From f3df7f06d4419a9ed41928124702bda222a9b4f0 Mon Sep 17 00:00:00 2001 From: Kurokabe Date: Thu, 18 Dec 2025 15:48:16 +0100 Subject: [PATCH 01/26] feat: experiment finetuning compatibility with peft --- .../components/huggingface_connector.py | 12 +- darts/models/forecasting/chronos2_model.py | 150 +++++++--- darts/models/forecasting/foundation_model.py | 36 ++- .../26-Chronos-2-finetuning-examples.ipynb | 276 ++++++++++++++++++ 4 files changed, 425 insertions(+), 49 deletions(-) create mode 100644 examples/26-Chronos-2-finetuning-examples.ipynb diff --git a/darts/models/components/huggingface_connector.py b/darts/models/components/huggingface_connector.py index 41fb1d1065..4006ec6085 100644 --- a/darts/models/components/huggingface_connector.py +++ b/darts/models/components/huggingface_connector.py @@ -13,12 +13,12 @@ from safetensors.torch import load_file from darts.logging import get_logger, raise_log -from darts.models.forecasting.pl_forecasting_module import ( - PLForecastingModule, -) +from darts.models.forecasting.pl_forecasting_module import PLForecastingModule logger = get_logger(__name__) +from darts.models.forecasting.foundation_model import FoundationPLModule + class HuggingFaceConnector: def __init__( @@ -109,10 +109,10 @@ def load_model_weights( def load_model( self, - module_class: type[PLForecastingModule], + module_class: type[FoundationPLModule], pl_module_params: dict, additional_params: Optional[dict] = None, - ) -> PLForecastingModule: + ) -> FoundationPLModule: """Load the model by creating an instance of the given module class and loading the weights. Some configuration files might contain external parameters that are not part of the module class constructor like `architectures`. They are filtered @@ -140,7 +140,7 @@ def load_model( **pl_module_params, **additional_params, ) - self.load_model_weights(module) + self.load_model_weights(module.model) return module def _get_file_path( diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index f6267c7484..54228c1d3a 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -23,14 +23,10 @@ _Patch, _ResidualBlock, ) -from darts.models.components.huggingface_connector import ( - HuggingFaceConnector, -) +from darts.models.components.huggingface_connector import HuggingFaceConnector from darts.models.forecasting.foundation_model import ( FoundationModel, -) -from darts.models.forecasting.pl_forecasting_module import ( - PLForecastingModule, + FoundationPLModule, ) from darts.utils.data.torch_datasets.utils import PLModuleInput, TorchTrainingSample from darts.utils.likelihood_models.torch import QuantileRegression @@ -51,7 +47,7 @@ class _Chronos2ForecastingConfig: time_encoding_scale: int | None = None -class _Chronos2Module(PLForecastingModule): +class _Chronos2Module(nn.Module): def __init__( self, d_model: int = 512, @@ -65,11 +61,12 @@ def __init__( rope_theta: float = 10000.0, attn_implementation: Literal["eager", "sdpa"] | None = None, chronos_config: Optional[dict[str, Any]] = None, + quantiles: list[float] = None, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, **kwargs, ): - """PyTorch module implementing the Chronos-2 model, ported from - `amazon-science/chronos-forecasting `_ and - adapted for Darts :class:`PLForecastingModule` interface. + """Core Chronos-2 model containing all the modules and forward logic. Parameters ---------- @@ -95,12 +92,12 @@ def __init__( Attention implementation to use. If None, defaults to "sdpa". chronos_config Configuration parameters for Chronos-2 model. See :class:`_Chronos2ForecastingConfig` for details. + quantiles + List of quantiles for probabilistic forecasting. **kwargs - all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` - base class. + Additional keyword arguments. """ - - super().__init__(**kwargs) + super().__init__() self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff @@ -114,6 +111,8 @@ def __init__( act_info = self.feed_forward_proj.split("-") self.dense_act_fn = act_info[-1] self.is_gated_act = act_info[0] == "gated" + self.device = device or torch.device("cpu") + self.dtype = dtype or torch.float32 if self.is_gated_act: raise_log( @@ -187,19 +186,11 @@ def __init__( num_layers=self.num_layers, ) - quantiles = self.chronos_config.quantiles + quantiles = quantiles or self.chronos_config.quantiles self.num_quantiles = len(quantiles) quantiles_tensor = torch.tensor(quantiles) self.register_buffer("quantiles", quantiles_tensor, persistent=False) - # gather indices of user-specified quantiles - user_quantiles: list[float] = ( - self.likelihood.quantiles - if isinstance(self.likelihood, QuantileRegression) - else [0.5] - ) - self.user_quantile_indices = [quantiles.index(q) for q in user_quantiles] - self.output_patch_embedding = _ResidualBlock( in_dim=self.d_model, h_dim=self.d_ff, @@ -334,14 +325,14 @@ def _prepare_patched_future( return patched_future, patched_future_covariates_mask - def _forward( + def forward( self, context: torch.Tensor, group_ids: torch.Tensor, future_covariates: torch.Tensor, num_output_patches: int = 1, ) -> torch.Tensor: - """Original forward pass of the Chronos-2 model. + """Forward pass of the Chronos-2 model. Parameters ---------- @@ -454,6 +445,88 @@ def _forward( return quantile_preds + +class _Chronos2PLModule(FoundationPLModule): + def __init__( + self, + d_model: int = 512, + d_kv: int = 64, + d_ff: int = 2048, + num_layers: int = 6, + num_heads: int = 8, + dropout_rate: float = 0.1, + layer_norm_epsilon: float = 1e-6, + feed_forward_proj: str = "relu", + rope_theta: float = 10000.0, + attn_implementation: Literal["eager", "sdpa"] | None = None, + chronos_config: Optional[dict[str, Any]] = None, + **kwargs, + ): + """PyTorch Lightning module wrapper for the Chronos-2 model, adapted for + Darts :class:`PLForecastingModule` interface. + + Parameters + ---------- + d_model + Dimension of the model embeddings, also called "model size" in Transformer. + d_kv + Dimension of the key and value projections in multi-head attention. + d_ff + Dimension of the feed-forward network hidden layer. + num_layers + Number of Chronos-2 encoder layers. + num_heads + Number of attention heads in each encoder block. + dropout_rate + Dropout rate of the model. + layer_norm_epsilon + Epsilon value for layer normalization layers. + feed_forward_proj + Activation of feed-forward network. + rope_theta + Base period for Rotary Position Embeddings (RoPE). + attn_implementation + Attention implementation to use. If None, defaults to "sdpa". + chronos_config + Configuration parameters for Chronos-2 model. See :class:`_Chronos2ForecastingConfig` for details. + **kwargs + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. + """ + + super().__init__(**kwargs) + + # Get quantiles for model initialization + chronos_config = chronos_config or {} + chronos_config_obj = _Chronos2ForecastingConfig(**chronos_config) + quantiles = chronos_config_obj.quantiles + + # gather indices of user-specified quantiles + user_quantiles: list[float] = ( + self.likelihood.quantiles + if isinstance(self.likelihood, QuantileRegression) + else [0.5] + ) + self.user_quantile_indices = [quantiles.index(q) for q in user_quantiles] + + # Create the core Chronos-2 model + self.model = _Chronos2Module( + d_model=d_model, + d_kv=d_kv, + d_ff=d_ff, + num_layers=num_layers, + num_heads=num_heads, + dropout_rate=dropout_rate, + layer_norm_epsilon=layer_norm_epsilon, + feed_forward_proj=feed_forward_proj, + rope_theta=rope_theta, + attn_implementation=attn_implementation, + chronos_config=chronos_config, + quantiles=quantiles, + device=self.device, + dtype=self.dtype, + ) + # TODO: fine-tuning support w/ normalized loss # Currently, Darts own `RINorm` is not used as Chronos-2 has its own implementation. Major differences # 1. Chronos-2 `RINorm` normalizes both target and covariates, while Darts normalizes target only. @@ -508,16 +581,16 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: # determine minimum number of patches to cover future_length num_output_patches = math.ceil( - future_length / self.chronos_config.output_patch_size + future_length / self.model.chronos_config.output_patch_size ) - # call original Chronos-2 forward pass + # call the core model's forward pass # Unlike the original, we remove `context_mask`, `future_covariates_mask`, `future_target`, # `future_target_mask`, and `output_attentions` parameters. They are not needed for Darts' # implementation. # We also remove `einops` rearrange operation at the end so the raw output tensor is returned, # in shape of `(batch, vars * patches * quantiles * patch_size)` - quantile_preds = self._forward( + quantile_preds = self.model.forward( context=context, group_ids=group_ids, future_covariates=future_covariates, @@ -532,15 +605,15 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: batch_size, n_variables, num_output_patches, - self.num_quantiles, - self.chronos_config.output_patch_size, + self.model.num_quantiles, + self.model.chronos_config.output_patch_size, ) # permute and reshape to (batch, time, vars, quantiles) quantile_preds = quantile_preds.permute(0, 2, 4, 1, 3).reshape( batch_size, - num_output_patches * self.chronos_config.output_patch_size, + num_output_patches * self.model.chronos_config.output_patch_size, n_variables, - self.num_quantiles, + self.model.num_quantiles, ) # truncate to output_chunk_length @@ -558,7 +631,7 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: class Chronos2Model(FoundationModel): # Fine-tuning is turned off for now pending proper fine-tuning support # and configuration. - _allows_finetuning = False + _allows_finetuning = True def __init__( self, @@ -881,11 +954,14 @@ def encode_year(idx): ) self.hf_connector = hf_connector - super().__init__(enable_finetuning=False, **kwargs) + super().__init__(**kwargs) - def _create_model(self, train_sample: TorchTrainingSample) -> PLForecastingModule: + def _create_model(self, train_sample: TorchTrainingSample) -> FoundationPLModule: pl_module_params = self.pl_module_params or {} - return self.hf_connector.load_model( - module_class=_Chronos2Module, + model = self.hf_connector.load_model( + module_class=_Chronos2PLModule, pl_module_params=pl_module_params, ) + model.apply_peft(self.peft_config) + + return model diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index 4c92b7e992..9508fc5f3e 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -10,11 +10,14 @@ """ from abc import ABC +from typing import Optional + +from peft import PeftConfig, get_peft_model +from torch import nn from darts.logging import get_logger, raise_log -from darts.models.forecasting.torch_forecasting_model import ( - MixedCovariatesTorchModel, -) +from darts.models.forecasting.pl_forecasting_module import PLForecastingModule +from darts.models.forecasting.torch_forecasting_model import MixedCovariatesTorchModel logger = get_logger(__name__) @@ -24,7 +27,7 @@ class FoundationModel(MixedCovariatesTorchModel, ABC): def __init__( self, - enable_finetuning: bool = False, + peft_config: Optional[PeftConfig] = None, **kwargs, ): """Foundation Forecasting Model with PyTorch Lightning backend. @@ -161,6 +164,8 @@ def encode_year(idx): # initialize `TorchForecastingModel` base class super().__init__(**self._extract_torch_model_params(**self.model_params)) + enable_finetuning = peft_config is not None + # extract pytorch lightning module kwargs self.pl_module_params = self._extract_pl_module_params(**self.model_params) @@ -174,8 +179,27 @@ def encode_year(idx): logger, ) - self._enable_finetuning = enable_finetuning + self.peft_config = peft_config @property def _requires_training(self) -> bool: - return self._enable_finetuning + return self.peft_config is not None + + +class FoundationPLModule(PLForecastingModule): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.model: nn.Module + + def apply_peft(self, peft_config: PeftConfig) -> None: + """Apply PEFT to the underlying model. + + Parameters + ---------- + peft_config + The PEFT configuration to apply. + """ + if not peft_config: + return + self.model = get_peft_model(self.model, peft_config) + self.model.print_trainable_parameters() diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb new file mode 100644 index 0000000000..13aa7c467a --- /dev/null +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da55dd6c", + "metadata": {}, + "source": [ + "# Chronos-2 Foundation Model\n", + "In this notebook, we will show how to use Chronos-2 in Darts. If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n", + "\n", + "Chronos-2 is a time series foundation model for zero-shot forecasting. That means that it can be used for forecasting **without any training or fine-tuning** since it has already been pre-trained on large-scale time series data. Chronos-2 supports multivariate time series forecasting with [covariates](https://unit8co.github.io/darts/userguide/covariates.html) (exogenous variables) and can produce probabilistic forecasts.\n", + "\n", + "Check out the [Amazon Science Blog](https://www.amazon.science/blog/introducing-chronos-2-from-univariate-to-universal-forecasting) and the [original paper](https://arxiv.org/abs/2510.15821) for technical details." + ] + }, + { + "cell_type": "markdown", + "id": "9ad51937", + "metadata": {}, + "source": [ + "
\n", + " Fine-tuning Chronos-2 on your own data is not yet supported in Darts, but may be added in the future.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "310fa52a", + "metadata": {}, + "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bfa59f65", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d510b54b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "The StatsForecast module could not be imported. To enable support for the AutoARIMA, AutoETS and Croston models, please consider installing it.\n", + "The `XGBoost` module could not be imported. To enable XGBoost support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "The `XGBoost` module could not be imported. To enable XGBoost support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "import numpy as np\n", + "\n", + "from darts.datasets import ElectricityConsumptionZurichDataset\n", + "from darts.models import Chronos2Model\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "import logging\n", + "\n", + "logging.disable(logging.CRITICAL)" + ] + }, + { + "cell_type": "markdown", + "id": "6b82a07a", + "metadata": {}, + "source": [ + "## Data Preparation" + ] + }, + { + "cell_type": "markdown", + "id": "70d7e392", + "metadata": {}, + "source": [ + "Here, we will use the [Electricity Consumption Zurich Dataset](https://unit8co.github.io/darts/generated_api/darts.datasets.html#darts.datasets.ElectricityConsumptionZurichDataset), which records the electricity consumption of households & SMEs (`\"Value_NE5\"` column) and business & services (`\"Value_NE7\"`) in Zurich, Switzerland, along with weather covariates such as temperature (`\"T [°C]\"`) and humidity (`\"Hr [%Hr]\"`).\n", + "Values are recorded every 15 minutes between January 2015 and August 2022.\n", + "\n", + "
\n", + "\n", + "Train-Test Split\n", + "\n", + "Even though Chronos-2 is pre-trained already, we still need to split the data into training and test sets. That is because `Chronos2Model` follows the Darts unified interface and will require calling the `fit()` method before forecasting. However, no training or fine-tuning will be performed during the `fit()` call.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "Data Scaling\n", + "\n", + "Unlike other deep learning models in Darts, Chronos-2 does not require data scaling since it has its own internal data normalization mechanism. Therefore, we will skip the scaling step in this notebook.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f87bcc5", + "metadata": {}, + "outputs": [], + "source": [ + "# convert to float32 as Chronos-2 works with float32 input\n", + "data = ElectricityConsumptionZurichDataset().load().astype(np.float32)\n", + "# extract households energy consumption\n", + "ts_energy = data[\"Value_NE5\"]\n", + "# extract temperature, solar irradiation and rain duration\n", + "ts_weather = data[[\"T [°C]\", \"StrGlo [W/m2]\", \"RainDur [min]\"]]\n", + "# split into train and validation sets by last 7 days\n", + "train_energy, val_energy = ts_energy.split_before(len(ts_energy) - 7 * 24 * 4)" + ] + }, + { + "cell_type": "markdown", + "id": "a3887f37", + "metadata": {}, + "source": [ + "Let's quickly visualize the last 7 days of the electricity consumption data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3b43a60a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "val_energy.plot(label=\"consumption\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "147c7e36", + "metadata": {}, + "outputs": [], + "source": [ + "from peft import LoraConfig" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "60b4811d", + "metadata": {}, + "outputs": [], + "source": [ + "peft_config = LoraConfig(\n", + " r=8,\n", + " lora_alpha=32,\n", + " target_modules=[\"q\", \"v\"], # optionally indicate target modules\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3cccd20e", + "metadata": {}, + "outputs": [], + "source": [ + "# use last 30 days of data to predict next 7 days\n", + "model = Chronos2Model(\n", + " input_chunk_length=30 * 24 * 4,\n", + " output_chunk_length=7 * 24 * 4,\n", + " peft_config=peft_config,\n", + " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "08715193", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "trainable params: 589,824 || all params: 120,067,488 || trainable%: 0.4912\n" + ] + }, + { + "ename": "MisconfigurationException", + "evalue": "No supported gpu backend found!", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mMisconfigurationException\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mseries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_energy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\utils\\torch.py:94\u001b[39m, in \u001b[36mrandom_method..decorator\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m fork_rng():\n\u001b[32m 93\u001b[39m manual_seed(random_instance.randint(\u001b[32m0\u001b[39m, high=MAX_TORCH_SEED_VALUE))\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdecorated\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:958\u001b[39m, in \u001b[36mTorchForecastingModel.fit\u001b[39m\u001b[34m(self, series, past_covariates, future_covariates, val_series, val_past_covariates, val_future_covariates, trainer, verbose, epochs, max_samples_per_ts, dataloader_kwargs, sample_weight, val_sample_weight, stride, load_best)\u001b[39m\n\u001b[32m 951\u001b[39m \u001b[38;5;66;03m# call super fit only if user is actually fitting the model\u001b[39;00m\n\u001b[32m 952\u001b[39m \u001b[38;5;28msuper\u001b[39m().fit(\n\u001b[32m 953\u001b[39m series=seq2series(series),\n\u001b[32m 954\u001b[39m past_covariates=seq2series(past_covariates),\n\u001b[32m 955\u001b[39m future_covariates=seq2series(future_covariates),\n\u001b[32m 956\u001b[39m verbose=verbose,\n\u001b[32m 957\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m958\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfit_from_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\utils\\torch.py:94\u001b[39m, in \u001b[36mrandom_method..decorator\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m fork_rng():\n\u001b[32m 93\u001b[39m manual_seed(random_instance.randint(\u001b[32m0\u001b[39m, high=MAX_TORCH_SEED_VALUE))\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdecorated\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:1150\u001b[39m, in \u001b[36mTorchForecastingModel.fit_from_dataset\u001b[39m\u001b[34m(self, train_dataset, val_dataset, trainer, verbose, epochs, dataloader_kwargs, load_best)\u001b[39m\n\u001b[32m 1091\u001b[39m \u001b[38;5;129m@random_method\u001b[39m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mfit_from_dataset\u001b[39m(\n\u001b[32m 1093\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1100\u001b[39m load_best: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 1101\u001b[39m ) -> \u001b[33m\"\u001b[39m\u001b[33mTorchForecastingModel\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 1102\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1103\u001b[39m \u001b[33;03m Train the model with a specific :class:`darts.utils.data.TorchTrainingDataset` instance.\u001b[39;00m\n\u001b[32m 1104\u001b[39m \u001b[33;03m These datasets implement a PyTorch ``Dataset``, and specify how the target and covariates are sliced\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1147\u001b[39m \u001b[33;03m Fitted model.\u001b[39;00m\n\u001b[32m 1148\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 1149\u001b[39m \u001b[38;5;28mself\u001b[39m._train(\n\u001b[32m-> \u001b[39m\u001b[32m1150\u001b[39m *\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setup_for_train\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1151\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1152\u001b[39m \u001b[43m \u001b[49m\u001b[43mval_dataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1153\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1154\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1155\u001b[39m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1156\u001b[39m \u001b[43m \u001b[49m\u001b[43mdataloader_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataloader_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1157\u001b[39m \u001b[43m \u001b[49m\u001b[43mload_best\u001b[49m\u001b[43m=\u001b[49m\u001b[43mload_best\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1158\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1159\u001b[39m )\n\u001b[32m 1160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:1309\u001b[39m, in \u001b[36mTorchForecastingModel._setup_for_train\u001b[39m\u001b[34m(self, train_dataset, val_dataset, trainer, verbose, epochs, dataloader_kwargs, load_best)\u001b[39m\n\u001b[32m 1306\u001b[39m train_num_epochs = epochs \u001b[38;5;28;01mif\u001b[39;00m epochs > \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m.n_epochs\n\u001b[32m 1308\u001b[39m \u001b[38;5;66;03m# setup trainer\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1309\u001b[39m trainer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setup_trainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_num_epochs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1311\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m model.epochs_trained > \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.load_ckpt_path:\n\u001b[32m 1312\u001b[39m logger.warning(\n\u001b[32m 1313\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAttempting to retrain/fine-tune the model without resuming from a checkpoint. This is currently \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1314\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mdiscouraged. Consider model `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.load_weights()` to load the weights for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1315\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mfine-tuning.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1316\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:525\u001b[39m, in \u001b[36mTorchForecastingModel._setup_trainer\u001b[39m\u001b[34m(self, trainer, model, verbose, epochs)\u001b[39m\n\u001b[32m 520\u001b[39m trainer_params[\u001b[33m\"\u001b[39m\u001b[33menable_model_summary\u001b[39m\u001b[33m\"\u001b[39m] = (\n\u001b[32m 521\u001b[39m verbose \u001b[38;5;28;01mif\u001b[39;00m model.epochs_trained == \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 522\u001b[39m )\n\u001b[32m 523\u001b[39m trainer_params[\u001b[33m\"\u001b[39m\u001b[33menable_progress_bar\u001b[39m\u001b[33m\"\u001b[39m] = verbose\n\u001b[32m--> \u001b[39m\u001b[32m525\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_init_trainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer_params\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrainer_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_epochs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:538\u001b[39m, in \u001b[36mTorchForecastingModel._init_trainer\u001b[39m\u001b[34m(trainer_params, max_epochs)\u001b[39m\n\u001b[32m 536\u001b[39m \u001b[38;5;66;03m# prevent lightning from adding callbacks to the callbacks list in `self.trainer_params`\u001b[39;00m\n\u001b[32m 537\u001b[39m callbacks = trainer_params_copy.pop(\u001b[33m\"\u001b[39m\u001b[33mcallbacks\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m538\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mTrainer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 539\u001b[39m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 540\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtrainer_params_copy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 541\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\utilities\\argparse.py:70\u001b[39m, in \u001b[36m_defaults_from_env_vars..insert_env_defaults\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 67\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mlist\u001b[39m(env_variables.items()) + \u001b[38;5;28mlist\u001b[39m(kwargs.items()))\n\u001b[32m 69\u001b[39m \u001b[38;5;66;03m# all args were already moved to kwargs\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m70\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:404\u001b[39m, in \u001b[36mTrainer.__init__\u001b[39m\u001b[34m(self, accelerator, strategy, devices, num_nodes, precision, logger, callbacks, fast_dev_run, max_epochs, min_epochs, max_steps, min_steps, max_time, limit_train_batches, limit_val_batches, limit_test_batches, limit_predict_batches, overfit_batches, val_check_interval, check_val_every_n_epoch, num_sanity_val_steps, log_every_n_steps, enable_checkpointing, enable_progress_bar, enable_model_summary, accumulate_grad_batches, gradient_clip_val, gradient_clip_algorithm, deterministic, benchmark, inference_mode, use_distributed_sampler, profiler, detect_anomaly, barebones, plugins, sync_batchnorm, reload_dataloaders_every_n_epochs, default_root_dir, model_registry)\u001b[39m\n\u001b[32m 401\u001b[39m \u001b[38;5;66;03m# init connectors\u001b[39;00m\n\u001b[32m 402\u001b[39m \u001b[38;5;28mself\u001b[39m._data_connector = _DataConnector(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28mself\u001b[39m._accelerator_connector = \u001b[43m_AcceleratorConnector\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 405\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevices\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[43m \u001b[49m\u001b[43maccelerator\u001b[49m\u001b[43m=\u001b[49m\u001b[43maccelerator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 407\u001b[39m \u001b[43m \u001b[49m\u001b[43mstrategy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstrategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 408\u001b[39m \u001b[43m \u001b[49m\u001b[43mnum_nodes\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_nodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[43m \u001b[49m\u001b[43msync_batchnorm\u001b[49m\u001b[43m=\u001b[49m\u001b[43msync_batchnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 410\u001b[39m \u001b[43m \u001b[49m\u001b[43mbenchmark\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbenchmark\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 411\u001b[39m \u001b[43m \u001b[49m\u001b[43muse_distributed_sampler\u001b[49m\u001b[43m=\u001b[49m\u001b[43muse_distributed_sampler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 412\u001b[39m \u001b[43m \u001b[49m\u001b[43mdeterministic\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdeterministic\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 413\u001b[39m \u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 414\u001b[39m \u001b[43m \u001b[49m\u001b[43mplugins\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplugins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 415\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 416\u001b[39m \u001b[38;5;28mself\u001b[39m._logger_connector = _LoggerConnector(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m 417\u001b[39m \u001b[38;5;28mself\u001b[39m._callback_connector = _CallbackConnector(\u001b[38;5;28mself\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\accelerator_connector.py:144\u001b[39m, in \u001b[36m_AcceleratorConnector.__init__\u001b[39m\u001b[34m(self, devices, num_nodes, accelerator, strategy, plugins, precision, sync_batchnorm, benchmark, use_distributed_sampler, deterministic)\u001b[39m\n\u001b[32m 142\u001b[39m \u001b[38;5;28mself\u001b[39m._accelerator_flag = \u001b[38;5;28mself\u001b[39m._choose_auto_accelerator()\n\u001b[32m 143\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._accelerator_flag == \u001b[33m\"\u001b[39m\u001b[33mgpu\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m144\u001b[39m \u001b[38;5;28mself\u001b[39m._accelerator_flag = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_choose_gpu_accelerator_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 146\u001b[39m \u001b[38;5;28mself\u001b[39m._check_device_config_and_set_final_flags(devices=devices, num_nodes=num_nodes)\n\u001b[32m 147\u001b[39m \u001b[38;5;28mself\u001b[39m._set_parallel_devices_and_init_accelerator()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\accelerator_connector.py:354\u001b[39m, in \u001b[36m_AcceleratorConnector._choose_gpu_accelerator_backend\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m CUDAAccelerator.is_available():\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mcuda\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[33m\"\u001b[39m\u001b[33mNo supported gpu backend found!\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mMisconfigurationException\u001b[39m: No supported gpu backend found!" + ] + } + ], + "source": [ + "model.fit(\n", + " series=train_energy,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e189768a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "darts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 76158c942817249bb8cce38f466b096d5260eb2e Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 19 Dec 2025 09:37:55 +0100 Subject: [PATCH 02/26] fix: properly set the model device and dtype --- darts/models/forecasting/chronos2_model.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index 54228c1d3a..f0a9cec9cb 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -62,8 +62,6 @@ def __init__( attn_implementation: Literal["eager", "sdpa"] | None = None, chronos_config: Optional[dict[str, Any]] = None, quantiles: list[float] = None, - device: Optional[torch.device] = None, - dtype: Optional[torch.dtype] = None, **kwargs, ): """Core Chronos-2 model containing all the modules and forward logic. @@ -111,8 +109,6 @@ def __init__( act_info = self.feed_forward_proj.split("-") self.dense_act_fn = act_info[-1] self.is_gated_act = act_info[0] == "gated" - self.device = device or torch.device("cpu") - self.dtype = dtype or torch.float32 if self.is_gated_act: raise_log( @@ -199,6 +195,14 @@ def __init__( dropout_p=self.dropout_rate, ) + @property + def device(self) -> torch.device: + return next(self.parameters()).device + + @property + def dtype(self) -> torch.dtype: + return next(self.parameters()).dtype + def _prepare_patched_context( self, context: torch.Tensor, @@ -523,8 +527,6 @@ def __init__( attn_implementation=attn_implementation, chronos_config=chronos_config, quantiles=quantiles, - device=self.device, - dtype=self.dtype, ) # TODO: fine-tuning support w/ normalized loss From 94a71b4df570d130ce7e2356e715699b355f893d Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Thu, 15 Jan 2026 14:08:39 +0100 Subject: [PATCH 03/26] fix: remove peft as a darts dependency --- darts/models/forecasting/chronos2_model.py | 1 - darts/models/forecasting/foundation_model.py | 23 +- .../26-Chronos-2-finetuning-examples.ipynb | 7958 ++++++++++++++++- 3 files changed, 7894 insertions(+), 88 deletions(-) diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index f0a9cec9cb..04630f0ffe 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -964,6 +964,5 @@ def _create_model(self, train_sample: TorchTrainingSample) -> FoundationPLModule module_class=_Chronos2PLModule, pl_module_params=pl_module_params, ) - model.apply_peft(self.peft_config) return model diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index 9508fc5f3e..01ddc1f4de 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -10,9 +10,7 @@ """ from abc import ABC -from typing import Optional -from peft import PeftConfig, get_peft_model from torch import nn from darts.logging import get_logger, raise_log @@ -27,7 +25,7 @@ class FoundationModel(MixedCovariatesTorchModel, ABC): def __init__( self, - peft_config: Optional[PeftConfig] = None, + enable_finetuning: bool = False, **kwargs, ): """Foundation Forecasting Model with PyTorch Lightning backend. @@ -164,8 +162,6 @@ def encode_year(idx): # initialize `TorchForecastingModel` base class super().__init__(**self._extract_torch_model_params(**self.model_params)) - enable_finetuning = peft_config is not None - # extract pytorch lightning module kwargs self.pl_module_params = self._extract_pl_module_params(**self.model_params) @@ -179,27 +175,14 @@ def encode_year(idx): logger, ) - self.peft_config = peft_config + self._enable_finetuning = enable_finetuning @property def _requires_training(self) -> bool: - return self.peft_config is not None + return self._enable_finetuning class FoundationPLModule(PLForecastingModule): def __init__(self, **kwargs): super().__init__(**kwargs) self.model: nn.Module - - def apply_peft(self, peft_config: PeftConfig) -> None: - """Apply PEFT to the underlying model. - - Parameters - ---------- - peft_config - The PEFT configuration to apply. - """ - if not peft_config: - return - self.model = get_peft_model(self.model, peft_config) - self.model.print_trainable_parameters() diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index 13aa7c467a..79d0bd8481 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "id": "310fa52a", "metadata": {}, "outputs": [], @@ -39,10 +39,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "id": "bfa59f65", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -54,25 +63,13 @@ "execution_count": null, "id": "d510b54b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "The StatsForecast module could not be imported. To enable support for the AutoARIMA, AutoETS and Croston models, please consider installing it.\n", - "The `XGBoost` module could not be imported. To enable XGBoost support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `XGBoost` module could not be imported. To enable XGBoost support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n" - ] - } - ], + "outputs": [], "source": [ "import warnings\n", "\n", "import numpy as np\n", "\n", - "from darts.datasets import ElectricityConsumptionZurichDataset\n", + "from darts.datasets import AirPassengersDataset\n", "from darts.models import Chronos2Model\n", "\n", "warnings.filterwarnings(\"ignore\")\n", @@ -116,19 +113,114 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "id": "2f87bcc5", "metadata": {}, "outputs": [], "source": [ "# convert to float32 as Chronos-2 works with float32 input\n", - "data = ElectricityConsumptionZurichDataset().load().astype(np.float32)\n", + "data = AirPassengersDataset().load().astype(np.float32)\n", "# extract households energy consumption\n", - "ts_energy = data[\"Value_NE5\"]\n", - "# extract temperature, solar irradiation and rain duration\n", - "ts_weather = data[[\"T [°C]\", \"StrGlo [W/m2]\", \"RainDur [min]\"]]\n", - "# split into train and validation sets by last 7 days\n", - "train_energy, val_energy = ts_energy.split_before(len(ts_energy) - 7 * 24 * 4)" + "# ts_energy = data[\"Value_NE5\"]\n", + "# # extract temperature, solar irradiation and rain duration\n", + "# ts_weather = data[[\"T [°C]\", \"StrGlo [W/m2]\", \"RainDur [min]\"]]\n", + "# # split into train and validation sets by last 7 days\n", + "train_passengers, val_passengers = data.split_before(len(data) - 2 * 12)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "a84830af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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#Passengers
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shape: (24, 1, 1), freq: MS, size: 96.00 B

" + ], + "text/plain": [ + " #Passengers\n", + "Month \n", + "1959-01-01 360.0\n", + "1959-02-01 342.0\n", + "1959-03-01 406.0\n", + "1959-04-01 396.0\n", + "1959-05-01 420.0\n", + "... ...\n", + "1960-08-01 606.0\n", + "1960-09-01 508.0\n", + "1960-10-01 461.0\n", + "1960-11-01 390.0\n", + "1960-12-01 432.0\n", + "\n", + "shape: (24, 1, 1), freq: MS, size: 96.00 B" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "val_passengers" ] }, { @@ -141,13 +233,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "id": "3b43a60a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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r6tSpdZvBSGQwGCEiIludP39e1q1bZ63wnj59elv3B4EI1qkxjdguXLhg6/74AYMRIiKy1apVq7QVvBOmaOJO1Vy/ft0KlCh8GIwQEZGtnJQvYjBvJLIYjBARka2WL19ubVetWlWcgMFIZDEYISIiR4yMpEmTRipWrChOwGAkshiMEBGRbU6ePCmbN2/W7dtuu00DEifImTOn3HLLLbrNtvDhx2CEiIhss2LFCsfli8QdHTl16pTs3r3b7t3xNAYjRERkGycmrxqcqokcBiNERGQbBiMEDEaIiMj2YCRjxoxSunRpcRIGI5HDYISIiGxx6NAh2bNnj25XqVJFW8E7ScmSJSVdunS6vXr1art3x9MYjBARke39RZw2RQOpUqWSChUq6PbWrVvl3Llzdu+SZzEYISIi2/NFnNLsLKGpGpT2rlmzxu7d8SwGI0REZAsnJ68azBuJDAYjREQUcRhpMMFIjhw5pGjRouJEDEYig8EIERFFHJqIHTlyxJqiSZEihThRpUqVrG0GI+HDYISIiCLODVM0kDVrVilcuLBVUXP9+nW7d8mTGIwQEVHEuSUYCZyqOXv2rOzYscPu3fEkBiNERBRxbgxGgFM14cFghIiIIgpTHWaBvPz580u+fPnEyRiMhB+DESIiiqgtW7bI6dOnHd1fJBCTWMOPwQgREUWUm6ZooHjx4pIhQwbdZjASHgxGiIgootwWjKRMmVIqVqyo2zt37pRTp07ZvUuew2CEiIgiyg1t4G+UN8JF80KPwQgREUXMlStXZNWqVdb0R/bs2cUNmMQaXgxGiIgoYtatWycXL150zRSNwWAkvBiMEBFRxLgtX8QwOSPAYCT0GIwQEVHEuDUYyZQpk04rwdq1a+XatWt275KnMBgh364YOnHiRKlTp448/fTT+j0Rhd/y5cutCpXbb79d3MRM1Vy4cEF7pVDoMBgh38HaEi1btpQ2bdrIggULZODAgbJy5Uq7d4vI85ArsmbNGt0uW7asZMyYUdyEFTXhw2CEfOPSpUvyzjvvSPny5eWXX36JdZtpTU1E4fPXX3/J1atXXTdFYzCJNXxShfFnEznG7Nmz5bHHHpNNmzZZ1+GsDKtwmoMkEYWXW/NFDAYj4cOREfK0gwcPSpcuXaRhw4ZWIIK56meeeUbWr19v3Y/BCFH4uT0YKVy4sGTJkkW3GYyEFoMR8iRkug8aNEjKlCkjo0ePtq6vUaOGTsl88sknUrBgQSlatKg1/4uVRIko/MFI6tSpYy0+5xYpUqSw9nvv3r1y/Phxu3fJMxiMkCez9RF0PPHEE9YaEtmyZZOvv/5aFi1aJLfddpt1X7N97tw52bZtm237TOR1WKXXjE5iuiNt2rTiRpyqCQ8GI+QZJ0+e1ACkWrVqVvkgdO/eXQ+CvXv31imaQIGBCadqiMIHI5KmhN6NUzQGg5HwYDBCrocDHKZiMCWDqRlzwEPVzPz582XEiBGSK1eueB/LYIQoMgJPEBiMULKrafr06aPd56KiovR7NK1Bn4YpU6bIW2+9JWnSpLHuO378eMmbN6+1HsGAAQNkz549+iHRv39/yZcvX1KfniiWjRs3apXMnDlzrOsyZMggb7zxhjYzw9z0jTAYIYoMN67UG58KFSroCCtyzBiM2Fza27dvX2nevPk/rq9SpYoMHjz4H9dfvnxZXnzxRR0mb9asmQwdOlT69eunX4mCcf78eXn77bflgw8+0FVADTQy+/TTTzU5NTFwP+STnDhxgsEIUQSCkfTp02vDM7eKjo6WUqVK6YkQTrJx/LnZSQ85pM8I5grxz2rdurV+36tXLy213Ldvn+TPnz/e4AWXQPiHs9qBYOrUqTrqsXPnTuu6IkWK6AhdixYt9PukvFYwOoKRlf3792spcO7cucOy35R85v/KY4G7HDlyxHq/Vq5c2RpZcCssmodgBJ9TGzZs0NESSljcXL2QBSMff/yxXhAdPvvss1KyZEm9Hm1+EWRkz55dOnToIG3bttXrt2/fbt0H0qVLJwUKFNDr4wtGMMc/ZMiQWNe1a9dO2rdvH8zukkcgeMVU34wZM6zrEORi6hBTNThj2bVrV5J/LgIZAz/7rrvuCtk+U3hgupfcY+7cudY2PjeCeZ86SaFChaztWbNm6SJ6lDDTQiGkwchTTz0lxYoV00hn7Nix+v2ECRM02sX3yBFBM6nnn39eh78RnGBRIczjB8L3GGqPT48ePaRz586xrsNZK4bUExNhkfcgQMVrLfA1U79+ffn88881cTU5EHzg5wNGRtDYiJwJZ9MIRHgscJfdu3db2w0aNHD9e6xu3bry0UcfWZ9Nbv99nCDJwUjgcFS3bt1k8uTJOiKCvg6B9+nYsaMOfSMYwRkr+jgEwveYO4wPkmADE2HNGTAOPjwA+Q/Ooh555BFrTYs8efLogaBTp07ahCi5EEgbSEjja8z5eCxwbyVN9erVXf+/C1xtGA0T3f77OEGy/4IJ/RPwIWFKLDGSsnXr1lgrN6J7Ha4nuhkEtSYQQYIq5moxchaKQASQTGcS0JjEShRa+BwwyasYLS9evLi4HdILkI4ArKixIRg5c+aMLF26VJN2kFCK3g7oqoeRkMWLF2tFAuDDAlM2derUsapssGLqpEmT9LHDhw/XD4D48kWI4lqwYIG1jcTVrFmzhvTnYxQO5ebmtYtpRSIKXa7XoUOHrJLeUJ1E2Am/g+k3gt/N/H4UoWAEZ6doKtWoUSNp2rSpfkigjBKrn/7xxx+aYHrnnXfKq6++Kl27dtX7mIM9SjC///57nedftWqVJiISJcbChQv1K0Yv0F01HEy/EeQkoI8OEYWG2xfHSwibn9mYM4Ihtm+//Tbe21BVg0tCcOb5ww8/JH0PyddwxrF582brrAr5R+EQ2PwMBxYvHTSJ7OSVZmc3C0aaNGli6/64HbNuyNGwsJ0RzpJbdmIlCg8/jIwgiZWSh8EIuSZfJJzBSOCBhcEIUeiSV00lDdo+eClPsFy5cpIq1f8mFzhNk3wMRsgV+SJQq1atsD0PkmJN8zMcWNzcHZLIKVBFidW0zaiIF5JXjbRp01o9jtCFFUUaFDwGI+RYZ8+e1WRnQMWWKaUL91QNnhfdgYkoebw6RRN3RBXFHQhIKHgMRsixlixZIteuXdNtVGmFG/NGiELLL8EIcKomeRiMkCumaCKxXgyDEaLQ8moljcFgJHQYjJArklcjMTLCJFai0MHUhZlmxUJpOXPmFK9hMBI6DEbIkdDhF91+zQqZgatkhgsWu8qSJYtuMxghSh7kUJiFLb04KmLWycLFBCNmCRRKOgYj5EgrV6602rJHYlQEkOlvpmrQwvrIkSMReV4iL/J6vkjc0ZFjx47pCr4UHAYj5EiRzhdJqBMrEQXHb8EI8JgRPAYj5EiRzhcxmMRKFNpgBCOOWCzVqxiMhAaDEXIczLuakRGsh4ROh5HCYIQo+dDozLx/sEJ7pkyZxKsqVapkbTMYCR6DEXKcjRs36vwr1K5dW1KmjNzLFIEPVgcGBiNEwZk5c6bVI6hx48biZejCipXpgcFI8BiMkOPYlS8COKiYkRgERRcvXozo8xN5wfTp063tZs2aiZfh5MUcM7DCuEm8p6RhMEK+XRzvZlM1OLNbt25dxJ+fyO3TrL/++qtuR0dHS926dcXrTN4I1rRau3at3bvjSgxGyLEjI+nSpbMl8Y15I0TBW716tVXiWr9+fX0fex2TWJOPwQg5Cvp77NixQ7erV69uzcVGEoMRouD5aYrGYDCSfAxGyFHsKukNxLbwRMFjMMJgJBgMRshR7ExeNVBOjNbw5sCCeWAiurlTp07JokWLdLtkyZJSvHhx8YMcOXJI/vz5rWkqtoVPOgYj5MiREZTz1qxZ0/YznTNnzljTRkSU+JJev4yKxD1mICDbvXu33bvjOgxGyFGNktasWWO9sTNnzmzbvjBvhCjp/DhFY3CqJnkYjJBjLF682BretCtfxGAwQhR8SS8qaPxQ0huIwUjyMBghx3BCvojBYIQoaTCqiWo4U9KLHiN+wmAkeRiMkGM4oZLGKFKkiDVNxGCE6Ob8PEVjEnZNAMZgJOkYjJAjoO36n3/+qdvIwM+XL5+t+4OVRs3oyN69e+Xo0aO27g+R0/k9GImKipIKFSro9rZt2+Ts2bN275KrMBghR1i+fLlcvnzZEaMi8U3V8EyHKGGnT5+2SnpLlCihFz8yUzXInzHJ+JQ4DEbIEZyUL2IwGCFKfEnv1atXfTsqYjBvJHgMRsgR7F4cLz5MYiVKHL9P0RgMRoLHYIRshw6nZog3d+7cmgjmBFgWPFWqVLrNYIQofpiSMMEISnrr1asnflWpUiVrm8FI0jAYIdthyW10LTT5IkgedYK0adNqQAIbNmzQJFsiSrikF4GI30p6A2XJkkUr8UxbeC4lkXgMRsh2TirpTWiqBvPh69evt3t3iByHUzTxT9WcO3dOtm/fbvfuuAaDEbKdE5NXDeaNEN0Yg5HYOFUTHAYjZPt8sxkZyZAhQ6wPfydgMEKUuJJe9AdySr6XnZjEGhwGI2SrXbt2WfPNWKXXJIw68cDCYIQoNpb0/hODkeAwGCFbOTlfBLJnzy4FCxa0ghEmpBH9f5yi+adixYpJxowZdZvBSOIxGCFbOTlfJO5UzZkzZ2Tnzp127w6R40p6UXnm55LeQClTppSKFStaI78nT560e5dcgcEIOWJkBNMz1atXFydi3ghR/CX5gSW96dOnt3uXHDlVgxJfujkGI2QbLD6H/h1QuXJlTWB1IgYjRP/EKZqEMRhJOgYjZBuThe/UfBGDwQjRPzEYSRiTWJOOwQjZxg35IoCOipkzZ9ZtHliI/lfSa96/SNhkSW9syBkxnaR5zEgcBiPkiEqa2rVri5MT0syZzu7du+X48eN27xKRrWbNmhWrpNcpSzg4Bapp0HfF5NZcu3bN7l1yPAYjZIvz58/LihUrdLtMmTKSK1cucbLAqRqe6ZDfcYrm5swJzIULF2TLli12747jMRghW/zxxx/WmZWTp2gM5o0QxV/SW79+fbt3yZGYN5I0DEbIFk5vdhYXgxGi/1m3bp3s3btXt+vWrcuS3kQEI4sXL7Z1X9yAwQjZwi3Jq0a5cuWsVvUMRsjPOEWTODiupUmTRre///57uXz5st275GgMRijiMD2zZMkS3b7lllu0WsXp0qVLJ2XLltXt9evXy6VLl+zeJSLbg5HmzZvbui9Oli1bNmnTpo1uHzlyRH755Re7d8nRGIxQxGH+9OzZs9bZg1sy8c2wK4IpBCREfoMlEVjSm3g9e/a0tocPH27rvjgdgxGKOLflixjMGyG/Q0nvlStXdJslvTfXsGFDa6HNadOmyf79++3eJcdiMEIR57Z8EYPBCPkd80WSJioqSrp166bbWPH722+/tXuXHIvBCEW8LNCMjKCraYUKFcSN2fEMRshvWNIbnO7du8eaqsHfkUIQjPTp00dq1aqlZ7S4PPXUU9ZtI0eOlEaNGkmDBg3k008/jfVHRzlYx44dtdMmfsaBAweS+tTkAVu3bpXDhw/rNl4LOHNwi5w5c0qBAgWsYIQHFfIT5Ent2bNHt1nSm3joxIpVjWHz5s0s8w3lyEjfvn317BaXgQMHWkPv48eP14Bk3Lhx+gefNGmS3oaSphdffFGDkdmzZ+sZZr9+/YJ5anI5t+aLxJ2qwdocO3futHt3iCKGUzTBYyLrzf2vcUIIIDkHZUzmzLFLly4yZcoUad26tbb9Tp06tW5Dr169NLFn3759kj9//n/8LAQvcWuykTSFOTfy1no0bvufIpCeOnWqbq9cuVIKFy5s9y75inm9uO1147VgpGnTpvwfJAE+GzNlyqTVSDhZ/+STT3T9Gr9ImTJleIKRjz/+WC+lSpWSZ599Vsu7duzYoS9Qo0SJErJt2zbd3r59e6wSMPRsQNCC6+MLRkaMGCFDhgyJdV27du2kffv2wewuOcicOXP0K5oB5cmTR3bt2iVugr4oxvz586Vy5cq27o9fmekCigyU4psTCVSHIGfEbe9du91zzz3a/Ax/y6+++kratm0rflG0aNHQByPIEUF9OSKdsWPH6vcTJkzQhc8yZMhg3Q/bWCAI8DXwNnM7HhOfHj16SOfOnWNdh5IovAkSE2GRMx08eNA6gN1xxx0azLpN48aNrW1M03BkJLJwNo5AhMeCyMKUuynpxYeqGxoVOs2TTz6pwQhg1uC5556ze5ccJcnBSGD1A0qWJk+eLGvWrNFkpnPnzlm3YTs6Olq38TXwNnN7QglQOGs2bXQNTPPg4MMDkHsFJm4h+dmN/0sko5nhVjRvc+Pv4AU8FkTWb7/9FqvrKv/2SVejRg1dVgKJwBhlQjK/G0/IwiXZryjzosQwDP64BqZocOAGjKQE3nbx4kVdaAnXk3+4PXnVvN5NiS9GeU6cOGH3LhFFrKQXJ4ks6Q0OGsQFJrKi2IOCDEZwNrh06VJNLsWQ3ejRo7WqAKMliJZ/+uknDTKOHTumt5l1C6pUqaJreWCoD49FNjHW+YgvX4S83+wMb0qUh7tVYPMzLg1OXrdhwwbZvXu3VdIbd8qdEg+FHaadwahRo3RpCQoiGMEfbtCgQdpLBMmqONNFPxFkBeNMFwk5mLrBVwxJtWrVyoqmP/jgA50vQ1S9atUqGTBgQFKemlwOQatpFIbgFYtIuRU7sZKfsKQ3dJC0j5wbkwc5Y8YMu3fJnTkj+AC5UTtbJJ7iEp/y5cvLDz/8kPQ9JE/AiJopBXRTC/j4sBMr+QmDkdDCVI3pwYVZAq58/D/MQqKI8EK+SGBgbYZaGYyQX0p6UUFTunRpu3fJ9RDQYYQEUABy9OhRu3fJERiMUES4dXG8+KA6rEyZMrqNzPi4DfqIvAIds83rm6v0hgYqQ7t27arbJveSGIxQBOBghmkaQF8O06XXzUzeCA4mCEiIvIhTNOERmM4wbNgwrnPFYIQiAW3TUc7thVERg0ms5LeSXiyASqGBatKaNWvqNvp0rVy5UvyOwQiFnZfyRQwGI+R1GzdutDom16lThyW9IcbF82JjMEJh56V8EYMVNeR1WPzU4BRN6GGtNdOlfMyYMdbyKX7FYITCCuW8JhjJkSOHDk96Qa5cuaymfWh8xjlf8hrmi4RX5syZdQFYOHnypEycOFH8jMEIhX2o9/jx47pdu3ZtT2Xjm6kaHEhMh0oir5X0IuncVI9RaHGq5v9jMEIRyxfxyhSNwbwR8iqW9EYGcnHMGm6zZs3SlcD9isEIRSxfxCvJqwaDEfIqTtFEBoI8U+YbExOj69X4FYMRisjICBK1KleuLF7CYIS8iCW9kYX13FL838jTiBEjrGUz/IbBCIXNnj17rNLA6tWr64HNS4oVK6aLRAKDEfJiSS+mVs1rnMIDTSCbNm2q2/i7z507V/yIwQiFjRdLegOlTJnSKvHFXC8SWYncjlM0kdeTiawMRih8vNjs7Eb9RlDiS+R2DEYi795775Xs2bPr9o8//ujLExsGIxT2kRGMIJjWx17DvBHyWknv/PnzdbtQoUKe6QvkdGnTppXOnTvrNpbO+OGHH8RvGIxQWJw4cULWrl1rfWBnypRJvIjBCHnJnDlzWNJrk54+n6phMEJhsXjxYqsrqRfzRYwKFSroyA8wGCG34xSNvSc2t99+u24vW7ZMF9DzEwYjFBZ+yBcxJcumO+W6deuss0oiN5f0pk6dmiW9No+OjBgxQvyEwQiFhdcraeKbqrly5Yps2LDB7t0hCsqmTZusDqB4z3p1atXJOnXqZLVA+Pbbb311csNghEIOCVgYZoSSJUtKnjx5xMuYN0JewCka+2XPnl3atGmj20ePHpWpU6eKXzAYoZBDIGIiei9P0cQXjLC8l9yKwYgz9PTpVA2DEQo5Ly+Od7NeIxwZITc6d+6czJs3T7cLFiwo5cqVs3uXfKthw4b6P4Bp06bJ/v37xQ8YjFDIeXlxvPjkzp1bbrnlFisYMVVERG7Bkl7niIqK0vVqAOvUIHfEDxiMUEhdu3ZNFi1apNvIFSlRooT4gZmqQX8VrMlD5CaconGW7t27x+o54ocTHAYjFFKojT99+rQ1KuKXMywmsZJXSnoxTUD2Kl68uNSrV0+3N2/erH2bvI7BCIWUn0p6AzEYIbfCh92OHTusEwiW9DpDT591ZGUwQiEVuPy1H/JFDCaxkltzRVq3bm19zyka57j//vutwHDs2LG6bpCXMRihkGbkm+HeHDlyxPqA9sOwaoYMGXSbwQg53aFDh+TBBx/ULqsbN27U6zJnziwdOnSwe9fo/6RPn14eeOAB69g6fvx48TIGIxQykydPlvPnz+t227ZtJVWqVOKnDPhKlSrpNoa8/bgEOLkjwXzw4MFSunRp+e6776zrq1evrqW9WKmXnKOnj6ZqGIxQyAQue92xY0fxm8C8kdWrV9u6L0RxrVixQmrWrCmPP/64nDp1Sq/Lli2bfPXVV5ogGfj6JWeoVq2a1fMF+XjI7/EqBiMUEihpNVM06Lnhp+RVg0ms5EQIPJ588kn9YDPLNAB6WWCKpk+fPtbK0+QsKVKkkB49eljfjxw5UryKr0AKiZ9//lkXioP27dvrtIXfMBghp5XsjhkzRqdkPv/8c22gBTjTxpQMPtjQsI+c7cEHH7SOp6NGjZKrV6+KFzEYoZBP0ZikK7+pUKGCdYbJYITsXoG3cePG0rlzZ01WNQmR7733nr4269SpY/cuUiLlyZNH7rnnHt1Ga/gZM2aIFzEYoWTDwW7WrFm6XaxYMbnjjjvEj3Cwx1korFu3zhopIoqUCxcuSN++faVixYrWexJQvrthwwZ58cUXtbEZuUtPHySyMhihZJswYYI1BIzEVb90Xb3RVA3W+TAlk0SRgEXVypcvL2+//bYVCBcpUkSmTJmi06islHGvZs2a6QiJqVo8cuSIeA2DEUq277//3tdVNIGYN0KRhrWQ7rvvPmnRooXVSRWjH6+++qqO0JkhfnKv1KlTS9euXXUbgebo0aPFaxiMULLs3r3bWhgPiXHIm/AzBiMUKfhQ+vDDD6Vs2bI68mHUr19f/v77bx0hwdQheUOPgKoaLy6ex2CEkgVtigMTV/08RRM3GPnjjz9s3RfyLpwAVK5cWV544QXtzgmojEEjM+SKIEAhbylbtqz2iTELkqJvjJcwGKFk8Xujs7jwgWCSWNFIylQyEIXC0aNHNZkR6z6tXbtWr8MJwGOPPaYVNKie8fsJgV9GR0aMGCFewmCEgoZugCtXrtTtqlWrSokSJezeJUfA/D1gGHXSpEl27w55BJKisax84IdQlSpVdARu0KBBkjVrVlv3j8KvQ4cOEh0drdvIG7l06ZJ4BYMRChpHRRJebdP46aefbN0X8lbVGhJSzaJ2aGSGQMSvpfR+lDlzZmnTpo3VWddLU8EMRigoOOsPrKLhap//H+byTRkl5u+5aB6FwsCBA61tjLhhjRk/djr2uyZNmljbc+bMEa9gMEJBwUJwpo8G1qEpUKCA3bvkGJizN1M1aN08depUu3eJXO7PP/+0zoLR0Kxu3bp27xLZpH79+tY2gxHyPbZ/vzETjACnaii5PvvsM2v7qaeeYpKqjxUqVEg7XcOSJUu0664XMBihoKZoTDCCYeK2bdvavUuOU6tWLWsRsl9//dUqvyRKqoMHD1ol9NmyZZNOnTrZvUvkkNGRy5cva0DiBQxGKMkwXLxz507dbtSokeTKlcvuXXIcBGlYDwRw5vLbb7/ZvUvkUl9//bXV3r13795sZEaCqipj7ty54gUMRijJ2P496VU1P/74o637Qu6EM98vv/xSt7EiNPqJENX3YN4IgxFKkmvXrsm4ceN0O02aNNbZP8V/9mJ6PyCJ1Us9ASgyEMQeOHBAt1u1aiWFCxe2e5fIAfLnzy8lS5a0RqrPnz8vbsdghJJk3rx5OocNzZs3Z6OlG0Cw1rJlS90+ffq0zJ492+5dIpcnrhLFHR3BFJ5ZH8zNGIxQkrDRWdKwqoaCtXz5cis5EQtQspyXvDxVkzI5fSbQ+W/o0KH6/ZQpU6R69erac8JczBk0oHMgPrxq164tffr0sYYeyV3z1+gCCRkyZODS5IlsUGQSDidOnKjTXESJwXJeupHA4NQLSaxBBSPXr1+Xjz/+WJeMD4R1EhYsWGBd8ubNa32IvfjiixqMYKj61ltvlX79+oXmN6CI+f333+XEiRO6fe+992pAQjeGQKRZs2bWImcLFy60e5fIBQ4fPmyNQqKcFwvgEQXKly+flClTRreXLVsmZ8+eFTdLFcyDMNyMYcPE/vJY6jh16tRWsmOvXr2kYcOGsm/fPk3EiQvBCy6BMC+GIIjsM2bMmFjt3/n/SBy87k01Db5i1JCCY15zXn/tffXVV9YxEMfLdOnSef53puCS5NEJG52e58+fL3fffbc4ESrBQh6MYJ0NlHaOHDlSPvroo1i3rVmzRoOM7Nmz64eVaYa1fft2K/MX8MZC+3BcH18wglUphwwZEuu6du3aSfv27ZO6uxQi6JVhVqDFYk2lS5eWXbt22b1brlCpUiVNZjXTXE8//XSi3pyUsD179ohX4cQLi+ABXicYheR7jeKDQQEDx+eyZcuKExUtWjT0wcjgwYO1/XemTJn+sTgYugRiamb9+vXy/PPP6/AighN8kMUd0sf3CZUj9ejR4x/Dkvv375eCBQvyIG6T8ePHW11EEWSWKlXK7l1yFbwPpk+frrlSR44ckWrVqtm9S66E0QEEIl4+FmB6BtM0gGos5NkRJdTL6IknntDtVatWubr0O0nBCIaDEGi89NJL/7gtcIQD0RryQ5Dhi4NwdHT0P9ph4/uEOgniLBKXQJjmwcHHqwcgpzO9RQDBKP8PST9oIBgxiaw1atSwe5dczcvHgkGDBsVKXPXq70nJlzdvXilfvrwWiCAdAqkTGLl2oyS9yleuXKnDhegv0bRpU01o/Oabb6R///7/uC8yv7GGCWBRn61bt1q3Xbx4Ufbu3Wst9kPOhh4Zv/zyi27nyZMnVkkZJQ6G2s2HCvJGzHuDKBA+UBYvXqzb+JDhe41uxrxGMGqIwhG3SpnUngk///yzjB49Wi916tTRXI5//etf+gYylRYYQcGUDW43VTboPok5LcybDx8+XOe24ssXIefBmbzpHor/N9ZdoaTB+j3m/YDAHGcyRDcq533yySdZzku+6TeSpGAEiac5c+a0LmnTptUpGOSPoCUtEkzvvPNOefXVV6Vr1646egKYcvnggw808RV/OMxtDRgwIFy/E4Wx0RmmaCg4bIBGN4I8EbPuEzobd+nSxe5dIpf0G0nxf0Grm4ORFDEuGS/esWOHJudw/jSy0BsD9ewoHStUqJD+H/g/CA6mJpF4aSps/v77b7t3yXUwFI2pYi8eC95++23p27evbj/33HPy4Ycf2r1L5BK33XabHk8QlBw7dkyLR9zGW+9mCjnkNyAQAZRre+0DIJJQzo4uxaaDcWAeFfkbynlRqQj4QHn88cft3iVyWb8RwNiCW/NG+MlCN2SGjYFTNKGdqkH+FZF5LaB9gSnnTUxfBiIv5Y0wGKEEoUMuuvoB+opgKJCSp02bNtY280bIGDhwoLXN1XkpqZAc7/a8EQYjdMPeIialCKMizOxPPnQirlixom4vXbpUAz7yN7RMMEvAY72vBg0a2L1L5DLZsmWT22+/XbeRO4K8EbdhMEKJqqJBEzsK/VQNyqbJ31jOS6Geqpk3b564DYMRite2bdvkzz//1G1Mz5jVISn5WOJLBpYGMHlZWbJkkQcffNDuXSKXJ7HC3LlzxW0YjFC80LTO4KhIaGGapkSJEtYZDMqnyZ+wIKhpKIjVeeOu4UWUWFgN3FQ7ujFvhMEI3bSKBiW9FDoYhjejI9euXZPJkyfbvUtkUznvF198odss56Xkwsgaup3D2rVrddTNTRiM0D/ghYwL1KxZU4oUKWL3LnkOp2oI+UJohAf33HMP1+qikOaNuG2qhsEI3XCKhr1FwuOOO+6w1mbCgpNYjJD8m7jKcl7ye78RBiMUC0p5zRQN5h+xMB6FHv62pucIFo+cNm2a3btEEYT1uUynTCwa2rBhQ7t3iTygdu3a1kKmHBkh1y9hjkoak52dN29eu3fJszhV418s56VwyJQpk466woYNG+TgwYPiFgxGKBa2f49s9nuOHDl0GyMjFy5csHuXKAJQPTVmzBjdZjkvhVp9l+aNMBihWCuimnyR1KlTxzpzp9BLlSqVtG7dWrfPnTsnM2bMsHuXKMLlvD179pSMGTPavUvkIfUdlDdy+PBhadq0aaLuy2CELAsXLrTak+MFlD17drt3yfM4VeMvWAGbq/NSONWqVUtPJp0QjHz99deJPsliMEIWtn+PPCQuYp4X0G8EvSfIH+W8LVq0kOLFi9u9S+QxGTJkkGrVqun2li1bbFv/KrCPTmIwGCHrhTN+/Hjdjo6OllatWtm9S76QNm1a7TEBJ0+edNUcLyU/cZXIq3kjP/30k+zfvz/R92cwQmr27NlWW3J8OHIeO3I4VeMPWE11/vz5uo21nho3bmz3LpFH1XdA3sjAgQOTdH8GI6RYRWOfu+++W9KlS6fbP//8s7aIJ2+PijzxxBMs56WwqVmzpqRJk8a2YAQtIhYvXqzb5cuXT9RjGIyQXLx4UT8EIXPmzNKsWTO7d8lXMAplMs4PHTokS5YssXuXKMSOHTsmo0ePtt5jXbt2tXuXyMOio6M1IIHt27fL7t27Hd9dmMEIyfTp06125OgKas7SKXLuv/9+a5tTNd4zdOhQDfpNOa9JWiYKl3r16tmSN4JyXjPSnjVrVuncuXOiHsdghFhF4wDI00HfEROMoC0/eaecd9CgQbrNcl7yet7IkCFDdIkLeOihh7S6JzEYjPjc2bNnZcqUKbqdM2dOrpFhk2zZskmDBg10e9euXbp2CXkDSrb37Nmj282bN5cSJUrYvUvkAzVq1LBGuSMVjKAq0/TRwfpbSQm8GYz4HA6Upg1527ZtrWY5FHmsqvGmwKoClvNSJNsG1KpVyzrB2bFjR9ifE7mHppy3ZcuWUqRIkUQ/lsGIz3GKxjnQ28VUWDAY8YbVq1fLvHnzdLt06dIs5yVPT9UMDAi8E5u4ajAY8bHjx4/Lr7/+qtu33HKLLtxG9sEKyVgC3Ky4iQt5q5wXQ9dEXkxiXblypSxatMgq5w0MhBKD7wwfw5CaaT/eoUMHHigdgFU13irn/e6773Qb1TPdunWze5fIZ6pVqybp06e3RkbCmRgft7twUvvo8NPHx9jozHlQWm0wGHG3YcOGWeW8PXr0YDkvRVyaNGms0VasibRt27awPM+RI0dilfN26dIlyT+DwYhPIbvfzCFisa6qVavavUskIoULF5YqVapYw547d+60e5comeW8ZoqGyKt5I0OGDJFLly7pdq9evRJdzhuIwYhPD5QPPvigXL9+3RoVYWtqZ1bVmM645L4qNdP1Eh2NS5YsafcukU/VD3MwErg6Lz5HHnvssaB+DoMRH+rfv7+V4V+gQAF55pln7N4lCsASX3fD1MzLL78cdFUBUShhpNWMVCCJNdR5IxMnTtQpIFPOW6xYsaB+DoMRn/n999/l7bff1u2oqCgt7c2RI4fdu0UBsKJr2bJldRvZ6QcPHrR7lxwBq0ojIdTpf48BAwbIli1brMZTTZo0sXuXyMdSp05tVUoeOHBANm/eHNbE1WAxGPERNKPBOgEmMn7nnXes5CZy5ugI/lc48/C78ePHa4CGipQWLVpYjZWc5u+//5b333/f+hDAmjSsUiOvTtWsWrVKFixYoNt4fyangzffJT6BZekRiCDr2cxjP//883bvFiWAJb7/g9dr+/bt9YKREThx4oQmyTlt/R7kYmEtDnyFV199NdHLpxO5MRiJuzpvsnIPY1xi+/btMdeuXbN7N1zrtddew5FbL/nz5485cuSI3btEN3D9+vWYIkWK6P8rVapUMceOHYvxm/Hjx8fkypXLet3ikiZNGmt78ODBMU7y0UcfWftWrly5mIsXL9q9S0TqypUrMZkzZ9bXZu7cufX4klz4DEmbNq3+zCxZssScOXMmWT+PIyM+MHPmTJ3HDswTwaJ45Fw4wzBTNTjTnjp1qvhpNARN+Nq1a2eN5GXPnl3GjBkTq7oII3smN8Nu27dvl759+1r/O0zPYG0QIidIlSqVlTdy+PDhkHR3jlvOmzFjxmT9PAYjHoeEpcA8kbfeekvuvPNOu3eLEsGPVTU//vijTm2MGzcuViO49evXawn63XffLZ06ddLrz58/ryXqZlrELnhvPfzww9aCk1iptGbNmrbuE1E4p2rwnjOr8yL4TsrqvAmKcQlO0yTd1atXY+rXr28NHd999938G7oI/ld58+bV/126dOmSPQzqZBjy7dChQ6wpmezZs8eMHj061pAy/iZr166NKVGihHW/AQMG2LrvI0eOtPalYMGCMadPn7Z1f4jis2LFCut1ev/998ckdwrV/KyWLVvGhAKDEQ97/fXXY+WJHD582O5doiR65JFHrP/huHHjYrzoxx9/1HnswECkVatWMQcOHPjHfXEMwLFg0aJFMSlTprRyapYvX27Lvh88eDAmW7Zs1n5PnTrVlv0gSszJadasWfV1miNHjmR9ntapU8d6zc+YMSMmFDhN41GzZs2SN99808oTwboBuXLlsnu3KIm8XFWD6hhMveB3xDw2ZMuWTUaPHq25IVjFOCHo3/HKK6/E6ihspkki6emnn9bqHsDvgrJjIieKioqSunXrWos4rl27Nujy9fnz51vlvI0aNQrJ/jEY8SA0hQrME0HyqkleInfBwQMf0IAkVrPwmtsh2EBuCJKpjVatWmluCHJCElMi+Nprr8ntt9+u20jI+/e//y2RNGXKFBk7dqxuo3Hgp59+GtHnJ0qqevXqWdvoxprccl6suRSypURiXILTNIkfimvQoIE1hNa0aVP+3VyuW7dunpkGOHr0aMwDDzwQa0oG0xzfffddosoNzTSNeU2vW7fOKi/EZfbs2RH4LWJiTp06FVOgQAHreb/55puIPC9Rcvz111/Wa7Z169ZBvX+Rv4bHo1Q4lHlsHBnxGFTLzJ49W7dvueUW+fbbb9kB0uW8UlWD0ZBy5cpZS43DvffeK+vWrdORvGDOsPDz3n33Xet7dGg9deqUhBumiMx6HGj3HsyS6USRVrFiRWv5D6xPZhZLTSyUrJvR2Z49eya7nDeWGJfgyMjNzZo1KyZFihQatSK5b968eXbvEoXA+fPnYzJkyGAlnqGBkZvgbKpTp07/GA359ttvk9x8Ke7IiLkusGqsa9euMeG0cOFC67nSp08fs2PHjrA+H1Eo3Xfffdbrd+XKlYl+HI47qBbD4/A5s2XLlpDuF0+ZQ2zXrl2yZ88eW/JEMNcemCdSp06diO8HhV50dLQ0b97cSjwLxzLg4TJp0iTNDUHDMgMre2I0BKMJoZhvxsjfyJEjJXPmzPr9N998E7YRJDR5Qst3A4tOFilSJCzPReSkfiOTJ0+2PtuQqF2iRInQ7liMS7hhZGTZsmU6IoGosXv37jF79+6NWJ5Iw4YNrWi3SZMmjv9bUfB1/e3bt49x42gIygqRW5GcVtTxjYwYo0aNsp4LI0jxlQYnV79+/aznuOOOO/S9R+Qma9asCapHSN26da3H/fbbbyHfLwYjIfTEE0/EOvhGR0frwSvczarefPNN6znz5csXc+jQobA+H0XepUuXrHVaUqdO7ej/MfatUKFCsd4L99xzT8y+ffuS/bNvFIwgyAkcgm7RokVI1uAwVq9erT1NTG+Tv//+O2Q/myhS8J4wxxKsKZOYgBqvdfO+KlOmTEjfVwanaULo119/jfU9+h5gugTDWejjj5VzQw3DbG+88YY1XI3kwNy5c4f8echeadKkkR49euj2lStXZNSoUeJUKHHdvXu3bmfNmlX3FUO8SKgOJ0z5fPXVV5InTx79/pdfftGEu1DAe7d3795W6/mXX35ZKlWqFJKfTRRJeJ+YEl8ke69atcq+ct5AMS7h9JGRrVu3WpFjtWrVYp555hk9gw08O6xQoULM9OnTQ9r90bQLx+Wtt94K2c8m50HCmPlfox16OM5Okgsr1ZqzLowebNu2LaQ//0YjIwbKn83fCYm/eG8m16effmr9zNKlS8dcuHAh2T+TyC6DBw+2Xs/vv/9+ksp5w7XcAUdGQuS3336LtbDXJ598og2cAjtoouNds2bNpGnTprJ69epkn6khARCJq9C4cWOrIyV5E0bYGjZsqNtbt251ZCLrhAkTrJV2UZJcrFixiO8DkuswigHnzp3Tct/kjEoiKf3VV1+1vscoZ7p06UKyr0ROT2IdNmyYVc6L0dlMmTKFZ6eCjWIwh1S1atWYIUOGWNeNGDFCEylRZvff//431pkbFrfCQli1atWK6d27d8z+/fs9NTKCRKCEyqVQCli9evVYoyRIcu3Zs2fQ8+hYHIx5Iv4zduxY6/+O95PT1KxZ09q/cJSWJ2ZkBHD2VrRoUWtf3n333aCeD8cwNA40P+fRRx8Ncs+JnOP69evWqHrGjBkTbBeA603+VzjKeQMFFYzgQICukKjnN8HIggULYpo3bx6zZ88eXYETGf8///yzlXyH2/A9hnE///zzmF69enkmGMHvZPpA5MmTJ8Hkuh9++CGmSJEisYIS9CnAgnZJSXKdO3eutUgYvs6ZMyfEvxG5JZHVSYsfIggPnJIMxzRSYoMRc0wyfXfwt1q1alWSnw+dYQMXmzx58mSQe07kLB07drRe20uXLo33Pj/99JN1H3yGh1NQ0zSo4a9QoYIULVrUum7atGk6PVGgQAHJmTOnTiHgOlixYoWkTp1aWrduLWnTppVevXrpWhL79u2L9+dfvnxZzp49G+uCpD10i3PiZcGCBTocbLoxQtz7IPBr166d9ld4//33JUuWLHq/8+fPS//+/aVUqVI6/Huz3xPTMliQy3TOe/3117WfiN1/A14ic0mVKpVOOwBeK+ivYfc+mcvnn39uvYcfffRRfc2H43nie3/Fd6lVq5a88MIL1t8Ki+nh/ZbY5zl06JAuhGfg98MQtd1/Z154uR6Ci1k0D9C1O777DBw4MFbiarDPlRipJIlOnjypFRs4CH700UfW9Tt27NBciMD57W3btun29u3bpWTJktZtmG9F0ILr8+fP/4/nGDFihH4wB8IHefv27cWJxo8fb21XqVJF55hvpG3bttKgQQM9uH333XeaoX/gwAHp06eP/k0xPx3fwnb4p3bv3l3vC7Vr19ZGZzd7PvIW5B19+OGHuj148GDNSwpLdnsSICvfNDZDi2gEyOF8XSa2sSDmuFHJs3HjRs3ZeuaZZxKdW/Xss89qkzmTh3LrrbfyvUaeUapUKWt7+vTp0rFjx1i34z1jFtPDwAPuH+zrP3DgImTBCA5+ODOPm8SCM44MGTJY32PbLOmNr4G3mdvxmIQOIFirItD+/fulYMGCjlxnZcmSJfoVHwj422Bk6GYKFy6siUEoEcRl4sSJev2mTZv0zBcjLB988IGOQBnvvPOOLFy4ULexvDqCIFPGSP6B1w6CWZzN7Ny5U08EAhPS7ICEbZPkhoAZa8aEAwJyBCJJORZgZeBq1arpiCtKffEeDTwrjA8OzugeC1g1GSdHfK+RlxQqVEgHAzBDgdmLfPnyaQuBwM+bwMA8MQFFsiRlTmfDhg0xnTt3tpqkINfB5Ixg/gm5DMb69et19Vgz7/r888/H+lnIKZk/f77rc0bQZTWwpDdY+Fugo2NgPgnyQR566CFN9kUyYGCeSKRWJyVnQv6ReZ3gvWcnvC+LFy9u7Q+OE+F8rmCOBShfNPtXuHBhXXX3RsmvgU3bhg8fHoI9J3Kezp07W6/zRYsWWdcfO3ZMm3bi+kyZMoWtnDdQkoYZVq5cqcM0WCcDUzK///67rgOBnAdETSg3NDBFU7x4cd1GeV/gbTiDwoqXdpT9hbOk9+677w7652BaZunSpTrUjTNfcxaIMzlMcWEoPjBPxO4zYbIX8q/MCBxyuEw5rR1mzJhhTcmi9LhMmTLiNP/617+sqU8cwzBdk5C+fftaTdvw+2Ckh8hPJb7Dhg2zZjbCWs4bKCmRCxr9oFLGXF5++eWYgQMHatQUWE2DJik4WwuspmnWrFnMxIkTdXvQoEGeqabBCI+JLBcvXhySn4m/83vvvacNZgJHSnBB6TTXwyDAaKN5XXz44Ye27QdavZv9QPZ9OAU7MgJ4HMoYzb7ieBTXkiVLrAocnBmGomEakVNt27Yt1mcL4PMFo4fm+s2bN0dkX5LVgTVwmgYwnImpmXr16t2wz4iZenB7MIIabCyFbhYBC/XS7ijbxHo3UVFRVtlwOBb/InfatGmTdcAoVaqULR1Z8b40H95YXjzU74FQBiMwbNgw62+GEunA/jw4USpfvrx1+wcffBDCPSdynuvXr1tTkuiyijYVGEQw7wEMIkQK28EnA0ZCzD+tXbt2Yf3Q+eyzz3TUiSgQGgya16Ad/WZefPHFiC5HkNxgBAffe++919rnVq1aWUFc//79reurVKkS9sCKyAm6desWq1EhBhTM96FcvuRmnFea4tKF8ZKTL3IzKKlCjTfKoYkCoRzc+PrrryP63Mj9wtwyoI/QQw89JE6HijdUxuTKlUu/R8UM2hRg6Ya33npLr4uKitJcLfR0IfK6ev+3aB6g3QSq9AC5iqZvViQwGAlRMBLYY4UoUtBo0CSy/vjjj3L06NGIPffYsWOtPhzoA+SW0lesah3YxwiNzdCkEY3RAI3SbrvtNhv3kMieJNbAnllPPvlkRFtpMBgJEg76y5Yt0+2KFSvG27yNKNzQ0dhUe6CPxqhRoyL23IMGDbK2H3/8cXGTVq1aaZUAnDlzxlpGHWeDr732ms17RxQ5qN6M20MEjQtNp+dIYTASJJQ1I+cm3FM0RDdjVqg1UzXmdRlOCMRNMI5RhJo1a4rb/Pe//7XK6A2MmERHR9u2T0R2iNsqAoF65syZI7oPDEYcni9ClJicIjPvu3nzZpk/f37ER0XsbkcfDBxsMZJk9h35NzfrzErkh2DkiSeeiPg+MBgJApqPmWZnaGuPNWKI/JLIiilKtFiHrFmz6vpIboXgY968eZqwGhhgEfktGImKirLWvgpctyZSGIwEYfXq1bqiJ2CNEMzbE9npvvvukxw5cuj2hAkTrMTScBg+fLhcunTJGs5Nnz69uBk6s2IlcVbPkF/lz59fq8qQf/bVV1/Zsg8MRoLAKhryayLrtWvX5IsvvrC+f/TRR8PyPEQUWagoGzFihC5CaQcGI0Fgvgj5NZEVq9lipWATiKP6hIgouRiMJNHp06dl0aJFul2iRAlrMUAiu5UuXdpKwNy0aZMsWLAg5M/h5nJeInIuBiNJhO50V69e1W2OipDTPPzww2FLZMXK22ZUECWxWL2biCgUGIwkEadoyOkdWcOVyBo3V8Rk3xMRJReDkSTAHLwJRtKkSROrpz+RE6RLl87qnIiKl2+++SYkP/f8+fNaRWOSZVF9QkQUKgxGkgANpXbt2qXbderU0R4jRH5IZP3+++/l5MmTut2hQwdrPRwiolBgMJIEnKIhNyhTpoyVyLpx40ZZuHBhsn4eghkmrhJRODEYSQIGI+TGjqzJbWK0dOlSayG5qlWrSrVq1ZK9f0REgRiMJNKFCxdk7ty5Vre6cuXK2b1LRDfsyJo9e/aQJLJyVISIwo3BSCJh8bGLFy9aoyJuXBiM/JvI+u233wb1cw4fPizjx4/XbQQ3yBchIgo1BiOJxCkacvviecEksmIBObSXB1TQREdHh3QfiYiAwUgSgxH0VmjUqJHdu0OUqERWVH3Bhg0brM7BiYXmfl9++aVuYySQ69AQUbgwGEkElPOiKgFq1Kihy6YTeT2RderUqbJnzx7dRrfVokWLhnz/iIiAwUgi/Pbbb9Y2p2jITe6//34rkRW5H8ePH0/0Y5m4SkSRwmAkEZgvQn5LZMVCezNnztRtLAaJFXqJiMKFwchNXLlyxTooo+tk5cqV7d4lorB3ZB08eLC1jVyRlCl5qCCi8OER5iaWLFkiZ86c0W2cHfKgTG5TtmxZueuuu3R7/fr1N01kPXv2rIwcOdIaWenRo0dE9pOI/IufrEmYouFQNXmlzPdGRo8eLadPn9btTp06WTknREThwmAkCcFIkyZNbN0XomC1bdtWsmXLptvjxo1LMJGV69AQkR0YjNzAwYMHrTU5kCuSJ08eu3eJKCSJrN99912898OiemvWrLHK2JkjRUSRwGDkBmbMmGFts4qGvJTIip4j8SWyclSEiOzAYOQG2F+EvASLO955551WIuvixYtj3X7gwAH58ccfdTtXrlzSrl07W/aTiPyHwUgCrl27ZgUjmTNn1iFrIrd7+OGHE0xkHTJkiLaAh4ceekjSpk0b8f0jIn9iMJKAlStXWsuuYy2a1KlT271LRCHpyBqYyHrixAmrn45pF4/y9UceecTW/SQif2EwkgB2XSUvwqq7Xbt21e2LFy9aiayTJk2S/fv363bLli2lUKFCtu4nEfkLg5EEsL8I+WXxPJbzEpHdGIzEA0PXS5cutbpX8iyRvJrIum7dOhk6dKjMnTtXvy9VqpQ0bNjQ5j0kIr9hMBIPrEVz/fp13eYUDXl9dCRwJOSxxx7jkgdEFHE86sSD+SLkh46sWbNmtZJXIX369FZjNCKiSGIwEgfmz00wgmS/OnXq2L1LRGFNZDW6dOliBShERJHEYCQOzKGbqoJ69eppG20ir0/VABNXicgurglG9u3bF5Hn4RQN+UX58uXlvvvu0218rVSpkt27REQ+5Zpg5IEHHpBdu3aF/XkYjJCfjB49WpYtWyZjxoyxe1eIyMdSxMS3WpYDpUiRQooUKaIliIULFw7Lc5w9e1Zy5Mghly9flqJFi8q2bdv0eYnIGVDlhpMSHANY9UPkHa56N+/cuVPq168fthESBDoIREyjMwYiRERE4eeaYAQjFbBjx46wBSScoiEiIoo81wQjmNNGd8jAgGT37t1hCUZSpUolDRo0COnPJiIiIpcHI3ny5JFZs2bFCkhQehuqgGTr1q2aIwJolZ0pU6aQ/FwiIiLySDACt9xyi8yZM0dKliwZ8oDkt99+s7Y5RUNERBQ5rgpGTECCRNPAgCQUUzbMFyEiIrKH64KR+AKS7du3a0CyZ8+eoH7epUuXZPbs2bqdN29eNn8iIiKKIFcGI/FN2SAgwZRNMAHJwoUL5fz589aoCEt6iYiIHByMvP3229qDo27dutKhQweZP3++Xj9lyhSpXr263HXXXdbl4MGDsdZ86dixo9SuXVvXxDhw4ECydz5//vwhCUg4RUNEROSiDqxoPIZRiTRp0miA8dhjj8mkSZNkwYIFMn36dBk8ePA/HoNGYm3atJHevXtLs2bNZOjQobJq1Sr9mljIDUmo6yLWrcE0zZYtW/T7YsWK6TROwYIFE/WzK1asKGvXrtURkSNHjmgXViJyHnZgJfKmVEl9AFqyG/jwvnr1qn6A38iKFSskderU0rp1a/2+V69e0rBhQw0iMLoRX/BiOqEaV65c0QNRfPLly6dlv+gNghJdk0OC624WkOzdu1cDEahWrZpky5YtwechInuZ9ybfo0TukZgThyQHI/Cf//xHp2WQ+IlplxIlSsjGjRtlzZo1GmRkz55dp3Datm2r90dwYKZSIF26dFKgQAG9Pr5gZMSIETJkyJBY17Vr107at29/w/365ptvrAX10DMEU0loloZgJSFjx461tjHNFInF+IgoeYJNVici+zqohzwYefnll+WFF17QEQ+zmFzlypX1gx3VKOvXr5fnn39eRxkQnFy4cEEyZMgQ62fge5M0GlePHj2kc+fOsa7bv3+/jnLcKMLC0C1yWPCcGCFBYNG1a1etlEHwEx/8DgYCqHAtwkdEyYcREQQiNzsWEJG7BBWMQFRUlE5rfP/993pgQNdSo0KFCpqsiuRSBAbR0dFy7ty5WI/H9+nTp4/3ZyMfBZdAmObBwedmB6BChQrp82KaxnRVxfQNckjiBiSYYpo5c6ZuI3DCyAgPcETOl5hjARG5R7LfzdeuXdO8i7gwWmJyY5FQisDAuHjxoj4G14cDgg4EJMWLF9fvEZAgOIm7n3/++aecPHlSt5s0aaIBFhERETk4GDl79qyWwWJ6xYwqLF++XG6//XZZvHixnDhxQu+H/BFM2dSpU0e/r1KliuaXoOoGianDhw+XsmXLxpsvEsqABKMhJiBBMISABEmzBkt6iYiIXFbai2Dkueeek02bNumoB6ZnevbsqdMgn3zyiUybNk3zQ3Lnzq3JppiqMVAGPGDAAJ3vLVeunLz55ps3TCxNSmnvjWA0BL1HzCJ4SLZFkIJACNNMy5Yts3JSkrI/RBR5LO0l8qYk9xmxS7DBCCAAwqhIYEAyYcIEHdHBr3/rrbfKX3/9FYa9JqJQYjBC5E2+eDdjBCcwhwRTNrVq1bJyWtBRloiIiOzhi2AkMCAxSbOBZcXMFyEiIrKPb4IRE5AgXySwigf9TtC4jYiIiOzhq2AkMCAxUzZoiha3pwkRERG5oOmZ2wMSrEeDpFV0jiUiIiL7+DIYMevj1KhRw+7dICIi8j3fTdMQERGRszAYISIiIlsxGCEiIiJbMRghIiIiWzEYISIiIlsxGCEiIiJbMRghIiIiWzEYISIiIlsxGCEiIiJbMRghIiIiWzEYISIiIlsxGCEiIiJbMRghIiIiWzEYISIiIluliImJibF3F4iIiMjPODJCREREtmIwQkRERLZiMEJERES2YjBCREREtmIwQkRERLZiMEJERES2YjBCREREtmIwQq61f/9+qV69ut27QUQ24nHAGxiMOMx9990nnTt3Fr9r2bKl/PXXX+I348ePl/vvv19q166tf4MhQ4bItWvXbviYKVOmyGOPPRaxfaTw43HA38cBPx4LUtm9A/T/rV27Vo4ePSqXL1+WHTt2SNGiRZP0eDTTxSVlSsaYbjRixAg9AL311ltSqVIl2b59u/Tt21eOHDkir776qt27RxHC4wCN8OGxwDGvVj9HwMb06dOlbt26OuQ4bdo06/qqVavKDz/8IC1atJCmTZvKN998Y932xhtvyHvvvSePPPKI3HnnnbJ3717xEvx+Q4cO9UTkfyNnz57V3/Oll16SypUrS6pUqaRUqVIyYMAAmThxouzatUtOnDgh//73v6Vx48bSsGFD+eyzz/T//e6778qKFSvkrrvukvbt24vb+f1YwOOAf48Dfj4WcGTEIa5evSq///67Rr9nzpyRL7/8Ut9sKVKk0NsXLlwoY8eO1TOmhx9+WMqUKSPVqlXT22bMmCGff/65lCxZ0ubfgoK1evVqfQ3ggyRQ6dKlJW/evLJ8+XKZPXu2buOAFBUVJZs3b5YCBQrIK6+8oh9ggwcPtm3/KTR4HKDVPj0WpHLiEOX777+v0V+mTJmkS5cu0rFjR73tq6++kj179siVK1dkyZIlOnz5zjvvSP78+cXtli5dqr9XzZo1dXgWv9eqVas0Mobu3btLxowZ9dKqVSs9YJmDUIMGDaRs2bI2/waUHCdPnpSsWbPqgSWu7Nmz6+0445kzZ45ER0fr9Ri+9TI/Hgt4HKCTPj0WOGaaxsCQFObE8IfGgeiLL76QjRs3Wrfj+nbt2mlkWLhwYfn666/FCxDN1qtXT1KnTi0ZMmSQWrVq6XUGomAjT548emYU+D25W5YsWfQgE1+C2vHjx/XAhAOROfj4gR+PBTwOUBafHgscF4xg2BEXJF+VK1dOM4n//vtv63acBWDuFAeqJk2ayJYtW8Ttzp8/L/PmzdODKuaCcfnjjz9k1qxZenYEBw8etO5/6NAhyZkzp/gB3nCXLl2yvj927Jh4Ec5s8JrGMHygTZs2yYEDB6RixYo6T3zx4sV/PNYM4XuN344FPA4kzC/HAT8fCxwXjGzbtk3nSBs1aqRJXDj7OXXqlHU7IkIjXbp0+gZ2Oxx8MmfOLD/++KOMHj1aLxMmTNAI2LwgkayGxKadO3fK5MmT9e/jB5j/XrRokf7uSNDC7+5FmIbo0aOHJiGuXLlS54zx4dqvXz+59957pUqVKjpU/9FHH+lrHgeiNWvW6GOzZcumH0x4jJf47VjA40DC/HIc8POxwHE5IxiOvf322+Xjjz/WAwyGaVGm5mUYhsX8b9yzHLzwzBAthms7dOig88mdOnXyTZOf5s2ba04AKgiKFCmiZ4uBZ8de8tBDD+mBCOV8OAPGhy0qS3r16qW343q8P3AdzoDatGmjZ0l33HGH3HLLLZpZj6F6VFx4gd+OBTwOJMxPxwG/HgscF4wg0kNyVtq0aTVxC9Ew5oO9bNCgQfFe/+STT+pXDEXXr1/fSt6LW/LmRXgdIIkLH0IffPBBvPfBmw7D2F6CDxpc4oOzHpTuxZUmTRot7fMavx0LeBz4J78eB/x4LHDcNA3eeGj2gmHZMWPGSJ06dezeJYowlK7hDDhfvnx27wrZiMcCf+NxwF9SOS0CxhDcpEmT4r0P6uoD4UwBddbkHW+//baWN2JIHmfE5D88FhCPA/6TIsYBk7CIgF988UWdF+ULj8i/eCwg8ifbR0YYARMR8FhA5F+OGBkhIiIi/3JcAisRERH5C4MRIiIi8lcwgo6CnTt31mY9WOzKwGwRvkdTG6zNgKYuaOxj9OnTRxv+YGlkXJ566inrNrQJRgMYNMLBcsoJ1esTkXMEeyyAUaNG6e0o90Xzr3Pnzlm3jRw5UjuTYuG4Tz/91NON0oi8IuLBCLoLIrDAgSLQlClTdA2GESNGyC+//KJrDwwZMiTWfbCs9oIFC/QycODAWAefrVu3ak8CXNAAh2V+RM4W7LFg3Lhx2o1z2LBhupZL//79dWE5QNt0HANwTMD9Fi9enGB5MBH5OBjBmQ6aGKHVbSAcRO677z7JnTu3rlbZrVs3mTp1aqJ+Jh6LMyys64C2uQ888IA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"text/plain": [ "
" ] @@ -157,7 +259,7579 @@ } ], "source": [ - "val_energy.plot(label=\"consumption\");" + "val_passengers.plot(label=\"passengers\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "3cccd20e", + "metadata": {}, + "outputs": [], + "source": [ + "# use last 30 days of data to predict next 7 days\n", + "model = Chronos2Model(\n", + " input_chunk_length=12,\n", + " output_chunk_length=6,\n", + " n_epochs=10,\n", + " pl_trainer_kwargs={\"accelerator\": \"mps\"},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "08715193", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=12, output_chunk_length=6, n_epochs=10, pl_trainer_kwargs={'accelerator': 'mps'})" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(\n", + " series=train_passengers,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f9c8b482", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0be2b2c066ac4d7f958b8a4b10778d4c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"actual\")\n", + "pred.plot(label=\"forecast\");" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c54a89eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('shared.weight',\n", + " tensor([[-1.4855e-06, 6.6741e-07, -2.3774e-07, ..., 2.5759e-07,\n", + " 1.6342e-06, 2.2832e-06],\n", + " [-2.4927e-03, 3.1087e-03, 1.2331e-03, ..., -1.2153e-02,\n", + " -7.9643e-04, -9.8041e-03]])),\n", + " ('input_patch_embedding.hidden_layer.weight',\n", + " tensor([[-1.6344e-03, -1.7315e-03, -1.7230e-03, ..., -6.8476e-03,\n", + " -1.5899e-03, -7.6784e-03],\n", + " [ 1.5054e-04, 1.0679e-04, 2.0166e-04, ..., 3.2022e-03,\n", + " 3.9019e-03, 3.4222e-03],\n", + " [-8.2940e-04, -8.7882e-04, -8.5499e-04, ..., -3.6055e-03,\n", + " 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+ }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.model.model.state_dict()" ] }, { @@ -167,86 +7841,236 @@ "metadata": {}, "outputs": [], "source": [ - "from peft import LoraConfig" + "from peft import LoraConfig, PeftModel, get_peft_model" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "id": "60b4811d", "metadata": {}, "outputs": [], "source": [ "peft_config = LoraConfig(\n", - " r=8,\n", + " r=32,\n", " lora_alpha=32,\n", - " target_modules=[\"q\", \"v\"], # optionally indicate target modules\n", + " target_modules=[\"v\"], # optionally indicate target modules\n", ")" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "3cccd20e", + "execution_count": 36, + "id": "61beeeae", "metadata": {}, "outputs": [], "source": [ - "# use last 30 days of data to predict next 7 days\n", - "model = Chronos2Model(\n", - " input_chunk_length=30 * 24 * 4,\n", - " output_chunk_length=7 * 24 * 4,\n", - " peft_config=peft_config,\n", - " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", + "model.model.model = get_peft_model(model.model.model, peft_config)\n", + "model._enable_finetuning = True" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "5f1a55b0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "64afd717f77145f08f46ae765ae9cd00", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"actual\")\n", + "pred.plot(label=\"forecast\");" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "cc169bfa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "trainable params: 589,824 || all params: 120,067,488 || trainable%: 0.4912\n" + "trainable params: 1,179,648 || all params: 120,657,312 || trainable%: 0.9777\n" ] - }, + } + ], + "source": [ + "model.model.model.print_trainable_parameters()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "f44f6966", + "metadata": {}, + "outputs": [], + "source": [ + "model.model.model.save_pretrained(\"chronos2-lora-passengers\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a6a4328e", + "metadata": {}, + "outputs": [], + "source": [ + "# use last 30 days of data to predict next 7 days\n", + "model_loaded = Chronos2Model(\n", + " input_chunk_length=12,\n", + " output_chunk_length=6,\n", + " n_epochs=10,\n", + " pl_trainer_kwargs={\"accelerator\": \"mps\"},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7e7adc07", + "metadata": {}, + "outputs": [ { - "ename": "MisconfigurationException", - "evalue": "No supported gpu backend found!", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mMisconfigurationException\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mseries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_energy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\utils\\torch.py:94\u001b[39m, in \u001b[36mrandom_method..decorator\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m fork_rng():\n\u001b[32m 93\u001b[39m manual_seed(random_instance.randint(\u001b[32m0\u001b[39m, high=MAX_TORCH_SEED_VALUE))\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdecorated\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:958\u001b[39m, in \u001b[36mTorchForecastingModel.fit\u001b[39m\u001b[34m(self, series, past_covariates, future_covariates, val_series, val_past_covariates, val_future_covariates, trainer, verbose, epochs, max_samples_per_ts, dataloader_kwargs, sample_weight, val_sample_weight, stride, load_best)\u001b[39m\n\u001b[32m 951\u001b[39m \u001b[38;5;66;03m# call super fit only if user is actually fitting the model\u001b[39;00m\n\u001b[32m 952\u001b[39m \u001b[38;5;28msuper\u001b[39m().fit(\n\u001b[32m 953\u001b[39m series=seq2series(series),\n\u001b[32m 954\u001b[39m past_covariates=seq2series(past_covariates),\n\u001b[32m 955\u001b[39m future_covariates=seq2series(future_covariates),\n\u001b[32m 956\u001b[39m verbose=verbose,\n\u001b[32m 957\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m958\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfit_from_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\utils\\torch.py:94\u001b[39m, in \u001b[36mrandom_method..decorator\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m fork_rng():\n\u001b[32m 93\u001b[39m manual_seed(random_instance.randint(\u001b[32m0\u001b[39m, high=MAX_TORCH_SEED_VALUE))\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdecorated\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:1150\u001b[39m, in \u001b[36mTorchForecastingModel.fit_from_dataset\u001b[39m\u001b[34m(self, train_dataset, val_dataset, trainer, verbose, epochs, dataloader_kwargs, load_best)\u001b[39m\n\u001b[32m 1091\u001b[39m \u001b[38;5;129m@random_method\u001b[39m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mfit_from_dataset\u001b[39m(\n\u001b[32m 1093\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1100\u001b[39m load_best: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 1101\u001b[39m ) -> \u001b[33m\"\u001b[39m\u001b[33mTorchForecastingModel\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 1102\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1103\u001b[39m \u001b[33;03m Train the model with a specific :class:`darts.utils.data.TorchTrainingDataset` instance.\u001b[39;00m\n\u001b[32m 1104\u001b[39m \u001b[33;03m These datasets implement a PyTorch ``Dataset``, and specify how the target and covariates are sliced\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1147\u001b[39m \u001b[33;03m Fitted model.\u001b[39;00m\n\u001b[32m 1148\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 1149\u001b[39m \u001b[38;5;28mself\u001b[39m._train(\n\u001b[32m-> \u001b[39m\u001b[32m1150\u001b[39m *\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setup_for_train\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1151\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1152\u001b[39m \u001b[43m \u001b[49m\u001b[43mval_dataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1153\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1154\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1155\u001b[39m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1156\u001b[39m \u001b[43m \u001b[49m\u001b[43mdataloader_kwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataloader_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1157\u001b[39m \u001b[43m \u001b[49m\u001b[43mload_best\u001b[49m\u001b[43m=\u001b[49m\u001b[43mload_best\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1158\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1159\u001b[39m )\n\u001b[32m 1160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:1309\u001b[39m, in \u001b[36mTorchForecastingModel._setup_for_train\u001b[39m\u001b[34m(self, train_dataset, val_dataset, trainer, verbose, epochs, dataloader_kwargs, load_best)\u001b[39m\n\u001b[32m 1306\u001b[39m train_num_epochs = epochs \u001b[38;5;28;01mif\u001b[39;00m epochs > \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m.n_epochs\n\u001b[32m 1308\u001b[39m \u001b[38;5;66;03m# setup trainer\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1309\u001b[39m trainer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setup_trainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_num_epochs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1311\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m model.epochs_trained > \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.load_ckpt_path:\n\u001b[32m 1312\u001b[39m logger.warning(\n\u001b[32m 1313\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAttempting to retrain/fine-tune the model without resuming from a checkpoint. This is currently \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1314\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mdiscouraged. Consider model `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.load_weights()` to load the weights for \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1315\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mfine-tuning.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1316\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:525\u001b[39m, in \u001b[36mTorchForecastingModel._setup_trainer\u001b[39m\u001b[34m(self, trainer, model, verbose, epochs)\u001b[39m\n\u001b[32m 520\u001b[39m trainer_params[\u001b[33m\"\u001b[39m\u001b[33menable_model_summary\u001b[39m\u001b[33m\"\u001b[39m] = (\n\u001b[32m 521\u001b[39m verbose \u001b[38;5;28;01mif\u001b[39;00m model.epochs_trained == \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 522\u001b[39m )\n\u001b[32m 523\u001b[39m trainer_params[\u001b[33m\"\u001b[39m\u001b[33menable_progress_bar\u001b[39m\u001b[33m\"\u001b[39m] = verbose\n\u001b[32m--> \u001b[39m\u001b[32m525\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_init_trainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer_params\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrainer_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_epochs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32md:\\Projects\\darts\\darts\\models\\forecasting\\torch_forecasting_model.py:538\u001b[39m, in \u001b[36mTorchForecastingModel._init_trainer\u001b[39m\u001b[34m(trainer_params, max_epochs)\u001b[39m\n\u001b[32m 536\u001b[39m \u001b[38;5;66;03m# prevent lightning from adding callbacks to the callbacks list in `self.trainer_params`\u001b[39;00m\n\u001b[32m 537\u001b[39m callbacks = trainer_params_copy.pop(\u001b[33m\"\u001b[39m\u001b[33mcallbacks\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m538\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mTrainer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 539\u001b[39m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 540\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtrainer_params_copy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 541\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\utilities\\argparse.py:70\u001b[39m, in \u001b[36m_defaults_from_env_vars..insert_env_defaults\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 67\u001b[39m kwargs = \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mlist\u001b[39m(env_variables.items()) + \u001b[38;5;28mlist\u001b[39m(kwargs.items()))\n\u001b[32m 69\u001b[39m \u001b[38;5;66;03m# all args were already moved to kwargs\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m70\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:404\u001b[39m, in \u001b[36mTrainer.__init__\u001b[39m\u001b[34m(self, accelerator, strategy, devices, num_nodes, precision, logger, callbacks, fast_dev_run, max_epochs, min_epochs, max_steps, min_steps, max_time, limit_train_batches, limit_val_batches, limit_test_batches, limit_predict_batches, overfit_batches, val_check_interval, check_val_every_n_epoch, num_sanity_val_steps, log_every_n_steps, enable_checkpointing, enable_progress_bar, enable_model_summary, accumulate_grad_batches, gradient_clip_val, gradient_clip_algorithm, deterministic, benchmark, inference_mode, use_distributed_sampler, profiler, detect_anomaly, barebones, plugins, sync_batchnorm, reload_dataloaders_every_n_epochs, default_root_dir, model_registry)\u001b[39m\n\u001b[32m 401\u001b[39m \u001b[38;5;66;03m# init connectors\u001b[39;00m\n\u001b[32m 402\u001b[39m \u001b[38;5;28mself\u001b[39m._data_connector = _DataConnector(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m404\u001b[39m \u001b[38;5;28mself\u001b[39m._accelerator_connector = \u001b[43m_AcceleratorConnector\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 405\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevices\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdevices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[43m \u001b[49m\u001b[43maccelerator\u001b[49m\u001b[43m=\u001b[49m\u001b[43maccelerator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 407\u001b[39m \u001b[43m \u001b[49m\u001b[43mstrategy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstrategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 408\u001b[39m \u001b[43m \u001b[49m\u001b[43mnum_nodes\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_nodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[43m \u001b[49m\u001b[43msync_batchnorm\u001b[49m\u001b[43m=\u001b[49m\u001b[43msync_batchnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 410\u001b[39m \u001b[43m \u001b[49m\u001b[43mbenchmark\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbenchmark\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 411\u001b[39m \u001b[43m \u001b[49m\u001b[43muse_distributed_sampler\u001b[49m\u001b[43m=\u001b[49m\u001b[43muse_distributed_sampler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 412\u001b[39m \u001b[43m \u001b[49m\u001b[43mdeterministic\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdeterministic\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 413\u001b[39m \u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 414\u001b[39m \u001b[43m \u001b[49m\u001b[43mplugins\u001b[49m\u001b[43m=\u001b[49m\u001b[43mplugins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 415\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 416\u001b[39m \u001b[38;5;28mself\u001b[39m._logger_connector = _LoggerConnector(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m 417\u001b[39m \u001b[38;5;28mself\u001b[39m._callback_connector = _CallbackConnector(\u001b[38;5;28mself\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\accelerator_connector.py:144\u001b[39m, in \u001b[36m_AcceleratorConnector.__init__\u001b[39m\u001b[34m(self, devices, num_nodes, accelerator, strategy, plugins, precision, sync_batchnorm, benchmark, use_distributed_sampler, deterministic)\u001b[39m\n\u001b[32m 142\u001b[39m \u001b[38;5;28mself\u001b[39m._accelerator_flag = \u001b[38;5;28mself\u001b[39m._choose_auto_accelerator()\n\u001b[32m 143\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._accelerator_flag == \u001b[33m\"\u001b[39m\u001b[33mgpu\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m144\u001b[39m \u001b[38;5;28mself\u001b[39m._accelerator_flag = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_choose_gpu_accelerator_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 146\u001b[39m \u001b[38;5;28mself\u001b[39m._check_device_config_and_set_final_flags(devices=devices, num_nodes=num_nodes)\n\u001b[32m 147\u001b[39m \u001b[38;5;28mself\u001b[39m._set_parallel_devices_and_init_accelerator()\n", - "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Kurokabe\\miniconda3\\envs\\darts\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\accelerator_connector.py:354\u001b[39m, in \u001b[36m_AcceleratorConnector._choose_gpu_accelerator_backend\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 352\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m CUDAAccelerator.is_available():\n\u001b[32m 353\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mcuda\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[33m\"\u001b[39m\u001b[33mNo supported gpu backend found!\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mMisconfigurationException\u001b[39m: No supported gpu backend found!" - ] + "data": { + "text/plain": [ + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=12, output_chunk_length=6, n_epochs=10, pl_trainer_kwargs={'accelerator': 'mps'})" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model.fit(\n", - " series=train_energy,\n", + "model_loaded.fit(\n", + " series=train_passengers,\n", " verbose=True,\n", ")" ] }, + { + "cell_type": "code", + "execution_count": 43, + "id": "6bc9e736", + "metadata": {}, + "outputs": [], + "source": [ + "model_loaded.model.model = PeftModel.from_pretrained(\n", + " model_loaded.model.model, \"chronos2-lora-passengers\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "3306a586", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7ea0d32c134f48ae9984a2179f2c138d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"actual\")\n", + "pred.plot(label=\"forecast\");" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "e189768a", + "id": "41eeeb2e", "metadata": {}, "outputs": [], "source": [] @@ -268,7 +8092,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.13.7" } }, "nbformat": 4, From 9057acbba7885f1cbda25311107455139d978f03 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 16 Jan 2026 09:54:19 +0100 Subject: [PATCH 04/26] wip: add transform callback for foundation model fine-tuning --- darts/models/forecasting/foundation_model.py | 146 + .../26-Chronos-2-finetuning-examples.ipynb | 7857 +---------------- 2 files changed, 279 insertions(+), 7724 deletions(-) diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index 01ddc1f4de..1d87b53d4a 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -10,7 +10,11 @@ """ from abc import ABC +from functools import partial +from typing import Any, Callable +import pytorch_lightning as pl +from pytorch_lightning.callbacks import Callback from torch import nn from darts.logging import get_logger, raise_log @@ -186,3 +190,145 @@ class FoundationPLModule(PLForecastingModule): def __init__(self, **kwargs): super().__init__(**kwargs) self.model: nn.Module + + +class ModelTransformCallback(Callback): + def __init__( + self, + transform_fn: Callable[[nn.Module], nn.Module], + model_attribute: str = "model", + ): + super().__init__() + self.transform_fn = transform_fn + self.model_attribute = model_attribute + self._transformed = False + + def _get_inner_model(self, pl_module: pl.LightningModule) -> nn.Module: + """Get the inner model from the Lightning module.""" + return getattr(pl_module, self.model_attribute) + + def _set_inner_model(self, pl_module: pl.LightningModule, model: nn.Module): + """Set the inner model on the Lightning module.""" + setattr(pl_module, self.model_attribute, model) + + def on_fit_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule): + """Apply transformation before training begins.""" + if not self._transformed: + inner_model = self._get_inner_model(pl_module) + transformed_model = self.transform_fn(inner_model) + self._set_inner_model(pl_module, transformed_model) + self._transformed = True + + # Log trainable parameters + trainable = sum( + p.numel() for p in pl_module.parameters() if p.requires_grad + ) + total = sum(p.numel() for p in pl_module.parameters()) + print( + f"Model transformed. Trainable: {trainable:,}/{total:,} ({100 * trainable / total:.2f}%)" + ) + + def on_save_checkpoint( + self, + trainer: pl.Trainer, + pl_module: pl.LightningModule, + checkpoint: dict[str, Any], + ): + """ + Handle checkpoint saving for transformed models. + + For PEFT models, we could optionally save just the adapter weights + or mark the checkpoint as requiring transformation on load. + """ + # Mark that this checkpoint was saved with a transformed model + checkpoint["model_transform_applied"] = True + + # TODO maybe replace in checkpoint["state_dict"] with pl_module.model.get_base_model().state_dict() + # and adapt the keys names accordingly + + def on_load_checkpoint( + self, + trainer: pl.Trainer, + pl_module: pl.LightningModule, + checkpoint: dict[str, Any], + ): + """ + Apply transformation before loading checkpoint weights. + + This ensures the model structure matches the saved weights. + """ + if checkpoint.get("model_transform_applied", False) and not self._transformed: + inner_model = self._get_inner_model(pl_module) + transformed_model = self.transform_fn(inner_model) + self._set_inner_model(pl_module, transformed_model) + self._transformed = True + + +class LayerFreezeCallback(ModelTransformCallback): + @classmethod + def _freeze_layers( + cls, model: nn.Module, freeze_patterns: list[str], unfreeze_patterns: list[str] + ) -> nn.Module: + for name, param in model.named_parameters(): + if any(name.startswith(layer) for layer in freeze_patterns): + param.requires_grad = False + if any(name.startswith(layer) for layer in unfreeze_patterns): + param.requires_grad = True + return model + + def __init__( + self, + freeze_patterns: list[str], + unfreeze_patterns: list[str] = None, + model_attribute: str = "model", + ): + unfreeze_patterns = unfreeze_patterns or [] + + super().__init__( + transform_fn=partial( + self._freeze_layers, + freeze_patterns=freeze_patterns, + unfreeze_patterns=unfreeze_patterns, + ), + model_attribute=model_attribute, + ) + + +class PeftCallback(ModelTransformCallback): + @classmethod + def _apply_peft(cls, model: nn.Module, peft_config) -> nn.Module: + try: + from peft import get_peft_model + except ImportError: + raise ImportError( + "Please install the `peft` package to use PeftCallback: `pip install peft`." + ) + peft_model = get_peft_model(model, peft_config) + return peft_model + + def __init__( + self, + peft_config=None, + model_attribute: str = "model", + ): + super().__init__( + transform_fn=partial(self._apply_peft, peft_config=peft_config), + model_attribute=model_attribute, + ) + self.peft_config = peft_config + + def on_save_checkpoint(self, trainer, pl_module, checkpoint): + checkpoint["peft_applied"] = True + # Optionally store config for reference + if self.peft_config is not None: + checkpoint["peft_config"] = self.peft_config.to_dict() + + def on_load_checkpoint( + self, + trainer: pl.Trainer, + pl_module: pl.LightningModule, + checkpoint: dict[str, Any], + ): + """Apply PEFT structure before loading weights.""" + if checkpoint.get("peft_applied", False): + self._apply_peft(pl_module, peft_config=self.peft_config) diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index 79d0bd8481..4cc65edb8e 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 1, "id": "310fa52a", "metadata": {}, "outputs": [], @@ -39,19 +39,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "id": "bfa59f65", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -60,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "d510b54b", "metadata": {}, "outputs": [], @@ -113,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "id": "2f87bcc5", "metadata": {}, "outputs": [], @@ -130,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "a84830af", "metadata": {}, "outputs": [ @@ -214,7 +205,7 @@ "shape: (24, 1, 1), freq: MS, size: 96.00 B" ] }, - "execution_count": 27, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "id": "3b43a60a", "metadata": {}, "outputs": [ @@ -243,7 +234,7 @@ "" ] }, - "execution_count": 28, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -263,70 +254,28 @@ ] }, { - "cell_type": "code", - "execution_count": 29, - "id": "3cccd20e", + "cell_type": "markdown", + "id": "1313019f", "metadata": {}, - "outputs": [], "source": [ - "# use last 30 days of data to predict next 7 days\n", - "model = Chronos2Model(\n", - " input_chunk_length=12,\n", - " output_chunk_length=6,\n", - " n_epochs=10,\n", - " pl_trainer_kwargs={\"accelerator\": \"mps\"},\n", - ")" + "# Full fine-tuning" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "08715193", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=12, output_chunk_length=6, n_epochs=10, pl_trainer_kwargs={'accelerator': 'mps'})" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(\n", - " series=train_passengers,\n", - " verbose=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "f9c8b482", + "execution_count": 10, + "id": "72832dff", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0be2b2c066ac4d7f958b8a4b10778d4c", + "model_id": "fd9f95a0195d448cbc9d961c5cb274d8", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Predicting: | | 0/? [00:00" + "Training: | | 0/? [00:00" + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=12, output_chunk_length=6, enable_finetuning=True, n_epochs=10, pl_trainer_kwargs={'callbacks': []})" ] }, + "execution_count": 6, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "pred = model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", + "from peft import LoraConfig\n", + "\n", + "from darts.models.forecasting.foundation_model import PeftCallback\n", + "\n", + "lora_config = LoraConfig(\n", + " r=16,\n", + " lora_alpha=32,\n", + " target_modules=[\"q\", \"v\"],\n", + " lora_dropout=0.1,\n", ")\n", - "val_passengers.plot(label=\"actual\")\n", - "pred.plot(label=\"forecast\");" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "cc169bfa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "trainable params: 1,179,648 || all params: 120,657,312 || trainable%: 0.9777\n" - ] - } - ], - "source": [ - "model.model.model.print_trainable_parameters()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "f44f6966", - "metadata": {}, - "outputs": [], - "source": [ - "model.model.model.save_pretrained(\"chronos2-lora-passengers\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "a6a4328e", - "metadata": {}, - "outputs": [], - "source": [ - "# use last 30 days of data to predict next 7 days\n", - "model_loaded = Chronos2Model(\n", + "peft_callback = PeftCallback(peft_config=lora_config)\n", + "\n", + "model = Chronos2Model(\n", " input_chunk_length=12,\n", " output_chunk_length=6,\n", + " enable_finetuning=True,\n", " n_epochs=10,\n", - " pl_trainer_kwargs={\"accelerator\": \"mps\"},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "7e7adc07", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=12, output_chunk_length=6, n_epochs=10, pl_trainer_kwargs={'accelerator': 'mps'})" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_loaded.fit(\n", - " series=train_passengers,\n", - " verbose=True,\n", - ")" + " pl_trainer_kwargs={\"callbacks\": [peft_callback]},\n", + ")\n", + "model.fit(train_passengers, verbose=True)\n", + "\n", + "# model.save(\"chronos2_lora_finetuned.pt\")\n", + "# # Save adapters using PEFT's native method\n", + "# model.model.model.save_pretrained(\"chronos2_lora_adapters/\")\n", + "\n", + "# # === Loading ===\n", + "# # Use callback with adapter_path to load\n", + "# load_callback = PeftCallback(adapter_path=\"chronos2_lora_adapters/\")\n", + "\n", + "# loaded = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")\n", + "# loaded.fit(train_passengers[:1]) # Initialize model structure\n", + "# loaded.predict(n=12, series=train_passengers) # Adapters applied via on_predict_start" ] }, { "cell_type": "code", - "execution_count": 43, - "id": "6bc9e736", + "execution_count": 7, + "id": "2716abc4", "metadata": {}, "outputs": [], "source": [ - "model_loaded.model.model = PeftModel.from_pretrained(\n", - " model_loaded.model.model, \"chronos2-lora-passengers\"\n", - ")" + "model.save(\"chronos2_lora_finetuned.pt\")" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "3306a586", + "execution_count": 14, + "id": "8122447b", "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7ea0d32c134f48ae9984a2179f2c138d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "RuntimeError", + "evalue": "Error(s) in loading state_dict for _Chronos2PLModule:\n\tMissing key(s) in state_dict: \"model.shared.weight\", \"model.input_patch_embedding.hidden_layer.weight\", \"model.input_patch_embedding.hidden_layer.bias\", \"model.input_patch_embedding.output_layer.weight\", \"model.input_patch_embedding.output_layer.bias\", \"model.input_patch_embedding.residual_layer.weight\", \"model.input_patch_embedding.residual_layer.bias\", \"model.encoder.block.0.layer.0.self_attention.q.weight\", \"model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.encoder.block.0.layer.0.self_attention.v.weight\", \"model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.encoder.block.0.layer.0.layer_norm.weight\", \"model.encoder.block.0.layer.1.self_attention.q.weight\", \"model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.encoder.block.0.layer.1.self_attention.v.weight\", \"model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.encoder.block.0.layer.1.layer_norm.weight\", \"model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.encoder.block.0.layer.2.layer_norm.weight\", \"model.encoder.block.1.layer.0.self_attention.q.weight\", \"model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.encoder.block.1.layer.0.self_attention.v.weight\", \"model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.encoder.block.1.layer.0.layer_norm.weight\", \"model.encoder.block.1.layer.1.self_attention.q.weight\", \"model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.encoder.block.1.layer.1.self_attention.v.weight\", \"model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.encoder.block.1.layer.1.layer_norm.weight\", \"model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.encoder.block.1.layer.2.layer_norm.weight\", \"model.encoder.block.2.layer.0.self_attention.q.weight\", \"model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.encoder.block.2.layer.0.self_attention.v.weight\", \"model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.encoder.block.2.layer.0.layer_norm.weight\", \"model.encoder.block.2.layer.1.self_attention.q.weight\", \"model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.encoder.block.2.layer.1.self_attention.v.weight\", \"model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.encoder.block.2.layer.1.layer_norm.weight\", \"model.encoder.block.2.layer.2.mlp.wi.weight\", \"model.encoder.block.2.layer.2.mlp.wo.weight\", \"model.encoder.block.2.layer.2.layer_norm.weight\", \"model.encoder.block.3.layer.0.self_attention.q.weight\", \"model.encoder.block.3.layer.0.self_attention.k.weight\", \"model.encoder.block.3.layer.0.self_attention.v.weight\", \"model.encoder.block.3.layer.0.self_attention.o.weight\", \"model.encoder.block.3.layer.0.layer_norm.weight\", \"model.encoder.block.3.layer.1.self_attention.q.weight\", \"model.encoder.block.3.layer.1.self_attention.k.weight\", \"model.encoder.block.3.layer.1.self_attention.v.weight\", \"model.encoder.block.3.layer.1.self_attention.o.weight\", \"model.encoder.block.3.layer.1.layer_norm.weight\", \"model.encoder.block.3.layer.2.mlp.wi.weight\", \"model.encoder.block.3.layer.2.mlp.wo.weight\", \"model.encoder.block.3.layer.2.layer_norm.weight\", \"model.encoder.block.4.layer.0.self_attention.q.weight\", \"model.encoder.block.4.layer.0.self_attention.k.weight\", \"model.encoder.block.4.layer.0.self_attention.v.weight\", \"model.encoder.block.4.layer.0.self_attention.o.weight\", \"model.encoder.block.4.layer.0.layer_norm.weight\", \"model.encoder.block.4.layer.1.self_attention.q.weight\", \"model.encoder.block.4.layer.1.self_attention.k.weight\", \"model.encoder.block.4.layer.1.self_attention.v.weight\", \"model.encoder.block.4.layer.1.self_attention.o.weight\", \"model.encoder.block.4.layer.1.layer_norm.weight\", \"model.encoder.block.4.layer.2.mlp.wi.weight\", \"model.encoder.block.4.layer.2.mlp.wo.weight\", \"model.encoder.block.4.layer.2.layer_norm.weight\", \"model.encoder.block.5.layer.0.self_attention.q.weight\", \"model.encoder.block.5.layer.0.self_attention.k.weight\", \"model.encoder.block.5.layer.0.self_attention.v.weight\", \"model.encoder.block.5.layer.0.self_attention.o.weight\", \"model.encoder.block.5.layer.0.layer_norm.weight\", \"model.encoder.block.5.layer.1.self_attention.q.weight\", \"model.encoder.block.5.layer.1.self_attention.k.weight\", \"model.encoder.block.5.layer.1.self_attention.v.weight\", \"model.encoder.block.5.layer.1.self_attention.o.weight\", \"model.encoder.block.5.layer.1.layer_norm.weight\", \"model.encoder.block.5.layer.2.mlp.wi.weight\", \"model.encoder.block.5.layer.2.mlp.wo.weight\", \"model.encoder.block.5.layer.2.layer_norm.weight\", \"model.encoder.block.6.layer.0.self_attention.q.weight\", \"model.encoder.block.6.layer.0.self_attention.k.weight\", \"model.encoder.block.6.layer.0.self_attention.v.weight\", \"model.encoder.block.6.layer.0.self_attention.o.weight\", \"model.encoder.block.6.layer.0.layer_norm.weight\", \"model.encoder.block.6.layer.1.self_attention.q.weight\", \"model.encoder.block.6.layer.1.self_attention.k.weight\", \"model.encoder.block.6.layer.1.self_attention.v.weight\", \"model.encoder.block.6.layer.1.self_attention.o.weight\", \"model.encoder.block.6.layer.1.layer_norm.weight\", \"model.encoder.block.6.layer.2.mlp.wi.weight\", \"model.encoder.block.6.layer.2.mlp.wo.weight\", \"model.encoder.block.6.layer.2.layer_norm.weight\", \"model.encoder.block.7.layer.0.self_attention.q.weight\", \"model.encoder.block.7.layer.0.self_attention.k.weight\", \"model.encoder.block.7.layer.0.self_attention.v.weight\", \"model.encoder.block.7.layer.0.self_attention.o.weight\", \"model.encoder.block.7.layer.0.layer_norm.weight\", \"model.encoder.block.7.layer.1.self_attention.q.weight\", \"model.encoder.block.7.layer.1.self_attention.k.weight\", \"model.encoder.block.7.layer.1.self_attention.v.weight\", \"model.encoder.block.7.layer.1.self_attention.o.weight\", \"model.encoder.block.7.layer.1.layer_norm.weight\", \"model.encoder.block.7.layer.2.mlp.wi.weight\", \"model.encoder.block.7.layer.2.mlp.wo.weight\", \"model.encoder.block.7.layer.2.layer_norm.weight\", \"model.encoder.block.8.layer.0.self_attention.q.weight\", \"model.encoder.block.8.layer.0.self_attention.k.weight\", \"model.encoder.block.8.layer.0.self_attention.v.weight\", \"model.encoder.block.8.layer.0.self_attention.o.weight\", \"model.encoder.block.8.layer.0.layer_norm.weight\", \"model.encoder.block.8.layer.1.self_attention.q.weight\", \"model.encoder.block.8.layer.1.self_attention.k.weight\", \"model.encoder.block.8.layer.1.self_attention.v.weight\", \"model.encoder.block.8.layer.1.self_attention.o.weight\", \"model.encoder.block.8.layer.1.layer_norm.weight\", \"model.encoder.block.8.layer.2.mlp.wi.weight\", \"model.encoder.block.8.layer.2.mlp.wo.weight\", \"model.encoder.block.8.layer.2.layer_norm.weight\", \"model.encoder.block.9.layer.0.self_attention.q.weight\", \"model.encoder.block.9.layer.0.self_attention.k.weight\", \"model.encoder.block.9.layer.0.self_attention.v.weight\", \"model.encoder.block.9.layer.0.self_attention.o.weight\", \"model.encoder.block.9.layer.0.layer_norm.weight\", \"model.encoder.block.9.layer.1.self_attention.q.weight\", \"model.encoder.block.9.layer.1.self_attention.k.weight\", \"model.encoder.block.9.layer.1.self_attention.v.weight\", \"model.encoder.block.9.layer.1.self_attention.o.weight\", \"model.encoder.block.9.layer.1.layer_norm.weight\", \"model.encoder.block.9.layer.2.mlp.wi.weight\", \"model.encoder.block.9.layer.2.mlp.wo.weight\", \"model.encoder.block.9.layer.2.layer_norm.weight\", \"model.encoder.block.10.layer.0.self_attention.q.weight\", \"model.encoder.block.10.layer.0.self_attention.k.weight\", \"model.encoder.block.10.layer.0.self_attention.v.weight\", \"model.encoder.block.10.layer.0.self_attention.o.weight\", \"model.encoder.block.10.layer.0.layer_norm.weight\", \"model.encoder.block.10.layer.1.self_attention.q.weight\", \"model.encoder.block.10.layer.1.self_attention.k.weight\", \"model.encoder.block.10.layer.1.self_attention.v.weight\", \"model.encoder.block.10.layer.1.self_attention.o.weight\", \"model.encoder.block.10.layer.1.layer_norm.weight\", \"model.encoder.block.10.layer.2.mlp.wi.weight\", \"model.encoder.block.10.layer.2.mlp.wo.weight\", \"model.encoder.block.10.layer.2.layer_norm.weight\", \"model.encoder.block.11.layer.0.self_attention.q.weight\", \"model.encoder.block.11.layer.0.self_attention.k.weight\", \"model.encoder.block.11.layer.0.self_attention.v.weight\", \"model.encoder.block.11.layer.0.self_attention.o.weight\", \"model.encoder.block.11.layer.0.layer_norm.weight\", \"model.encoder.block.11.layer.1.self_attention.q.weight\", \"model.encoder.block.11.layer.1.self_attention.k.weight\", \"model.encoder.block.11.layer.1.self_attention.v.weight\", \"model.encoder.block.11.layer.1.self_attention.o.weight\", \"model.encoder.block.11.layer.1.layer_norm.weight\", \"model.encoder.block.11.layer.2.mlp.wi.weight\", \"model.encoder.block.11.layer.2.mlp.wo.weight\", \"model.encoder.block.11.layer.2.layer_norm.weight\", \"model.encoder.final_layer_norm.weight\", \"model.output_patch_embedding.hidden_layer.weight\", \"model.output_patch_embedding.hidden_layer.bias\", \"model.output_patch_embedding.output_layer.weight\", \"model.output_patch_embedding.output_layer.bias\", \"model.output_patch_embedding.residual_layer.weight\", \"model.output_patch_embedding.residual_layer.bias\". \n\tUnexpected key(s) in state_dict: \"model.base_model.model.shared.weight\", 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\"model.base_model.model.encoder.block.11.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.final_layer_norm.weight\", \"model.base_model.model.output_patch_embedding.hidden_layer.weight\", \"model.base_model.model.output_patch_embedding.hidden_layer.bias\", \"model.base_model.model.output_patch_embedding.output_layer.weight\", \"model.base_model.model.output_patch_embedding.output_layer.bias\", \"model.base_model.model.output_patch_embedding.residual_layer.weight\", \"model.base_model.model.output_patch_embedding.residual_layer.bias\". ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m loaded \u001b[38;5;241m=\u001b[39m \u001b[43mChronos2Model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchronos2_lora_finetuned.pt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpl_trainer_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpeft_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Projects/Darts/darts/darts/models/forecasting/torch_forecasting_model.py:2072\u001b[0m, in \u001b[0;36mTorchForecastingModel.load\u001b[0;34m(path, pl_trainer_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 2070\u001b[0m path_ptl_ckpt \u001b[38;5;241m=\u001b[39m path \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.ckpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2071\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(path_ptl_ckpt):\n\u001b[0;32m-> 2072\u001b[0m model\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_from_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_ptl_ckpt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2073\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2074\u001b[0m model\u001b[38;5;241m.\u001b[39m_fit_called \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m~/Projects/Darts/darts/darts/models/forecasting/torch_forecasting_model.py:2204\u001b[0m, in \u001b[0;36mTorchForecastingModel._load_from_checkpoint\u001b[0;34m(self, file_path, **kwargs)\u001b[0m\n\u001b[1;32m 2198\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Loads a checkpoint for the underlying :class:`PLForecastingModule` (PLM) model.\u001b[39;00m\n\u001b[1;32m 2199\u001b[0m \u001b[38;5;124;03mThe PLM object is not stored when saving a :class:`TorchForecastingModel` (TFM) to avoid saving\u001b[39;00m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;124;03mthe model twice. Instead, we recover the module class with the module path and class name stored\u001b[39;00m\n\u001b[1;32m 2201\u001b[0m \u001b[38;5;124;03min the TFM object. With the recovered module class, we can load the checkpoint.\u001b[39;00m\n\u001b[1;32m 2202\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2203\u001b[0m pl_module_cls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(sys\u001b[38;5;241m.\u001b[39mmodules[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_module_path], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_module_name)\n\u001b[0;32m-> 2204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpl_module_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_from_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/utilities/model_helpers.py:125\u001b[0m, in \u001b[0;36m_restricted_classmethod_impl.__get__..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m instance \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scripting:\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 122\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe classmethod `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmethod\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` cannot be called on an instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Please call it on the class type and make sure the return value is used.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 124\u001b[0m )\n\u001b[0;32m--> 125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/core/module.py:1611\u001b[0m, in \u001b[0;36mLightningModule.load_from_checkpoint\u001b[0;34m(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;129m@_restricted_classmethod\u001b[39m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mload_from_checkpoint\u001b[39m(\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 1530\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint it stores the arguments\u001b[39;00m\n\u001b[1;32m 1532\u001b[0m \u001b[38;5;124;03m passed to ``__init__`` in the checkpoint under ``\"hyper_parameters\"``.\u001b[39;00m\n\u001b[1;32m 1533\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1609\u001b[0m \n\u001b[1;32m 1610\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1611\u001b[0m loaded \u001b[38;5;241m=\u001b[39m \u001b[43m_load_from_checkpoint\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1614\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1615\u001b[0m \u001b[43m \u001b[49m\u001b[43mhparams_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1616\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1617\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1618\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(Self, loaded)\n", + "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/core/saving.py:91\u001b[0m, in \u001b[0;36m_load_from_checkpoint\u001b[0;34m(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _load_state(\u001b[38;5;28mcls\u001b[39m, checkpoint, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, pl\u001b[38;5;241m.\u001b[39mLightningModule):\n\u001b[0;32m---> 91\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43m_load_state\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 92\u001b[0m state_dict \u001b[38;5;241m=\u001b[39m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_dict\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m state_dict:\n", + "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/core/saving.py:187\u001b[0m, in \u001b[0;36m_load_state\u001b[0;34m(cls, checkpoint, strict, **cls_kwargs_new)\u001b[0m\n\u001b[1;32m 184\u001b[0m obj\u001b[38;5;241m.\u001b[39mon_load_checkpoint(checkpoint)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# load the state_dict on the model automatically\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_state_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstate_dict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m strict:\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys\u001b[38;5;241m.\u001b[39mmissing_keys:\n", + "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/torch/nn/modules/module.py:2624\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m 2616\u001b[0m error_msgs\u001b[38;5;241m.\u001b[39minsert(\n\u001b[1;32m 2617\u001b[0m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 2618\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing key(s) in state_dict: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2619\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m missing_keys)\n\u001b[1;32m 2620\u001b[0m ),\n\u001b[1;32m 2621\u001b[0m )\n\u001b[1;32m 2623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2624\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 2625\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2626\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)\n\u001b[1;32m 2627\u001b[0m )\n\u001b[1;32m 2628\u001b[0m )\n\u001b[1;32m 2629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n", + "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for _Chronos2PLModule:\n\tMissing key(s) in state_dict: \"model.shared.weight\", \"model.input_patch_embedding.hidden_layer.weight\", \"model.input_patch_embedding.hidden_layer.bias\", \"model.input_patch_embedding.output_layer.weight\", \"model.input_patch_embedding.output_layer.bias\", \"model.input_patch_embedding.residual_layer.weight\", \"model.input_patch_embedding.residual_layer.bias\", \"model.encoder.block.0.layer.0.self_attention.q.weight\", \"model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.encoder.block.0.layer.0.self_attention.v.weight\", \"model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.encoder.block.0.layer.0.layer_norm.weight\", \"model.encoder.block.0.layer.1.self_attention.q.weight\", \"model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.encoder.block.0.layer.1.self_attention.v.weight\", \"model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.encoder.block.0.layer.1.layer_norm.weight\", \"model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.encoder.block.0.layer.2.layer_norm.weight\", \"model.encoder.block.1.layer.0.self_attention.q.weight\", \"model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.encoder.block.1.layer.0.self_attention.v.weight\", \"model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.encoder.block.1.layer.0.layer_norm.weight\", \"model.encoder.block.1.layer.1.self_attention.q.weight\", \"model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.encoder.block.1.layer.1.self_attention.v.weight\", \"model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.encoder.block.1.layer.1.layer_norm.weight\", \"model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.encoder.block.1.layer.2.layer_norm.weight\", \"model.encoder.block.2.layer.0.self_attention.q.weight\", \"model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.encoder.block.2.layer.0.self_attention.v.weight\", \"model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.encoder.block.2.layer.0.layer_norm.weight\", \"model.encoder.block.2.layer.1.self_attention.q.weight\", \"model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.encoder.block.2.layer.1.self_attention.v.weight\", \"model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.encoder.block.2.layer.1.layer_norm.weight\", \"model.encoder.block.2.layer.2.mlp.wi.weight\", \"model.encoder.block.2.layer.2.mlp.wo.weight\", \"model.encoder.block.2.layer.2.layer_norm.weight\", \"model.encoder.block.3.layer.0.self_attention.q.weight\", \"model.encoder.block.3.layer.0.self_attention.k.weight\", \"model.encoder.block.3.layer.0.self_attention.v.weight\", \"model.encoder.block.3.layer.0.self_attention.o.weight\", \"model.encoder.block.3.layer.0.layer_norm.weight\", \"model.encoder.block.3.layer.1.self_attention.q.weight\", \"model.encoder.block.3.layer.1.self_attention.k.weight\", \"model.encoder.block.3.layer.1.self_attention.v.weight\", \"model.encoder.block.3.layer.1.self_attention.o.weight\", \"model.encoder.block.3.layer.1.layer_norm.weight\", \"model.encoder.block.3.layer.2.mlp.wi.weight\", \"model.encoder.block.3.layer.2.mlp.wo.weight\", \"model.encoder.block.3.layer.2.layer_norm.weight\", \"model.encoder.block.4.layer.0.self_attention.q.weight\", \"model.encoder.block.4.layer.0.self_attention.k.weight\", \"model.encoder.block.4.layer.0.self_attention.v.weight\", \"model.encoder.block.4.layer.0.self_attention.o.weight\", \"model.encoder.block.4.layer.0.layer_norm.weight\", \"model.encoder.block.4.layer.1.self_attention.q.weight\", \"model.encoder.block.4.layer.1.self_attention.k.weight\", \"model.encoder.block.4.layer.1.self_attention.v.weight\", \"model.encoder.block.4.layer.1.self_attention.o.weight\", \"model.encoder.block.4.layer.1.layer_norm.weight\", \"model.encoder.block.4.layer.2.mlp.wi.weight\", \"model.encoder.block.4.layer.2.mlp.wo.weight\", \"model.encoder.block.4.layer.2.layer_norm.weight\", \"model.encoder.block.5.layer.0.self_attention.q.weight\", \"model.encoder.block.5.layer.0.self_attention.k.weight\", \"model.encoder.block.5.layer.0.self_attention.v.weight\", \"model.encoder.block.5.layer.0.self_attention.o.weight\", \"model.encoder.block.5.layer.0.layer_norm.weight\", \"model.encoder.block.5.layer.1.self_attention.q.weight\", \"model.encoder.block.5.layer.1.self_attention.k.weight\", \"model.encoder.block.5.layer.1.self_attention.v.weight\", \"model.encoder.block.5.layer.1.self_attention.o.weight\", \"model.encoder.block.5.layer.1.layer_norm.weight\", \"model.encoder.block.5.layer.2.mlp.wi.weight\", \"model.encoder.block.5.layer.2.mlp.wo.weight\", \"model.encoder.block.5.layer.2.layer_norm.weight\", \"model.encoder.block.6.layer.0.self_attention.q.weight\", \"model.encoder.block.6.layer.0.self_attention.k.weight\", \"model.encoder.block.6.layer.0.self_attention.v.weight\", \"model.encoder.block.6.layer.0.self_attention.o.weight\", \"model.encoder.block.6.layer.0.layer_norm.weight\", \"model.encoder.block.6.layer.1.self_attention.q.weight\", \"model.encoder.block.6.layer.1.self_attention.k.weight\", \"model.encoder.block.6.layer.1.self_attention.v.weight\", \"model.encoder.block.6.layer.1.self_attention.o.weight\", \"model.encoder.block.6.layer.1.layer_norm.weight\", \"model.encoder.block.6.layer.2.mlp.wi.weight\", \"model.encoder.block.6.layer.2.mlp.wo.weight\", \"model.encoder.block.6.layer.2.layer_norm.weight\", \"model.encoder.block.7.layer.0.self_attention.q.weight\", \"model.encoder.block.7.layer.0.self_attention.k.weight\", \"model.encoder.block.7.layer.0.self_attention.v.weight\", \"model.encoder.block.7.layer.0.self_attention.o.weight\", \"model.encoder.block.7.layer.0.layer_norm.weight\", \"model.encoder.block.7.layer.1.self_attention.q.weight\", \"model.encoder.block.7.layer.1.self_attention.k.weight\", \"model.encoder.block.7.layer.1.self_attention.v.weight\", \"model.encoder.block.7.layer.1.self_attention.o.weight\", \"model.encoder.block.7.layer.1.layer_norm.weight\", \"model.encoder.block.7.layer.2.mlp.wi.weight\", \"model.encoder.block.7.layer.2.mlp.wo.weight\", \"model.encoder.block.7.layer.2.layer_norm.weight\", \"model.encoder.block.8.layer.0.self_attention.q.weight\", \"model.encoder.block.8.layer.0.self_attention.k.weight\", \"model.encoder.block.8.layer.0.self_attention.v.weight\", \"model.encoder.block.8.layer.0.self_attention.o.weight\", \"model.encoder.block.8.layer.0.layer_norm.weight\", \"model.encoder.block.8.layer.1.self_attention.q.weight\", \"model.encoder.block.8.layer.1.self_attention.k.weight\", \"model.encoder.block.8.layer.1.self_attention.v.weight\", \"model.encoder.block.8.layer.1.self_attention.o.weight\", \"model.encoder.block.8.layer.1.layer_norm.weight\", \"model.encoder.block.8.layer.2.mlp.wi.weight\", \"model.encoder.block.8.layer.2.mlp.wo.weight\", \"model.encoder.block.8.layer.2.layer_norm.weight\", \"model.encoder.block.9.layer.0.self_attention.q.weight\", \"model.encoder.block.9.layer.0.self_attention.k.weight\", \"model.encoder.block.9.layer.0.self_attention.v.weight\", \"model.encoder.block.9.layer.0.self_attention.o.weight\", \"model.encoder.block.9.layer.0.layer_norm.weight\", \"model.encoder.block.9.layer.1.self_attention.q.weight\", \"model.encoder.block.9.layer.1.self_attention.k.weight\", \"model.encoder.block.9.layer.1.self_attention.v.weight\", \"model.encoder.block.9.layer.1.self_attention.o.weight\", \"model.encoder.block.9.layer.1.layer_norm.weight\", \"model.encoder.block.9.layer.2.mlp.wi.weight\", \"model.encoder.block.9.layer.2.mlp.wo.weight\", \"model.encoder.block.9.layer.2.layer_norm.weight\", \"model.encoder.block.10.layer.0.self_attention.q.weight\", \"model.encoder.block.10.layer.0.self_attention.k.weight\", \"model.encoder.block.10.layer.0.self_attention.v.weight\", \"model.encoder.block.10.layer.0.self_attention.o.weight\", \"model.encoder.block.10.layer.0.layer_norm.weight\", \"model.encoder.block.10.layer.1.self_attention.q.weight\", \"model.encoder.block.10.layer.1.self_attention.k.weight\", \"model.encoder.block.10.layer.1.self_attention.v.weight\", \"model.encoder.block.10.layer.1.self_attention.o.weight\", \"model.encoder.block.10.layer.1.layer_norm.weight\", \"model.encoder.block.10.layer.2.mlp.wi.weight\", \"model.encoder.block.10.layer.2.mlp.wo.weight\", \"model.encoder.block.10.layer.2.layer_norm.weight\", \"model.encoder.block.11.layer.0.self_attention.q.weight\", \"model.encoder.block.11.layer.0.self_attention.k.weight\", \"model.encoder.block.11.layer.0.self_attention.v.weight\", \"model.encoder.block.11.layer.0.self_attention.o.weight\", \"model.encoder.block.11.layer.0.layer_norm.weight\", \"model.encoder.block.11.layer.1.self_attention.q.weight\", \"model.encoder.block.11.layer.1.self_attention.k.weight\", \"model.encoder.block.11.layer.1.self_attention.v.weight\", \"model.encoder.block.11.layer.1.self_attention.o.weight\", \"model.encoder.block.11.layer.1.layer_norm.weight\", \"model.encoder.block.11.layer.2.mlp.wi.weight\", \"model.encoder.block.11.layer.2.mlp.wo.weight\", \"model.encoder.block.11.layer.2.layer_norm.weight\", \"model.encoder.final_layer_norm.weight\", \"model.output_patch_embedding.hidden_layer.weight\", \"model.output_patch_embedding.hidden_layer.bias\", \"model.output_patch_embedding.output_layer.weight\", \"model.output_patch_embedding.output_layer.bias\", \"model.output_patch_embedding.residual_layer.weight\", \"model.output_patch_embedding.residual_layer.bias\". \n\tUnexpected key(s) in state_dict: \"model.base_model.model.shared.weight\", \"model.base_model.model.input_patch_embedding.hidden_layer.weight\", \"model.base_model.model.input_patch_embedding.hidden_layer.bias\", \"model.base_model.model.input_patch_embedding.output_layer.weight\", \"model.base_model.model.input_patch_embedding.output_layer.bias\", \"model.base_model.model.input_patch_embedding.residual_layer.weight\", \"model.base_model.model.input_patch_embedding.residual_layer.bias\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.0.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.0.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.0.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.1.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.1.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.1.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.2.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.2.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.2.mlp.wi.weight\", 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" + ] } ], "source": [ - "pred = model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - ")\n", - "val_passengers.plot(label=\"actual\")\n", - "pred.plot(label=\"forecast\");" + "loaded = Chronos2Model.load(\n", + " \"chronos2_lora_finetuned.pt\", pl_trainer_kwargs={\"callbacks\": [peft_callback]}\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "41eeeb2e", + "id": "a7a64516", "metadata": {}, "outputs": [], "source": [] From 34b08c1bcf922c7a9dee9df9a28fd1ac66b1ba6a Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 16 Jan 2026 17:45:39 +0100 Subject: [PATCH 05/26] fix: make lora finetuning work --- darts/models/forecasting/foundation_model.py | 63 +- .../26-Chronos-2-finetuning-examples.ipynb | 617 +++++++++++------- 2 files changed, 448 insertions(+), 232 deletions(-) diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index 1d87b53d4a..b526b4aee0 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -10,6 +10,7 @@ """ from abc import ABC +from copy import deepcopy from functools import partial from typing import Any, Callable @@ -185,6 +186,19 @@ def encode_year(idx): def _requires_training(self) -> bool: return self._enable_finetuning + @property + def internal_model(self) -> Any: + """ + Returns the underlying PyTorch model (nn.Module). + This gives access to the actual internal mechanics of the model, which can be useful + for advanced usage like accessing PEFT adapters, inspecting weights or custom saving/loading. + + If the model has not been initialized yet, returns None. + """ + if hasattr(self, "model") and hasattr(self.model, "model"): + return self.model.model + return None + class FoundationPLModule(PLForecastingModule): def __init__(self, **kwargs): @@ -211,8 +225,8 @@ def _set_inner_model(self, pl_module: pl.LightningModule, model: nn.Module): """Set the inner model on the Lightning module.""" setattr(pl_module, self.model_attribute, model) - def on_fit_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule): - """Apply transformation before training begins.""" + def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str): + """Apply transformation before training begins (before optimizer setup).""" if not self._transformed: inner_model = self._get_inner_model(pl_module) transformed_model = self.transform_fn(inner_model) @@ -243,9 +257,6 @@ def on_save_checkpoint( # Mark that this checkpoint was saved with a transformed model checkpoint["model_transform_applied"] = True - # TODO maybe replace in checkpoint["state_dict"] with pl_module.model.get_base_model().state_dict() - # and adapt the keys names accordingly - def on_load_checkpoint( self, trainer: pl.Trainer, @@ -318,10 +329,44 @@ def __init__( self.peft_config = peft_config def on_save_checkpoint(self, trainer, pl_module, checkpoint): - checkpoint["peft_applied"] = True - # Optionally store config for reference - if self.peft_config is not None: - checkpoint["peft_config"] = self.peft_config.to_dict() + # We replace the state_dict in the checkpoint with the one from the base model + # (with adapters merged), so that the model can be loaded as a regular model. + peft_model = getattr(pl_module, self.model_attribute, None) + try: + from peft import PeftModel + except ImportError: + return + + if isinstance(peft_model, PeftModel): + # Merge adapters into the base model weights + model_copy = deepcopy(peft_model) + setattr(pl_module, self.model_attribute, peft_model.merge_and_unload()) + try: + # Get the state dict of the base model + # This returns the weights including the merged adapters + # base_state_dict = peft_model.get_base_model().state_dict() + + # We need to prepend the model attribute name to the keys + # because the PL module expects keys to start with `model.` (or `model_attribute.`) + prefix = self.model_attribute + "." + new_state_dict = { + prefix + k: v + for k, v in getattr(pl_module, self.model_attribute) + .state_dict() + .items() + } + + # # Update the checkpoint + checkpoint["state_dict"] = new_state_dict + + # Remove "peft_applied" so that on_load_checkpoint() does not try to re-wrap the model + # This allows loading the model as a regular (non-PEFT) model + checkpoint.pop("peft_applied", None) + checkpoint.pop("peft_config", None) + + finally: + # Unmerge adapters to keep the current model in PEFT mode + setattr(pl_module, self.model_attribute, model_copy) def on_load_checkpoint( self, diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index 4cc65edb8e..50fc3d1e3b 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -5,27 +5,23 @@ "id": "da55dd6c", "metadata": {}, "source": [ - "# Chronos-2 Foundation Model\n", - "In this notebook, we will show how to use Chronos-2 in Darts. If you are new to Darts, please check out the [Quickstart Guide](https://unit8co.github.io/darts/quickstart/00-quickstart.html) before proceeding.\n", - "\n", - "Chronos-2 is a time series foundation model for zero-shot forecasting. That means that it can be used for forecasting **without any training or fine-tuning** since it has already been pre-trained on large-scale time series data. Chronos-2 supports multivariate time series forecasting with [covariates](https://unit8co.github.io/darts/userguide/covariates.html) (exogenous variables) and can produce probabilistic forecasts.\n", - "\n", - "Check out the [Amazon Science Blog](https://www.amazon.science/blog/introducing-chronos-2-from-univariate-to-universal-forecasting) and the [original paper](https://arxiv.org/abs/2510.15821) for technical details." + "# Chronos-2 Foundation Model Fine-Tuning" ] }, { - "cell_type": "markdown", - "id": "9ad51937", + "cell_type": "code", + "execution_count": 1, + "id": "bfa59f65", "metadata": {}, + "outputs": [], "source": [ - "
\n", - " Fine-tuning Chronos-2 on your own data is not yet supported in Darts, but may be added in the future.\n", - "
" + "%load_ext autoreload\n", + "%autoreload 2" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "310fa52a", "metadata": {}, "outputs": [], @@ -37,18 +33,6 @@ "%matplotlib inline" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bfa59f65", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -77,31 +61,6 @@ "## Data Preparation" ] }, - { - "cell_type": "markdown", - "id": "70d7e392", - "metadata": {}, - "source": [ - "Here, we will use the [Electricity Consumption Zurich Dataset](https://unit8co.github.io/darts/generated_api/darts.datasets.html#darts.datasets.ElectricityConsumptionZurichDataset), which records the electricity consumption of households & SMEs (`\"Value_NE5\"` column) and business & services (`\"Value_NE7\"`) in Zurich, Switzerland, along with weather covariates such as temperature (`\"T [°C]\"`) and humidity (`\"Hr [%Hr]\"`).\n", - "Values are recorded every 15 minutes between January 2015 and August 2022.\n", - "\n", - "
\n", - "\n", - "Train-Test Split\n", - "\n", - "Even though Chronos-2 is pre-trained already, we still need to split the data into training and test sets. That is because `Chronos2Model` follows the Darts unified interface and will require calling the `fit()` method before forecasting. However, no training or fine-tuning will be performed during the `fit()` call.\n", - "\n", - "
\n", - "\n", - "
\n", - "\n", - "Data Scaling\n", - "\n", - "Unlike other deep learning models in Darts, Chronos-2 does not require data scaling since it has its own internal data normalization mechanism. Therefore, we will skip the scaling step in this notebook.\n", - "\n", - "
" - ] - }, { "cell_type": "code", "execution_count": 4, @@ -111,136 +70,56 @@ "source": [ "# convert to float32 as Chronos-2 works with float32 input\n", "data = AirPassengersDataset().load().astype(np.float32)\n", - "# extract households energy consumption\n", - "# ts_energy = data[\"Value_NE5\"]\n", - "# # extract temperature, solar irradiation and rain duration\n", - "# ts_weather = data[[\"T [°C]\", \"StrGlo [W/m2]\", \"RainDur [min]\"]]\n", - "# # split into train and validation sets by last 7 days\n", - "train_passengers, val_passengers = data.split_before(len(data) - 2 * 12)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a84830af", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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#Passengers
Month
1959-01-01360.0
1959-02-01342.0
1959-03-01406.0
1959-04-01396.0
1959-05-01420.0
......
1960-08-01606.0
1960-09-01508.0
1960-10-01461.0
1960-11-01390.0
1960-12-01432.0

shape: (24, 1, 1), freq: MS, size: 96.00 B

" - ], - "text/plain": [ - " #Passengers\n", - "Month \n", - "1959-01-01 360.0\n", - "1959-02-01 342.0\n", - "1959-03-01 406.0\n", - "1959-04-01 396.0\n", - "1959-05-01 420.0\n", - "... ...\n", - "1960-08-01 606.0\n", - "1960-09-01 508.0\n", - "1960-10-01 461.0\n", - "1960-11-01 390.0\n", - "1960-12-01 432.0\n", - "\n", - "shape: (24, 1, 1), freq: MS, size: 96.00 B" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "val_passengers" + "train_passengers, val_passengers = data.split_before(\n", + " len(data) - 2 * 12\n", + ") # last 2 years for validation" ] }, { "cell_type": "markdown", - "id": "a3887f37", + "id": "b9251561", "metadata": {}, "source": [ - "Let's quickly visualize the last 7 days of the electricity consumption data." + "# Model prediction out-of-the-box\n", + "Let's see how the model behaves on the validation data without any fine-tuning. For that we:\n", + "- Create the model\n", + "- Call fit to load the model internally (no training is done)\n", + "- Predict on the validation set" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "3b43a60a", + "execution_count": 13, + "id": "ea8456ae", "metadata": {}, "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a08b5ab19e164a3086e0a3bf59c7f2c3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -250,7 +129,18 @@ } ], "source": [ - "val_passengers.plot(label=\"passengers\")" + "model = Chronos2Model(\n", + " input_chunk_length=24,\n", + " output_chunk_length=6,\n", + ")\n", + "model.fit(train_passengers, verbose=True)\n", + "\n", + "prediction = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "prediction.plot(label=\"Forecast\")" ] }, { @@ -258,19 +148,23 @@ "id": "1313019f", "metadata": {}, "source": [ - "# Full fine-tuning" + "# Full fine-tuning\n", + "\n", + "In this method, all the model weights are retrained. This is done with `enable_finetuning=True` in the model constructor.\n", + "\n", + "The model is saved then loaded to show that Darts model saving and restoration continue to work with the different fine-tuning methods" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "72832dff", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fd9f95a0195d448cbc9d961c5cb274d8", + "model_id": "c73e0b0c4ed942a0b8c1c3915d6de446", "version_major": 2, "version_minor": 0 }, @@ -284,10 +178,11 @@ ], "source": [ "model = Chronos2Model(\n", - " input_chunk_length=12,\n", + " input_chunk_length=24,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=10,\n", + " n_epochs=100,\n", + " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", ")\n", "model.fit(train_passengers, verbose=True)\n", "model.save(\"full_finetuned.pt\")\n", @@ -296,6 +191,104 @@ "loaded = Chronos2Model.load(\"full_finetuned.pt\")" ] }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9bbd219e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "607d823d5a9f436289c2a9d8789c21a0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred_trained = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "pred_loaded = loaded.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "pred_trained.plot(label=\"Forecast of the trained model\")\n", + "pred_loaded.plot(label=\"Forecast of the loaded model\")" + ] + }, + { + "cell_type": "markdown", + "id": "d3dc22f4", + "metadata": {}, + "source": [ + "We can also verify that the prediction of the trained model is identical to the prediction of the loaded model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "599402d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_trained == pred_loaded" + ] + }, { "cell_type": "markdown", "id": "3cabab8a", @@ -306,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "id": "33fa7fc4", "metadata": {}, "outputs": [ @@ -320,7 +313,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76747d3e90b64bf1a861dffa31b5b87c", + "model_id": "970d4406f6974c1085b70f9585de66c4", "version_major": 2, "version_minor": 0 }, @@ -344,8 +337,8 @@ " input_chunk_length=12,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=10,\n", - " pl_trainer_kwargs={\"callbacks\": [freeze_callback]},\n", + " n_epochs=100,\n", + " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [freeze_callback]},\n", ")\n", "model.fit(train_passengers, verbose=True)\n", "model.save(\"partial_finetuned.pt\")\n", @@ -354,6 +347,106 @@ "loaded = Chronos2Model.load(\"partial_finetuned.pt\")" ] }, + { + "cell_type": "code", + "execution_count": 30, + "id": "50830283", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fbb261b4924248879c6f3224ae0b331a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred_trained = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "pred_loaded = loaded.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "pred_trained.plot(label=\"Forecast of the trained model\")\n", + "pred_loaded.plot(label=\"Forecast of the loaded model\")" + ] + }, + { + "cell_type": "markdown", + "id": "3c01daaa", + "metadata": {}, + "source": [ + "Again, we verify that the prediction of the fine-tuned model is the same as the loaded model to make sure that saving/load works correctly" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "01717b70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_trained == pred_loaded" + ] + }, { "cell_type": "markdown", "id": "b0d126dc", @@ -364,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6981052c", "metadata": {}, "outputs": [ @@ -372,13 +465,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model transformed. Trainable: 1,179,648/120,657,312 (0.98%)\n" + "Model transformed. Trainable: 1,206,912/120,684,576 (1.00%)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "253112b961e349e4abb07e1b22606f15", + "model_id": "6e469e8f90df46a0b3673668261f603b", "version_major": 2, "version_minor": 0 }, @@ -388,16 +481,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=12, output_chunk_length=6, enable_finetuning=True, n_epochs=10, pl_trainer_kwargs={'callbacks': []})" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -406,80 +489,168 @@ "from darts.models.forecasting.foundation_model import PeftCallback\n", "\n", "lora_config = LoraConfig(\n", - " r=16,\n", - " lora_alpha=32,\n", - " target_modules=[\"q\", \"v\"],\n", - " lora_dropout=0.1,\n", + " r=8,\n", + " lora_alpha=16,\n", + " target_modules=[\n", + " \"q\",\n", + " \"v\",\n", + " \"k\",\n", + " \"o\",\n", + " \"output_patch_embedding.output_layer\",\n", + " ],\n", + " # lora_dropout=0.1,\n", ")\n", "peft_callback = PeftCallback(peft_config=lora_config)\n", "\n", - "model = Chronos2Model(\n", - " input_chunk_length=12,\n", + "model_lora = Chronos2Model(\n", + " input_chunk_length=24,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=10,\n", - " pl_trainer_kwargs={\"callbacks\": [peft_callback]},\n", + " n_epochs=100,\n", + " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [peft_callback]},\n", + " log_tensorboard=True,\n", ")\n", - "model.fit(train_passengers, verbose=True)\n", + "model_lora.fit(train_passengers, verbose=True)\n", + "\n", + "# Fully save the model including adapters\n", + "model_lora.save(\"chronos2_lora_finetuned.pt\")\n", + "loaded = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")\n", + "# loaded_full = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")\n", "\n", - "# model.save(\"chronos2_lora_finetuned.pt\")\n", "# # Save adapters using PEFT's native method\n", "# model.model.model.save_pretrained(\"chronos2_lora_adapters/\")\n", "\n", - "# # === Loading ===\n", - "# # Use callback with adapter_path to load\n", - "# load_callback = PeftCallback(adapter_path=\"chronos2_lora_adapters/\")\n", - "\n", - "# loaded = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")\n", - "# loaded.fit(train_passengers[:1]) # Initialize model structure\n", - "# loaded.predict(n=12, series=train_passengers) # Adapters applied via on_predict_start" + "# # # === Loading ===\n", + "# model = Chronos2Model(\n", + "# input_chunk_length=12,\n", + "# output_chunk_length=6,\n", + "# enable_finetuning=True,\n", + "# n_epochs=10,\n", + "# pl_trainer_kwargs={\"callbacks\": [peft_callback]},\n", + "# )\n", + "# model.fit(train_passengers, verbose=True)\n", + "# model.model.model.load_adapter(\"chronos2_lora_adapters/\")" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "2716abc4", + "execution_count": 6, + "id": "41e8a82f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "56b8b427e22348b19709bde1107f6367", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "model.save(\"chronos2_lora_finetuned.pt\")" + "pred_trained = model_lora.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "pred_loaded = loaded.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "pred_trained.plot(label=\"Forecast of the trained model\")\n", + "pred_loaded.plot(label=\"Forecast of the loaded model\")" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "8122447b", + "execution_count": 8, + "id": "9a96ca55", "metadata": {}, "outputs": [ { - "ename": "RuntimeError", - "evalue": "Error(s) in loading state_dict for _Chronos2PLModule:\n\tMissing key(s) in state_dict: \"model.shared.weight\", \"model.input_patch_embedding.hidden_layer.weight\", \"model.input_patch_embedding.hidden_layer.bias\", \"model.input_patch_embedding.output_layer.weight\", \"model.input_patch_embedding.output_layer.bias\", \"model.input_patch_embedding.residual_layer.weight\", \"model.input_patch_embedding.residual_layer.bias\", \"model.encoder.block.0.layer.0.self_attention.q.weight\", \"model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.encoder.block.0.layer.0.self_attention.v.weight\", \"model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.encoder.block.0.layer.0.layer_norm.weight\", \"model.encoder.block.0.layer.1.self_attention.q.weight\", \"model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.encoder.block.0.layer.1.self_attention.v.weight\", \"model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.encoder.block.0.layer.1.layer_norm.weight\", \"model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.encoder.block.0.layer.2.layer_norm.weight\", \"model.encoder.block.1.layer.0.self_attention.q.weight\", \"model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.encoder.block.1.layer.0.self_attention.v.weight\", \"model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.encoder.block.1.layer.0.layer_norm.weight\", \"model.encoder.block.1.layer.1.self_attention.q.weight\", \"model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.encoder.block.1.layer.1.self_attention.v.weight\", \"model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.encoder.block.1.layer.1.layer_norm.weight\", \"model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.encoder.block.1.layer.2.layer_norm.weight\", \"model.encoder.block.2.layer.0.self_attention.q.weight\", \"model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.encoder.block.2.layer.0.self_attention.v.weight\", \"model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.encoder.block.2.layer.0.layer_norm.weight\", \"model.encoder.block.2.layer.1.self_attention.q.weight\", \"model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.encoder.block.2.layer.1.self_attention.v.weight\", \"model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.encoder.block.2.layer.1.layer_norm.weight\", \"model.encoder.block.2.layer.2.mlp.wi.weight\", \"model.encoder.block.2.layer.2.mlp.wo.weight\", \"model.encoder.block.2.layer.2.layer_norm.weight\", \"model.encoder.block.3.layer.0.self_attention.q.weight\", \"model.encoder.block.3.layer.0.self_attention.k.weight\", \"model.encoder.block.3.layer.0.self_attention.v.weight\", \"model.encoder.block.3.layer.0.self_attention.o.weight\", \"model.encoder.block.3.layer.0.layer_norm.weight\", \"model.encoder.block.3.layer.1.self_attention.q.weight\", \"model.encoder.block.3.layer.1.self_attention.k.weight\", \"model.encoder.block.3.layer.1.self_attention.v.weight\", \"model.encoder.block.3.layer.1.self_attention.o.weight\", \"model.encoder.block.3.layer.1.layer_norm.weight\", \"model.encoder.block.3.layer.2.mlp.wi.weight\", \"model.encoder.block.3.layer.2.mlp.wo.weight\", \"model.encoder.block.3.layer.2.layer_norm.weight\", \"model.encoder.block.4.layer.0.self_attention.q.weight\", \"model.encoder.block.4.layer.0.self_attention.k.weight\", \"model.encoder.block.4.layer.0.self_attention.v.weight\", \"model.encoder.block.4.layer.0.self_attention.o.weight\", \"model.encoder.block.4.layer.0.layer_norm.weight\", \"model.encoder.block.4.layer.1.self_attention.q.weight\", \"model.encoder.block.4.layer.1.self_attention.k.weight\", \"model.encoder.block.4.layer.1.self_attention.v.weight\", \"model.encoder.block.4.layer.1.self_attention.o.weight\", \"model.encoder.block.4.layer.1.layer_norm.weight\", \"model.encoder.block.4.layer.2.mlp.wi.weight\", \"model.encoder.block.4.layer.2.mlp.wo.weight\", \"model.encoder.block.4.layer.2.layer_norm.weight\", \"model.encoder.block.5.layer.0.self_attention.q.weight\", \"model.encoder.block.5.layer.0.self_attention.k.weight\", \"model.encoder.block.5.layer.0.self_attention.v.weight\", \"model.encoder.block.5.layer.0.self_attention.o.weight\", \"model.encoder.block.5.layer.0.layer_norm.weight\", \"model.encoder.block.5.layer.1.self_attention.q.weight\", \"model.encoder.block.5.layer.1.self_attention.k.weight\", \"model.encoder.block.5.layer.1.self_attention.v.weight\", \"model.encoder.block.5.layer.1.self_attention.o.weight\", \"model.encoder.block.5.layer.1.layer_norm.weight\", \"model.encoder.block.5.layer.2.mlp.wi.weight\", \"model.encoder.block.5.layer.2.mlp.wo.weight\", \"model.encoder.block.5.layer.2.layer_norm.weight\", \"model.encoder.block.6.layer.0.self_attention.q.weight\", \"model.encoder.block.6.layer.0.self_attention.k.weight\", \"model.encoder.block.6.layer.0.self_attention.v.weight\", \"model.encoder.block.6.layer.0.self_attention.o.weight\", \"model.encoder.block.6.layer.0.layer_norm.weight\", \"model.encoder.block.6.layer.1.self_attention.q.weight\", \"model.encoder.block.6.layer.1.self_attention.k.weight\", \"model.encoder.block.6.layer.1.self_attention.v.weight\", \"model.encoder.block.6.layer.1.self_attention.o.weight\", \"model.encoder.block.6.layer.1.layer_norm.weight\", \"model.encoder.block.6.layer.2.mlp.wi.weight\", \"model.encoder.block.6.layer.2.mlp.wo.weight\", \"model.encoder.block.6.layer.2.layer_norm.weight\", \"model.encoder.block.7.layer.0.self_attention.q.weight\", \"model.encoder.block.7.layer.0.self_attention.k.weight\", \"model.encoder.block.7.layer.0.self_attention.v.weight\", \"model.encoder.block.7.layer.0.self_attention.o.weight\", \"model.encoder.block.7.layer.0.layer_norm.weight\", \"model.encoder.block.7.layer.1.self_attention.q.weight\", \"model.encoder.block.7.layer.1.self_attention.k.weight\", \"model.encoder.block.7.layer.1.self_attention.v.weight\", \"model.encoder.block.7.layer.1.self_attention.o.weight\", \"model.encoder.block.7.layer.1.layer_norm.weight\", \"model.encoder.block.7.layer.2.mlp.wi.weight\", \"model.encoder.block.7.layer.2.mlp.wo.weight\", \"model.encoder.block.7.layer.2.layer_norm.weight\", \"model.encoder.block.8.layer.0.self_attention.q.weight\", \"model.encoder.block.8.layer.0.self_attention.k.weight\", \"model.encoder.block.8.layer.0.self_attention.v.weight\", \"model.encoder.block.8.layer.0.self_attention.o.weight\", \"model.encoder.block.8.layer.0.layer_norm.weight\", \"model.encoder.block.8.layer.1.self_attention.q.weight\", \"model.encoder.block.8.layer.1.self_attention.k.weight\", \"model.encoder.block.8.layer.1.self_attention.v.weight\", \"model.encoder.block.8.layer.1.self_attention.o.weight\", \"model.encoder.block.8.layer.1.layer_norm.weight\", \"model.encoder.block.8.layer.2.mlp.wi.weight\", \"model.encoder.block.8.layer.2.mlp.wo.weight\", \"model.encoder.block.8.layer.2.layer_norm.weight\", \"model.encoder.block.9.layer.0.self_attention.q.weight\", \"model.encoder.block.9.layer.0.self_attention.k.weight\", \"model.encoder.block.9.layer.0.self_attention.v.weight\", \"model.encoder.block.9.layer.0.self_attention.o.weight\", \"model.encoder.block.9.layer.0.layer_norm.weight\", \"model.encoder.block.9.layer.1.self_attention.q.weight\", \"model.encoder.block.9.layer.1.self_attention.k.weight\", \"model.encoder.block.9.layer.1.self_attention.v.weight\", \"model.encoder.block.9.layer.1.self_attention.o.weight\", \"model.encoder.block.9.layer.1.layer_norm.weight\", \"model.encoder.block.9.layer.2.mlp.wi.weight\", \"model.encoder.block.9.layer.2.mlp.wo.weight\", \"model.encoder.block.9.layer.2.layer_norm.weight\", \"model.encoder.block.10.layer.0.self_attention.q.weight\", \"model.encoder.block.10.layer.0.self_attention.k.weight\", \"model.encoder.block.10.layer.0.self_attention.v.weight\", \"model.encoder.block.10.layer.0.self_attention.o.weight\", \"model.encoder.block.10.layer.0.layer_norm.weight\", \"model.encoder.block.10.layer.1.self_attention.q.weight\", \"model.encoder.block.10.layer.1.self_attention.k.weight\", \"model.encoder.block.10.layer.1.self_attention.v.weight\", \"model.encoder.block.10.layer.1.self_attention.o.weight\", \"model.encoder.block.10.layer.1.layer_norm.weight\", \"model.encoder.block.10.layer.2.mlp.wi.weight\", \"model.encoder.block.10.layer.2.mlp.wo.weight\", \"model.encoder.block.10.layer.2.layer_norm.weight\", \"model.encoder.block.11.layer.0.self_attention.q.weight\", \"model.encoder.block.11.layer.0.self_attention.k.weight\", \"model.encoder.block.11.layer.0.self_attention.v.weight\", \"model.encoder.block.11.layer.0.self_attention.o.weight\", \"model.encoder.block.11.layer.0.layer_norm.weight\", \"model.encoder.block.11.layer.1.self_attention.q.weight\", \"model.encoder.block.11.layer.1.self_attention.k.weight\", \"model.encoder.block.11.layer.1.self_attention.v.weight\", \"model.encoder.block.11.layer.1.self_attention.o.weight\", \"model.encoder.block.11.layer.1.layer_norm.weight\", \"model.encoder.block.11.layer.2.mlp.wi.weight\", \"model.encoder.block.11.layer.2.mlp.wo.weight\", \"model.encoder.block.11.layer.2.layer_norm.weight\", \"model.encoder.final_layer_norm.weight\", \"model.output_patch_embedding.hidden_layer.weight\", \"model.output_patch_embedding.hidden_layer.bias\", \"model.output_patch_embedding.output_layer.weight\", \"model.output_patch_embedding.output_layer.bias\", \"model.output_patch_embedding.residual_layer.weight\", \"model.output_patch_embedding.residual_layer.bias\". \n\tUnexpected key(s) in state_dict: \"model.base_model.model.shared.weight\", \"model.base_model.model.input_patch_embedding.hidden_layer.weight\", \"model.base_model.model.input_patch_embedding.hidden_layer.bias\", \"model.base_model.model.input_patch_embedding.output_layer.weight\", \"model.base_model.model.input_patch_embedding.output_layer.bias\", \"model.base_model.model.input_patch_embedding.residual_layer.weight\", \"model.base_model.model.input_patch_embedding.residual_layer.bias\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.0.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.0.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.0.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.1.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.1.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.1.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.2.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.2.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.2.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.2.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.3.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.3.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.3.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.3.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.3.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.v.lora_B.default.weight\", 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", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m loaded \u001b[38;5;241m=\u001b[39m \u001b[43mChronos2Model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mchronos2_lora_finetuned.pt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpl_trainer_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mpeft_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Projects/Darts/darts/darts/models/forecasting/torch_forecasting_model.py:2072\u001b[0m, in \u001b[0;36mTorchForecastingModel.load\u001b[0;34m(path, pl_trainer_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 2070\u001b[0m path_ptl_ckpt \u001b[38;5;241m=\u001b[39m path \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.ckpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2071\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(path_ptl_ckpt):\n\u001b[0;32m-> 2072\u001b[0m model\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_from_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_ptl_ckpt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2073\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2074\u001b[0m model\u001b[38;5;241m.\u001b[39m_fit_called \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/Projects/Darts/darts/darts/models/forecasting/torch_forecasting_model.py:2204\u001b[0m, in \u001b[0;36mTorchForecastingModel._load_from_checkpoint\u001b[0;34m(self, file_path, **kwargs)\u001b[0m\n\u001b[1;32m 2198\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Loads a checkpoint for the underlying :class:`PLForecastingModule` (PLM) model.\u001b[39;00m\n\u001b[1;32m 2199\u001b[0m \u001b[38;5;124;03mThe PLM object is not stored when saving a :class:`TorchForecastingModel` (TFM) to avoid saving\u001b[39;00m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;124;03mthe model twice. Instead, we recover the module class with the module path and class name stored\u001b[39;00m\n\u001b[1;32m 2201\u001b[0m \u001b[38;5;124;03min the TFM object. With the recovered module class, we can load the checkpoint.\u001b[39;00m\n\u001b[1;32m 2202\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2203\u001b[0m pl_module_cls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(sys\u001b[38;5;241m.\u001b[39mmodules[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_module_path], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_module_name)\n\u001b[0;32m-> 2204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpl_module_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_from_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/utilities/model_helpers.py:125\u001b[0m, in \u001b[0;36m_restricted_classmethod_impl.__get__..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m instance \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scripting:\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 122\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe classmethod `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmethod\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` cannot be called on an instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Please call it on the class type and make sure the return value is used.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 124\u001b[0m )\n\u001b[0;32m--> 125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/core/module.py:1611\u001b[0m, in \u001b[0;36mLightningModule.load_from_checkpoint\u001b[0;34m(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;129m@_restricted_classmethod\u001b[39m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mload_from_checkpoint\u001b[39m(\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 1530\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint it stores the arguments\u001b[39;00m\n\u001b[1;32m 1532\u001b[0m \u001b[38;5;124;03m passed to ``__init__`` in the checkpoint under ``\"hyper_parameters\"``.\u001b[39;00m\n\u001b[1;32m 1533\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1609\u001b[0m \n\u001b[1;32m 1610\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1611\u001b[0m loaded \u001b[38;5;241m=\u001b[39m \u001b[43m_load_from_checkpoint\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1614\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1615\u001b[0m \u001b[43m \u001b[49m\u001b[43mhparams_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1616\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1617\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1618\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(Self, loaded)\n", - "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/core/saving.py:91\u001b[0m, in \u001b[0;36m_load_from_checkpoint\u001b[0;34m(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _load_state(\u001b[38;5;28mcls\u001b[39m, checkpoint, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, pl\u001b[38;5;241m.\u001b[39mLightningModule):\n\u001b[0;32m---> 91\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43m_load_state\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 92\u001b[0m state_dict \u001b[38;5;241m=\u001b[39m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_dict\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m state_dict:\n", - "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/pytorch_lightning/core/saving.py:187\u001b[0m, in \u001b[0;36m_load_state\u001b[0;34m(cls, checkpoint, strict, **cls_kwargs_new)\u001b[0m\n\u001b[1;32m 184\u001b[0m obj\u001b[38;5;241m.\u001b[39mon_load_checkpoint(checkpoint)\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# load the state_dict on the model automatically\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m keys \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_state_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstate_dict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m strict:\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys\u001b[38;5;241m.\u001b[39mmissing_keys:\n", - "File \u001b[0;32m~/anaconda3/envs/darts/lib/python3.13/site-packages/torch/nn/modules/module.py:2624\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m 2616\u001b[0m error_msgs\u001b[38;5;241m.\u001b[39minsert(\n\u001b[1;32m 2617\u001b[0m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 2618\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing key(s) in state_dict: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2619\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m missing_keys)\n\u001b[1;32m 2620\u001b[0m ),\n\u001b[1;32m 2621\u001b[0m )\n\u001b[1;32m 2623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2624\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 2625\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2626\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)\n\u001b[1;32m 2627\u001b[0m )\n\u001b[1;32m 2628\u001b[0m )\n\u001b[1;32m 2629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n", - "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for _Chronos2PLModule:\n\tMissing key(s) in state_dict: \"model.shared.weight\", \"model.input_patch_embedding.hidden_layer.weight\", \"model.input_patch_embedding.hidden_layer.bias\", \"model.input_patch_embedding.output_layer.weight\", \"model.input_patch_embedding.output_layer.bias\", \"model.input_patch_embedding.residual_layer.weight\", \"model.input_patch_embedding.residual_layer.bias\", \"model.encoder.block.0.layer.0.self_attention.q.weight\", \"model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.encoder.block.0.layer.0.self_attention.v.weight\", \"model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.encoder.block.0.layer.0.layer_norm.weight\", \"model.encoder.block.0.layer.1.self_attention.q.weight\", \"model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.encoder.block.0.layer.1.self_attention.v.weight\", \"model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.encoder.block.0.layer.1.layer_norm.weight\", \"model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.encoder.block.0.layer.2.layer_norm.weight\", \"model.encoder.block.1.layer.0.self_attention.q.weight\", \"model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.encoder.block.1.layer.0.self_attention.v.weight\", \"model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.encoder.block.1.layer.0.layer_norm.weight\", \"model.encoder.block.1.layer.1.self_attention.q.weight\", \"model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.encoder.block.1.layer.1.self_attention.v.weight\", \"model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.encoder.block.1.layer.1.layer_norm.weight\", \"model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.encoder.block.1.layer.2.layer_norm.weight\", \"model.encoder.block.2.layer.0.self_attention.q.weight\", \"model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.encoder.block.2.layer.0.self_attention.v.weight\", \"model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.encoder.block.2.layer.0.layer_norm.weight\", \"model.encoder.block.2.layer.1.self_attention.q.weight\", \"model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.encoder.block.2.layer.1.self_attention.v.weight\", \"model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.encoder.block.2.layer.1.layer_norm.weight\", \"model.encoder.block.2.layer.2.mlp.wi.weight\", \"model.encoder.block.2.layer.2.mlp.wo.weight\", \"model.encoder.block.2.layer.2.layer_norm.weight\", \"model.encoder.block.3.layer.0.self_attention.q.weight\", \"model.encoder.block.3.layer.0.self_attention.k.weight\", \"model.encoder.block.3.layer.0.self_attention.v.weight\", \"model.encoder.block.3.layer.0.self_attention.o.weight\", \"model.encoder.block.3.layer.0.layer_norm.weight\", \"model.encoder.block.3.layer.1.self_attention.q.weight\", \"model.encoder.block.3.layer.1.self_attention.k.weight\", \"model.encoder.block.3.layer.1.self_attention.v.weight\", \"model.encoder.block.3.layer.1.self_attention.o.weight\", \"model.encoder.block.3.layer.1.layer_norm.weight\", \"model.encoder.block.3.layer.2.mlp.wi.weight\", \"model.encoder.block.3.layer.2.mlp.wo.weight\", \"model.encoder.block.3.layer.2.layer_norm.weight\", \"model.encoder.block.4.layer.0.self_attention.q.weight\", \"model.encoder.block.4.layer.0.self_attention.k.weight\", \"model.encoder.block.4.layer.0.self_attention.v.weight\", \"model.encoder.block.4.layer.0.self_attention.o.weight\", \"model.encoder.block.4.layer.0.layer_norm.weight\", \"model.encoder.block.4.layer.1.self_attention.q.weight\", \"model.encoder.block.4.layer.1.self_attention.k.weight\", \"model.encoder.block.4.layer.1.self_attention.v.weight\", \"model.encoder.block.4.layer.1.self_attention.o.weight\", \"model.encoder.block.4.layer.1.layer_norm.weight\", \"model.encoder.block.4.layer.2.mlp.wi.weight\", \"model.encoder.block.4.layer.2.mlp.wo.weight\", \"model.encoder.block.4.layer.2.layer_norm.weight\", \"model.encoder.block.5.layer.0.self_attention.q.weight\", \"model.encoder.block.5.layer.0.self_attention.k.weight\", \"model.encoder.block.5.layer.0.self_attention.v.weight\", \"model.encoder.block.5.layer.0.self_attention.o.weight\", \"model.encoder.block.5.layer.0.layer_norm.weight\", \"model.encoder.block.5.layer.1.self_attention.q.weight\", \"model.encoder.block.5.layer.1.self_attention.k.weight\", \"model.encoder.block.5.layer.1.self_attention.v.weight\", \"model.encoder.block.5.layer.1.self_attention.o.weight\", \"model.encoder.block.5.layer.1.layer_norm.weight\", \"model.encoder.block.5.layer.2.mlp.wi.weight\", \"model.encoder.block.5.layer.2.mlp.wo.weight\", \"model.encoder.block.5.layer.2.layer_norm.weight\", \"model.encoder.block.6.layer.0.self_attention.q.weight\", \"model.encoder.block.6.layer.0.self_attention.k.weight\", \"model.encoder.block.6.layer.0.self_attention.v.weight\", \"model.encoder.block.6.layer.0.self_attention.o.weight\", \"model.encoder.block.6.layer.0.layer_norm.weight\", \"model.encoder.block.6.layer.1.self_attention.q.weight\", \"model.encoder.block.6.layer.1.self_attention.k.weight\", \"model.encoder.block.6.layer.1.self_attention.v.weight\", \"model.encoder.block.6.layer.1.self_attention.o.weight\", \"model.encoder.block.6.layer.1.layer_norm.weight\", \"model.encoder.block.6.layer.2.mlp.wi.weight\", \"model.encoder.block.6.layer.2.mlp.wo.weight\", \"model.encoder.block.6.layer.2.layer_norm.weight\", \"model.encoder.block.7.layer.0.self_attention.q.weight\", \"model.encoder.block.7.layer.0.self_attention.k.weight\", \"model.encoder.block.7.layer.0.self_attention.v.weight\", \"model.encoder.block.7.layer.0.self_attention.o.weight\", \"model.encoder.block.7.layer.0.layer_norm.weight\", \"model.encoder.block.7.layer.1.self_attention.q.weight\", \"model.encoder.block.7.layer.1.self_attention.k.weight\", \"model.encoder.block.7.layer.1.self_attention.v.weight\", \"model.encoder.block.7.layer.1.self_attention.o.weight\", \"model.encoder.block.7.layer.1.layer_norm.weight\", \"model.encoder.block.7.layer.2.mlp.wi.weight\", \"model.encoder.block.7.layer.2.mlp.wo.weight\", \"model.encoder.block.7.layer.2.layer_norm.weight\", \"model.encoder.block.8.layer.0.self_attention.q.weight\", \"model.encoder.block.8.layer.0.self_attention.k.weight\", \"model.encoder.block.8.layer.0.self_attention.v.weight\", \"model.encoder.block.8.layer.0.self_attention.o.weight\", \"model.encoder.block.8.layer.0.layer_norm.weight\", \"model.encoder.block.8.layer.1.self_attention.q.weight\", \"model.encoder.block.8.layer.1.self_attention.k.weight\", \"model.encoder.block.8.layer.1.self_attention.v.weight\", \"model.encoder.block.8.layer.1.self_attention.o.weight\", \"model.encoder.block.8.layer.1.layer_norm.weight\", \"model.encoder.block.8.layer.2.mlp.wi.weight\", \"model.encoder.block.8.layer.2.mlp.wo.weight\", \"model.encoder.block.8.layer.2.layer_norm.weight\", \"model.encoder.block.9.layer.0.self_attention.q.weight\", \"model.encoder.block.9.layer.0.self_attention.k.weight\", \"model.encoder.block.9.layer.0.self_attention.v.weight\", \"model.encoder.block.9.layer.0.self_attention.o.weight\", \"model.encoder.block.9.layer.0.layer_norm.weight\", \"model.encoder.block.9.layer.1.self_attention.q.weight\", \"model.encoder.block.9.layer.1.self_attention.k.weight\", \"model.encoder.block.9.layer.1.self_attention.v.weight\", \"model.encoder.block.9.layer.1.self_attention.o.weight\", \"model.encoder.block.9.layer.1.layer_norm.weight\", \"model.encoder.block.9.layer.2.mlp.wi.weight\", \"model.encoder.block.9.layer.2.mlp.wo.weight\", \"model.encoder.block.9.layer.2.layer_norm.weight\", \"model.encoder.block.10.layer.0.self_attention.q.weight\", \"model.encoder.block.10.layer.0.self_attention.k.weight\", \"model.encoder.block.10.layer.0.self_attention.v.weight\", \"model.encoder.block.10.layer.0.self_attention.o.weight\", \"model.encoder.block.10.layer.0.layer_norm.weight\", \"model.encoder.block.10.layer.1.self_attention.q.weight\", \"model.encoder.block.10.layer.1.self_attention.k.weight\", \"model.encoder.block.10.layer.1.self_attention.v.weight\", \"model.encoder.block.10.layer.1.self_attention.o.weight\", \"model.encoder.block.10.layer.1.layer_norm.weight\", \"model.encoder.block.10.layer.2.mlp.wi.weight\", \"model.encoder.block.10.layer.2.mlp.wo.weight\", \"model.encoder.block.10.layer.2.layer_norm.weight\", \"model.encoder.block.11.layer.0.self_attention.q.weight\", \"model.encoder.block.11.layer.0.self_attention.k.weight\", \"model.encoder.block.11.layer.0.self_attention.v.weight\", \"model.encoder.block.11.layer.0.self_attention.o.weight\", \"model.encoder.block.11.layer.0.layer_norm.weight\", \"model.encoder.block.11.layer.1.self_attention.q.weight\", \"model.encoder.block.11.layer.1.self_attention.k.weight\", \"model.encoder.block.11.layer.1.self_attention.v.weight\", \"model.encoder.block.11.layer.1.self_attention.o.weight\", \"model.encoder.block.11.layer.1.layer_norm.weight\", \"model.encoder.block.11.layer.2.mlp.wi.weight\", \"model.encoder.block.11.layer.2.mlp.wo.weight\", \"model.encoder.block.11.layer.2.layer_norm.weight\", \"model.encoder.final_layer_norm.weight\", \"model.output_patch_embedding.hidden_layer.weight\", \"model.output_patch_embedding.hidden_layer.bias\", \"model.output_patch_embedding.output_layer.weight\", \"model.output_patch_embedding.output_layer.bias\", \"model.output_patch_embedding.residual_layer.weight\", \"model.output_patch_embedding.residual_layer.bias\". \n\tUnexpected key(s) in state_dict: \"model.base_model.model.shared.weight\", \"model.base_model.model.input_patch_embedding.hidden_layer.weight\", \"model.base_model.model.input_patch_embedding.hidden_layer.bias\", \"model.base_model.model.input_patch_embedding.output_layer.weight\", \"model.base_model.model.input_patch_embedding.output_layer.bias\", \"model.base_model.model.input_patch_embedding.residual_layer.weight\", \"model.base_model.model.input_patch_embedding.residual_layer.bias\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.0.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.0.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.0.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.0.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.0.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.0.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.1.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.1.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.1.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.1.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.1.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.1.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.2.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.2.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.2.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.2.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.2.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.2.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.3.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.3.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.3.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.3.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.3.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.3.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.4.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.4.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.4.layer.1.self_attention.o.weight\", \"model.base_model.model.encoder.block.4.layer.1.layer_norm.weight\", \"model.base_model.model.encoder.block.4.layer.2.mlp.wi.weight\", \"model.base_model.model.encoder.block.4.layer.2.mlp.wo.weight\", \"model.base_model.model.encoder.block.4.layer.2.layer_norm.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.k.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.v.base_layer.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.v.lora_A.default.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.v.lora_B.default.weight\", \"model.base_model.model.encoder.block.5.layer.0.self_attention.o.weight\", \"model.base_model.model.encoder.block.5.layer.0.layer_norm.weight\", \"model.base_model.model.encoder.block.5.layer.1.self_attention.q.base_layer.weight\", \"model.base_model.model.encoder.block.5.layer.1.self_attention.q.lora_A.default.weight\", \"model.base_model.model.encoder.block.5.layer.1.self_attention.q.lora_B.default.weight\", \"model.base_model.model.encoder.block.5.layer.1.self_attention.k.weight\", \"model.base_model.model.encoder.block.5.layer.1.self_attention.v.base_layer.weight\", 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" - ] + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "loaded = Chronos2Model.load(\n", - " \"chronos2_lora_finetuned.pt\", pl_trainer_kwargs={\"callbacks\": [peft_callback]}\n", - ")" + "np.allclose(pred_trained.values(), pred_loaded.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "527ad900", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "model_without_lora = model_lora.model.model.merge_and_unload()\n", + "\n", + "assert len(loaded.model.state_dict().keys()) == len(\n", + " model_without_lora.state_dict().keys()\n", + ")\n", + "\n", + "for key_loaded, key_lora in zip(\n", + " loaded.model.state_dict().keys(), model_without_lora.state_dict().keys()\n", + "):\n", + " assert torch.equal(\n", + " loaded.model.state_dict()[key_loaded], model_without_lora.state_dict()[key_lora]\n", + " )" ] }, { "cell_type": "code", "execution_count": null, - "id": "a7a64516", + "id": "ce2fcd82", "metadata": {}, "outputs": [], "source": [] @@ -501,7 +672,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.12.12" } }, "nbformat": 4, From eeb93b4a9cd73177935e38433c02bc3b9d44f667 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Mon, 19 Jan 2026 15:36:46 +0100 Subject: [PATCH 06/26] fix: improve callbacks to allow only saving the adapter --- darts/models/forecasting/foundation_model.py | 112 +++-- .../26-Chronos-2-finetuning-examples.ipynb | 417 +++++++++++++----- 2 files changed, 389 insertions(+), 140 deletions(-) diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index b526b4aee0..b77d17fc49 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -199,6 +199,27 @@ def internal_model(self) -> Any: return self.model.model return None + @internal_model.setter + def internal_model(self, model: nn.Module): + """ + Sets the underlying PyTorch model (nn.Module). + This allows replacing the internal model, which can be useful for advanced usage like loading PEFT adapters. + + Parameters + ---------- + model + The new PyTorch nn.Module to set as the internal model. + """ + if hasattr(self, "model"): + self.model.model = model + else: + raise_log( + AttributeError( + "The internal model cannot be set because the outer model is not initialized yet." + ), + logger, + ) + class FoundationPLModule(PLForecastingModule): def __init__(self, **kwargs): @@ -211,10 +232,30 @@ def __init__( self, transform_fn: Callable[[nn.Module], nn.Module], model_attribute: str = "model", + verbose: bool = False, ): + """ + A PyTorch Lightning callback that applies a transformation function to an internal model + within a LightningModule. + + This is useful for modifying model architectures (e.g., applying PEFT or freezing layers) + just before the training starts, while ensuring the transformation is correctly handled + during checkpoint saving and loading. + + Parameters + ---------- + transform_fn + A function that takes an ``nn.Module`` and returns a transformed ``nn.Module``. + model_attribute + The attribute name of the model within the LightningModule. Default: ``"model"``. + verbose + Whether to log information about the model transformation, such as the number of + trainable parameters. Default: ``False``. + """ super().__init__() self.transform_fn = transform_fn self.model_attribute = model_attribute + self.verbose = verbose self._transformed = False def _get_inner_model(self, pl_module: pl.LightningModule) -> nn.Module: @@ -232,15 +273,15 @@ def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str): transformed_model = self.transform_fn(inner_model) self._set_inner_model(pl_module, transformed_model) self._transformed = True - - # Log trainable parameters - trainable = sum( - p.numel() for p in pl_module.parameters() if p.requires_grad - ) - total = sum(p.numel() for p in pl_module.parameters()) - print( - f"Model transformed. Trainable: {trainable:,}/{total:,} ({100 * trainable / total:.2f}%)" - ) + if self.verbose: + # Log trainable parameters + trainable = sum( + p.numel() for p in pl_module.parameters() if p.requires_grad + ) + total = sum(p.numel() for p in pl_module.parameters()) + logger.info( + f"Model transformed. Trainable: {trainable:,}/{total:,} ({100 * trainable / total:.2f}%)" + ) def on_save_checkpoint( self, @@ -292,7 +333,24 @@ def __init__( freeze_patterns: list[str], unfreeze_patterns: list[str] = None, model_attribute: str = "model", + verbose: bool = False, ): + """ + A callback to freeze or unfreeze specific layers of a model based on name patterns. + + Parameters + ---------- + freeze_patterns + A list of strings. Parameters whose names start with any of these patterns will be frozen + (``requires_grad=False``). + unfreeze_patterns + A list of strings. Parameters whose names start with any of these patterns will be unfrozen + (``requires_grad=True``). This is applied after ``freeze_patterns``. Default: ``None``. + model_attribute + The attribute name of the model within the LightningModule. Default: ``"model"``. + verbose + Whether to log the trainable parameter count after freezing. Default: ``False``. + """ unfreeze_patterns = unfreeze_patterns or [] super().__init__( @@ -302,6 +360,7 @@ def __init__( unfreeze_patterns=unfreeze_patterns, ), model_attribute=model_attribute, + verbose=verbose, ) @@ -321,10 +380,27 @@ def __init__( self, peft_config=None, model_attribute: str = "model", + verbose: bool = False, ): + """ + A callback to apply Parameter-Efficient Fine-Tuning (PEFT) to a model using the ``peft`` library. + + It wraps the internal model with a PEFT adapter (e.g., LoRA) and manages the merging of + weights during checkpointing so that the saved state can be loaded as a standard model. + + Parameters + ---------- + peft_config + A PEFT configuration object (e.g., ``LoraConfig``) from the ``peft`` library. + model_attribute + The attribute name of the model within the LightningModule. Default: ``"model"``. + verbose + Whether to log the trainable parameter count after applying PEFT. Default: ``False``. + """ super().__init__( transform_fn=partial(self._apply_peft, peft_config=peft_config), model_attribute=model_attribute, + verbose=verbose, ) self.peft_config = peft_config @@ -339,6 +415,7 @@ def on_save_checkpoint(self, trainer, pl_module, checkpoint): if isinstance(peft_model, PeftModel): # Merge adapters into the base model weights + # TODO: This might be inefficient for large models, think about a better way model_copy = deepcopy(peft_model) setattr(pl_module, self.model_attribute, peft_model.merge_and_unload()) try: @@ -356,24 +433,9 @@ def on_save_checkpoint(self, trainer, pl_module, checkpoint): .items() } - # # Update the checkpoint + # Update the checkpoint checkpoint["state_dict"] = new_state_dict - # Remove "peft_applied" so that on_load_checkpoint() does not try to re-wrap the model - # This allows loading the model as a regular (non-PEFT) model - checkpoint.pop("peft_applied", None) - checkpoint.pop("peft_config", None) - finally: # Unmerge adapters to keep the current model in PEFT mode setattr(pl_module, self.model_attribute, model_copy) - - def on_load_checkpoint( - self, - trainer: pl.Trainer, - pl_module: pl.LightningModule, - checkpoint: dict[str, Any], - ): - """Apply PEFT structure before loading weights.""" - if checkpoint.get("peft_applied", False): - self._apply_peft(pl_module, peft_config=self.peft_config) diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index 50fc3d1e3b..a413b4ad76 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -5,7 +5,15 @@ "id": "da55dd6c", "metadata": {}, "source": [ - "# Chronos-2 Foundation Model Fine-Tuning" + "# Chronos-2 Foundation Model Fine-Tuning\n", + "This example notebook presents how fine-tuning can be applied to the Chronos-2 model.\n", + "\n", + "The following fine-tuning methods will be shown:\n", + "1) Full fine-tuning : all the models weights will be retrained\n", + "2) Partial fine-tuning : some layers of the model will be frozen\n", + "3) PEFT fine-tuning : the HuggingFace peft library will be used to apply LoRA fine-tuning (requires `pip install peft` since it is not a darts dependency)\n", + "\n", + "To be useful, a fine-tuned model should be easily saved and loaded. For each fine-tuning method, how to save and load the model will be shown with an example (straightforward for (1) and (2), slightly different for (3))" ] }, { @@ -58,7 +66,8 @@ "id": "6b82a07a", "metadata": {}, "source": [ - "## Data Preparation" + "## Data Preparation\n", + "Here we just load an example dataset with 144 samples as a fast demo. The data is split between train and validation, with the 2 last years (24 samples) for validation" ] }, { @@ -89,14 +98,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "ea8456ae", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a08b5ab19e164a3086e0a3bf59c7f2c3", + "model_id": "426a22d045d34b1a842eec9a09e6302a", "version_major": 2, "version_minor": 0 }, @@ -113,7 +122,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -148,23 +157,23 @@ "id": "1313019f", "metadata": {}, "source": [ - "# Full fine-tuning\n", + "# 1. Full fine-tuning\n", "\n", "In this method, all the model weights are retrained. This is done with `enable_finetuning=True` in the model constructor.\n", "\n", - "The model is saved then loaded to show that Darts model saving and restoration continue to work with the different fine-tuning methods" + "The model is saved and loaded with the usual `save` and `load` methods, so the behavior is the same as other darts models." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "72832dff", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c73e0b0c4ed942a0b8c1c3915d6de446", + "model_id": "f706e79ab1704fea9809dae3e7db1b78", "version_major": 2, "version_minor": 0 }, @@ -177,30 +186,38 @@ } ], "source": [ - "model = Chronos2Model(\n", + "full_finetuned_model = Chronos2Model(\n", " input_chunk_length=24,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=100,\n", + " n_epochs=50,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", ")\n", - "model.fit(train_passengers, verbose=True)\n", - "model.save(\"full_finetuned.pt\")\n", + "full_finetuned_model.fit(train_passengers, verbose=True)\n", + "full_finetuned_model.save(\"full_finetuned.pt\")\n", "\n", "# Load\n", - "loaded = Chronos2Model.load(\"full_finetuned.pt\")" + "full_finetuned_loaded_model = Chronos2Model.load(\"full_finetuned.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "ba806d95", + "metadata": {}, + "source": [ + "We can compare the prediction with the ground truth, as well as checking that the loaded model behaves similarly to the fine-tuned model." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "9bbd219e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "607d823d5a9f436289c2a9d8789c21a0", + "model_id": "467be790d94d48b09ea2ad2de1567125", "version_major": 2, "version_minor": 0 }, @@ -214,7 +231,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9815ecb465814e6b8f484402b3cb5f0d", + "model_id": "fb68e0d824904fc183dc196fd93a88be", "version_major": 2, "version_minor": 0 }, @@ -228,16 +245,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -247,17 +264,21 @@ } ], "source": [ - "pred_trained = model.predict(\n", + "pred_full_finetuned = full_finetuned_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", ")\n", - "pred_loaded = loaded.predict(\n", + "pred_full_finetuned_loaded = full_finetuned_loaded_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "pred_trained.plot(label=\"Forecast of the trained model\")\n", - "pred_loaded.plot(label=\"Forecast of the loaded model\")" + "pred_full_finetuned.plot(label=\"Forecast of the full finetuned model\", linestyle=\"-.\")\n", + "pred_full_finetuned_loaded.plot(\n", + " label=\"Forecast of the loaded full finetuned model\",\n", + " linestyle=\"--\",\n", + " title=\"Full finetuning\",\n", + ")" ] }, { @@ -265,28 +286,28 @@ "id": "d3dc22f4", "metadata": {}, "source": [ - "We can also verify that the prediction of the trained model is identical to the prediction of the loaded model" + "We can also verify numericaly that the prediction of the trained model is identical to the prediction of the loaded model" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "id": "599402d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "False" + "True" ] }, - "execution_count": 22, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pred_trained == pred_loaded" + "np.allclose(pred_full_finetuned.values(), pred_full_finetuned_loaded.values())" ] }, { @@ -294,26 +315,64 @@ "id": "3cabab8a", "metadata": {}, "source": [ - "# Partial fine-tuning with layer freezing" + "# 2. Partial fine-tuning with layer freezing\n", + "With this method, top layers of the model will be frozen. That means that their weights won't be updated during the fine-tuning. \n", + "\n", + "This is done with the `LayerFreezeCallback` available in darts. \n", + "\n", + "
\n", + "LayerFreezeCallback\n", + "\n", + "\n", + "Here is the source code of the callback method.\n", + "\n", + "It extends the `ModelTransformCallback` which applies a transform function to the model attribute (by default `model`) on the setup callback of `pytorch_lightning`.\n", + "\n", + " ```python\n", + "class LayerFreezeCallback(ModelTransformCallback):\n", + " @classmethod\n", + " def _freeze_layers(\n", + " cls, model: nn.Module, freeze_patterns: list[str], unfreeze_patterns: list[str]\n", + " ) -> nn.Module:\n", + " for name, param in model.named_parameters():\n", + " if any(name.startswith(layer) for layer in freeze_patterns):\n", + " param.requires_grad = False\n", + " if any(name.startswith(layer) for layer in unfreeze_patterns):\n", + " param.requires_grad = True\n", + " return model\n", + "\n", + " def __init__(\n", + " self,\n", + " freeze_patterns: list[str],\n", + " unfreeze_patterns: list[str] = None,\n", + " model_attribute: str = \"model\",\n", + " verbose: bool = False,\n", + " ):\n", + " unfreeze_patterns = unfreeze_patterns or []\n", + "\n", + " super().__init__(\n", + " transform_fn=partial(\n", + " self._freeze_layers,\n", + " freeze_patterns=freeze_patterns,\n", + " unfreeze_patterns=unfreeze_patterns,\n", + " ),\n", + " model_attribute=model_attribute,\n", + " verbose=verbose,\n", + " )\n", + "```\n", + "
" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "id": "33fa7fc4", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model transformed. Trainable: 72,280,224/119,477,664 (60.50%)\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "970d4406f6974c1085b70f9585de66c4", + "model_id": "5f830deafaf54b249882fcad5c672577", "version_major": 2, "version_minor": 0 }, @@ -333,30 +392,30 @@ " unfreeze_patterns=[\"output_patch_embedding\"],\n", ")\n", "\n", - "model = Chronos2Model(\n", + "partial_finetuned_model = Chronos2Model(\n", " input_chunk_length=12,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=100,\n", + " n_epochs=50,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [freeze_callback]},\n", ")\n", - "model.fit(train_passengers, verbose=True)\n", - "model.save(\"partial_finetuned.pt\")\n", + "partial_finetuned_model.fit(train_passengers, verbose=True)\n", + "partial_finetuned_model.save(\"partial_finetuned.pt\")\n", "\n", "# Load - no callback needed, structure unchanged\n", - "loaded = Chronos2Model.load(\"partial_finetuned.pt\")" + "partial_finetuned_loaded_model = Chronos2Model.load(\"partial_finetuned.pt\")" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "id": "50830283", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fbb261b4924248879c6f3224ae0b331a", + "model_id": "3f92740814334e009f614fb2668b88c2", "version_major": 2, "version_minor": 0 }, @@ -370,7 +429,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b52f68fea154de68b9bd3f5887178bb", + "model_id": "be8c720307a84928a25298ca935b3992", "version_major": 2, "version_minor": 0 }, @@ -384,16 +443,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -403,19 +462,25 @@ } ], "source": [ - "pred_trained = model.predict(\n", + "pred_partial_finetuned = partial_finetuned_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", " random_state=42,\n", ")\n", - "pred_loaded = loaded.predict(\n", + "pred_partial_finetuned_loaded = partial_finetuned_loaded_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", " random_state=42,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "pred_trained.plot(label=\"Forecast of the trained model\")\n", - "pred_loaded.plot(label=\"Forecast of the loaded model\")" + "pred_partial_finetuned.plot(\n", + " label=\"Forecast of the partial finetuned model\", linestyle=\"-.\"\n", + ")\n", + "pred_partial_finetuned_loaded.plot(\n", + " label=\"Forecast of the loaded partial finetuned model\",\n", + " linestyle=\"--\",\n", + " title=\"Partial finetuning\",\n", + ")" ] }, { @@ -428,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "id": "01717b70", "metadata": {}, "outputs": [ @@ -438,13 +503,13 @@ "True" ] }, - "execution_count": 31, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pred_trained == pred_loaded" + "np.allclose(pred_partial_finetuned.values(), pred_partial_finetuned_loaded.values())" ] }, { @@ -452,26 +517,24 @@ "id": "b0d126dc", "metadata": {}, "source": [ - "# LoRA fine-tuning" + "# 3. LoRA fine-tuning\n", + "This fine-tuning method uses HuggingFace `peft` library. This library makes it easy to use **P**arameter **E**fficient **F**ine-**T**uning methods such as LoRA which greatly reduces the number of fine-tuned parameters.\n", + "\n", + "More information about peft can be found in the [official documentation](https://github.com/huggingface/peft)\n", + "\n", + "To use LoRA fine-tuning, the `PeftCallback` is used. A `peft_config` is required. In this example, a `LoraConfig` is used with the same parameters used in the [official Chronos-2 implementation](https://github.com/amazon-science/chronos-forecasting/blob/f889ae66477b53f6beb130f5c7b13590b29a1035/src/chronos/chronos2/pipeline.py#L202)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "6981052c", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model transformed. Trainable: 1,206,912/120,684,576 (1.00%)\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e469e8f90df46a0b3673668261f603b", + "model_id": "f747f8932c964382b4e2c45025fac6ec", "version_major": 2, "version_minor": 0 }, @@ -481,6 +544,16 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=50, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -498,7 +571,6 @@ " \"o\",\n", " \"output_patch_embedding.output_layer\",\n", " ],\n", - " # lora_dropout=0.1,\n", ")\n", "peft_callback = PeftCallback(peft_config=lora_config)\n", "\n", @@ -506,42 +578,43 @@ " input_chunk_length=24,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=100,\n", + " n_epochs=50,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [peft_callback]},\n", - " log_tensorboard=True,\n", ")\n", - "model_lora.fit(train_passengers, verbose=True)\n", - "\n", + "model_lora.fit(train_passengers, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e86085e3", + "metadata": {}, + "source": [ + "## 3.1 Full-model saving\n", + "Darts `save` and `load` methods can be used to save the full model weights." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "49b2c2e8", + "metadata": {}, + "outputs": [], + "source": [ "# Fully save the model including adapters\n", "model_lora.save(\"chronos2_lora_finetuned.pt\")\n", - "loaded = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")\n", - "# loaded_full = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")\n", - "\n", - "# # Save adapters using PEFT's native method\n", - "# model.model.model.save_pretrained(\"chronos2_lora_adapters/\")\n", - "\n", - "# # # === Loading ===\n", - "# model = Chronos2Model(\n", - "# input_chunk_length=12,\n", - "# output_chunk_length=6,\n", - "# enable_finetuning=True,\n", - "# n_epochs=10,\n", - "# pl_trainer_kwargs={\"callbacks\": [peft_callback]},\n", - "# )\n", - "# model.fit(train_passengers, verbose=True)\n", - "# model.model.model.load_adapter(\"chronos2_lora_adapters/\")" + "model_lora_loaded = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "41e8a82f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56b8b427e22348b19709bde1107f6367", + "model_id": "9423ea48aac342ea9600ae6f45f168ae", "version_major": 2, "version_minor": 0 }, @@ -555,7 +628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "90797a2c2c8740cea37643d332b9f7e1", + "model_id": "01bbd79720504a9db991aed2291c721e", "version_major": 2, "version_minor": 0 }, @@ -569,16 +642,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -588,24 +661,36 @@ } ], "source": [ - "pred_trained = model_lora.predict(\n", + "pred_lora_trained = model_lora.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", " random_state=42,\n", ")\n", - "pred_loaded = loaded.predict(\n", + "pred_lora_loaded = model_lora_loaded.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", " random_state=42,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "pred_trained.plot(label=\"Forecast of the trained model\")\n", - "pred_loaded.plot(label=\"Forecast of the loaded model\")" + "pred_lora_trained.plot(label=\"Forecast of the LoRA trained model\", linestyle=\"-.\")\n", + "pred_lora_loaded.plot(\n", + " label=\"Forecast of the loaded LoRA model\",\n", + " linestyle=\"--\",\n", + " title=\"LoRA finetuning - Save all\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "32cef0b5", + "metadata": {}, + "source": [ + "Again, we verify that the prediction of the fine-tuned model is the same as the loaded model to make sure that saving/load works correctly" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "9a96ca55", "metadata": {}, "outputs": [ @@ -615,42 +700,144 @@ "True" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "np.allclose(pred_trained.values(), pred_loaded.values())" + "np.allclose(pred_lora_loaded.values(), pred_lora_trained.values())" + ] + }, + { + "cell_type": "markdown", + "id": "c633f2ad", + "metadata": {}, + "source": [ + "## 3.2 Adapter saving\n", + "Another method is to save only the adapter. This results in a light-weight folder containing only the LoRA weights which can be plugged to the original model.\n", + "\n", + "For that, we need to access the internal chronos model with the `internal_model` attribute to save only the adapter." ] }, { "cell_type": "code", - "execution_count": 106, - "id": "527ad900", + "execution_count": 16, + "id": "ce2fcd82", "metadata": {}, "outputs": [], "source": [ - "import torch\n", - "\n", - "model_without_lora = model_lora.model.model.merge_and_unload()\n", + "model_lora.internal_model.save_pretrained(\"chronos2_lora_adapters/\")" + ] + }, + { + "cell_type": "markdown", + "id": "6e2f159a", + "metadata": {}, + "source": [ + "Then, a new model can be created, and the internal model can be replaced with the loaded adapter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "630bb5bc", + "metadata": {}, + "outputs": [], + "source": [ + "from peft import PeftModel\n", "\n", - "assert len(loaded.model.state_dict().keys()) == len(\n", - " model_without_lora.state_dict().keys()\n", + "model_new = Chronos2Model(\n", + " input_chunk_length=24,\n", + " output_chunk_length=6,\n", ")\n", + "model_new.fit(train_passengers) # Initialize model\n", "\n", - "for key_loaded, key_lora in zip(\n", - " loaded.model.state_dict().keys(), model_without_lora.state_dict().keys()\n", - "):\n", - " assert torch.equal(\n", - " loaded.model.state_dict()[key_loaded], model_without_lora.state_dict()[key_lora]\n", - " )" + "# Replace _Chronos2Module with PeftModel containing _Chronos2Module + adapters\n", + "model_new.internal_model = PeftModel.from_pretrained(\n", + " model_new.internal_model, \"chronos2_lora_adapters/\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c1fddf83", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "73a4c844302a4b458ad0780a7e2898f4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred_lora_trained = model_lora.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "pred_new = model_new.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "pred_lora_trained.plot(label=\"Forecast of the trained model\", linestyle=\"-.\")\n", + "pred_new.plot(\n", + " label=\"Forecast of the loaded model\",\n", + " linestyle=\"--\",\n", + " title=\"LoRA finetuning - Save adapters only\",\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "ce2fcd82", + "id": "6f74bc02", "metadata": {}, "outputs": [], "source": [] From 76a8efa3893ca031be55647d7231ee5b119b3f64 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 30 Jan 2026 17:05:28 +0100 Subject: [PATCH 07/26] feat: modify foundation model to integrate full and partial fine-tuning --- darts/models/forecasting/foundation_model.py | 273 +++--------------- darts/utils/callbacks/__init__.py | 5 + darts/utils/callbacks/fine_tuning.py | 237 +++++++++++++++ .../progress_bar.py} | 0 .../26-Chronos-2-finetuning-examples.ipynb | 133 +++------ 5 files changed, 331 insertions(+), 317 deletions(-) create mode 100644 darts/utils/callbacks/__init__.py create mode 100644 darts/utils/callbacks/fine_tuning.py rename darts/utils/{callbacks.py => callbacks/progress_bar.py} (100%) diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index b77d17fc49..3a24e65435 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -10,17 +10,14 @@ """ from abc import ABC -from copy import deepcopy -from functools import partial -from typing import Any, Callable +from typing import Any -import pytorch_lightning as pl -from pytorch_lightning.callbacks import Callback from torch import nn from darts.logging import get_logger, raise_log from darts.models.forecasting.pl_forecasting_module import PLForecastingModule from darts.models.forecasting.torch_forecasting_model import MixedCovariatesTorchModel +from darts.utils.callbacks.fine_tuning import LayerFreezeCallback logger = get_logger(__name__) @@ -31,6 +28,8 @@ class FoundationModel(MixedCovariatesTorchModel, ABC): def __init__( self, enable_finetuning: bool = False, + freeze_patterns: list[str] | None = None, + unfreeze_patterns: list[str] | None = None, **kwargs, ): """Foundation Forecasting Model with PyTorch Lightning backend. @@ -58,6 +57,13 @@ def __init__( enable_finetuning Whether to enable fine-tuning of the foundation model. If set to ``True``, calling :func:`fit()` will update the model weights. Default: ``False``. + freeze_patterns + A list of strings. Parameters whose names start with any of these patterns will be frozen + (``requires_grad=False``). This is only used if ``enable_finetuning=True``. Default: ``None``. + unfreeze_patterns + A list of strings. Parameters whose names start with any of these patterns will be unfrozen + (``requires_grad=True``). This is applied after ``freeze_patterns``. This is only used if + ``enable_finetuning=True``. Default: ``None``. batch_size Number of time series (input and output sequences) used in each fine-tuning pass. Default: ``32``. n_epochs @@ -164,13 +170,7 @@ def encode_year(idx): whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. """ - # initialize `TorchForecastingModel` base class - super().__init__(**self._extract_torch_model_params(**self.model_params)) - - # extract pytorch lightning module kwargs - self.pl_module_params = self._extract_pl_module_params(**self.model_params) - - # validate and set fine-tuning flag + # validate fine-tuning flag if enable_finetuning and not self._allows_finetuning: raise_log( ValueError( @@ -180,6 +180,41 @@ def encode_year(idx): logger, ) + if not enable_finetuning and (freeze_patterns or unfreeze_patterns): + logger.warning( + "`freeze_patterns` or `unfreeze_patterns` are specified, but `enable_finetuning` is False. " + "These patterns will be ignored." + ) + + if enable_finetuning and (freeze_patterns or unfreeze_patterns): + pl_trainer_kwargs = self.model_params.get("pl_trainer_kwargs") + if pl_trainer_kwargs is None: + pl_trainer_kwargs = {} + else: + pl_trainer_kwargs = dict(pl_trainer_kwargs) + + callbacks = pl_trainer_kwargs.get("callbacks") + if callbacks is None: + callbacks = [] + else: + callbacks = list(callbacks) + + callbacks.append( + LayerFreezeCallback( + freeze_patterns=freeze_patterns or [], + unfreeze_patterns=unfreeze_patterns or [], + ) + ) + pl_trainer_kwargs["callbacks"] = callbacks + # we must update model_params to be picked up by super().__init__() + self.model_params["pl_trainer_kwargs"] = pl_trainer_kwargs + + # initialize `TorchForecastingModel` base class + super().__init__(**self._extract_torch_model_params(**self.model_params)) + + # extract pytorch lightning module kwargs + self.pl_module_params = self._extract_pl_module_params(**self.model_params) + self._enable_finetuning = enable_finetuning @property @@ -225,217 +260,3 @@ class FoundationPLModule(PLForecastingModule): def __init__(self, **kwargs): super().__init__(**kwargs) self.model: nn.Module - - -class ModelTransformCallback(Callback): - def __init__( - self, - transform_fn: Callable[[nn.Module], nn.Module], - model_attribute: str = "model", - verbose: bool = False, - ): - """ - A PyTorch Lightning callback that applies a transformation function to an internal model - within a LightningModule. - - This is useful for modifying model architectures (e.g., applying PEFT or freezing layers) - just before the training starts, while ensuring the transformation is correctly handled - during checkpoint saving and loading. - - Parameters - ---------- - transform_fn - A function that takes an ``nn.Module`` and returns a transformed ``nn.Module``. - model_attribute - The attribute name of the model within the LightningModule. Default: ``"model"``. - verbose - Whether to log information about the model transformation, such as the number of - trainable parameters. Default: ``False``. - """ - super().__init__() - self.transform_fn = transform_fn - self.model_attribute = model_attribute - self.verbose = verbose - self._transformed = False - - def _get_inner_model(self, pl_module: pl.LightningModule) -> nn.Module: - """Get the inner model from the Lightning module.""" - return getattr(pl_module, self.model_attribute) - - def _set_inner_model(self, pl_module: pl.LightningModule, model: nn.Module): - """Set the inner model on the Lightning module.""" - setattr(pl_module, self.model_attribute, model) - - def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str): - """Apply transformation before training begins (before optimizer setup).""" - if not self._transformed: - inner_model = self._get_inner_model(pl_module) - transformed_model = self.transform_fn(inner_model) - self._set_inner_model(pl_module, transformed_model) - self._transformed = True - if self.verbose: - # Log trainable parameters - trainable = sum( - p.numel() for p in pl_module.parameters() if p.requires_grad - ) - total = sum(p.numel() for p in pl_module.parameters()) - logger.info( - f"Model transformed. Trainable: {trainable:,}/{total:,} ({100 * trainable / total:.2f}%)" - ) - - def on_save_checkpoint( - self, - trainer: pl.Trainer, - pl_module: pl.LightningModule, - checkpoint: dict[str, Any], - ): - """ - Handle checkpoint saving for transformed models. - - For PEFT models, we could optionally save just the adapter weights - or mark the checkpoint as requiring transformation on load. - """ - # Mark that this checkpoint was saved with a transformed model - checkpoint["model_transform_applied"] = True - - def on_load_checkpoint( - self, - trainer: pl.Trainer, - pl_module: pl.LightningModule, - checkpoint: dict[str, Any], - ): - """ - Apply transformation before loading checkpoint weights. - - This ensures the model structure matches the saved weights. - """ - if checkpoint.get("model_transform_applied", False) and not self._transformed: - inner_model = self._get_inner_model(pl_module) - transformed_model = self.transform_fn(inner_model) - self._set_inner_model(pl_module, transformed_model) - self._transformed = True - - -class LayerFreezeCallback(ModelTransformCallback): - @classmethod - def _freeze_layers( - cls, model: nn.Module, freeze_patterns: list[str], unfreeze_patterns: list[str] - ) -> nn.Module: - for name, param in model.named_parameters(): - if any(name.startswith(layer) for layer in freeze_patterns): - param.requires_grad = False - if any(name.startswith(layer) for layer in unfreeze_patterns): - param.requires_grad = True - return model - - def __init__( - self, - freeze_patterns: list[str], - unfreeze_patterns: list[str] = None, - model_attribute: str = "model", - verbose: bool = False, - ): - """ - A callback to freeze or unfreeze specific layers of a model based on name patterns. - - Parameters - ---------- - freeze_patterns - A list of strings. Parameters whose names start with any of these patterns will be frozen - (``requires_grad=False``). - unfreeze_patterns - A list of strings. Parameters whose names start with any of these patterns will be unfrozen - (``requires_grad=True``). This is applied after ``freeze_patterns``. Default: ``None``. - model_attribute - The attribute name of the model within the LightningModule. Default: ``"model"``. - verbose - Whether to log the trainable parameter count after freezing. Default: ``False``. - """ - unfreeze_patterns = unfreeze_patterns or [] - - super().__init__( - transform_fn=partial( - self._freeze_layers, - freeze_patterns=freeze_patterns, - unfreeze_patterns=unfreeze_patterns, - ), - model_attribute=model_attribute, - verbose=verbose, - ) - - -class PeftCallback(ModelTransformCallback): - @classmethod - def _apply_peft(cls, model: nn.Module, peft_config) -> nn.Module: - try: - from peft import get_peft_model - except ImportError: - raise ImportError( - "Please install the `peft` package to use PeftCallback: `pip install peft`." - ) - peft_model = get_peft_model(model, peft_config) - return peft_model - - def __init__( - self, - peft_config=None, - model_attribute: str = "model", - verbose: bool = False, - ): - """ - A callback to apply Parameter-Efficient Fine-Tuning (PEFT) to a model using the ``peft`` library. - - It wraps the internal model with a PEFT adapter (e.g., LoRA) and manages the merging of - weights during checkpointing so that the saved state can be loaded as a standard model. - - Parameters - ---------- - peft_config - A PEFT configuration object (e.g., ``LoraConfig``) from the ``peft`` library. - model_attribute - The attribute name of the model within the LightningModule. Default: ``"model"``. - verbose - Whether to log the trainable parameter count after applying PEFT. Default: ``False``. - """ - super().__init__( - transform_fn=partial(self._apply_peft, peft_config=peft_config), - model_attribute=model_attribute, - verbose=verbose, - ) - self.peft_config = peft_config - - def on_save_checkpoint(self, trainer, pl_module, checkpoint): - # We replace the state_dict in the checkpoint with the one from the base model - # (with adapters merged), so that the model can be loaded as a regular model. - peft_model = getattr(pl_module, self.model_attribute, None) - try: - from peft import PeftModel - except ImportError: - return - - if isinstance(peft_model, PeftModel): - # Merge adapters into the base model weights - # TODO: This might be inefficient for large models, think about a better way - model_copy = deepcopy(peft_model) - setattr(pl_module, self.model_attribute, peft_model.merge_and_unload()) - try: - # Get the state dict of the base model - # This returns the weights including the merged adapters - # base_state_dict = peft_model.get_base_model().state_dict() - - # We need to prepend the model attribute name to the keys - # because the PL module expects keys to start with `model.` (or `model_attribute.`) - prefix = self.model_attribute + "." - new_state_dict = { - prefix + k: v - for k, v in getattr(pl_module, self.model_attribute) - .state_dict() - .items() - } - - # Update the checkpoint - checkpoint["state_dict"] = new_state_dict - - finally: - # Unmerge adapters to keep the current model in PEFT mode - setattr(pl_module, self.model_attribute, model_copy) diff --git a/darts/utils/callbacks/__init__.py b/darts/utils/callbacks/__init__.py new file mode 100644 index 0000000000..5e0391b415 --- /dev/null +++ b/darts/utils/callbacks/__init__.py @@ -0,0 +1,5 @@ +from darts.utils.callbacks.progress_bar import TFMProgressBar + +__all__ = [ + "TFMProgressBar", +] diff --git a/darts/utils/callbacks/fine_tuning.py b/darts/utils/callbacks/fine_tuning.py new file mode 100644 index 0000000000..8583191b94 --- /dev/null +++ b/darts/utils/callbacks/fine_tuning.py @@ -0,0 +1,237 @@ +from copy import deepcopy +from functools import partial +from typing import Any, Callable, Optional + +import pytorch_lightning as pl +from pytorch_lightning.callbacks import Callback +from torch import nn + +from darts.logging import get_logger + +logger = get_logger(__name__) + + +class ModelTransformCallback(Callback): + def __init__( + self, + transform_fn: Callable[[nn.Module], nn.Module], + model_attribute: str = "model", + verbose: Optional[bool] = None, + ): + """ + A PyTorch Lightning callback that applies a transformation function to an internal model + within a LightningModule. + + This is useful for modifying model architectures (e.g., applying PEFT or freezing layers) + just before the training starts, while ensuring the transformation is correctly handled + during checkpoint saving and loading. + + Parameters + ---------- + transform_fn + A function that takes an ``nn.Module`` and returns a transformed ``nn.Module``. + model_attribute + The attribute name of the model within the LightningModule. Default: ``"model"``. + verbose + Whether to log information about the model transformation, such as the number of + trainable parameters. If ``None``, it will be set to ``True`` if the trainer has a + progress bar callback enabled (e.g. when ``model.fit(..., verbose=True)``). + Default: ``None``. + """ + super().__init__() + self.transform_fn = transform_fn + self.model_attribute = model_attribute + self.verbose = verbose + self._transformed = False + + def _get_inner_model(self, pl_module: pl.LightningModule) -> nn.Module: + """Get the inner model from the Lightning module.""" + return getattr(pl_module, self.model_attribute) + + def _set_inner_model(self, pl_module: pl.LightningModule, model: nn.Module): + """Set the inner model on the Lightning module.""" + setattr(pl_module, self.model_attribute, model) + + def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str): + """Apply transformation before training begins (before optimizer setup).""" + if not self._transformed: + inner_model = self._get_inner_model(pl_module) + transformed_model = self.transform_fn(inner_model) + self._set_inner_model(pl_module, transformed_model) + self._transformed = True + + verbose = self.verbose + if verbose is None: + verbose = trainer.progress_bar_callback is not None + + if verbose: + # Log trainable parameters + trainable = sum( + p.numel() for p in pl_module.parameters() if p.requires_grad + ) + total = sum(p.numel() for p in pl_module.parameters()) + logger.info( + f"Model transformed. Trainable: {trainable:,}/{total:,} ({100 * trainable / total:.2f}%)" + ) + + def on_save_checkpoint( + self, + trainer: pl.Trainer, + pl_module: pl.LightningModule, + checkpoint: dict[str, Any], + ): + """ + Handle checkpoint saving for transformed models. + + For PEFT models, we could optionally save just the adapter weights + or mark the checkpoint as requiring transformation on load. + """ + # Mark that this checkpoint was saved with a transformed model + checkpoint["model_transform_applied"] = True + + def on_load_checkpoint( + self, + trainer: pl.Trainer, + pl_module: pl.LightningModule, + checkpoint: dict[str, Any], + ): + """ + Apply transformation before loading checkpoint weights. + + This ensures the model structure matches the saved weights. + """ + if checkpoint.get("model_transform_applied", False) and not self._transformed: + inner_model = self._get_inner_model(pl_module) + transformed_model = self.transform_fn(inner_model) + self._set_inner_model(pl_module, transformed_model) + self._transformed = True + + +class LayerFreezeCallback(ModelTransformCallback): + @classmethod + def _freeze_layers( + cls, model: nn.Module, freeze_patterns: list[str], unfreeze_patterns: list[str] + ) -> nn.Module: + for name, param in model.named_parameters(): + if any(name.startswith(layer) for layer in freeze_patterns): + param.requires_grad = False + if any(name.startswith(layer) for layer in unfreeze_patterns): + param.requires_grad = True + return model + + def __init__( + self, + freeze_patterns: list[str], + unfreeze_patterns: list[str] = None, + model_attribute: str = "model", + verbose: Optional[bool] = None, + ): + """ + A callback to freeze or unfreeze specific layers of a model based on name patterns. + + Parameters + ---------- + freeze_patterns + A list of strings. Parameters whose names start with any of these patterns will be frozen + (``requires_grad=False``). + unfreeze_patterns + A list of strings. Parameters whose names start with any of these patterns will be unfrozen + (``requires_grad=True``). This is applied after ``freeze_patterns``. Default: ``None``. + model_attribute + The attribute name of the model within the LightningModule. Default: ``"model"``. + verbose + Whether to log the trainable parameter count after freezing. If ``None``, it will be + set to ``True`` if the trainer has a progress bar callback enabled + (e.g. when ``model.fit(..., verbose=True)``). Default: ``None``. + """ + unfreeze_patterns = unfreeze_patterns or [] + + super().__init__( + transform_fn=partial( + self._freeze_layers, + freeze_patterns=freeze_patterns, + unfreeze_patterns=unfreeze_patterns, + ), + model_attribute=model_attribute, + verbose=verbose, + ) + + +class PeftCallback(ModelTransformCallback): + @classmethod + def _apply_peft(cls, model: nn.Module, peft_config) -> nn.Module: + try: + from peft import get_peft_model + except ImportError: + raise ImportError( + "Please install the `peft` package to use PeftCallback: `pip install peft`." + ) + peft_model = get_peft_model(model, peft_config) + return peft_model + + def __init__( + self, + peft_config=None, + model_attribute: str = "model", + verbose: Optional[bool] = None, + ): + """ + A callback to apply Parameter-Efficient Fine-Tuning (PEFT) to a model using the ``peft`` library. + + It wraps the internal model with a PEFT adapter (e.g., LoRA) and manages the merging of + weights during checkpointing so that the saved state can be loaded as a standard model. + + Parameters + ---------- + peft_config + A PEFT configuration object (e.g., ``LoraConfig``) from the ``peft`` library. + model_attribute + The attribute name of the model within the LightningModule. Default: ``"model"``. + verbose + Whether to log the trainable parameter count after applying PEFT. If ``None``, it will be + set to ``True`` if the trainer has a progress bar callback enabled + (e.g. when ``model.fit(..., verbose=True)``). Default: ``None``. + """ + super().__init__( + transform_fn=partial(self._apply_peft, peft_config=peft_config), + model_attribute=model_attribute, + verbose=verbose, + ) + self.peft_config = peft_config + + def on_save_checkpoint(self, trainer, pl_module, checkpoint): + # We replace the state_dict in the checkpoint with the one from the base model + # (with adapters merged), so that the model can be loaded as a regular model. + super().on_save_checkpoint(trainer, pl_module, checkpoint) + peft_model = getattr(pl_module, self.model_attribute, None) + try: + from peft import PeftModel + except ImportError: + return + + if isinstance(peft_model, PeftModel): + # Merge adapters into the base model weights + # TODO: This might be inefficient for large models, think about a better way + model_copy = deepcopy(peft_model) + setattr(pl_module, self.model_attribute, peft_model.merge_and_unload()) + try: + # Get the state dict of the base model + # This returns the weights including the merged adapters + # base_state_dict = peft_model.get_base_model().state_dict() + + # We need to prepend the model attribute name to the keys + # because the PL module expects keys to start with `model.` (or `model_attribute.`) + prefix = self.model_attribute + "." + new_state_dict = { + prefix + k: v + for k, v in getattr(pl_module, self.model_attribute) + .state_dict() + .items() + } + + # Update the checkpoint + checkpoint["state_dict"] = new_state_dict + + finally: + # Unmerge adapters to keep the current model in PEFT mode + setattr(pl_module, self.model_attribute, model_copy) diff --git a/darts/utils/callbacks.py b/darts/utils/callbacks/progress_bar.py similarity index 100% rename from darts/utils/callbacks.py rename to darts/utils/callbacks/progress_bar.py diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index a413b4ad76..8c3f9d92d2 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -105,7 +105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "426a22d045d34b1a842eec9a09e6302a", + "model_id": "8547492bdce140e5a935ba4db13a6b21", "version_major": 2, "version_minor": 0 }, @@ -119,7 +119,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -128,7 +128,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -149,7 +149,7 @@ " series=train_passengers,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "prediction.plot(label=\"Forecast\")" + "prediction.plot(label=\"Forecast\", title=\"Base model (not finetuned yet)\")" ] }, { @@ -173,7 +173,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f706e79ab1704fea9809dae3e7db1b78", + "model_id": "16d446f7866f4d0ca54ded638d7e66e3", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ " input_chunk_length=24,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=50,\n", + " n_epochs=100,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", ")\n", "full_finetuned_model.fit(train_passengers, verbose=True)\n", @@ -217,7 +217,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "467be790d94d48b09ea2ad2de1567125", + "model_id": "30691d9dda1d4f998b5ed5001809a6b5", "version_major": 2, "version_minor": 0 }, @@ -231,7 +231,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb68e0d824904fc183dc196fd93a88be", + "model_id": "9cba0f4cc07645f69c14457c61b59d40", "version_major": 2, "version_minor": 0 }, @@ -254,7 +254,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -316,63 +316,19 @@ "metadata": {}, "source": [ "# 2. Partial fine-tuning with layer freezing\n", - "With this method, top layers of the model will be frozen. That means that their weights won't be updated during the fine-tuning. \n", - "\n", - "This is done with the `LayerFreezeCallback` available in darts. \n", - "\n", - "
\n", - "LayerFreezeCallback\n", - "\n", - "\n", - "Here is the source code of the callback method.\n", - "\n", - "It extends the `ModelTransformCallback` which applies a transform function to the model attribute (by default `model`) on the setup callback of `pytorch_lightning`.\n", - "\n", - " ```python\n", - "class LayerFreezeCallback(ModelTransformCallback):\n", - " @classmethod\n", - " def _freeze_layers(\n", - " cls, model: nn.Module, freeze_patterns: list[str], unfreeze_patterns: list[str]\n", - " ) -> nn.Module:\n", - " for name, param in model.named_parameters():\n", - " if any(name.startswith(layer) for layer in freeze_patterns):\n", - " param.requires_grad = False\n", - " if any(name.startswith(layer) for layer in unfreeze_patterns):\n", - " param.requires_grad = True\n", - " return model\n", - "\n", - " def __init__(\n", - " self,\n", - " freeze_patterns: list[str],\n", - " unfreeze_patterns: list[str] = None,\n", - " model_attribute: str = \"model\",\n", - " verbose: bool = False,\n", - " ):\n", - " unfreeze_patterns = unfreeze_patterns or []\n", - "\n", - " super().__init__(\n", - " transform_fn=partial(\n", - " self._freeze_layers,\n", - " freeze_patterns=freeze_patterns,\n", - " unfreeze_patterns=unfreeze_patterns,\n", - " ),\n", - " model_attribute=model_attribute,\n", - " verbose=verbose,\n", - " )\n", - "```\n", - "
" + "With this method, top layers of the model will be frozen. That means that their weights won't be updated during the fine-tuning. " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "33fa7fc4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f830deafaf54b249882fcad5c672577", + "model_id": "1a38e2f67955406d8c399db728eb96e2", "version_major": 2, "version_minor": 0 }, @@ -385,19 +341,14 @@ } ], "source": [ - "from darts.models.forecasting.foundation_model import LayerFreezeCallback\n", - "\n", - "freeze_callback = LayerFreezeCallback(\n", - " freeze_patterns=[\"encoder.block.0\", \"encoder.block.1\", \"encoder.block.2\"],\n", - " unfreeze_patterns=[\"output_patch_embedding\"],\n", - ")\n", - "\n", "partial_finetuned_model = Chronos2Model(\n", " input_chunk_length=12,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=50,\n", - " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [freeze_callback]},\n", + " freeze_patterns=[\"encoder.block.0\", \"encoder.block.1\", \"encoder.block.2\"],\n", + " unfreeze_patterns=[\"output_patch_embedding\"],\n", + " n_epochs=100,\n", + " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", ")\n", "partial_finetuned_model.fit(train_passengers, verbose=True)\n", "partial_finetuned_model.save(\"partial_finetuned.pt\")\n", @@ -408,14 +359,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "50830283", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f92740814334e009f614fb2668b88c2", + "model_id": "0c6375a46aa043aba1397eb195e2b3fe", "version_major": 2, "version_minor": 0 }, @@ -429,7 +380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "be8c720307a84928a25298ca935b3992", + "model_id": "5117f22a8ec94ad5842ea8709e74fd4d", "version_major": 2, "version_minor": 0 }, @@ -446,13 +397,13 @@ "" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -527,14 +478,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "6981052c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f747f8932c964382b4e2c45025fac6ec", + "model_id": "cd7cd86f70dc4759ab3ed10c73fd7afe", "version_major": 2, "version_minor": 0 }, @@ -548,10 +499,10 @@ { "data": { "text/plain": [ - "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=50, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=50, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -559,7 +510,7 @@ "source": [ "from peft import LoraConfig\n", "\n", - "from darts.models.forecasting.foundation_model import PeftCallback\n", + "from darts.utils.callbacks.fine_tuning import PeftCallback\n", "\n", "lora_config = LoraConfig(\n", " r=8,\n", @@ -578,7 +529,7 @@ " input_chunk_length=24,\n", " output_chunk_length=6,\n", " enable_finetuning=True,\n", - " n_epochs=50,\n", + " n_epochs=100,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [peft_callback]},\n", ")\n", "model_lora.fit(train_passengers, verbose=True)" @@ -595,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "49b2c2e8", "metadata": {}, "outputs": [], @@ -607,14 +558,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "id": "41e8a82f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9423ea48aac342ea9600ae6f45f168ae", + "model_id": "80fe22ec79024e3e8d10bc5165d2f93e", "version_major": 2, "version_minor": 0 }, @@ -628,7 +579,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01bbd79720504a9db991aed2291c721e", + "model_id": "1588da65b96e44559a7b0086ac5b3796", "version_major": 2, "version_minor": 0 }, @@ -645,13 +596,13 @@ "" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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OCkGZOuXl5bC1tTX0NBiGMSE2nU3Bkbhs3D6iE0K8ODnCnGAPipFw//33w9raGseOHcPcuXPRo0cPdO7cGTNnzsS///6LGTNmyK8l7wF5VchgcXJywuuvvy4ep8e6dOkiTvLdunXDzz//LL8nLi5OvO/UqVPyY+SBoMfII6HpmdixYwcGDRoksg5GjBiBqKioGnN96623hHeH2mXfeeedKC0tbfB70eeOHz9ejD08PMTyb7vtNjkk87///U+EZby9vTF16tQm59nY8ojq6mo88cQT8PT0FEWxKPzEMIx5kl5QigdXncTy/XGY+fk+HL6cZegpMTqEDRQjICsrC1u3bsXSpUuFwdGcDAA68c6aNUt4WO644w789ddfIjz02GOP4dy5c7jnnntw++23Y+fOnVrP59lnn8X7778vjCUymmj5KlavXi0+m7w89DwVnfriiy8aXFZwcDDWrFkjxmTopKSk4OOPP5af//HHH4VBtX//fnz11VdNzq05y6N1ePjwYbzzzjt45ZVXsG3bNq3XAcMwxs/xuBxUVCnEOKe4Aou+P4zVxxINPS1GR7SbEM+MT/cho6CsTT/Tx8UO6x8Y1eTrYmJiRLVJ8npoQl4FlXeCjJe3335bfm7BggXCAFExf/584UkgTwzx6KOP4tChQ3jvvfdkj0NzIY/M2LFjxfipp57C9OnTxTxIF/PRRx8JrwndiNdeew3bt29v0ItCBanIm0FQgSoKYWnStWtXYUioIA9JYzS1vIiICLz44ovysj/77DPhEZo8ebJW64BhGOPnZGJNHRoZK0/8eQYx6YV48prusLKseWHHmBbtxkAh4yQ1v+FQhDFy5MgREbJYuHAhyspqGlcUgtHkwoULWLJkSY3HSNOi6V1oLnSSV6Eqy00VMENCQsTn3HvvvTVeP3z48BZ5aoiBAwdCl2jOXTV/VYlxhmHMi5MJOfL4poFB+ON4khh/s+cyLmcU4eN5/eBk125Oc2ZHu9ly5M0w1s+krB0K4dTWepAGRbNMtiYNhYIawtJSiuaRp0ZFQxUtNQW3qtASGUr6oPb30Gae9VFbLEzz19fcGYYxHBVV1TiTlCfGIZ6OePemvugb7I4X10WiqlqB7RfSMOfLA/j+tsHo4F73GMoYP+3GQGlOqMVQUM8gCkFQOOKBBx7Q2vggSFRLOo7FixfLj9H9nj17irGPj4/4T5qN/v37i7GmEFWbzyF9x6233io/RqGkxlBl5lCfpKZozjy1WR7DMObJxZQClFVKFx/9Q6RQ76Jhoejo5YT7Vh5HQWklLqYWYOZn+/HNrQMxIMTDwDNmtIVFskYCCU0rKytF6Ob3338XoRTyqPzyyy+4ePFik83Fli1bhhUrVohMnujoaHzwwQdYu3YtHn/8cdkLM2zYMJGBQ8vevXs3nnvuOa3nSULcH374AcuXL8elS5eE3iMyMrLR94SGhgpPxoYNG5CRkYHCwsIGX9uceWqzPIZhzJOTierwTv9gtRZtVFdv/HX/SIQqU44zC8sw75tD+OfUVYPMk2k5bKAYCZQefPLkSUyaNAlPP/00+vbtK4yVTz/9VBgZr776aqPvv+GGG4TehESxvXr1wtdffy2MCErlVUGGBRlBpPug1F4SuGrLzTffjOeff16k8tJy4uPjcd999zX6Hqrh8vLLLwvBLaUnU2pxYzQ1T22XxzCM+XEyQS2Q7V/LOxLm64y/7x+JYZ0lQX15ZTUeWnUKH2y7hOpqdfiYMW4sFJrBfhMhPz8fbm5uyMvLg6ura43nKJvkypUr6NSpk8g6YRiGMQb42KRbxr27E3FZxbC1tsS5l6aK/7Uhw+SFf85h1VF16vH0iAC8d2NfONg27pVm2v78XRv2oDAMwzAmRXZRuTBOiD4d3Oo1Tgh6/M3ZffDc9B5QlZL690wK5n1zEOkmltXZHmEDhWEYhjHZ9GJN/Ul9kF7trtGd8d2tg+Ck9JqcTsrD9Z/tx7mrUhYQY5ywgcIwDMOYjf6kISb28MOf942QU46pLtZNXx3E5nOpepsn0zrYQGEYhmFMNoNnQGjjHhRNegS44u+lIzFAmZZcUlGFe385ji92SdW8GeOCDRSGYRjGZKAibKcTpdCMv6s9AtwctC6g+evdw3BDv0D5sXc2R+GxP06jrJJrKxkTbKAwDMMwJgP12Sksq6xRoE1b7G2s8OHN/fD4lHD5sbUnrmLht4eRVdi2PduYhmEDhWEYhjFNgWwLDRSVePZ/E7rii4UDYG8jnQqPxedg5uf7cSmtQCdzZVoHGygMwzCMWQtkG+PaPgFYfc9w+LlKvdOSckow+4sD2BnFTUYNDRsoDMMwjMkJZK0tLdA70E0ny4wIcsc/S0ehdwepcBiFkO5ccRQ/HYzTyfKZlsEGCmMSpKamioaK1EjR3b35bt24uDjhym1JY0RG4rbbbhOtFPTNSy+9hH79+hnFat+1a5fYb3Jz1VfrTdGxY0d89NFHep1Xeye/tALR6YVyRo4uq8H6u9kLT8o1vfzFfaqI/9K6SMRlFunsMxjtYAPFiE4CdECsfYuJiYGpQs0LtTEmGuPDDz8UHY7J0KAmhYY8keriu9H7VdvY0tISAQEBos9RQkJCva/v3r077OzshKHW1id66vFE82UYQ3MmMQ+qbODW6E8awtHWWmhSbh4ULBspR+Kydf45TPNgA8WIuOaaa8RJWPNGPYVaQnl5OcyJ2NhY0Tywa9eu8PX1hTlAfShoG1+9ehVr1qwR3atvuummOq/bt28fSkpKcOONN+LHH3/U2edXVFQ063XUN0NXhibDGINAtjEsLS0wUyMFOZKrzRoMNlCMCLpC9vf3r3GzspJcmLt378aQIUPEa+hqmzr5UsdfFdS1mLr6Uvdfb29vTJ06VTx+7tw5TJs2Dc7OzqLz7y233ILMzEz5fdXV1XjnnXcQFhYmlh0SEoLXX39dfv7JJ59EeHg4HB0d0blzZ9HJWPPEdvr0aYwfPx4uLi7ihEtGxLFjx4SL/PbbbxcNoVSeArqyb4gvv/xSdHS2tbVFt27d8PPPP9dwndMJ/KeffhLLIU9JbWjZdPL+559/5M+jOai4fPmymCd9D+oUffDgwTpGwOjRo+Hg4IDg4GA8+OCDKCpquWuXPCEzZ84U653Wy9y5c5GWllbjNTRH2sa0PUeMGIE777wTR44cEc20NPn++++xYMECse2o03NjkKeDOj3TdlGtB5X3g8a0nq+//noRKqPtXFVVJT6XDGH67rTuyWPSmGeK9jVaP9TR2tPTU3yH2tuWQiN33XUXfHx8xPefMGGCmJMmb731ltgnad+hOVAzveaEXbZs2YL+/fuL+dJy09PTsWnTJvTo0UN8Fq2r4mKpTwtRVlYm5kuGLTUQHTVqFI4ePVpj2Rs3bhT7OS2T9hMKDdZG1/sIoz0nEzUEssGtF8g2RC8Nbcu55Jq/R6YNUWhJUlKSYuHChQpPT0+Fvb29onfv3oqjR4/Kz1dXVyuef/55hb+/v3h+4sSJikuXLtVYRlZWlmLBggUKFxcXhZubm+KOO+5QFBQUNHsOeXl55OQT/2tTUlKiOH/+vPhvSixevFgxc+bMBte5o6Oj4v7771dcuHBB8ddffym8vb0VL774ovyasWPHKpydnRXLli1TXLx4UdxycnIUPj4+iqefflq878SJE4rJkycrxo8fL7/viSeeUHh4eChWrFihiImJUezdu1fx7bffys+/+uqriv379yuuXLmiWLduncLPz0/x9ttvy8/36tVLsWjRIrF82s6rV69WnDp1SlFWVqb46KOPFK6uroqUlBRxa2gbr127VmFjY6P4/PPPFVFRUYr3339fYWVlpfjvv//E8+np6YprrrlGMXfuXLGc3NzcOsugZdPz9DrV59EcaN60r3Tv3l2xYcMGsfwbb7xRERoaqqioqBDvpe/t5OSk+PDDD8V3oO/bv39/xW233dbg9lq+fLnYd+ujqqpK0a9fP8WoUaMUx44dUxw6dEgxcOBAsY0aen9aWprYLvS9CwsL5cfz8/PF3M6dO6eorKwU63/Pnj0Nzqu4uFjx2GOPie2iWg/0GEHrwdfXV/HDDz8oYmNjFfHx8Yry8nLFCy+8IH7Dly9fVvzyyy9iX/v9998b3Dfpe9B2femll8T6+vHHHxUWFhaKrVu3yq+ZNGmSYsaMGWK59Bqak5eXl/jtE7R8Ozs7xXfffSf21WeffVYcD/r27dvgd9u5c6f4DsOGDVPs27dP7M9hYWFiPlOmTBH3ad3Q57z11lvy+x588EFFYGCgYuPGjYrIyEjxfWifV80lISFBzOXRRx8Vc6F1QOuZPot+Q83dR2ifoucbwlSPTcYCnVv6vbxFEfrkBvGf7uuTUW/vEJ/V/blNisoq/X5WeyKvkfN3bbQyULKzs8WPkH6Uhw8fFge0LVu2iB+vCjow0IH377//Vpw+fVpx/fXXKzp16lTjR0knEToQ0YGbToh0kJk/f75OvmCjB4H9nyoU73Vv/e1yrRME3Vc9R5/RAuigSScnOgiqbnQiJZ555hlFt27davwg6WROBgmdDAk6SNMBUxMyLujArUliYqJYd3SippMfHZg1DZKmePfdd8XJVgWdVMi40fYkrsmIESMUd999d43HbrrpJsW1114r36cTJK0jbY08lYFCJ0IVdJKix8ioIu68807FkiVLaryP9ktLS8sGTyaNfTc6UdO2pBNf7c88cuSI/H66T9uZDAIa041Oppp88803wthR8dBDDzW5Hshwre9ET8t/+OGHFU2xdOlSxZw5cxo1UMj40mTw4MGKJ598Ul53ZMCUlpbWeE2XLl0UX3/9tRgPHz5cGNyaDB06tFkGyvbt2+XH3nzzTfEYGVwq7rnnHsXUqVPFmIw9Mn5XrlwpP09GGRks77zzjrhPBnzPnj1rfBZ9F00DpTn7CBso+uVyRqEwGOh2+3Lpd6RP7v35mPx50WnNv4BmdGegWGvjbXn77beFa3P58uXyY5oaCToGkor9ueeeE+5tgtzy5Mb9+++/MW/ePFy4cAGbN28WLtZBgwaJ13z66ae49tpr8d577yEwUB370zllBUBBcuuXU1VW975qufQZLYRcy+SCV0FueILW2fDhw4V7W8XIkSNRWFiIpKQkEZYhKLyiCbnUd+7cKcIM9Wk6yA1P7u+JEyc2OKfff/8dn3zyiXg9fR6FlciNruLRRx8VrnwKyUyaNEloKChUow30/ZYsWVLjMfp+tUMNrSEiIkIeU0iFoNAAiU9pPZ05cwYrV66ssS9T+OvKlSsidKDt96HfCd1U9OzZU+g46LnBgweLxyi0ceLECREyoxAFfb5meI2gkM6iRYvk+zQeO3as+M3Q+7VF9ZvT5PPPPxefQ2Ep0rqQfqkpka3m+lStU1qfBK1P2le8vLxqvIaWTfsRQevh3nvvrfE87eO0vzaF5mfTsUUVftR8jEJlBH0erV/an1TY2NiIcCnNQTWXoUOH1pmLJrreRxj9djDWBb07uGGTspFgZHIewnzrHkcZ/aKVgbJu3TqhbaCTEGkiOnTogPvvvx933323eJ5+qJRlQCcqTYEd/fgp5k8GCv2nA7XmgZJeT5kMhw8fxqxZs+p8Lp1E6aaidoy+2di5AC46MICs7OreVy2XPqOFkEFCWpDWvF8TOknMmDFDGJa1oRMK6TIag7bVwoULhaaBtjtty1WrVuH999+XX0PaA4r5//vvv+Ik++KLL4rX1LcdDQmdlFSoDD06uajW0z333CM0BbVRGX/6gPZ51famExydTO+77z5Zf3P+/HkcOnRInGxJC6SCdCO0jlW/u9bsI7Scxx9/XGxTOimT0fPuu++K32Jz16dqnWquT9q/NDVAKnQhtq29LRubi64w1D7C1F+gbUCo/vQnKnoGqi/Ezl3Nw8x+HXhzGLOBQic0usKnq+ZnnnlGeEHoB0vCxsWLF8spkHQFowndVz1H/2tnYVhbWwuxXUMplG+++aY4SbaaEf+Tbrqm02jgMelqTB/QyYtEonTFpjq57t+/X5xMgoKCGnzfgAEDxPtIZErruDaUEUOCvx07dggvSG0OHDiA0NBQPPvss/Jj8fHxdV5H4kK6PfLII5g/f77wsJGBQvsFnUyb8/3o+9A+pILuk9dBG5r7efWtJzIGWmMc1v4+iYmJ4qbyotDyyWPV2Hci4TN5n2g90pxIHDtmzBjh4dCE1i8915CBos16oPVMAl260FCh8nK0FJo7/ZZpn6N9r6F1REbQrbfeKj9GxpiuUQmv6XvSvkyQR4WOXSQoV82FLr40qT0XXe8jTMsLtNEhMCJINwXaGkOzCNy5qyyUNfosHroqoR/qG2+8IVT05Jang+RXX32lvxkCePrpp0U2iOpGB/72BJ086Ds/8MADuHjxoshUIU8FGYp0Fd4QS5cuRXZ2tjAa6IBMJx7KgKDsGjqBUUYDXZlTNgaF4uh5OjDTyU9lwJDbn66y6TkK9fz11181XPaUOURXymS40EmAPkfl7qaTE115kgFEmUOamRWaLFu2TGSakPEbHR2NDz74AGvXrhVX9tpAn0dueErXpc9rbhotrQMyxui7UJ0VmgOtY7rfGLQO6fWaNwoXkEewT58+wvtEIRzygNCJmEIz9YVYVJAxQ4bdCy+8IOZOnhTadr17965xI2OSTu6RkZENrgfyZtJ8aD1oeh9rQ9uYsq5ov6D6MpSlVTvDRVvo+5M3hjJ/tm7dKjJiaP2SoUufRTz00EMirETGFn0u7c8NfZ/WQB4j8krRPkahZTIy6JhF+yJlDhEUaqJtTq+hfefXX3+tU/elpfsIoxtKyqtwIUUKn4f7usDFvqbXTB9Q12NV+XsK8UgyLsZoDRRy29a+AqSTkaq4FKUbErXTKem+6jn6r4pVqyBdA51IVa+pDaW/ku5B89aeoFAapUHSiY5SZOmASgdX0vo0Bul5yGigE+mUKVPESZOuGsnNrjJs6IT02GOPiZMibUsqFqbaPpSOSlfzdBAmTQIdoOn1KigFOisrS5x8yYNCqbSU0qzydtGVOc2VlknpppTOXB90IiO9CWmQevXqha+//lqcuCidVRvoxENpsmQE0OfRd28OpGmgkCWdKCmNlIxvWh9N6aHI+KLXat4opEZeLjp5eXh4CA8InbBJI0F6nqag9U3hMjLSaN3WFyqj7UQ3lSFZmzlz5oiaOqRpovXw22+/Nfh5FLaYPXu22EYUiqXP1PSmtAT6/rS/0ncnY5j2DQrvkhGr8q7S59G+RMYxaafoOTIk9AGlM9M6oTRtusCi4odkkNH2UYVoyNNIOjn6fdEFF12E6WIfYXTD2at5qKKqaXqsf9KYFyW/tFL06GHaFgtSyjb3xaQ1oCv5vXv31jig0tUcnbxoUfSDpStfOump9CIU0qErEpVIlowcupJSiTrpKosOqCT4bM4PnpZJegjyptQ2VqiWAl09kniXPAQMwzDGAB+bWs7Xu2Px5qaLYvz2nD64eXDb6H4+2HYJn+yIFuMvFw7AtD6SwJ5pOY2dv1vlQSFjhEIAdHVBVyHkCv3mm29EKEF15URX6K+99pqI6Z49e1ZcXZPRoSr0RFd+ZIzQ1S55BOgql67QyXjhqxGGYRhG3x2Mm0svTaFscl6bfS7TApEspUeSBoE0Ia+88orwUlBaMcXaVZDLlqorkj6FRIFUtZFiv5reDErVI6OE0lsp1EDuV9I3MAzDMIwm5Jk/oUwxdrGzRphP26X7UqqxChbKGrmBQlx33XXi1hDkRSHjhW4NQRk75H1hGIZhmMZIyStFeoEk9O4bTPo5dT0ofRPoZg8PRxvkFFfIQlnNelSMfuFePAzDMIyJhHfatmklGSMqL0pmYblsKDFtAxsoDMMwjNGiCu8YwkCp0ziQOxu3KWygMAzDMCZR4r6fHjsYN0soywXb2hQ2UBiGYRijpKyyCueSpSquHb0c4elk2+ZzqCGU5UyeNoUNFIZhGMYooeqx5ZXVbZ5eTJSW5EBRXY1QT0c420n5JOeVxhLTNrCBwjAMwxh9eGdAG+pP9hz+GONXjcbcH/ujsqJQbhx4NbcE2UXlbTaP9g4bKIxJQM3nJk+eLHqraNMRl/rAkBKf+qfoEuriTOX/9Y0uPkdf68AcoT5GVNvJ2PaD9oohCrRlpEdi6cXvUGhpgYuW1TgWu7GGDoXSjZm2gQ0UI+G2224TJ5HaN6rYa6pQewNtjInG+PDDD5GSkiJOstQPpaF1qKpYzGgH9T1Sdfdt6ftV+ywVZaT+O9SFvL5OGgcPHhR9nKZPn86biWlWB2N7G0t083fR/9pSKLBxy4M1HkqwVHBnYwPBBooRQS0A6CSseaNqvS2hvNy83JDUTZl6N1H3XertxBgf1L6C9lnqCEzVpqmZXn2dzqnJIXXm3rNnD5KTkw0yV8b4ySgoQ2K21KAvooM7bKza4HR1fAUWxx7DTflS52QioSChhlCWPShtBxsoRgR1baaOzpo3utIkqJPqkCFDxGuoq/RTTz0lukBrXsFS+wC6Cvb29sbUqVPF4+fOnRMdhp2dnUUnWerompmZKb+vurpadBkOCwsTy6bOrq+//nqNNvN0Nezo6Cg68lIH2oqKCvn506dPi665Li4uovETGRHUCHLXrl2iky01hFJdWZM7vCG+/PJLdOnSBba2tqIj8c8//1zD7U7dZn/66SexHPKU1IaW/eOPP4ouwqrPozmouHz5spgnfQ/qWEtX8Zrs27dPdKl1cHBAcHAwHnzwQdGyobnQeqTqyUFBQWI9ktufWjxo0tS6VHXepe1E65M6VlODudp89913oqcVeSq6d++OL774osbz1OOKuu3S89TZ+eTJk2gttP6p0zR9N9oe77//fp3X0PeifTY0NFRse+oAvG3btjodoKmrM3UuJg8Kedmagj6P+ntRXy/aj2n51OsrIyMDM2fOFI/RZ9F+p82cqWs3dZ+mbU4XAtSCozbUruOuu+4SXaFp/54wYYLY5xn9cyqxjQu0ZV8Gtjwrhkty1WLYxPxEdPFxgp21dLqMZKGs8Za6N2V+jPwRP53/qcnX9fTsiU8nflrjsQd2PIDz2eebfO+tPW/F4l6LoUuuXr2Ka6+9VpyY6SR98eJFcbVKJyDNkz6doOnATw0YVQdXOqDSAZZCJCUlJeIkOXfuXPz333/iNXSl++2334rnqW8SXQHT8lXQiZJOItTIkZo/0ufSY9RziaA+THQyJAODjCkKwdjY2GDEiBEilk9X0XRFTdCJpD6ov9NDDz0kXj9p0iRs2LBBnODoZE9GxdGjR8XJiU4QH3/8sTih1IY6aFOnbOqUuXz5crmlguoK/dlnn8V7770nPDA0nj9/vgifWVtbC+8Mea/oJPjDDz+IEx8Ze3RTLaspaF50Avz666/F+qDlXH/99YiMjBSf2Zx1uXr1arE9P//8c7EtyEijHlVkzKigkyit088++0x8DhkftBzS5ixevFgYANSKgvQ6v/zyi+jsTeu2NRw/flzsMzS3m2++WXQuv//+++Hl5VWvsUhhHTL4aD9SfXcV9B3JqCIjdNGiRcKgpn2wqfLhtH9Sk1Iy6mhMhjbtY3fccQfeffddsV/TPkLrm5bVnDnTf9o/du7cKfZZMkrJaNHkpptuEvvbpk2bRAdW2r7UQ4zCjLR/MW0jkNW7gVJdBfx1L1AhXZT4RiyEXcFBlFWVIb4gHtZWlugR4CqMpiuZRSgorYCLvY1+58SIg4nJkZeXR4Ft8b82JSUlivPnz4v/tfn85OeK3it6N3lb8O+COu+lx5rzXvqMlrB48WKFlZWVwsnJSb7deOON4rlnnnlG0a1bN0V1dbX6u3z+ucLZ2VlRVVUl7o8dO1bRv3//Gst89dVXFVOmTKnxWGJiolh3UVFRivz8fIWdnZ3i22+/bfY83333XcXAgQPl+y4uLooVK1bU+9rly5cr3NzcmlzmiBEjFHfffXeNx2666SbFtddeK9+fOXOmWEeNQc/T6zS5cuWK+L7fffed/FhkZKR47MKFC+L+nXfeqViyZEmN9+3du1dhaWlZ735EvPjii4q+ffvK9wMDAxWvv/56jdcMHjxYcf/99zd7XQ4fPrzO64cOHVrjc7p06aL49ddf62xnei/x9ddfK7y8vGrM+8svvxTf9+TJkw3Ohfafhx56qN7nFixYoJg8eXKNx5YtW6bo2bNnjffb2NiI/Zb+0+fZ29sr9u/fX2dbf/TRR2JcUVGh8Pb2VuzcuVPRGKGhoYpFixbJ91NSUsTyn3/+efmxgwcPisfouebMmfZ/ev2RI0fk52l/oMc+/PBDeR9wdXVVlJaW1lgObQNaz/XtB43R2LGJqcu8rw8qQp/cIG6pefpdZyvX36nY/LafQvGiq0LxUYRCUVqguGHlSHFM77+8t6KyokzxzNoz8nwOxWbyJtPD+bs27cqD4mTjBF/HpvULnnae9T7WnPfSZ7QU8haQJ0JelpO0LPIMDB8+vMZV5siRI8XVclJSkgjLEBRe0YRc0XR1WJ/ngrwG5GEpKysTV4QNQe54uoqn19PnUViJPBkqHn30UeGhoat98n7QFSeFarSBvh91v9aEvh95JXQFhQBUUIiMoKtlupqn9XTmzJkaLn7yAlDYhjwQFE5pDPLa0JU4zbn2d9AMBzS1Lmk93HvvvTWWQdudtiFBISd6L4V+yGuigpZDV/eqZdB31eweTstoDbRMCqXU/m7k8aqqqpLDkORNI+9UTk4OXnzxReHhoJsK8qRR+Ik8ZgR5r8i7QZoUClE2d/tRCIzo06dPncdom1KYqak50/P0+Zq/GdoXNEXdtO1oO5HXRRPyRNJ2YPRHVbUCp5Ny5YZ9fq7q/VnXXIxah3czD6HSzwe7C4rw+oyvYGHnjJAqBShFocICSEs/g94d1Md/Kh43tHPN/YLRPe3KQKHQS0vDL7VDPvqADBLSgrTm/ZrQwZVi7G+//Xad19JJmnQZjUE6DTrpvPzyy0LTQifBVatW1Yjlkwt9wYIF+Pfff4UbnE5M9JpZs2bBmCAXvgqVoUcGiGo93XPPPcLFXxuV8ddamrMum4LmSVBIbujQoTWeUxkJhoS+k2r/pVAOjYcNGyYMV4IMETKmKMSlaQiSRoRCViojq7nbr7FtqgtofdPvRFPLpEJX2WlM/USlFqC4vErv6cXlZQV4+sDzqFR2SPYJ6A+LUMmgD3HwBYry4VGtQFbOFfQOVIcrWSjbNrQrA8VUoSt4EvxptvomnQnpF0in0RADBgwQ7yOBIF0t1ob0ARRf37Fjh/CC1Ibi9iRIpKtiFfHx8XVeR8JPuj3yyCNC20G6DTJQSPBKV6vN+X70fUhDoYLu9+zZE9rQ3M+rbz2dP3++xcYheUHopEtzHjt2rPw43Sdhc3PXJa2Hw4cPCy2FikOHDtXwEtDnkGFJxk590DLIm0XiWpUXRXMZLUG1fTSh+7TNGzKMyGtH2hfSBpFOhrYL6afIIJsyZUqN11Jq+G+//VbHe6TPOZO3hIwl0qoMHjxY9vCQV1Fzv6D6O/Tbod8Q0/bpxfrWn3y2/lbEWEpGbXi1JZZepxZt3zHhPdxtZQtXt2C57L61pQUqqxWI5J48bQJn8ZgAJO5LTEwUqZkkPKRMFfJUUHjF0rLhTbh06VJkZ2cLo4GEpuSW3rJlixCg0gmDTmAkLiSRJp086Hk6mdGVrsqASUhIEFf69ByFJ1TueZWrm4SkdIVJJ1s6AdDnqEIidFCnq1AygChzqLi4uN55Llu2TIhHKbwVHR2NDz74AGvXrhUnN22gz6NQDZ1o6PNqZ8g0BK0DMiDou5DIl+ZA65juNxf6DuSpojAOfT5lWdGyVALVptYlQa8lcS0ZeCTCpG1Mok9NyAND9UXo/fQaEtvS62mdEeTNIiOWQkBkdG3cuFGIg5sDiYNpzpq3tLQ0PPbYY2Ibvvrqq+IzSYxNHo+mtg95pej1ZCST8JlCPxSe6t27d43bnDlz5H1OVzQ1ZxLpkjCa5khGIRkqZKRrCrDJ80PhMTKgtm7dKgre0X5CRmbtjCFGnwXa9GOgHDu1AisKo8XYRqHAG6PfhK2dutaKh2cX2Tgh7KytEO4nPR+dXoASpYeH0SOKdiSSNWbqE3hqsmvXLiG6tLW1Vfj7+yuefPJJITJsSuR46dIlxaxZsxTu7u4KBwcHRffu3RUPP/ywLLglke1rr70mhIgkbgwJCVG88cYbNYSFJLokQe7NN98sBIQq4WtZWZli3rx5iuDgYDEvEor+73//q7Hu7733XvF+2l4kKGyIL774QtG5c2cxh/DwcMVPP/1U4/nmiGTT09OFMJLmSp9H4kuVSFZTIJqTkyM/r4LEkqr3ktAzIiKijuhVk9riSFqPL730kqJDhw7iO9BzmzZtqvGextalCvpMEo7Sa+j7PvHEE3VEmCtXrlT069dPrHMPDw/FmDFjFGvXrq0hGKX30PP0ujVr1jRLJEuvqX0jAS7x559/CoGpah8hgW/t99e3/91zzz2KXr16Ka677roaomdNDh8+LD7r9OnT9T5P+6ZKuKqCXv/XX3/J9+vbzk3NmQS106dPF0Jxep72udqfRULyBx54QOzbtBza1xcuXKhISEgQz7NIVj9MeG+nEKOGPfOvoqS8UufLLyxIUUz9Xp3c8P3625v1vsdXn5KFsifis3U+r/ZAnhYiWQv6AxODRIkUr6YaG5oiQ4Jc2yRspLoGmkJBhmEYQ8LHpuaRV1yBvq9sFeO+we74Z2lN8bkuePG3yVhbnirGAxS2+GHRQVhZN90p+ccDcXhxneTVfPWG3rhlWKjO52bu5Ddy/q4Na1AYhmEYo+GUMnuH6B+s+/DOzoPvycaJY7UCr0/+vEHj5J//nsLp9FNILsvBF4v2o3cH9Qn1PPfk0TtsoDAMwzDtokBbdVUlPr7wE6DUdj8ZfA2CgoY1+PrtSXuwSyGVvU9PP4seAX1AeQoUdzjHQlm9wyJZhmEYxigFsgN0nGJsaWWNb6/7DaMtnDDOwgWzJrzT6OtDHHzkcULqcTjaWqOzt5OcCl1eqbu0dqYubKAwDMMwRkF1tULuwePtbIsgj7ptLVqLj28vfL7oAN6+cR0sGsmCJEJc1RqThEyp1YmqcWB5VbXI5mH0h9kaKCao/WUYxozhY1LTXMkqQl6JVB6gX7BHkz2aWgoZJo6O3k2+LtiruzxOyLsi/vcO1OxsrG4qyOgeszNQVNUlG6q5wTAMYwhUxyTNCriM/uufkO7km3W3IC83Tuv3hgaoWyEklEiNJHtpCGUjr+bpZI5MOxHJUpVIKkOt6kpKLeD1ZYUzDMM0x3NCxgkdk+jYZAxtCdqTQPaXLffj05xT+H3tdXij34MYOqBm36/G8PfrD2uFApUWFkiolFpN9NLwoFBPHkZ/mJ2BQlCzMKJ263SGYRhDQcaJ6tjENO5BodY4EUGtN1BiMs7h4/QD1KwJ6VbaX6hS+nFQtSXirBRIRBWqq6vg5mCDYE8HJGaX4HxyvmhsaKXs5cPoFrM0UMhjQk2+fH19m13unGEYRl9QWIc9J41TVFaJi6mSR4JKyjvbte70VFFVgWcOvYJypQd9kWMnrbwnKkKtnRCnKESppQUyMiLh5xchdChkoJRUVOFKZiHCfNUl8hndYZYGigo6IPBBgWEYxvg5k5SHamVugy46GH915itcyL4gxp3dOuOhab+0aDnBlGpcLIV3ElKOSQZKBzdsOpcqC2XZQNEPZieSZRiGYdp3B+PTGafx3dnvxNjawhpvjH4D9hqNALVhgEcPXFtYhHtz8uBbIqUV9wpUC2XPsVBWb5i1B4VhGIYxxQJtLTdQiosz8eyWe1CtkIqo3dP3HvTy6tXi5U3uOBmT930l3SnMriuU5YqyeoM9KAzDMIzBM51UBoqrPVVrdW7xsj5Yfyviq4rEOMK1M+7qc1frJufZRT3Oviz++bjYwc/VTowjk/O4xo2eYAOFYRiGMShJOSXILCwT434hHrBsYVZM5Mkf8HtpohjbUyPAAY/B2rKVgQK3YEC1jGypWJtmwbb80koxf0b3sIHCMAzDGJSTyvL2repgXJyNnjvexisZWXCsrsZjAePRMXRM6ydnZQ24h4LyQa/kx0FRLYWOeilL3hOsQ9EPbKAwDMMwpl+gbePjsChMxazCIqyz6oKbp3yss/k97+6AQR2Dcb2/BzIzpcyg3ppC2WSuKKsP2EBhGIZhjEYg268lHpSzfwLn1khje3f43fBVk40AtcHJ3hPVynoqlGpc14PCFWX1ARsoDMMwjMEoq6wSFVmJzj5OcHe01er9aWlnsGf7E+oHpr8PuAbqdI7BLsF1mgYGutnDw1Hqq8RCWf3ABgrDMAxjMKjQWXmVpOvoH6x9gbaXNi/BUk8nvOzlgeKeM4E+N+p8jiHdrpfHic6ecsVyKthGZBaWI71AEvkyuoMNFIbR4GxSHo7HS7UOGIYx7g7GiYkHsQ9SSvEeJ2dUTHkF+iDEu4c8js+Pl8c166GwDkXXsIHCMAAuZxTizhVHMeOzfZjz5UHsjc7g9cIwbcCJVghko+J3yeNZHr3h5t4R+iDQORBWFlIX6sQCKY2Z6N1Bs6Is61B0DVeSZdo1+aUV+HRHNFYciENFlbIRCIAdF9IxuquPQefGMO2BU0oPioONFbr5aVeO/lLGGXnczScC+sLG0kYYKWScJBQkiMJsFOKp4UHhTB6dwwYK0y6hFul/HEvEu1uikFVUXud5dtcyjP5Jyy/F1VypyFlEkBusrbRz6kcVJsjjbiE6qHnSCCGWDiDfSVFFEbKyLsHbuxtCPR1F1+XCskpEcohH53CIh2l3HLmSjes/24en1p6VjRM7a0s8MCEMHdwdZOEeGTEMw7SV/kR7geylCims4lCtQFCHYdAnwWWl8jgx+aj4TxVveyrroSTnlSK7nosdpuWwgcK0G5JyirH01xOY+/VBYYComN4nANsfHYvHpnQTV3FESUWV0KUwDGOcHYyLClORJMlC0BU2sKSKr3ok1FWdapyYeb5OyXtVujGjOzjEw5g9xeWV+Gr3ZXy9OxZllVI6I9EjwBUvzuiJYZ295McobXDTuVQxPns1D121jIkzDNM2GTzRcf/J4672+teLTep3D3pljEJw4CB4eYY3KJRl7ZruYAOFMVtIyLbudDLe3HgRqflq96ynky2WTe2GuYOCYVWrKVkfjeqQZKDMHhDUpnNmmPZCZVU1ziRJBkqQhwN8Xey1ev+lq4flcTcPtcGgL/wD+otbbVgoayQhnpdeekkolzVv3bt3l58fN25cnefvvffeGstISEjA9OnT4ejoCF9fXyxbtgyVlZW6+0YMA4gD341fHcRDq07Jxom1pQXuGtUJOx8fh/lDQuoYJ4Sq8BLBQlmG0R8XUwtQWlHdYv1JdmkWrBWSTiw8YDAMRRcfJ6FhI1QVcRkDeVB69eqF7du3qxdgXXMRd999N155RV0shwwRFVVVVcI48ff3x4EDB5CSkoJbb70VNjY2eOONN1r+LRhGSXp+Kd7ZEoU/jyfVWCcTuvvi2ek90MXHudF1Rd4VEspSZgHpVKqrFS1u/c4wjP46GN878xfcWVaEK4l7EKJngWxjUOYRhYtPJebiSmYRCkor4GIvlcBn2thAIYOEDIyGIIOkoee3bt2K8+fPCwPHz88P/fr1w6uvvoonn3xSeGdsbbXrwcAwmv08ftgXh8/+i0ZReZX8OPX2eP66nhjfzbfZK4tiymSgFJdX4XJmEcJ8GzdqGIYxTAdjGzsnhIdNa7PVH3lhDU7H/4fE/ATcO+VTuTAcHTPIQFF5UYZq6NqYNsziiY6ORmBgIDp37oyFCxeKkI0mK1euhLe3N3r37o2nn34axcXF8nMHDx5Enz59hHGiYurUqcjPz0dkZGSDn1lWViZeo3ljGJXOZEtkKiZ/sAdvb74oGycu9tbCMNny8BitjJPaOhQO8zCMfgu02VpZyqm6xs5fZ77Hm2l78EtJHK4kHWhAh8LnJ4N4UIYOHYoVK1agW7duIjzz8ssvY/To0Th37hxcXFywYMEChIaGCgPmzJkzwjMSFRWFtWvXivenpqbWME4I1X16riHefPNN8VkMo0lUagFe2RCJ/TFZ8mMUjSF9yaOTw+HlbNeiFda7llD2hv4deMUzjA7JKSoX3kmiVwdX2Fkr84WNnBCXIKBUKnWfmBGJfvWlGnPBNsMYKNOmqV1pERERwmAhg2T16tW48847sWTJEvl58pQEBARg4sSJiI2NRZcuXVo8SfLEPProo/J98qAEB6tz0pn2x+pjiXhqzRlo1lIb1tkTL1zXq9VXY7UNFIZhdIsqHNLSDsartzyEQ+knEO4ailnDnoCfn/7K3GsS4tkNyDgoxvG5sfLj4f7OQoRfWa2oUWOJMWChNnd3d4SHhyMmJqbe58mAIVTPkzYlLS2txmtU9xvTtdjZ2cHV1bXGjWnf6Ymvrj8vGyeUovjVogH47e5hOnEVezvbIcDNXo4nk1CWYRjj0Z8cSj+ObdW5+Dz3NIpL2q77eIifymcCJBSlyGPyAIUrayZFpxegREMHxxjIQCksLBTeEfKU1MepU6fEf9Xzw4cPx9mzZ5Geni6/Ztu2bcLg6NmzZ2umwrQj6AqloExKTR/d1VtUgb2md4BIa9cVKi8K9diIy5Jc0QzD6CGDpwUGyqVKyUthX61ASNCINtssQYFDYKFMbU5UltmvXbCNrmcuprIXpc0NlMcffxy7d+9GXFycSBOeNWsWrKysMH/+fGGoUEbO8ePHxfPr1q0TKcRjxowR4SBiypQpwhC55ZZbcPr0aWzZsgXPPfccli5dKrwkDNMcDl1Wa06m9vKHvY3u49e1C7YxDKMbyCOpEsj6uNjJ/a+aS3FFMRKUZ64w11BYWbdd9qetnQsCqqULoQSUk0pffo6FsgY2UJKSkoQxQiLZuXPnwsvLC4cOHYKPj49IEab0YTJCqHjbY489hjlz5mD9+vXy+8mY2bBhg/hP3pRFixYJI0azbgrDNMVBDQNFs0y9LuFMHobRD7EZhbIHlOqfaOv5jMmNgQKqAm2D0NYEW0nh33xLS+TmxtVb8v489+Rpe5HsqlWrGnyORKvkXWkKEtVu3LhRm49lmBr6k6NXsuWrL6riqA9YKMsw+u+/MyC0BR2Mcy7J4/A2KHFfmxA7LxwuuyrGCclH4O7RSYypWBvZWuRUoZ48TOvhbsaMSUE1BlS1Tsh7okvdiSZk/Pi5SmHHyKsslGUYvXQwbkEFWUMbKKEu6gzShIxz8tjR1lquVE0lEMo1GpMyLYObBTImqz+htGJdUlFWhDMX/0RM6nG4OXihT4drkZafLtzRCdnF6OitH28Nw7RHDwr1wuoTpNZ6NZdLl7fJ43D3rmhrOnv1QNfkPQitqIRncU19Wu9AV8SkF6K8qlpk82jqUhjtYQ8KY8IGipdOjJJfNt2LyopSFJdk4LZT7+G11J34/fJ6DvMwjI6hrLiotAIx7u7vIrwO2qCorsal0gwx9qtSwM2+ZSXyW8Poztdi7dVUfJieiRElJTWe0zRIyPPKtA42UBiT1Z90bqVHo6wsH4/+Phlvp+/H86unwdk5ED5VkvguRlGK3gFSXQOCS94zTOs5k5grJ760JL04NfUkCpTNO8OtDdQjy4P67yhDy9mXazxFVXFVRLJQttWwgcKYDJTuqyv9SUllCR7cugS7FNLV3LbyDMRc2YYwK6n7dq6lBUKc1EUFOdWYYXTdwVh7gaxjTjyezczGjfkFGOfWzTCbxMYecFW2v8hWV5MlONVYt7CBwpgMhy6rK0YOb0V4h+ooLN2xFAeypQaVDtUKfN73QXTrOh1hjuqig1lZh+DrYid7UKgxIcMwhqsg65Z1GfMKCvFiVg7mdp5huE3h1Vn8qyrJQXVRpnp+DjYI8XSUq1BXcRXqVsEGCtOuBLIF5QW4Z9s9OJp6VNx3snHCVxO/wNABUh+prhpZATFpJ2UdSn5pJRKza8abGYZpPmTgn1AKZOlE3qklIdo0ddYM/HobbPX/5miLmR0CMLhjMM7H76zxnKoeSklFFa5kFhpohuYBGyiMSVBRVY1jcZIHhbwaLTm45eXGYcnfc3AqQ2rB4GLrgm8nf4sBIWPk14QFDJHHMXmXWSjLMDqCMuGyi8pl70mLQrRpktcTVnaAV5jBtk2pgzsu29qgwsICCennGg7zsFC2VbCBwpgE51qpP8nOjsFdf83EuRKpwZe7nTu+n/I9+vj0qfG6LqFj5XFMaSaXvGcYPRRoa4n+pKw0F0eLEpFnaQn4dgesDFclI8RDnd4cn1dbh8JCWV3BBgpj/uXtC9LwztobcdFSKpzkVa3AD+M+QQ+vHnVe6ujsiw7KRqQxqOBMHobRESdaqT+JubIDd/j7YFRoEN521a5/j64J6TxJHid6qgu3EexB0R1soDCmJ5DtooWBkncVWHEtnkqOR9fycvhWKbB8/Gfo6t+/wbeEKdMXiy0tUF0cCW9nOzmTh4WyDNMy9kZLYlLKEu7bkgqyVw/J4wC3UINuhiCfXvI4oSCxxnNUAsHfVerXcy6ZjxmtgQ0UxqT0J1R+vqOXpJJvkpx4YPk0ICsG7tXV+LbIFismf4tOHcc1+rZw5yB0LK/ApKJiVGZGoY9S9JZXUoGkHBbKMkxLGgReySwS40EdPYVIVlsuZV9U/0b9Bhp0IzhYO8DX0VeME2sZKJphngIW17cKNlAYo4c8F8Va6k8SE/Yjb8W1QG689IBHJ3jdthHBwcObfO8DXW7C+qspolJkaEFGDaEsF2xjGO3Zfl5dU2hyD78WrcKoYkk/RoR3mmjwzRDiEiL+Z5dmi+xATXppHDO4YFvLYQOFMbvy9pev7MDi7ffgfscKFJEx4x0O3L4JcK8ZK24ICz8NbUr6Bc7kYZhWsuNCujye2EPyPGhd4l5RKsZU7dnT03AZPCpCbdRGSELigTo9eVRQmIdpGWygMEbPwdjmGyhR0f/i9l0PIcPKAmfs7fBuh87Abf8CruoCbE3i1RWwsJLG6ec5k4dhWkFOUTmOxUshWmpP0VnZ8Vcb0jPOIU9V4t7KOJp2Bms0CkzU0McQNb2u3JOnpbCBwpiA/kRS/5PwrDH9SeTFtbhz35PIVh7IelRb4eE5awBnX+1LWXt1EcPKjEvwc7aEl5OtuM8VZRlGO3ZGpUNVUHVSz5aFdy7F75LH4U6BRrEJaqQa58TUeC7AzR6efMxoNYZLJGeYZnAmKU9UZFRVj21If3L63G+47+jrciOxiGprfDl7HVzdmhfWqc1KTy+stfXHFVsbrE06JK6Idl/KQE5xBa7mliDIo5lCXYZp59QI73TXPrxDRKWdkMfh3oarIKtJn7Br8XxxOkK8eqBr6Pgaz9FxioSylLmUVVSOtPwy+LtJmT1M82EPCmPy+pNjp1ZgiYZxMkBhi29u2tRi44TIc/TEJTtbUSkyJmm/XL6aYJctwzSP8spqYdgT7o42GBiqfYE24lLeFXkcHjTCKFa/v38/zJ3yEYYNvAdepHOrhWY9FBbKtgw2UBiTNlAOHv8K9518T9QsIYbCHl/O3QInZ/9WfW6Yt7rOQXRWTR0KZ/IwTPM4fCULhWWVYjy+my+srVp2yimproClQgFrhQKdgtWtKYwZvqhpPRziYYz66ktTfxJaS38SeWEN/nf2M5QrjZNRcMKHczfB3qFlV2mahHWZBsSuEuMYNz/M0DBQKO2ZYRjt0osntTC9mPj01gMoLcnB1eRjsLEzDpFsU/TW7MnDmTwtgj0ojNFy9mqurD+h6rG19SfhYdMwyko6CIy3cMXH87bpxDghQvwiYGMpFZOKyY1BB3cHeDhK91koyzBNQ1WXtyv1JzZWFhgT7t2q1Ua/7S5dJhvVqs/JjsXRk99jzbbHkJoqNSFVEeLpCBc7yQcQyRc1LYINFMYkytuTQLY2NjaOeHfuZjzmNRTvz98GWzsXnX22taU1Ort1FuP4/HhUVFfIqYMkekvJk2oyMAxTPxdTC4SgXPr9esHFXvvqscbOmn2v4I4zH+Gl5K04Hb2+xnOWlhbooayHkpxXKndyZpoPGyiMSQtkySi57brvhLGia8I8pGJQVYoqXMm7wvVQGEYLdlxIa3X2jrETrDxGEAk50Y2GeVgoqz1soDBGrz+hmgLkLm1rwuzVMfPY6H9rFF9ily3DNI4qvENMbIX+5KXfpuCJX8biu/W3obLCuDyXob595XFCYXKd51ko2zrYQGGMkjNJav1J7f47G3Y9j5Wb7se583/o9YDVtUpZXYp0KEkH2YPCMM0kvaAUpxJzxbi7vwuCW3iBQTqWnaXJ2FSVjZ8zj8HKSiqYaCwEdxgijxPKpQuqBivKslBWa9hAYYw+vDO8Vnhn1ZV/8Vb6Xsw/+goKCq7qbQ5hwaPkcXTRVQR5OMhdWM9ezRcHT4Zh6rLzYut676jIyomRK0OHWzrCwtK4TllUzsBLeSGTUF1W53kq7W9nLc2Zva7aY1xbm2HqFciqDZTysgJcsJDEZqFVgIenVJJeHwT4D8ATPiPwda/78OK074UXR1UPJbOwTFSHZBimLtvOp+skvfhSiVTkjQgPGWeUqzrE0k78z7SyQHGh+nsTVPelR4AklI3LKkZBaYVB5miqsIHCGKf+RNlcLNDNHsGeDvJzUXmxKFeGeyI8NboO6wFLK2vccu3XGDHofnj79KjjsuV6KAxTl9KKKuyLkQwLb2c79A1yb/FqupRzSR6HB480ytUdYqsubZCYfKRRHcr5ZG4cqA1soDBGqT8praiuV39yOvOcPI7oPrvN51ZT9MYF2ximNvtjMuXfL2XvULptS4nKiZLH4R51y8kbAyHO6k7pCelnmijYxgaKNrCBwhgdB2MbTi8+k6E+APT1USvo2wouec8w2mTvtC69WOVBsbKwQhd3/YVzW0OIuzrVOF7D46OCs/9aDpe6Z4yOQ1c0BLJdahkomZKBYm9lj64a7c71RUVZEc5Hr0d0yhF4OPlhwvAn4GpvjfzSSg7xMEwtqqsVcv0TEoeO6urdqt/eZWVtkU4OvrA1sgweFcG+fWB9+Q8EVVTCvkTKXNKkq58zrC0tUFmt4EweLWEDhTEqyiqrcDxeStej8vKUOaMiM+sSrhZKWTu9PLuLaq/6JjcvDouOvy7GI+CAiSOeFFdEB2KzkF5QhvT8Uvi6cht1hlGl0tLvghgZ5g1H25b/Rq8k7kElpAyZrmXGK0jvHjIOR1clSidT67peHjtrK4T7ueB8Sj5i0gtRUl4FB1srQ0zV5OAQD2NUnEnKk+PXQzt71tCfnIn6Wx5HlBS1yXy8vXvArVo6SMZUFdcJ87BQlmHqD++0JnuHiErcL4+7KdtOGCNWjp6wdlR6irIvN6pdo0PJxVTWoTQXNlAYo+JQI/qT0ymH5XHfgKFtMh+quxBmIaURpltZIC8vgTN5GKYZ3Ytbqz+hi5Ans3Iwq6AQAwOHG/c691QaUAXJQLl0IdNwwTY2UJoLGyiMUXGwkQJtvfOzcE1hEQIqKxERdl2bzSnMXn2gjY3bVfNgc5UPNgxDJOeWiDCGysvo18rQZ2hWPBblF+CVzGz069p2v/dWGShEzpU6T/fS7MnD2X/Nhg0Uxmj1JzXKY1dVYvLVi3g3Iwtb863h49urzeYVpqHSj0k9jlCNNuqcaswwdZsDtja8I0iLlP47eAAu6lReY+SwgyMe8/HC3EB/7Lm8sc7zPQJcoIpWR7IHpdmwgcIYDacT81BWqa5/UoP0SKBSat2OoEFtOq8w/4HyODo3WtR16KWMKafmlyJDKQpkmPaMLtOLUZQJFKZKY7/ekM/uRkqWoyu2Ojvhgp0tYrIu1HmexMJdfJzFOCq1QBSjZJqGDRTGKPvvDOvsWfPJpKPqcdDgNpwVEBY6Vh7HlEgHYa6HwjBqCssq5fpF1H28V6C6oGFLSE3Yi5N2tigkw4QMFCMn1LePPE5QZhrWprdynZRXVSM6vaDN5mbKsIHCGKmBUtODkpCwF6Wqq6hgdQfRtsDdoxN8lA3BYhSlUFRXs1CWYTTYF50hTrwq74lm9l1L2HplE24N9MfwjsHYaGfc3hMiKEgt4k108an3NTULtrF2rTmwgcIYv/4EwNK8ExgeGoRbA/yhMMAVVZiVNJ9cSwtkZUWxB4Vh9NAcUMWlXHW6bmhA24Z0W4KbSwe420k9h+KLlaGpWvTU8CpRvRimadhAYYyCUwm5sv6kdvXY3JwriLMCKi0sUGXrBAubti+M1tUxAEEVFRhXVIyS9HPo6OUEZxbKMgyqqhXYGSUZKI62VnX1Yy3gUlmm+G+pUKBLxwkmsZZDXEPE/7TiNJRWljaayXPkSjYUCskryzQMGyiMUXDostS9uN7+O5fWyeMI5yAYgsc7z8GmpBR8mp6J4IJMIZRVXREl55Uiq5CFskz75FRiDrKLysV4TFcf2Nu0rkpqZUUpYi0qxTi02hL2lMVjAoS4SAYKkVSQVOd5Nwcb9AuWvCwXUwtwKrFuWXymJmygMEanPxnaqaZA9kzyQXnc10+dUdOWWPhppDWnnxf/uKIsw9QM77Q6e4dCJAl7Ua7UsITbSid0UyDEQf3dE5LUxyxNFgxVGzG/Hk5ok3mZMmygMAantKIKJxIk/Qn13qmtPzmTr45H923DAm018OmmHqdLaYScycMw6vonZFOM7956A+VSkrrEfbhrqMms4uBCtRc4IWF3va+ZEREIF3uphtL6M8nIK65os/mZImygMAbndKJaf1I7vFNdVYmz1VLpaMqk8ffvb5A5ws4ZcJeufqrSL3AmD8OQtyOrCNHphWJdDAjxgLez1BaiNURlnpPH4b4G+r23gFAfjVTjekI8BDUJnDNAClNTz7G1J+t/HSPBBgpj1OXtL8f9h0JLyd0bYeMmeuMYip+9/ESlyKEB7khJPYFO3k5CFEhwyXumPaLT4mxKLhWpT9rdQtQ1iIyd0KDhmG8fgid9R2Jm79safN1CjTDPysMJLJZtBK2O9i+99JLIb9e8de/eXX6+tLQUS5cuhZeXF5ydnTFnzhykpanLHxMJCQmYPn06HB0d4evri2XLlqGyUhJEMe2TGvqTWgXazlzZKo8j3DXCLAYgy9FdVIoss7RETOJeWFFFWaVQ9mpuCXKUQkGGaY/NASfrorw9GSiVkkfGpdqAHtMW4OoWjGdu/heLpn2Fvr3nNfi6rn4uGNJROs7FpBeKjB6mfrS+HO3VqxdSUlLk2759++TnHnnkEaxfvx5//PEHdu/ejeTkZMyePVt+vqqqShgn5eXlOHDgAH788UesWLECL7zwgrbTYMxKfyKp2YM9HRDkUUt/knFGHvcNHgNDEubVQx5Hp5+pU3zpLDcBY9oRpJ84EiedXEM8HRHmK5Vybw2lJTmwhwUsFAp0tbAzqMdUnywcVtOLwtSPpNbRAmtra/j7+9d5PC8vD99//z1+/fVXTJgg5a0vX74cPXr0wKFDhzBs2DBs3boV58+fx/bt2+Hn54d+/frh1VdfxZNPPim8M7a2ttpOhzFxKNVO1ZdiWKe69RMyK6SS0FYKBXqGXw9D0rXLNUD832Ic4+ZXbybPmPD6q0gyjLmx61K6qIGiKs7W2uqxBKUUb7jjLIoL05Gbb74n7mt6+8PTyVakZ28+lyrKFHjpQL9jbmhtnkZHRyMwMBCdO3fGwoULRciGOH78OCoqKjBp0iT5tRT+CQkJwcGDUsoV/e/Tp48wTlRMnToV+fn5iIxUdq6sh7KyMvEazRtj/uXtic9uPYg9M/7Gt/0ehYNjrf48bUzHwEGwspA0J7F5UmYRZ/Iw7ZUdGvqTSTrSn6hwdPZFYKDxV5CtDbXByMmOxalzvyI15WSDr7OztsJNAyWxLLUI+PM4i2VbbaAMHTpUhGQ2b96ML7/8EleuXMHo0aNRUFCA1NRU4QFxd6+Zt07GCD1H0H9N40T1vOq5hnjzzTfh5uYm34KDg7WZNmPEqBqMEcNqVZBV4eHZBYP73QFDY2dlh2AXad+LzY1FVXUVOvs4w0FZmIrLVzPthYqqauxSVo+ltNnBtWoXtVf+3fMixqy/AbccfxNbT37Z6GvnD9GoiXIkAdVKbxTTQgNl2rRpuOmmmxARESE8Hxs3bkRubi5Wr14NffL000+LEJLqlpiYqNfPY9pOf3JSWU2RYtjUg8fY6erRVfwvry5HYkGiEMqqKsomZpcgt5iFsoz5czQuG/mlUnLDuG6+sLEyT62ItgR592gy1VhFR28njArzFuP4rGLsj5XK+zNqWrVXkbckPDwcMTExQpdC4lcyWDShLB6VZoX+187qUd2vT9eiws7ODq6urjVujOlzMkFDf1Ire8dYCXNQewBjYjbWE+bh8CNj/ugjvFNVWY4Fy/vjmZUT8NeOJ2CKhASqO60nlDZtcNRIOT5kvpobgxgohYWFiI2NRUBAAAYOHAgbGxvs2LFDfj4qKkpoVIYPl1pR0/+zZ88iPV29c2/btk0YHD179mzNVBgz059QLHfxioF4edVUbN7zCoyFsHJ15cfoRKniJWfyMO0JanK3XVk9ljyI48J1Y6AkJB3AWctKrK/MwN7kAzBFPNw7i/RoIqGqpMnXT+rpBx8XSRy77UIa0vLrNhlsz2hloDz++OMifTguLk6kCc+aNQtWVlaYP3++0IbceeedePTRR7Fz504hmr399tuFUUIZPMSUKVOEIXLLLbfg9OnT2LJlC5577jlRO4W8JEx7rn9S00BJSNyPExbl+LMsGRviNsFYCAsaIY9jCqUrHhbKMu2J2IxCEZIgBnf0gJujjU6WeynlqDzu6mI6Je41obToYGVybIqlAhVlRY2+nkJj8wZLujbKiFp9lOULLTZQkpKShDHSrVs3zJ07VxRkoxRiHx8ptfLDDz/EddddJwq0jRkzRoRt1q5dK7+fjJkNGzaI/2S4LFq0CLfeeiteecV4rpCZNtSfJDSsPzmj0csiwj3MaDZLSNAIPOI5GF/0uBtPTP5cPNbFxwn2NtJPiWuhMO2pOSClF+uKS/b28ji8m2FLCrSGEBtJglBtYYEkDaOrIeYNCYGyWDZ+O5Igp24zWtZBWbVqVaPP29vb4/PPPxe3hggNDRXiWqZ9Q80BKb2uvvL2xGmNi7KI7jfCWLC2sccdM36o+ZiVJXoEuAqDKyG7WBSw0tVVJcMYa3NAnRsoOZfkcbiGp9LUCHH0Bwqk5qeJaSfRqeO4Rl9PF2ckNP7vYjqS80pFdtREHa5XU4al14xBOHRZXd55WJe6AllVBVkLWKBPJ3VtHWNFM8wTmZxn0LkwjL6ggmKqzuNUOZYyUXTFpWzJQHGycUIH5w4wVULcO8vj+KyoZr2ndn8eRoINFMbw+pNaFWSLK4rlq6kwjzBxwDJ2WCjLtAd2RmVAFYHQVXNAoqC8AMlFyWLc1b0rLC1M99QU4t1bHicUNE9TQh6UQDcpxLUzKh1JOZLGp71junsBY9L6k1NK/UmolyMCa+lPzmedR5WiSowjvCNgbJDwLfLCGvy940nsPfJJvSXvGcbcwzu6ag5IREerw/7h9lJtEFMlJHCw+E/9hApLJW9TU1A2lKpwm0IB/M5iWQEbKEybcyJerT+pr//O6dPL5XFfV7W71FhIz4zEvCMv4fmkjVh9UdJldfV1hp219HM6xwYKY4aUVVZhz6UMMaY+Mv1DPHS27KjEvfI4vEK6ODFVPD274u+0PByNT8Sbuc33hNw8OFgYKsSqo4miWm97hw0UxqDhneH1lLc/k35KHkc4BMDYCPAfAEelnzumsqCGUJaIyypGfqm6XgrDmIturKhcMh7GdfORT6a64FJutDzuFjgUpgylGndxDYUdHSJyE4Cq5h0LfF3tZa9URkEZtp+vWdS0PcIGCmNQgezQWhVkqUDbmQopREIFjzp1HA9jw9LKGl0gZekkWQHFxVLFyN4d1BWOI7miLGNmaJ4wdRneIWYXleKxrBzMKChCWMcJMHk8lZ5fClWTkdJMFg5jsawmbKAwbUpJeRVOKfvvdPRyRIBbTf2JIi8Bb6SnY2lOLuZbegpjwBgJs1d7fi7H7RT/uWAbY87VY1X6E1srS4wOl2pf6YTqavRJjcZt+QV4o9IZzi7G5zVtsYFCZEudz5vDyC7eQpdH7IvJRFxm44XezB02UJg25aRG/ZPa5e3FDnn1OIaXluHe3Hw8EHKN0W6dMDf1AShaWYyJM3kYc+V8Sr6o0aHqOu5sp8MLh5wrQIXyROzfB+ZAorMXvnR3xTPeXtgat7XZ77O0tMACjS7Hvx1p3ynHbKAwbcrBRvrvCBI1Ki8GSWp4YyTMt588jsmWah2E+7nAloWyjBmij+aAMmmR6rFfL5gD6Q4u+MLDHetdnHAi54JW771xYJDwUhGrjyUKcXJ7hQ0UxmgaBAqSNAyUDgNhrHQNGSuPY4tT5b4aPfxdxPhyZhEKWCjLmGF6sa6rnJ5P3IszdrYotrAwGwMlJGCQPI4vabqrsSZezna4pre/GOcUV2DzOen40h5hA4UxiP6kk7cT/JWFiVSUlxVgdWE0omxtUOkVBjjWrTBrLHh794CbMpMnukodJ9YM85xPzjfI3BhGl1CH3dNJknCdMtVq981qLd+kH8DCQH8MCw3CVRcdalsMfHxwgJUYJzpImhJtqFFZ9lD7DfOwgcK0GVQiu6JKOqkPq5W9Q1yI/heverrhxg4BeNVLdzUW9JZKaCF14E63skBennQQYR0KY25QjxgVk3Ud3qEU4wrJkLdXAAGBQ2AO0PEhxENqcnq1NAeV1ZVavX9IJ0/RSoA4EpeNS2lSOYP2BhsojNGEd04nSNkwRC8v43f1drX3RUBlJUYXl6AoVeodxJk8jDmnF+s6vFNUmIpEydGArrAx2qy9lhDiKnlBKhWVSClK0eq9FhYWNbwov7bT/jxsoDBtxsHYxg2UM9lqMVlEx4kwdp7pNAtbE5PxRVoGAvPT1EJZpcCNS94z5hCWpXRXwsfFroYBrgui4/6Tx+EOuvfOGJJgl2B5nJCvvYExu38Q7G2kY8maE0liW7Q32EBh2oTi8kqcTpL0J529neDnWlN/Qpwplwq4OVQrENZ5itFvGUtNQV+6ZFxRFk83DaFsYZl2rl2GMSbIOCmrrJazdygNVpdcSj4ij8Pdu8KcCHFRe0ASUk9o/X43RxvMiAgU44LSSqw/IzVTbE+wgcK0CSfic2X9ydB6vCfpaeeQYiUd/Hpb2MPapq4BY3T49lCPMy7KQ5UOhZp+XUhhoSxjJtk73XUb3iEuKVP0ifAA4y0r0BJCStS6kYTL21q0jAWaYtl2GOZhA4UxgP6krkD2bMwGeRzh0tE0tgplGTlL6YCKtEhRpr92yfuzyuwHhjE1qqsV2K6sf0KhhpFhuu8yfKlULcAN7zQJ5kSI/wB5nFgiNVnUln7B7uip7PF1OjG33TUiZQOFMQ6BbIra1RsRYDpK/p98/LEwwA/DfB2RlX1JPMZCWcYcOHM1D5mFZWI8KswbDrZKNauOIIP+kkJafmAV4OLaAeaEj3dPDIcD5tgGYELgyBYtw4LEshr9eX5tZ5Vl2UBhjEJ/crowXh5HhF9vMlslxdENZ+ztUGxpiZj43eIx0qDYKMNVLJRlzCG8M0nH2TtEcsoxFCk1LeHWkm7LnKCMpG8WH8FL87di9qT3Wrycmf06wElpHP5z8mq70rWxgcLonePxGvVPutT1nlRUFOO88kqqQxUVOepuMlulq0e4PI5JPyn+21lbiWweIjajUBhoDGNqbNNIL57QXfcZNtm58eL3TnR1DtL58s0FZztr3NBf8i4VlVfh75NX0V5gA4UxeHinoCAZI63d4F2lQISt8VaPrY+wLuqGhjGu6oO4KsxDxWa5oixjaiTlFONiqiTy7BvsDt96vJ6tpU+vm7D5jrM4MGszbh3/ts6Xb04sqCWWpe7S7QHzqYrDGC2HLkvpw8SwTnUNEE/PMHx0y34Rky4tlUJBpkKXoOHyODovtmYmz9FEMSZh26COpmV4Me2bGs0B9eA90cTctCcNFaSrqCiBu0enFr2/V6CbEMxSqxDKDDyZmIsBIcZdbVsXsAeF0b/+RNl/p7OPU6NXYlQe2sGI++/Uh5ONEzo4SwfYmJwY+cqmZsl7TjVmTIvtemwO2J44f/FvjF3eG8PWTMa32x9u1bIWtsPKsmygMHrXn1Qqm+rV273YDAhzl3puFFcWyyWtu/u7wFopAGxvqYGMaUNX6aqwLDUG7BFgfgLWtsLTLRTZyuNAQgtTjVVcFxEIV3sp6LH+dDLyiitg7rCBwrRZH4/6DBQSyFaUqbsBmyJhjlItFCImdpP4b29jha5KoWx0ekG7LFPNmF7X4kdXn8INn++XRe1UPZZSXXVN5MW1mLe8H174bRIOHv8K5oqvbx/YKS/QEioLW7UsB1srzBkoiYmpui+Vvzd32EBh9EZ5ZTXWnZbKM9tZW2J8t7qt1A+e+BrDfhuKW1YMwM6DLU/FMyRhZVIGEhGdsE8e91EWbBNCWa4oyxgppRVV+HxnDMa/twtrT6gzRML9nPHgRP2Un7+QuB+RllX4qzwNl9NPw1yhVONghXSaTbSsRlVluc7CPCsPx5u9WJYNFEZv7IpKR47SDTmllz9c7G3qvOb01YMot7DAKYsKVFSWmuTW6Bo4VB7H5sfJY82CbZHJHOZhjAs6uW0+l4LJH+7Gu1uiUKz08lEY4cUZPfHvg6Ph5Wynl8++VKD+nYT7D4I5E6ys8VJhYYH0dKnreUsJ83XBEGWiQWxGEQ5fUScgmCOcxcPoDc2rsdkD6lfqn65UZ+1EdJ1uklujY8hoPHixP8J8ItA9dLz8eC9NoSyXvGeMCMoEeWX9eRzUKAFAUomFQ0PxyORweDrZ6vXzLzm6AMpWNV3DpsGcCXH0AYokoXx8ynEEBA5qtRfliNIwIbGsuWr7CDZQGL2QW1yO/y5KqYreznYYXU8fj6rqKpyrlH64vg7e8PfrZ5Jbw87eDXdf/1Odx6mHhpWlBaqqFVxRljEKsovK8f7WKPx2JEGEHlWM6OKFF2b0RHd/dR8pfXpuLuVIbSF8HX3h7iJ17DVXQlw7AkVSCYKErAsY1srlXdPbXxiQtC03nUtBZmFPcYw1RzjEw+iFDWdSUF4lNc+7oV8grK3q7mqX8y6jqEISyPb17U+NJ8xqawihrK+zGEenF4pYP8MYgoqqavyw7wrGvbtTFPpSGSchno74+paBWHnX0DYxToi04jQUlEvuk24e3WDuhHipu54n5qlDWy3FztoKNw2SxLIkZv7zuPmKZdlAYfTCWg2F+ewB9ZexPp2hFsdFeEeY5ZZQ1UMhLwq51Rmmrdl9KQPXfLQHr2w4j/xSqe2Co60VnrimG7Y+MgZTe/nrJVOnIVTeEyJco1WEuRISMFAex7cy1VjFgiE1a6JQ52lzhA0URudcySzCiYRcuR5Iz8D6r8zOpJ+SxxE+pm2gUKr0xah1WL/zORw6/rX8OHc2ZgzF5YxC3LniKBb/cEQIKlXcODAIux4fh/vHhQkvX1sTFfm7PA530G+VWmPAz7cvbJTZNgmVSuFNKwn1csLorlLYPCG7GPtiMmGOsAaF0Tl/1fCeNFzG+kz0emEiW8MCPT3VblBTJDH5MG469KwYT7Fyx7CB99SpKHuOK8oybUB+aQU+3RGNFQfi5HomRP8Qd7w0o5forWNILmWclcfd7OqWHjA3rKxt8HGJHXxy4hGssAaqqwHL1vsGFg4Nwd7oTDnleEy4+a1LNlAYnUKuxrXKbpuUFUCtwusjPy8RsZaSRqVbtSXsbRxMekuEBI0QV0mUShhbnldDKEvrgTywZ7miLKNHKIz4x7FEkTKcVaSut+Hvao+npnXHzH6BbRrKaYhL5bmAFcTvJTRkNNoDo926Amkx5GsFCpIBt9Z3b57Yww++LnZILyjD9gvpotCenx6aOhoSDvEwOuVYfA6SckrEeFRXnwZ/MFEx/8rjCEfTbxZmbWOPzgrJXR5vWY3ysgK5+mOYUih7Ka2gXZSnZtqew5ezMOPTfXhq7VnZOKHiiA9OCMN/j4/FDf07GIVxgopSLMtMx0PZubil0l78btoFXl3U4+zLOlmkjZUl5g0Olo3T35XNSc0JNlAYvYlj5zQS3hlckIf/EpLwUVoGbgidYhZbIcxWKqBUaWGBuER1RdlRYZLrlXoSbTmfarD5MeZHVmEZlv56Ajd/c6hGteLpEQHY8dhYPDqlGxxtjchRnnEBo4pLcFdePh7xGox2g2cnnRsoxM1DQoSHlqDU8Upl5qS5wAYKozMojfbfM1KzPCdbK0zpqe5RU4eko/CpqsbE4hL0DJ9hFlshzFWtrI+5ekgeX9c3oEb6NcPoCjJOVL85VUjx9yXD8PmCAQjycDS+FZ0WqR7790Z7ocg1ENsdHfCDmwu2Je3R2XI7uDtgfDdJaJySV2p2Ylk2UBidse18GgrKpDTGaX0CRHijXkjRnnRUGjt6Ax4aVxcmTFeNTKSYrPPyuH+wuziQEPtjMkWBJYZpLcfjs3HoslRR1N3RBm/O7oP1D4zCUGOuLKppoPj1Qnshx8kLj/j54ENPD/ybH6XTZd+obCBI7IrSTRqzscAGCqOn2ieN6EqyYoFSZYn7oMFmU6CtS9BIeRxdqC7zT7H/6yIC5Fjx5nMc5mFaz7d7rsjjZ6/tgflDQkTlYmNmb9oRnLe1QRlN06/9eFAC/PvDWqGbrsa1GRHmLYd56ALInGADhdEJGQVl2KNMeQt0s8ewTg1fxe05/yve8PTABidHZAeYz0EqMGAQHJQFk2Jr1TuY0Vddznu9ssMzw7SUuMwiWc9EmRzX9zP+cvGK6mo8U52GmzsE4NrgYMCpbvsLc8XK2hZBlpIXNdHKQqddiN0cbOTUcapYnZpnmk1X64MNFEYnrDudLLwDxKwBHWDZyJXc7uQD+M3NBU/7eiPazdesWquHQerYnGSpQHGx+mqmV6ArOnpJmoDDV7KQXmA+BxGm7fl+3xURKSVuG9lRlD83djIyziNXeVwIszJCfYyeCekwRPwvVVQhQ0cVZVVo9jozJx0KGyiMzsM7s/o3nuN/pkQS9VkqFOgdfr1ZbYEwey/4VFZiWGkp8lNO1grzSFe5ZMdtOsthHqZlkIbpj+OJshidOhCbApfid8rjbk6mX1pAW0Jc1CL6+Px4nS57pIaBYk5hHjZQmFZzMTUfkclSimPfIDe57kd9kFfhkoXUNC9MYQUn50YyfUyQ50Ovx3+JyfgmNQP++VI35/qzeTjMw7SMnw/Go7RCSie9eXCIcPGbAlFpJ+RxV6/2I5BVEaKR5ZdYoNuaJf1DPER/JZUHRZchJEPCBgrTav46cbXJxoAqIi+tQ7VSFBvhYF7GCWGjKfxLV2fyEN38XGTj7WhcDlLypIJ2DKNNKv9PB6WOuCSIvX1kR5NZeVF56vof4UHD0Z49KAlZF3W6bFtrSwxTZm+RHjAqTTc9fwwNGyhMqyDdyd+nJAPF2tKihhi0Pk4nqmsARPj0Nb+179tTPU6/UOMpzWweQrN+BcM0h7UnrsqVYq/tE4BgT9PQcpBA9kR5lhiTkLxzx/Fob4RYqKvmJkRv1PnyR2nqUJQJC+3aQHnrrbfEQffhhx+WHxs3bpx4TPN277331nhfQkICpk+fDkdHR/j6+mLZsmWorJTqZzCmxYHYTKTll4nx+O6+8HSybfT1Z3Ki5XHfztfA7HD2A+wlRb2iloFCqHQoBBdtY7Ttc/XdXrUX4u7RplM/6GryEaRZSZ7TvpaOsLExDcNKlwT49JK7Gp+vyBNGmy4ZpexubE5C2RbXQD569Ci+/vprRESoi1OpuPvuu/HKK6/I98kQUVFVVSWME39/fxw4cAApKSm49dZbYWNjgzfeeKOl02EMeEXXnNL2BP0gz1TmAVYWcKlWoGPIGJgdFhb40S8YO8ptEWNjiU15CXBzU7t2KcTT3d8FF1MLcCoxF4nZxSZzFcwYlh0X03E5s0iMh3X2RESQYbsSa8OxqL/k8UD3cLRHqO/QQAtH5FWXY4xHd1RUFMHWzkVny+/q6ww/VztxwXj4cjbKKqtMIrtL5x6UwsJCLFy4EN9++y08PDzqPE8GCRkgqpurq6v83NatW3H+/Hn88ssv6NevH6ZNm4ZXX30Vn3/+OcrLucKmKVFYVikXHSOhHnlQGiM55RiylFdREZZOIi3XHEl0cMFJe3sUWFkiNm5Xnec1w2DsRWGayzd7YuXxPWM0ms+ZAMfTjsvjQaGT0F75fN4OrL79FP43a5VOjROCohWqbJ6SiiqciFcWw2xvBsrSpUuFF2TSpPp3tJUrV8Lb2xu9e/fG008/jeLiYvm5gwcPok+fPvDz85Mfmzp1KvLz8xEZqVEGWYOysjLxvOaNMTxknNAPgSBtRVPW+pkYddy1r5tpHWC1Icw9TB7HpB6r8/yMGmEezuZhmuZEQo4QVquulMeGSw0oTQUPO1f4Vylgq1CgT/dZaK/o2ihpTIdiDunGWl/Crlq1CidOnBAhnvpYsGABQkNDERgYiDNnzuDJJ59EVFQU1q5dK55PTU2tYZwQqvv0XH28+eabePnll7WdKqNn/jqZ1OzsHaJr0Ajck30RZ/IvY2DoBJgrYWFTgTTJcxLtWvdEEuLliIggN5xJyhPp2Vcyi9DJ28kAM2VMhZrak86NFkI0Rh6dsxaPAsjMvAg7ezdDT8dsGaVhoOyNycTjU7uh3RgoiYmJeOihh7Bt2zbY26sVyZosWbJEHpOnJCAgABMnTkRsbCy6dGnZVTN5YR59lHZvCfKgBFOpZMZgJOeW4ECspMqnCqkDQpqOh4d1mYL/dZkCcycsaJQ8jtFIrdSEPE5koBAbTifjgYld22x+jGkRn1Ukh1K9ne0ws7/xl7VvCG/v7oaeglFAery4hD2orqpAly6TdbZcX1d7Uc6A0ozPJuUir7gCbo6mUSen1SGe48ePIz09HQMGDIC1tbW47d69G5988okYkwC2NkOHDhX/Y2JixH/SpKSlpdV4jeo+PVcfdnZ2QseieWMMC6UWq2oBkfeE4p+MhLu9O3wcJM9JTG5MvUWTpnM2D9NMfth3RVQfJqjuiakLH9s7SUmHcN2Kvrh+9wP4+uBrOl/+KGU2D+0zlGVpymhloJAn5OzZszh16pR8GzRokBDM0tjKqu4Phx4nyJNCDB8+XCyDDB0V5JEho6NnT40aEozRQidczeydWf3bX9nqpujiLnkLc8tykVUqeZo06eDuIHud6Gon2kwKKzG6JaeoHKuPSaFUqhS6cKg6I8xU0OxJxQD+fv2QYyFZnPsqslBRodZo6rweSkw7MlBcXFyE8FXz5uTkBC8vLzGmMA5l5JCnJS4uDuvWrRMpxGPGjJHTkadMmSIMkVtuuQWnT5/Gli1b8NxzzwnhLXlKGOPn7NU8xKRLLcOHdPRsVppsTOxWXL6yA9VV7aPeTZiT2g0fc3l7va/RrImynou2MfXwy6F4WYg+d1Aw3B0brzNkbGRlXsKI1eMwb3k//Lb5f4aejtGkG4+ykaq+Flha4NS533S6/KGdPWGjzJZsVwZKU9ja2mL79u3CCOnevTsee+wxzJkzB+vXr5dfQ16WDRs2iP/kTVm0aJEwYjTrpjDGjab3ZHYTtU9UfHHgVczc8zBG/dRPuDjNna4lUr0KIiahbqoxMT0igMqmyDoUc+mfweiurP2PyrL2pIm9c5TpFGZTceLin6iysECkZRVSCtXHjfbO2KCx8nh3jPr8qAscba0xIEQq/xGfVSxqLZkqrS5EsWuX+uBLwlXSpDQFZfls3Kj7Ur+M/qmoqsa608ly/4drNUq3N8aZihxRoI38JwH+A2DuhAUMBq5uEeOY3PqFsn6u9sIDdfhKtijAdT4lH70COcOBkfj75FVkFkq1oaaZUFl7TY4VqPf9QcFmWJixhYzqdwcs49aKvmS7Cy7jcV0vP8xbHFdUXpT5Q0wvNEhwLx5GK3ZHZYh278SUnn5wtW9aIZ5akCyXue5j7Qora9NyU7eEsNDxWOreFx92WYC7xzQshLuOi7YxDZS1/1YjtXjJ6M4muZ6OV0jFwixggX69Fxl6OkaDm3tH9IMkaYizUiA+fq/+yt5Hm26Yhw0URivWatQ+mdOM2ifE2Wx1Ab6+vea1izXu6OyLe2f+gkmjnkaHDkMafN203v7Cfa8q2sZhHobYGZWO2AwpTDikkyf6BptOWXsVeWV5uJRzSYy7e3aHq5NpFZfTN2O91M1S90T+otNlRwS5w9VeCpDsj80UTV1NETZQmGZDOfXbz0vZV97OthitYaU3xun00/I4wqdu76b2DNW1GNFFWo+J2SVybRSmffP1HtP3npxMPwkFpBPjQL+Bhp6O0TGu1wJ5vDvjhE6XbWVpIR9XcosrEJlsmscVNlCYZrPhbDLKq6QOnNf37QBrq+btPmcyz8jjPt59eI3XU7RNXsdc+r7dQ00kjyj1A118nDChiR5XxsqxZLUYfpDfIIPOxRjp1HECgpSlw44rSlBYoNu2FyPNoLsxGyhMs/mrBdk7FeXFOJ95ToyDnIPg5SCl17UHqL5BdMxmbN7zMo6f/rHB113T2x/WyjjPv2dShP6Aab98a+Jl7VUcP/+7PB7gow5nMBIWlpYY69wRjtXVGFdcgrzozTpdNaPDTF+HwgYK0+xy28fipWZlVEq5V2Dzqvleit2EsuoKMe6rMM/uxQ0Re3k7Zu9fhmVX/sSacw0bKFTbQiVqS84rxclEaT0z7Q9KCd10NkUOo95gokUQiwpTccFC+t2HVVnAw7F54eD2xn0RS7A3PgkfpmeiQ/wRnS471MsRQR4OYnwsLgcl5XUrvRs7bKAwLap90tzS9qfi1EXKIpxNM9WtpXQKHQtLZW2TmHLJZd8QNYq2nZZOUEz743uNsvaLh3eEvY1plrWPurQeUjAYGOhkmkZWW+AWNgW2ts7SneitQLXujAgLCwtZJ0ih+aNxjR+DjBE2UJjmlbZXZu+QXTKzX/MPOJVZsfBU9mjq38n8GwVqQl1bQ6qln1isRSWqKqX07PqY0ssPtkpNz79nU0xWdc+0nNxiKmufKMYONlZYNCzUZFfngJwU7E1IwqepGZjTabqhp2O8WNsBXcZL4+Is4OpxnS5+pImXvWcDhWkSCu1QhomqAJC/W/2drOtQXYXFSRexK+Eq1mQWoVtY+ztQdbWRQmHlFhZITDrY4OuonszYblIaZkZBmSySZNoPKw8noFjphp87KAgeTiZcLyj+ANyqFRhXUoIePW409GyMm/BrxL8yCyAh8g+dLnpkF2+5WvVeE9ShsIHC6KW0vSD1DFCaB/p9hAeNhKVV+9KgEGHOwfI45uqBRl/L2Tztl7LKKizfry5rf4cJlrWXqShRewI8uwAu9XepZyQqu0zEQ77eGB0ShIdTtup0tXg42aK3sjr1hZR8ZBaWmdRqZwOFabIfiCr1lbqpTu2lxcHmyh71uFP7LHMd5t1LHkdnqgvW1cekHn6wt5F+kpvPpaJSmdLNmD//nEyWTx6U1RXq5QSTJekYUKUMZ4aOMPRsjB5r1wBkOLiixNIS0ZbVSE4+prcwz34TC/OwgcI0yo4L6SgolToQT+sdIBpRNZeKyxp9mTqpm2O1J8KC1Afo2IKERl/rZGct17zIKirHwctZep8fY3gorfybWqnFpszv55bjBW9PrHN2QkEw1z9pDmM9e8vjPZc36XR7jDaysvfllc2/8GIDhWmUtSc0S9s3P7xTUVaE8RVRuN3fF796BwLeXdvlmg4JGgEbOZNH6kvS3GyeDZzN0y7YfSkDMemFYjy4owf6KzvRmipbs8/iLxdnPOvjhQJ/9YmXaZix/e6Wx7tLdVuwbWCoB+ysLWWhrCHbaZBXePaX+5v9ejZQmAYhl/OuSxliHOBmj2Gdm19k7VzUX8izssQxB3uc9fCX0n/aIdY29uiskFJF4yyrkZ8nZWk0xPhuviKURmyOTNXqaoMxTb7ZYz7eE7owOV0tCeoDq4DAQPagNIduIaPh6yh5T4+kHEFxRbHOtgmlqlM/JyIlr1R0TjcUWyLTEJfZ/O/GBgrTIOtOJcvprlQwSpuKloc13JRD/Ae367U82ClEVIucXFSMwujG3bcOtlZCi0LklVRgX4xkIDLmydmkPDmU19nbSd72pgpdmJQpjxMD7bg5oDY1S8YGSWHw8upyHE45rNPtQtmXhg7zkOfmmz2xWr2HDRSmQf46qZG9o2VFyyO5UfJ4aI+57Xot39P/f9iTkIR3M7IQGKshHG5ONg+HecwaTe3JXSZc1l7F8bht8niQ7wCDzsXUUBkoxO7EnTpdtqpStSHTjY/G5eC0ls1Q2UBh6uVSWgHOXpV2poggN3T1c2n2miopzsYplIoxNcNq725e9y6TYOegPEBEbwfKG3dxUj0UF2Wr9K3n00QmFWOeZe03KsvaeznZapfCb6Qcy7kojweGzzToXEyNIQFDYGchhXf3XFoLRbXuwrs9/F3FPkYcupxlkAxBzVBmc2EDhWm69omW3pNTF1ajQqk5Geqg9ga0WyytgO7KInWVJUDsjkZfbmdthSk9pXTuwrJKIaJkzA+qe6IKod5qwmXtVVRWlOJktaRv8K5SICR4pKGnZFI4WDtgKKTeORmWFjh/6W+dLZs8cyOUYR46ppxOalqwr0tIBL79QpoY+7naNft9bKAwdaCD5t/K8A512Z3RV51Z0hyOaPTfGRrIdRAEPa8X/4otLHDu3G9NrsPr+mqEec5wbx5zI6+4AquOSmnnlGFxy3DTLWuvIirmXxQrQ1SDbD1Ft15GO8b6DxH/O1YBuQXJeutu3NZhnu/3qb0nt2jRwqH9lfZkmuRgbBZS86UQzbhuPvBybr7FSxzJj5VN38G95vEaJzqOwfN+/thsbw2bwjPYXVYEGzunRkVt7o42yC2uwI4LaaITKQloGfNg5ZF4uaz9TYOC4GnKZe2VHLu8WR4P9O5r0LmYKlOHPoYhObPRsaPu60Zp6lBIKPvwpHC0BdS6Y43SI+9iZ405A4PwUDPfyyYuUwdVY0Bi9oAgrdZQQf5VnNNos+7t3Z3XMGFti0rXQJRaWqLA0gKHT//Q6HqxsbLENcqqvXQi++9iOq9HMyprv0JZ1p4ioXeNMu3UYhXHstSVkgd1nWHQuZgqbm4hejFOiEB3B3T2kS6KTibmoqBUOk7rm58PxsnlEuYPDYGLvU2z38sGClODorJKUWadcLVXVzZtLpHRG1Ct0p84m77bWpdM6jhVHm9PP6Zd0TZluwHGPNL30wuksvZTe/qjo7cJl7XX4O6BD+FBj/6YZOmGzh0nGHo6TCPpxhTGP3xZ/w1JyfP706F4WS5w24iOWr2fDRSmBlsiU2XX83V9A7UW7g0beA92Xrsab3ecjesj7uS1q8GI/ktgbym58ncWkUCy8eycYZ09ZeU9eVBI3MaYNlQL4lvNsvZjzMN7QkT0uhl3X/8TPrxlX7tsDKoP8nIlT5te6qG0QV+eP48nijA1QVpG8uJoAxsojM6yd1R4+/TAtWNfRs/uN/Da1cDB0ROjgqSmidml2TiZfrLR9WNtZYlpfaQwT1llNbafl1TwjOlCGVmX0grlEuR0Y5ja/Pjv3bhxeV9M/us6lJVqVzukMYZ18YKVUsisbwOFvDTf7bvSqirJbKAwMil5JdgfK+20IZ6OfPDUAxNDJ8rjHQmNpxsTMzjMY1bU8J6YeFl7Rn/E5F1BlGU1SiwtcPTsTzpbrqu9DfoGuUmfkV4ojvn6YmtkKuKziuWGhT0DXbVeBhsojMzfJ5Oh6iNFRaOo/DKjW8YEjYG1peT+3h63pcliTIM7esp1A+jqm8rfM6bJuat52B8jlbXv6OWIyT1Nu6y9CtqHV21+ABei/kFVZbmhp2MWjA1RX8js1nF341FdffRe9p5CmV/roMcUGyiMgFTWfxxXN7Kb3V+77B3im3W34PXfr8X2fW/q1C1pTrjaumKYSxcxTi3JwPmotU0WWLq2j1QTpaJKIa5KGNP3nlBZe5Wr3dS5Er8Lr6ftwtxDz2HZbyyO1QXD+90Oa+XV4p6iRJ1WlSVvhgp9hXmOx+fgVKJUDK67v0uNz9QGNlAYwWv/nsfljCK55XuIl6PWa2Zd5imsKk3EspiVqKrmK/2GmOSkzm7afn6Vltk8XLTNVMvaq7Yd1TyZo2X6vjFzPHq9PO7t3ja1NcwdJ2d/DLaQjsHJVkDM5a06W3a/YHc4KWsq7Y/JFN4OfXfobqk3ng0UBn8eT8JPB6VUMFtrSzx/XU+t10pqeiTilQk/EbCDo2PLLOb2wLj+98BSoUDnKgv4ODXt5h8Q4o4OSvU7HVByitiNbkrQCeCZv87KZe2pkqY5Fd07Zqm+GBnY5VqDzsWcGOur7mG2+0LTFzLNhWosDevsJcaZheW4mFoAXXI5oxDbNMraa1uJXBM2UNo51O6dDp4qXpvZGxFB7lov53B+jDweGjJeZ/MzR7y8w7Fpygr8c8cZLLjm8yZfT1cf05UdjiurFdjMYR7ZK/HahvP4ft8VVCtP/sbIr0cS5NLivi52uGNkJ5iT8XUs54LcS6Znd24QqCvG9L5FHu/OOgN9VpXVJZS5o3LK3D6yk7jobSlsoLRjsgrLcM/Px+Qqf4uGhWDu4OAWLetI6hF5PLTXfJ3N0VzRtsPzdUoDhVh/un0XbaNOrF/vjsXkD3eLg+GrG87jGw19hzGRkFWM1/+VTuDE2zdGwM2x+ZU0jZ2kwiSkF0tVjvv69IWNpfl8N0MTHDwcXaqk0MhplCMnO9bodSiZhWVYc1yqRO5sZ40FQ0NatTw2UNrxQf6B304iOU/quUP1GF64rleLr6IOpxwWY3sre0R4R+h0rgzQp4ObSP1WtUun/hbtkdOJuZjx2X68uekiSivUwsH3tkThZEIOjAkK6Tz+x2m58OH8IcEY3027yszGzvG04/J4kJ92RjfTNGPcwsR/hYUF9jXRHkMbuvg4y9mBh69kifYLuuDng/GiZhMxb3CwSGtuDWygtFPe3nwRB2KllEcfFzt8sXBAi11xCQUJSCuWYo4D/AbAxoqvorRafwn7mrw6ojDPDGWHY4pmbDrXvsSyVEX3pXWRuOGL/biQki8eI91d32B3OfRFBrcxpWEv338FR+KkcuJBHg54drr22i5j5/ilf+TxQL+BBp2LOTI2TAqZ2SgUSElpuj1Gc6HjyagwKd2YDH3KutFFWfuflWXtKUPt9lGtD2WygdIO+efUVXy7V6rwZ2NlgS8XDoCfq32Ll3f42JfyeIhXH53MsT1w4vRPmL28L6bvvA/rDr6lXTbP6fZjoFBq9eQPdmPFgTg5tk2pi2vvG4E/7x0uRMREUk6J0FPpIytBW2LSC/DOlij5/ns39RUub3PjWKp00rRVKNDHkxuD6pq+PW/GR9nF2BufhCXx54GqCv2EeXSgQ1lzIgnZSgE/haRVwv7WwAZKO4OuPp9coxZcvTCjFwZ19GzVMg8n75fHQ+3No/hUW+DpFopoS8kduj1D7SpvCDopd1F2Iz0an41UZXjOXKHvRxqpJT8fR4ryu9rbWOKpad2x/oFR6B/iITISPp7XXzS2JP49k4Lfj6rr+RgqfPro6tOytotEsaqsCXMiNfUUkpTJSH1gDztbZ0NPyeywtrHHxJAJcCKjuywfSDios2WP1OjLQ9mBrQ1nklhd11WS2UBpR+QWl+Oen4/LsfubBgZhUStFTNVVlThaIbkHXaoV6BHOKv7mQm3Vw5QiuFMWFchIV7erb8gtq/Ki0PHq37Pm6UWhg91PB+Mw6YPd2BKp7j80JtwHWx8ei3vHdhGGiYpgT0e8PUete3ppfSSi03SbOqkNX+yKxZkkqVAhtbd/4ppuMEfKEg7hmsIieFdWYZCbVHyQ0QPh6i7ouLRFZ4ul0D5d9BBnruaJ80NL2XY+DVcypTpaI7p4oXcHqZx+a2EDpZ1AB/2HVp1CQrbUGyEiyA2v3tC71eXsFenn8U5aOu7OzcNN1r6wspa67zLNY5KHWpj838mvm3y9SodCbDiTbJYevjlfHsAL/0TK3Zu9nW3x8bx++PH2wQ0WEJzWJwALlcY2GeD/+/UkSit0I/zTtpz9JzuixZgKxX4wt5/WHcFNhdC0C3g3Iwv/JV7Fkt7cuVxvhE0ELKR9qCpqo166G9MFj0qT2NoqyUt02KGbDZR2wgfbokQvF8LLyRZfLRqokwOnVdw+DCstw4M5eXik61wdzLR9ManXQnm8LbVp922Yr4t81XMyIVfUAjEHSGD31qaLmPHpPrlENnHzoGBsf3QsZvZrujcUFRjs5ietm6i0ApF+3JZQJsRjq08LwS5x/7gwUbXTbIk/IP5ZWFrDNnSkoWdjvjh44HBIfzzl44WxzmWIi9utn3ooLQzzHI/PlkW29PsbG67u9dNa2EBpB2w+l4rPd8bK6urPFgxAoA4ETIIrGj+WTmN0s8x2RHjYtQhSXugfU5QgN0cdx21OTRRzCPPsuZSBqR/twVe7Y+WTO4VGfl8yTNQNcXdsnleODO7PFvQXOhVi5eEEbGrD9fPR9mhhGBFkRD44sSvMlsJ0IPOSNA7sD9hK2ihGP1z06YR/nZ2QZ2WF3ZErdbbcIZ08YasMl7ZUKPvtHvUx667RnXTaZJYNFDOHsgkeW31Kvv/0tO4Y3kVHgr2qSiBOKZB18gF8e+hmue0IC0tLTHKVah1UWVhg14mvtOzNY7phHirq9NCqk7j1hyNy6JEOlg9P6opND43G0BYIS7v6ueClGeqw2RNrzrSJl4muIKl4nCoz7sOb+7WqgqaxkxOzDSWqE1HoCENPx+wZ03uRPN6TdVpny3W0tcaAUMnLR79BKiyoDXGZRdhyPlWuknx9v5aXta8P8/0FMcgvrcCSn46jSFkoama/QNypg9x0FVdiNuF3OwXirK2h6DhaKkzBaM3E7jfJ4x1X9zb5+o7eTujdwVWMz13NFwcJU4LSgFcfTcTE93fjn1PJNa7mNj40Gg9PCoeddcvDjzcPDpa9TAWllcIIqqjSXTfY2hSXV4qCbKpq+zT/HgHS9jFXvrr0G0aEBuGWAD/E+ZqnCNiY6BgyBiHWUvjyBMqRXy7VAtIFo7v6tDjMQ5k7qqz+20Z2bNXvtj7YQNEzBaUVwovR1rUZqDfJo7+fxmXlyYsOmG/NjtCp+2171J94zdsTM4IDsc6DmwO2lIgec+FbJe0fB6rzUVQoXZE0xgwNL4rmSd7Yic0oxLxvDgnPhqqompuDDd6e0wer7h6GMN/Wp6rSPv7G7D4I9pTCmCcScvHhNmU4Qg+8szlKzmAgzck9OhQJGivHihJRaWGBM3a28Oo0ztDTaRee1jFdpQzJSkUlDiRL+h9dCmWJfTGSTrE5UM2TP45LKf2OtlZYOETdpV1XsIGiR4rKKjH1wz2Y9MEezP36II5ckapKtgWf7YzBdmVHSToBfL1ooM47qB7OUYsQB3S7QafLbk9YWlljglMI7KqrMbK4BLlRG5p8z3V9A2WH1aqjCaL2hjFDAtKPt0dj2kd7cVjjd0BevR2PjcXNg0NgSWkvOoJKbH8yrz+slcv8cneszpuiEQdiMkUBOcLO2hLvz+0La40UaHMkLzcO0RaSV7abwhourh0MPaV2wdjgsfJ4T+IenS2XUoLpHEHsj8mSu243p6y9qmTFvMEheukxZd6/JANDWTOqXjdH43KEkXLb8iMiFVGf/HcxDR9ul64Y6ST2yfz+DaZntpSy0jycqi4R44AqBYI6DNfp8tsb9/S9D3sSruKT9Ex0uLyvyddTlcaJ3aW+LlTEjOoQGCt0wLv1+yNinyxXGlLk3fjxjiGiyJq3s9QTRNdQIbdlU6XwAzkwH1l9Sqc9jMg7uuxPddHDJ6/pLnqcmDsnLvwpesMQA51bV0eJaT4DfQfCyUYSI++9uhdV1bpJo6fECapdQpBXsznnJ0rhp1pFqvffPrIj9AEbKHpE5cHQZFdUBq77dB+Wrjwh3N26hlzNVO9EFVGiA7Qu075UnLnwJ8qUV6dD7f2FC5JpOd5dp8HRXpmSemkrUNF0ldjFI9QHBdVVvLH+DlReEzqYUaE1Krimj/2yNlTRkgq8EWScPEZakWZeITYFpTFfzZWM9GGdPXGbxvYwZ44nqQ3oQR04vbitsLGywQjfAWKcW5aLM5GrDJZuvPbEVWQpy9pf2ydAFEvUB3xW0eNVIxkjhJOtlYixa/YmoPRQ6i/yxJ+n5YOcLkJKVBqchIHEtN7+uG+sfio8Ho7bJo+HBAzTy2e0K6ysgW7TpXFFEXB5Z5NvGdnFW6TjEmQAXEzVnXBOl2iWwP58wQBRql7X4caGoLDR+zf1lb00lNL83T51UamWsuNCGlYfS5J/3+/e2FenISpj5lih2hge0P1Gg86lvTHWUi2+3n3pL/3oUJoIhZKB/51GYba7R+su8UKnBspbb70lBGkPP/yw/FhpaSmWLl0KLy8vODs7Y86cOUhLq+lJSEhIwPTp0+Ho6AhfX18sW7YMlZXSSdVcoGJTqsZJpJKmGPt/j4/Fy9f3kg+WdCFHB7nx7+7Cy+sjRdplSyER7hN/nsGlNMkr09XXGe/e1FenolhNDuepRYdDenKBNp3QY4b4R9LRS+eavjqiE+Li4eqr9p8OSp1EjYmzSXmy9or6CE3p2fa9mqik90c395M1OyRq1SwGpy05ReV4au3ZGgXi9HUFaWyQgPuChXSsDqu2hIcnl7hvS0b3vRMWSvd4ZGGCzpYb6uUki8opZZ4KJzbEjovpcvIFeQ4jgtyNz0A5evQovv76a0REqHtgEI888gjWr1+PP/74A7t370ZycjJmz54tP19VVSWMk/Lychw4cAA//vgjVqxYgRdeeAHmBOlAVEzoIWkFKAWL3PJ7nhgnQi+qBmcUl1++Pw5j3tmJ97ZEtahl/Nd7LstFu1zsrPH1LQP11j21qDAN5yAZXx2rLODnV3MfYFpI53F41ccXY0KDcEf+cVRUNF2TYPaADuIKnvjrxFXkFeuu26ku+F7DW3HHqE4G8zKQC5tCSwQVg3vwt5MiDb8lvLAuUtayjOtGFx/BaC+cPP8HqlX6E0cWx7Y1nl5d8VLQNfhj2Ov45pZDOl32qDAf+Xx0JK7hhI5v9kj1fnRd1l5nBkphYSEWLlyIb7/9Fh4eHvLjeXl5+P777/HBBx9gwoQJGDhwIJYvXy4MkUOHpJW5detWnD9/Hr/88gv69euHadOm4dVXX8Xnn38ujBZzYceFdPGffsvju0kGimZxnKXjw7D3iQlYOr4LHJQl54vLq0T2DRkqX+6KbdSK1WRvdAbe2XxRvk9FojrrUax34vzvIsWQGOoUpLfPaXfY2KPALRCFlpbIs7TA8TM/NfkWF3sbzBkobYOSiio57c9YuhFvOCMZzR6ONpjd37D7yqOTw9E/RF2U6pm1Z7VO/6fCeOtPJ2ukR+s2dd/YOZ6orhw9iEO7BmH2pPfQvdv1Otf9jdbUoUTXn258IiFHJHyovPTjwmue23RNi74hhXDICzJp0qQajx8/Tld9FTUe7969O0JCQnDwoNRnhP736dMHfn5qV+/UqVORn5+PyMj6u7mWlZWJ5zVvxkxSTjEupkolr/sGuQsXc31QWtayqd2x+4lxQmBHFSgJ8qC8vfkixry7UyilVW3b64OqZD7w20m5SNRDE7tikp7d6EcS1PqIoR1G6fWz2hsTQ9W/ne2pzbtCulUjzPPzoXidiUBby48H4+TS9QuHhraZ7qQhqAMypR67KD2XZDytPtZ8gy69oBTP/X1Ovv/KzF7wc7VHeyKlVN1QblAPdYFBxvQZ3tlLDoPubUCHUlN70lnvHlGtDZRVq1bhxIkTePPNN+s8l5qaCltbW7i714xJkTFCz6leo2mcqJ5XPVcf9Flubm7yLTjYuF2qOy9K3hNClQraGL4u9njp+l7477FxuHFgkOiCSpAbmbq6Tnh/F9YcT6qTn04elnt+Po5cpVufPosMFH3Ty7cfxlm4wKVagcG95uv989oTo/vfA1sL6QS6oyge1Yqm65tQcTOVyC0+q1huCmlIqLrqr4elGDkZ3rcO130Rp5ZAWhHyeqh4cV2kKKTYFORpeXrNWfm3dm0ff1zfV7dlvU2BtxbuxK7pa/B597vg7cOtLcwJDydb9OngJsZ0gV07JT8+q0j0dSNIRzmzv/73f60MlMTERDz00ENYuXIl7O3b7srh6aefFuEj1Y3mYcyQiKi2/qS5B8/3buqLrY+MERk4KpJySkR65DUf7RE7CB0s6fbMX2dxPkXyJnXydsIHN/drkxj/NWNexKe3HsDeW07A3UN/Cu72iKOzL0YESV6pzJJMnMlQ19lobsoxeS4MDRnUKi3VjL6B8DUiTwOlRS4YKtXvoEJT//v1pKjr0Bh/Hk+Sf9fezrZ4dWbvdhXa0cTLOxxjhj5k6Gm0a5KTj+G7dYtxy4oB2HnwPb1k8xyIrelF+WHfFdlTf7seytq32kChEE56ejoGDBgAa2trcSMh7CeffCLG5AkhHUlubk2FPGXx+PtLJ1z6XzurR3Vf9Zra2NnZwdXVtcbNWKErxwOxkhs0wM0ePVvQkyPM1wVfLhqIdf8bKddwIKLTC3HvL8cx8/P9eHn9efx18qpcZphEsapqgG2FlXXzuswy2jEpRCPME7+9We+Z0N1XTmOn9HZV6XVDQCGmH/arjSRd9n/SFS9c11O0hlddLb72r7oqcm2oDMAr69XPvzk7Al56Ki7HMM0hJnE/Ps45gVMWFdgVt0UvBopmmIcy11Rp9aSZXKg08I3KQJk4cSLOnj2LU6dOybdBgwYJwaxqbGNjgx07dsjviYqKEmnFw4dLlUbpPy2DDB0V27ZtE0ZHz549YepQDrlKM0InjdZcZVH61k93DMGqJcMwMFQtRj6TlFejMBd5XcKVB1vG9BkXPA5WFtLVyfb4rVBUNx3moQJot2iEUagMtaH472K6bCBRXLtXoOQ2Nibsbazw6YL+sLeRDoG/HErA5nOSoLe2sUW1igrKpNTaOQOCMNkAqdIMo8mQiFthr3Rn7C5JQTV1ltcBAzt6yL8JOpepROS/HIoXInyCstbcHW2Nz0BxcXFB7969a9ycnJxEzRMakz7kzjvvxKOPPoqdO3cKj8vtt98ujJJhw6RiXlOmTBGGyC233ILTp09jy5YteO6554Twljwlpg4dnFVM1CK80xjDOnvhz3uH44fbBtXpkkqpk+SybivOnf8DFWWm1T3X1HCzc8NgVyl972pRCqKim+7NQ9w8KFj0gyH+OJYoCvcZujCbMXpPVJBR/+KMXvJ9qiNEAndNfjkcL/qTqDyiL8ww/YuolkC/+bnL++HVVddgz+EPDT2ddo+9gweGWUnngiwrC5yP0k3RNgrbDOkklb1PzS9FbEaRCH+qwsakIGjL37TOK8l++OGHuO6660SBtjFjxoiwzdq1a+XnrayssGHDBvGfDJdFixbh1ltvxSuvvAJTh662VAYKWaEjuuiuwy95YiZ098O/D4wSvXXIFUcV/FS9RtqC9LRzmH/0FYz8dSje+4ObA+qTSQ7qlNxt51c2W+RGzfcIuuJXhQDbksjkPBy8nCXrosiLaMzMGxyM6RGSgZ9fWinaRKgaL5IX6M2N6vT9d26MaPMwqrFwLuovXLCswuqyq9gSu97Q02EAjPEfKq+H3VHqc2xrGV2jqmwG/j55FZmFUgmQaXosa18fra7ktWvXrhr3STxLNU3o1hChoaHYuHEjzI3I5HykK5XPZECQG1nXkAiWsgcMkUFw5MLv4n+JpQXsrE3f22XMTOh/D17f+B8Cqy2ER6W5UMqxKlZMKeoUK25LMaem9+SOkR2Nvvw7rZs3Z/fB6cRcIUanKpofbY/GI5PD8fgfp2W39i3DQkVF6PbK8WSpTAQxUNkPhjEsY/reDqRIGrXduRewVEfLHVlLhxKXpfaYLxmt38JsteFePHpqDkjeDnPjSJk6fXVox8kGnYu54+PbC/+M+wybbjuNW6/9RqvW6YOUeiVqe6DyZrQF6fmlNYqYqQrIGTuu9jbCK2mtNKY+3xWDB1edFMYKEerlKPoHtWeOWamznAb1XmjQuTASVMG7R7V0EUzerbS05mX8NUV3fxeRqUZQ5hqFeYghnTzRN1h/Ze3rgw0UPelPjN213RKOKJuE2Vraoi8fpPROp47jWlQt8laNlOOfDrSdWJZ6AVVUSaI6SuOlismmwoAQDzyuDJeSLvBfZQVccj6RCN1JT20jTIHK6kqcTD8pxj4OPgj262foKTFKxrqra9HsPbMCuoC8nppeFEN5T8Rc2vwTzZS0/FKcvZonxr0CXeHvZjx1H3RBYkEirhZKmob+vv1hZ6PuzMwYF9f08oevsnrx1vOpOuuW3RhUNJAEpYR1rSaGpgIdgDXLfauqZQ7u6In2zMXsiyiulMTDg/wGtdv6L8bI2G7qPne7U3TXm6e2gUJd0w1x0c0GioGqx5oaR1KOyOMhAUMMOpf2SGrKSeTlNq8Am621pVyIjDIRVx7Svxdl7ckkucrqdREBJmmg05XjB3P7yd3Gw/2cRf+e9s7xtOPyeKDfQIPOhalJz26z4KX0Wh6tykNFqW7awNRnqBtCT8YGio7YrmwOSEzsYX76k8NX1MWAhgao1eOMfjl1diUWLO+PyVtvxfqDbzX7fWSgqHo7rTqa2GSl1NZmr9VMLW57V7CuoL5Zf90/Ai/N6IlVS4brRehuahw7vVweD/TmzuXGhKWVNe537oo30jOxKTEZNnH7dLLcADcHuew9eWNn9TdM52o2UHQAHfz3x0hV9+jqS7VhzQUqFHbk6gExdoIFenmpa0cw+sXF0RdnLaV6JtvTjjX7fdTfaVpvKX02u6hc7iqsD6j3z2UNIV2fINPe/ymN8raRneDpxJWSq6sqcLxMOrZ5VCvQxbPtyhowzWNu33swo6gYHlTQ8eQv0BUfz+uHpeO74Ne7hxrMUGcDRQdQpoQqHXFCdx+jT63UltgrO0QxIGKghROsLduvYLCt6dxpIjpWSev+BEqRmamuy9EUi0eoK8v+eCBOrgrZXguzMdoTHbsFBcrj2QBr9xaJthk9EzYJcFEW67y0GcjXzcVIZx9nLJvaXbReMRS8t+mA/zTCO+aYXpwZvxtBFZK+YIhXH0NPp11BJ4RJbpIOQmFhgV0nv9EqM6V3B6naJAm4TybW7JGlCy6k5GOf0ntI6biTzDC82Z7pmBmH71LScG9OHq7zl6qBM0aGlTXQf5E0VlSh6uRPMBfYQGkldFW6Q1n/xNbKso64yBwYln4Fm5JSsDnxKmb0WWzo6bQ7JvWcL4+3J+9v9vso24IKt6n4SaN/k66gDqcqbh/RUfQEYswHu8TDGFpahqW5eZjU+1ZDT4dpiP634KSdHZ7x9sKc2F901pvH0LCB0kqoE2pyXqkYD+viZX71EqqrgLi9YtjBxg2eQVLTR6ZtlfoBSqX+YUUR8vMSm/1eqjjs4SiVZ//3bArSC6R9VRfQsv45JRVmc7G3xk2DgnW2bMYIoJBgvKQ9A1Uz9mPtmdHiEYqvAzthvYsTYq2AQ1p4Wo0ZNlB02RzQDNOLkXoGKJXqu6DTaMrFNPSM2mWYZ6KLlBlTaWGB3Se+bvZ7Sdx282Ap5ZiKqK060nzjpimoA3C5sm/NgiEh5mect3cyLwHFUvgOocMpZcTQM2IaYU6XmfJ4TbTuevMYEj7btBJVeMdcq8dWxu5U3+k0xpBTaddMCp8jj3ckaWyTZrBoWIjoQkqsPEzVXiWjorWZa9SCnaCwzmKN6rWMeXAo8jf87uKMGBsbVIew59TYGTfkYXhaSN7S/yqzkFXSdm0u9AUbKK0gq7BMFh5283Np0y6PbcUDl3/H3EB/vOfpjrKQEYaeTrulX6/58KSqawD2V+ahWHVl2wyCPNTi1bT8MmyJTG31fKjDKaUvE9f2CUCgO1cWNjfWJu3Ea96emBUUgEiPtm9OymiHja0jbuh1i9yeYF3sOpg6bKC0gp1RGSJMS0zoYX7ek4qyIhyvLsIFO1tscnaBrU/7bphmSKysbTHBoQOsFQr0LytFdtQGrd6/WIf9eUgYzqnF5g3VPjpeLl2BO1Qr0D38OkNPiWkGc7qqPa1ro9fqrbRAW8EGSiv472KaWetPzl5cgxJlbGConS/XQDAw9/a5G7sSruKb1AwEXW5+Ng8xoosXwnydxfhIXDbOJ7e8JPae6ExEpxeKMXVO7tfGHU4Z/ZN09SDSlbWP+lk6wsbG/LzD5kiIawiG+EutSOLy43As5TBMGTZQWkh5ZTX2XFJWWHS0Qf8QqcW9OXH4ymZ5PMR/sEHnwgB+3a6Hm42LuiBTpRRiaW7K8eLh6sJtPx1secrxd3svy+O7RnNhNnPk2KV/5PEgD64ea6pi2T93PgVThg2UFnI0LhuFZVKu+fhuvmZZ/+Fw7iV5PLTHXIPOhaE2wbZAt2ukVVGWD1zZo9VqmT0gCC7KTJu/T11FbnHzDRwVl9IKsDdaMsyDPR0wuac/bxoz5Jhmg8DQSQadC6MdE4PGwl2pV9tekdnsJqPGCBsoLWS7ZvaOGepPSoqzcRpSzYyQKiAgkLuYGgU9Zoh/ZBrHnVul1VspDXjOwCAxLq2oxupjia0qzHbbiE5maZjrC1MqnnW8VCqfYKtQoE/32YaeDqMFdvZumOHcRYwDqy1xNfUUTBU2UFpcPVb6AVtbWmB0Vx+YGyfPrxI1N4ghjqzgNxq6TMRb3j6YENIBi3MPoUqLMA9xq0aY5+dD8ahSXmk1h8zCMqw9eVWMyRMzd5Bk7DBNk5h4EDf8OADX/NAHW/a+atSrbPu+N3FVWfIkAvawtTNcLxamZSwc8SxW9Hsc6247hZ7db4CpwgZKC4jNKEJCdrEYD+7oCTcHKffcnDgcv0MeDw3k9GKjwdYR6W7+yLGyQralBU6cW6l1A7Ax4ZJBnZhdgp0ahQabYiUVZquUaqjcPDgYLvbmt9/rKyPmtf8ewhUrhTjx/xH5ExRr7lYXQDQift54Dx6J/VW+P4p7b5kkHToMwcC+i00+scG0Z28M2TtmGN4hjuSrhZCDNXrBMIZnYvAEebzjqnY6FEJTLPtjM8WyVJjt50PSaymqc9tILszWXLbufx0HUCLffyEzGxZnVwNfjgTi9sGYmNjvbjgrvWoTLF2xaPJHhp4S045hA6UFbNfoXjzRDLu3UhGwKAupe3HXakt4eUvddBnjYMyAe2FtIfngtxcnal3rYFw3X4QoiwqS4DU2Q0oZbox1p5ORWSiFk6b1DhDF35imKSpMxTvRv8v3n3LqjhAr5bqjnkorrkPcxkdQXlZgFKszMHAQXuxyE17uMBUfLdwr9AyM6RMdsxmFBSkwNdhA0RLKfDgenyPGnb2d0MnbCeaGo6M39szZhs+734Gl3RYYejpMLVxcAzFcGXZLK05DZFakVuuIhK23DNPQohxsvHAbGUCa4tg7ObW42Xy1+T65nshoCycsmP07cN9+IHSUeCzfErg7ZTPm/zoKl2LUaf1tQULCPiz7ZYwwojS5ZsyLmD3pPZMPDzDAqXO/YuHyAZi9fxk27H/D5FYJ74FasvtShiwsNMfeOyqcXQIwZugjmDjiSUNPhamHSRqpnxuvbNR6Hd00KAj2NtLP/8/jSXLKfH3sj8kSXbuJ/iHuGGCGNX/0ddX6c2G0nA3z9Pj3pZO+ewiweB0w+VW84+WFVGtrXLKsxrx9j+PHM9+iWtH6XklNGZxr9r6EG3fci81VOXhr3SK9fh5jOBxsXXHGUvKG/5myR+ihTAk2UFrRvdgc04sZ02BC8ATYWtqK8d/Rf2nVm4dwd7TFrP4dxJiMk7Unkhp87Xf7NAqzjZK6KjPNEMbuexZVyky4u9z7Ijh4pPoF1Bl45IO4ZfInCKuWDsMVFhZ47+QnuHvr3Ugtan2/pPrILs3GgzsfxEuX1VWiT5SmIZ/CTYzZ0S38OvSulmofRVlWI/LiGpgSbKBoQWVVNXZFZYixi721yOBhGEPgbu+OawOkE15BRSE2tCB19dbhaqHrjwfi6tWyxKQXyPt8B3cHTO1lfporfbB+13M4YSFpdoKrgDuu+aLe13ULn45V8/fgVu9B8mNHUo9g9j+z8W/Mep3OaU/SHrHcXYm75MdutAvEH3N3wNUtWKefxRgPNwapRfV/nv4WpgQbKFpA2pO8EsldNjbcBzZW5rf69hz+EM+snIB//nsK2dkxhp4O0wgLg9VhnpVXd2rtvu0R4IohnTzl1PkDsXXbs3+/T53lc/vIjrA2on0+Oicat22+Dct2L0NxhZT2byyE+PQSAnPimd5LGhWb0nPLpi/Hd1O+g7+TVJm3oKIAT+1/Bk/8MhZ5eQmtLrr42ppZWLpjKbJKpW3sae+JT8Z/ghfnbYGjM3uCzZlpw5+Eo1KWsKk0GUWF6ixUY8d4jjYmFt4x1/TinVc2Y31lBp5L/BcXL2819HSYRuje7XoMVNjBq0qBazx7o6KiSOv1tVjDi7LiQM2U4+yicjn042RrhbmDjecq+3zWedy+5XYcTzuOzXGb8fKBl4wqvt6vz0L8vuggPu56C0YNeaBZ7xkaMBRrrl+D6Z2ny49tqsrGojXTUVkhVXXWlsgLazB31Tj8Xqi+2BgTNEZ8zviQ8S1aJmNaODr7Ypq9VGyz2NICmw6+A1OBDRQt2KE0UCh0Oy7cPA2UwyVSKpq1QoH+PecZejpME7w59RtsW3gY993wa4sqfk7p5Qd/V3sx3nEhDYnKAoTEr4fjUaYszEbGiauRFGY7fWEN7tpyJ/LK1IXONsZtwsotS2FMUAfgCSOe0Oo9rraueGv0W3i3y3y4KK96F3UYD2sbaRs1m+oqYM+7OLX5EcRZScuxr1bg+Yil+GzCZ/B28NZueYxJc1O/e+TxmqT/YCqwgdJM4rOKEKNsMT8w1AMeTpJA0ZxILkxGoqV0MOvrHAwHR9bYGDsBAQNgY9fyVHcKUy4cGiLGdD785bCUclxWWYUflenHZJDfPsI4uhYfO7UCSw69KHQ3RGcXdbr0+2l7xfOG7LWjq34714x6Bmun/YJ7XXtjrrbF0nLigOXXAv+9hvl5eRheUoJe1VZYPf4zzO1/r+hszbQvenabhe7KkOM5y0pcvKRbfZO+YAOlmah67xATupunUJDEeSqGdr3eoHNh2o55Q0Jgq9SW/H40UVSN3XA6BRkFZeKxKT39EeJl+MJsiedW476T7wk3NTHUzg+/zViNO5ylQoK+1RZwtHc32PzW7FiGW34ajPMX/9bJ8vz9+2HprN/q1CN5/89Z+H797XX6MFGIK/LA+8CXo4DEQ+IxSwtLvNtlHn5edACdOo7TybwY08PC0hJzAsbI9/88+SVMATZQ9Kw/qagoRk52LEyBIykaBor/UIPOhdGelOTj+HjNjSjIlxr6NRcfFztMjwgQ49ziCqw7lYzvNAqz3WUMhdkubUHQX//D/HypHssoCyd8NvMPONo44oHrV2KJa0/8PmudwRqj0W/846tbccayEvMPPYfExP16+ZwDx77AiqIYfJR9DHf8MhxXr0q/2dycK3hs5RjMv7QcRyyVhot7KHD7JrhNfk2Em5j2zfQRT8Gpuhrjioox4epFoNy4hOX1ISVIM41SUFqBw1ck9XuQhwO6+jo3a40VF6Zj3upJSLOoxuaZ/8DDU2qBbYycjfwDe+K3ibGDtQP6ePcx9JQYLfhj6yN4PXmbqLvhtf9VLJr2ldZdjv9Sdip+a/NFIZAl+ga5iZCmQbmwHvjjdlhUV+CRnDJ09O6J625cLWtuSJ/xwCx1OXlD8NHJj5Gn9HRMs/GpWfNEh0SlHoeFQgGFhYVIY56z9Q4s9ojAH9lnkEEVay0s8KyPF9Z5joXDtHcAe1e9zIMxPVxcO2C7+0g4xyt/K+f/BvoZd6Vw9qA0g33RmaiokrQZk3r4NTuGu3rXM6KD6SMB42Fna5wtyyvKi/DZX/Nwy9GXkVclZQqMCBwBGyvjEEQyzaN/l2lyUbBfU/bXcf83Rb9gd0QESamwKuOEuHN0Z4NqFnJP/gSsXgxUS+n9Fr3nYPa8dU0KginrhYzutuBU+imsTdopxs6WNnh8Sv01T3TB7dd9jx/6PY7AKul+kaUFvsg7KxknANyqFXii2y1wmPUVGydMHZwH3aW+c/xHGDtsoGjZHLC55e1LywqxIkOKA3+dvBMp6WfUz5XkGEdzsLTzUPwwFTszTsont16WTnhqyFOGnhmjJWFdpmAYHMQ40QrYd+wzrd5PRohmyjER4GaPab2luhyG4O8dT2Laqbdx0lZqjIi+C4DZ3wJNGM9ZmZewZOUo3H7kZZ3pQRqisroSrx16Tb7/v0GPwdunh14/c1C/2/DnjZtxvU3NY9FwOGDttaswedQzev18xoQJGgyo9k/SKaVfhDHDBkoTUN+dXVGSgeJoa4WhnZuX2bL28jpkKcV8/W290KWzuqjW++sW4uZfR+Fi1DoYBEpB3P8x8M1Y2KaexRsZWXCorsb97n3x87ydcrEoxrRY2E2dFv5L1Cqt3086FE+N7LTbRnQ0WDHC1VsewvNJG1FoaYn7/H2R2O9mYObnUon4Jvhl9zM4alGGMksLPHLgeaHP0Be/R/2OqJwoMe7u2R03d7sZbeWuf33BDnwUthDjLFzwfMAEfLXoAHz9erfJ5zMmioUFMHAxyB+53dEBa/ZpX4G6LWEDpQlOJ+UiS+nyHt3VG3bWTR8gK6oq8MO5H+T7SzT0AAePf4VVpYmIsazG/IPP4Jt1t7S4CFNLSEw8iMsrpgDbXgCqpO/Vzb0Ltoz/EvfN/AU2NtJVOGN6jBn8oCirThxCCWJjJU1Rc7G3scLS8WFyWft5g6X047bm54334NVUda2GWU6dEXT9V0Azu+ved91yRFRLXpZkK+CJdTdrHfJqDhnpkfjsiLro1bNDn4W1ZdvK+iaOfAqf3noAc6d8DEsrlhQyTVPRew5mBHfAI34++DDvNMpK1fWEjA2TNlBWH9N/g6v/NMI7E3s0L714Xew6pBVL5YTHBo1Fdy+1y9fLLRTdlPnolRYW+DTnFBb/MhxxcbuhTygFkYSUc7bfjScqk4QFDVgAw/8HLNkNj47qFDTGNKET1Hx/tThz5dH3tV7GnaM6YcMDo7DufyPh5tj2OqTv1i3GOxkH1PNxDscTN/5TJ9W2MUif8sG1K+CpLHR2ECX47B/diwHf27YUhZAK2c32GoB+vv10/hkMo2tsnH3Rz146l+VZWmDH4Q/QVihO/gpsfb59GCivrD+Pb/boN4V3+wV134Lx3ZrWn5A35Luz6oZMSyKW1Hg+PGwafltwAHe7dIelsjkbpSbetHMpVm66X2eFnjRJTzuH+38egVdStosOplF2tvjZLwS47V9g6uuAtlUqGaPlhlHPy3031pcktaiPS+8ObvBytkNbQgb053/Nx8c5J+TH7nfrg4dm/aGVcaLCzy8C7/V/FFbK39h3hVHYvv9Nnc03PvIPbKrIlIWpD494QWfLZhh9M6fXLfJ4TcGlNlnhf//3FJ488Bwqjy9vHwYK8cbGi/hga1S9nVhby9XcElxMlcSsfYPdRb2Ipti07zUkFUrpmsP9hyLCJ6LOa6jy54Oz/8BPg55DqNIlX2ppgbfS92LJL8ORmqI+SLeWTbtfwqyN87AP6j4tN9oG4uZbtgEd9ZMKyRgO0ibc4NRR3qfW7n3Z6DcHGScfrp2Dr/LPyY897DlIlO9viXGiYnC/O/CYr3off/bSSly+ooMy35XlCN35Dn5NTkPPsjI8HDTFqEsIMExtBkXcho4uUgj3SNY5xOdLVaP1xV87nsALCRuwydkJL3t5th8Dhfjkvxi8suE8qpVXjnopztaM7B3yfnx7RS18XeI3qtHX9+09D3/M240FDupy3YdRilmbb0X0wY+BVhhdJAx8/JcxeCJuDfKVYl2fKgU+734XXpy/BU7OLIQ1V+YPVfd/2Zp2GNCDV06XvLtmFpYXqZvZPek7EnfOaP5VVmMsuuZLTLOSDohUgfbhnQ+jqDC1dQs99DmQeQm9y8vxq0UQZk8wneZrDEOQ4T87/EaoWBO9Bvpi7fbH8WLiRlG7h3D37tY+DJSnpqm/6PL9cXhq7RmRdaMr/tMI7zQnvTj25PdIs5BcIgMUtiIdsCmo383Tczfg297/g7+y1kp4eRk6b3kBWLUQKMzQet57Dn+MWX/NwJaqHPmxa6w8sHbWeowZ+pDWy2NMi46hY3CrhQdey8jCiqREIGojjJke3n1E8THi+YCJWheZa+pA/NLsNeiq1H1RXaLv/17YcuM/NxHYrTRILCxhdd2HLE5lTJLru1wvi7r/iflHJHfomj+3PYoXr26RjZNFDh3x6A2/tw8DZdGwjnjnxgjRzIxYfSwJD/52EuXKDqytobi8Evtjpeqx1O21V2ATFRkVCnQ9+jO2Jibjvpw83N9boyBOMxg28B6svXELbrTxx2sZ2RC5QlH/Al8MBc43Px05KekQHrzwLTKVhZtcqxV4p+McvLtoD9w9jKBkOdMmLBvxImYWFsGOzsOHdXfC1wczxr+GFztMxWvB0zF3ipaN8ZqBo6M3Ppr4uegOfFN+Ae6NOQbsb9nn7P73flRVKEuED74LCOir28kyTBvh5eCFCcETxDi7NBu7tKyd1BSUlPFysjqT8BbHzloL3k3aQCHmDgrGp/MHwEZ5Qv73bAqW/HxMNDxrDftjsmRDZ0IP36araV7aAqSdhRvVE3HsgqED722RfuDFBdsQPOdHwFHZDr04C6f+uRPPrpyA/Lyms5aCgobhDlcpa2gUnPDXtaswbexLWs+FMXG6TAC8pSZ6iN8PpKgLBRqD5qQ2cya/j5kT3tLbZ4aEjMLaQc/jhawciEovO14BYqXqr81l75FP8L/KOMwP9MdZNz9g/LP6mi7DtAlzfAbJ4z8v/qaz5a7e+pBIylCx2LELls35S2tNmckbKKoCU9/cOgh21tLX2RWVgcU/HBE9dFrKfxfTmq8/IXfxnnfV98cskwritJQe1wH3HwK6X4diCws8Q701KjMwe800HDilrq9CUA577RoP901fIbwmX9zChZvaLbT/Db1HvltEugkjgCooP7JyNP7avqzNP9u/z83AuKelO4pq4M87gNzmZTlR9ec3zknZeRfsbBHXby7gYLjOyQyjC4Z1vwkdlNfyBxXForZPa/l9y4N4NUUtRr/dKQyPzVnbIsG7Vu/48ssvERERAVdXV3EbPnw4Nm3aJD8/btw44WnQvN17b01PQkJCAqZPnw5HR0f4+vpi2bJlqKxsvYiPUoB/vGMInO2kmNrhK9lY9N1h5Gj0FWkulBG0Q1n/hIyeEV2U3owGiI/8HXmqzBuq5Bh+DVqNsw9w8y+InvQccpQFmNKsLHDP6Q/x+qHXUVxRjMiLa3Hzr6OxYpP6RKTKEiKvSWsyIBgzoO98nHX2xDIfL0zI3YfsbLUQ1RCUlubjwVWTsaM6Hy8mbcK/uwyQmjvmCfn3GVuZjyfW3oCy0twm3/bD5vuRpKzROEhhh+vG/r+9OwGLqmz7AP5nB0VxQQVDIwUXck2KcM1dXDK1NJc09dU0t9TILD9TS0V7vczeTM001Mw1MS33Bfe9MJfS3JfccEEURYHzXfczzAAqIjowZ2b+v+uaizNz5hwODDxzz7Pc96icvlKiXMmd1Ll4bXTOUwpL60xCkaIvPtP57t9PQNS/m033u3oGYmDrn5/6vShbR/n5+SEiIgL79u3D3r17Ua9ePbRs2RKHDqVFXT169MCFCxdMt/Hj02a4Jycnq+Dk3r172L59O2bNmoXIyEgMH26ehurVUoUx9z8hKJCaYGr/uTi8/d1OXL6ZvUyth/69icvxiWq7ZoA3PIy1QDIxYu9/0ahEcUws6IU7Nfo9ccbLLDk4oHKNDxHVZI6pzoqYf2Q+WkY1R6edw3HcScPkq3tw9FhaoEikuObFqucrYZVnXiQ4OmLxVsstOZaAuu+mgdiWutzdXQMK53su9y9E/jdbTcPmIv7oUNwHK53u44slbz5y2Mno7NltmBF3QG07axo+rTWGwT/ZjA5NJiP8rV9Q6oX6z3wuF5c8mNZqKcqnOKG7Z1kMbLX4mf5XsnVkixYt0LRpUwQGBqJMmTIYPXo0PD09sXOnoSiekJ4RHx8f0016WozWrFmDw4cP48cff0SVKlUQFhaGzz//HJMnT1ZBizlIvpIFPUNNOUuOXIpH22k7cO566sS2bCZnk/knj7MvJhJ7HRLVG8CGfAXgGtQa5ubjWxXTOm3HJ68MhbuTIanaxTtXVCZaEaA5w9kprYYKkVH70E9MCQEXXD+I+6lVgXNTYnIi3l//PnZd3K3u53VwxpSXwtXEcIvwKIBijcciRTIpSwKp+5ew6PdHTxCUwGXMhsG4l/q/9o5noCrMSESP5lXAH5Ht1mFAq4XPHMg/9dHSGzJ//nzcvn1bDfUYzZ07F97e3qhQoQKGDh2KhIS0wGDHjh2oWLEiihVLSxnfuHFj3Lx5M0MvzLMq65MPi94LVfVExKmrCXhr6g4cv3Ir2/lPslpePH3/FNN2D//mcHJ2zbk05uU7YFGLRabkb04OTuhVOBhzO203S/RLtsfPLwSveRoSt11GEtadTpu4lhtkuHT4tuHYd2mfup/PNR++C5uFapW7wJLKBjTFiFJtTPfH/vUD9l/Z/9DzNuwYb0pyWCxZQ68m03L1Ooly2+N6Ex9FhmofXMAhK+fMMcUg22c4cOCA6jVxc3NT80uioqIQFBSk9nXo0EH1jmzcuFEFJ3PmzEGnTp1Mx168eDFDcCKM92VfZhITE1UQk/6WFX/vvFjUKxSlvPOq+xfi7qLt1B04/O/jj5XhoD/PGYonBfnmh69X5sXzDhxahG0wBGAy0Sis1jDkNH8vf8xqMgtTG0zFL2/8gj7Nf1DdakSZ6VgjbQh17l9zc/UXNXVFD6w4acjD4uHsgekNpz8yu7IlNKszEp3KGKoPJ6UkYVD0IMTeMaSvFwkJsYg48qPp/keBbyOPZ9b5kIisUWzs35i5vBtej6yCixdjnuiY2St64uPTUXhvyeuIv2nIoG5O2Q5QypYti5iYGOzatQu9e/dGly5d1LCN6Nmzp+oRkV6Sjh07Yvbs2SqAOX782erljB07Fl5eXqZbiRIl1ON798967HHFC3hgYa9QlPc1DDNJVeK3v9uBfafTEpg9aOORtN6TBlkM73y3b6Jpu7tfg1wLFCS5To3nauD5/GkZaIky87LPyyhT0LDkWHoJDsampZTPSVJm4dvYXWrbAQ4YW2ssXvR+tkl45jYoZAiqFaumti8nXEb4pnDcT63yPW3le7iYmr6gOjzQsMYnFr1Wopy0aOsoTLy2B6ecNETtTLcqNROzYqbgyys71PZBxySs3pX94qRmD1BcXV0REBCAatWqqcChcuXKmDRp0iOfGxISor4eO2ZYPSBzUi5dSpvfIYz3ZV9mpDcmLi7OdDt71tCd9PGBb3HyVPRjr9fb0w3ze7yKl0oalgTevJuEd2bswrZjaZ+U0luXrnpxvcdULz5y9FdEa4Y6PUWTNbTkrH7SKVlN17F8R9P9uftzfpji5olofH5iken+wMIvo35J/Q1Duji64L91/ouiHoYPI3sv7cXEJW+p7RQtRc3fcdE0fPLaBE6MJZvWKiTcNF8t6tr+h9JXpBd5MBL/3f9thsKebzY0f1XkZx4kSklJUUMwjyI9LcLX11d9lbkqMkR0+XJaELB27Vo1kdY4TPQoMpxkXNpsvIlbjg7ov6F/ll1LUjZ+TvcQ1AgorO4n3EtG18g9WHc4Y7Akyd22/mMIXLw9XVHpOa9Mzzl9d9rqpG6+tVSJdyK9avpCUxRwNvTwrTq70Sz5DjJ1/TTyL+6Oby5eQcHkZLR29cG7TdMqfOuNt4c3Jrw2Ac6pk2bnJJxQvT+D34zCgtAx+L8STfH887UsfZlEOUoWY9Rw9FTbF5wcsOP3R3+QmXlwJibsS+st6RPUVRX2zAnZClCkJ2Pz5s04deqUCjTkfnR0tBrOkWEcWZEjS5Bl/7Jly9C5c2fUrl1b5U4RjRo1UoHIO++8g/3792P16tUYNmwY+vTpo4KQpyHdUUOWtnlstCfyujljRpeX0SC1V0SyxL734z78EpMW3Ow8cRV3UjPQSl4VR2MO/QdIRdQ1SdfUdqEUDa1fG/NU106UW9yd3fGmu2FoVFZ/Ldr2Rc58o7txwE/tgIRYvJSYiAWOJTGs9VLd9z5UKVoFQ33qqm35FBl7+4LaLlf2dbSqz2KAZB/aBLQybS8+Mv+h/d9v+AgT001t6Fe1H3q9PCjHridbrYb0fEjQIfNQ6tevjz179qggo2HDhmroZ926dSoIKVeuHAYPHow2bdpg+fLlpuOdnJzw66+/qq/SmyITaOV8o0Y9XdIjqTMjtmi38b9f2mf5fHcXJ0zp9BJaVimu7kthwQ8WxOCnXWcerl78mOGdObvGmYofdSnyCjw8Cj7V9RPlpnbVh8FJ01AmxRGlChvKIZiTlnQfWNQVuPKX4YHCAfBtN08lDrQGbzWcqBJWTa3YF+805Wodsj+1X+4H79SitZuS4xBr/F+WUYNlnTHpbFq+rQEvDUDPSj1z9HocNFkHaGVkFY9Mll0XPRGDT36P5NRgYZx/azStk3UyKglMhi09iHm709Jcf9K0HGZtP43zN+7A1ckRvw9vaMpK+6Bb8RcwP3ooll35HfParkFez8znzxDpyfHja9WSdLP3aGgaxixsBs+LB9H3ehwcJWj/z3qgcGnzfh8iylFfL3kL0+P/VtsDClXDf1pEYt2Kvhh4ZZPpOR+UbIbudSOe6f1b5pOmz5NmcwGK/IDLtw1BxOWt6nG3FA2zQkfhxXJZJ0uTH3vsyr/x3eYTD+2rFeit5qxkeY6UFN13XRPlhrkr30fE5S1qu9mtO4hoNgvwr8FfPpGVOXt2B5puMPSM+CUDv73QAcmbIjCkqDfW5s2jJrx3a56xJlxOBShW/+7aofFktHI1DMckOjpgwPbhiL1iiP6yWtkwNKwcBjdMrfiaTpbFAY3nYHBChM27vsL4S2n1N6qXf4vBCZGVKlEi1FRaRepP7d41EVI8ZtzlWEzyafhMwUl2WX2AIkHCsNZRqJziYiqot335f4CkxKyPdXBAv/qBGN484wqizOafSEVTIltx8PAi/LSqzzOd48g/vyH88PdISR1m7eFZDq/XG2umKyQiS2hTqoX66pGSgjMuhvdWl0ajUa+x+ZcS23SAImSJ71fN56JUkoZJl67g9TMHgBUfqnHxJ9Gt5guY2K4yXvDOi/dfK40ShfI8Msteg/m1ELGwxRNn2SPSqwFzaqD9nlEYf3ETLl7446nOIRPo+m4ZgoTU1W4NHQug7xvzzHylRJTb6r8yCJ/dTMSGM+fRNv4W0HgsUL1vrl+HTQQowrtIeSxpHIl691KDkt9nA3u+f+LjW1X1w8YPX8NHTco9cv/sTZ8gztEBc++cwpwtn5nrsoksoqxnSfVVJpjP3z4628dLb2L/XzuYMq1WSHHG6Da/qJpRRGTdXNzy4s0mk+HpXwt4YyoQ+r5FrsNmAhTh5BcMvP6/tAdWDkHCsbXPfN4bd29gwe2TattV09CldvYbdCI9aVtzOJxTexgX3/wbdxIMeX2eREpyEj79+XUccExS932SNfyv+Vx45CmUY9dLRLkssCHQZTlQJesUHjnFpgIUpVJboHp/SNM739MDzTZ/gHPndj7TKef+PRcJmqExblWsOooWq2CmiyWyXI9jE5cialt6Bldse/Kge+Zv3bEm+YbazpOi4Zta4+BdJPNM0ERET8P2AhTRYAQWlArGaO9CiHVyxIA17yHhVloStuyIvxdvqgDr7OCM7rWyzrNCZA06Vk3rtv3x3LonLrPeNLgfAlMcVcbVL4O6o2xgsxy8SiKyV7YZoDg6oWnLSPgbstbjqFMKhm3/P1X8K7sWHFmgghTxesDr8PU01BUisnYVgt4yrX475piC3TEznui44sWDMfvNVZhYpjNqhwzM4askIntlmwGKpMH3KoFJ9b6GJ5zU/bUXtmPan9lLX52QEIvZMYZjHB0c0b1C9xy5ViJL6VS6pWl77sHIJz7OM58v6lX/KIeuiojIhgMUUcq/LsbV/xoOqVVKv435FutPr3/i4xdHf4LrKXfVdljBiiiZ37DygchW1A8NR9HU2hvRKXEqi+SD4uLOqOX1TztMSkT0NGw6QBG1/WqrokZGQ7cOxdHT0Vkel3g3DpEXt5vu9wjqkmPXSGQpLi558Lb3S2pbCmCu3p1WqVTcv5+AwUvbqOX1XRY1ZA4gIso1Nh+giG4VuiHshTC1fSfpDvqv74cb1w3LhjMV8xO63biBoklJaOjohdKlG+bOxRLlsjY1h6NBwl18f+ESuh/dCSTeMtWrGr15KHbB0It4GclISjJsExHlNLsIUCSl/cjqI1Ee7ur+eSdg2PIOmR+QdA9uOyaj0814rDz7Lz6pybwnZLsKFQrAxOKNEHI3EQ6JccB+QzbY2Ydn4+dzG9S2qwZMCh4KP79XLXy1RGQv7CJAER7OHvi60XcolKLBN1lD35ChmT/5zwVA3Fm16RrYCN4v1Mm9CyWyhJBeadu7pmHD6fWYsHeC6aFRtcagSsWOfG2IKNfYVV5qH9+qmBL6BXyKVlCfGh8pOQnYmq4gUu3wXLs+IovxqQg8XxM4vRWH40/hg+iB0FS6Q6B35d5oVtpQPIyIKLfYTQ+KUVC5NzIPTgCs2voFRjpcx1lnJ+CF2kCJV3L1+ogsJTmkJxbny4t2z/maghOZuyUBChFRbrO7AOVBskrh6yVvqdUJUmNk6vElWJw/H1r4FceJ4M6Wvjyi3FMmDNMLFjbdrexSEJ/X+FzN4SIiym12HaDISp5ec2tjevzfGLDyXazYMhLHnQyfHCvBDS+Ub2PpSyTKNU7OrvigtOFvPiDFEZMafQ83Jze+AkRkEXY1B+VBmpaC8ymJkGSzhx2TMexUlCz5Uft6Br0LB0e7jt/IDoXVGYEaVbrDI09hlSOFiMhS7PoduGCh0vi69nh4pBh6TZJTg5OgFCfUCO5j4asjslyZCAYnRGRpdh2giDIBYRgTmHH5ZM+y7dl7QkREZEF2H6CIBjWHYnDhEDhpGmo55EXdkMGWfE2IiIjsnoMm+aytzM2bN+Hl5YW4uDjkz5/fbOeV+jtu7l5mOx8RERE93fs3e1DSYXBCRESkDwxQiIiISHcYoBAREZHuMEAhIiIi3WGAQkRERLrDAIWIiIh0hwEKERER6Q4DFCIiItIdBihERESkOwxQiIiISHcYoBAREZHuMEAhIiIi3WGAQkRERLrDAIWIiIh0xxlWSNM0U9lmIiIisg7G923j+7jNBShXr15VX0uUKGHpSyEiIqJsio+Ph5eXl+0FKIUKFVJfz5w5k+UPmF0vv/wy9uzZo/tz5tR5ea38HVjT35Z8GpMPKmfPnkX+/Pnt8v8gp87La+XvNSf+DqTnpFq1aihevHiWz7XKAMXR0TB1RoITczZKwsnJySrOmVPn5bXyd2Btf1tCzmvOc1vT/0FOnZfXyt9rTv0duLq6mt7HH4eTZB/Qp08fqzhnTp2X18rfgbX9beUEa/o/yKnz8lr5e7X035aD9iQzVXRGunWl9yQuLi7HPpERkf6xLSCyXVbZg+Lm5obPPvtMfSUi+8W2gMh2WWUPChEREdk2q+xBIcqMg4MDli5dyl8QkZ1jW2D9GKDo1I4dO9Ts6WbNmsGevfvuu3jjjTdgj2TpbLdu3dRyPJn1/vzzz2PAgAGmPEBZiY6OVo30jRs3cvxaKeewLTBgW9DN7toCBig6NWPGDPTr1w+bN2/Gv//++0znSk5ORkpKitmujXLeiRMnEBwcjH/++Qfz5s3DsWPHMHXqVKxfvx6hoaG4du0aXwY7wbbAvp2w47aAAYoO3bp1CwsWLEDv3r1VD0pkZORDkfBvv/2GSpUqwd3dHa+++ioOHjxoeo48v0CBAli2bBmCgoLUREJJamft/P398dVXX2V4rEqVKhgxYgRsjSzDk09Ka9asQZ06dVCyZEmEhYVh3bp1OH/+PD799FP1vMTERAwZMkQlK5PXOSAgQL2hnTp1CnXr1lXPKViwoPqbkU+gZF3YFjwa24Iwu2gLdBmg2HNXnli4cCHKlSuHsmXLolOnTpg5c+ZDdQvCw8MxYcIEleGvSJEiaNGiBe7fv2/an5CQgHHjxuH777/HoUOHULRoUQv8JPQ05BPR6tWr8f7778PDwyPDPh8fH3Ts2FEFsPI30blzZ/Wp6uuvv8Zff/2FadOmwdPTUzVSP//8szrmyJEjuHDhAiZNmmR1LwjbArYF9uyanbcFVplJ1tZJ1CuBiWjSpInK97Jp0ya89tprpufIMuuGDRuq7VmzZsHPzw9RUVFo27atekyClW+//RaVK1e20E9BT0u6cqXBKV++/CP3y+PXr19XwakEs2vXrkWDBg3UvlKlSj1UEkKCU+lRI+vDtsC+/WPnbYEue1DSW7VqFWrWrKl+qYULF0bz5s1x/Phx037pvpIuqyVLlqhurDx58qg3ZZlYZo0kwt29ezfat2+v7js7O6Ndu3aqoUpPxh7T//FJb4tEzUYyPCBDQGS9ssoAIH/7MpFahoDsAdsCtgX2SrPTtkD3Acrt27cxaNAg7N27V00Kkvz9rVq1emjSp4zDffjhh4iJiUGZMmXUG3xSUhKsjQQict0yW1uCE7lNmTJFddFJT8qTku5ACdxsibz2D/6jph/WshUydiyvXfqAMz15XMaSH+zytXVsC9gWGLEtsI+2QPcBSps2bdC6dWvVaMuESJmPceDAARw+fDjD8yQ4kQmlEpyMHDkSp0+fVrOdrYkEJrNnz1ZzSyTQMt7279+vAhYZXzTauXOnaVu6+I4ePZppN6CtkLk2Mn6aPs35yZMnYWukp1CG72SI7s6dOxn2Xbx4EXPnzlW9ahUrVlSBugz/PYr0ohlXcdkCtgVsC4zYFsAu2gJHaxiDk94QGU+Tujsye1s8uCol/XCGr6+v+nr58mVYk19//VUFG927d0eFChUy3KRxTj/MM2rUKNWjJKt3ZCKht7e3zU8srlevHubMmYMtW7aoILVLly6qW9MWffPNN2pWfuPGjdVSc8mJIkMcErg899xzGD16tPpfkN+B5EqR5HQSrMkqLxmLFpIrQXpi5O/qypUrakWINWNbwLbAiG3BKrtoC3QfoMjqFJnJPH36dOzatUvdxL179zI8z8XFxbRtHNqwttwfEoDIBCcphPggCVBkmOvPP/9U9yMiIlSinmrVqqlIevny5aYo2ZbIayjDXGLo0KFqjFXmIUlvmQRkpUuXhi0KDAxUr7cE5jLxWX7Onj17qnlWMr/KOOlNhv/efPNNNctfVn716NFDDYUIabykN/Hjjz9GsWLF0LdvX1gztgUGbAvYFvS0l7ZA06EuXbpoLVu21GJjY2XCgbZ582bTvi1btqjHoqKi1P2TJ0+q+3/88YfpOdevX1ePbdy4UbM18jPJzyY/oz1o3Lix1qdPH0tfBlkI24LMsS0gW6frZcYy+UfG47/77js1bCPDOhIBku2Toa5t27apbspevXpZ+nLIwtgW2C+2BfbLWc/d+jJTe/78+ejfv7+ahyFLaSUJTfp8IGSbZCxV1vYPHjwYLVu2tPTlkIWwLSC2BfbLQbpRoDOSnExW7chEQSKyX2wLiOyXo9668mSWsXTrG7PhEZH9YVtARLoa4mFXHhGxLSAi3Q7xEBERkX3T1RAPERERkWCAQkRERLpjsQBF0ndLZkipMSOZXyU9b3qXLl1SKdxlv1Qoltn8kuo6PVluLMemvz2YM0PSwVevXh358uWDj48PhgwZYpVFBIlslTnaAiFZNSUFet68eVVZjNq1a2eoZSQZqTt27Kj2SXV0KSlhLSm/ieyRxQIUScFbuXJlTJ48+aF9Mi1G0pifOHECv/zyC/744w9VS0BW9hhT9xpJOl8pIGe8jR8/3rRPiuw1bdpUNWhyjgULFmDZsmVM9kakI+ZoCyQ4kf/zRo0aYffu3SqHjqTzllxKRhKcHDp0CGvXrlWrBSUwkpThRKRTmg6kT10vjhw5oh47ePCg6bHk5GStSJEi2vTp002P1alTRxswYECm5x06dKgWHByc4bFly5Zp7u7u2s2bN83+cxCRZdqCkJAQbdiwYZme9/Dhw+o8e/bsMT22cuVKzcHBQTt//jxfNiId0uUcFKniKtzd3U2PySchNzc3bN26NcNzpdy0VPKVTLNSTC4hISHDedKfQ3h4eODu3bvYt29fjv8cRJTzbYFULZciokWLFlXDuVIMTYpKpm8rpIdFhnWCg4NNj0kvjJzLWICUiPRFlwGKVGIsWbKkCjgkYZNULh43bhzOnTunhnGMOnTogB9//BEbN25Uz50zZw46depk2i+l6rdv34558+YhOTkZ58+fx6hRo9S+9OchIn16krZAhn/EiBEj1JDvqlWr8NJLL6F+/fqmuSpS8VsCmPSknIZUgpV9RKQ/ugxQXFxcsGTJEhw9elQ1IDIxToKQsLCwDGPKMn4sQUjFihXV+PLs2bMRFRWF48ePq/0yHv3ll1+qibPyiatMmTJqTopIfx4i0qcnaQukXo9477330LVrV1StWhUTJ05Utbtmzpxp4Z+AiJ6Wbt+lq1WrhpiYGNy4cUN9UpJPRVevXkWpUqUyPSYkJER9PXbsmOmxQYMGqXNIJeTY2FhT4bnHnYeIrKctkErnIigoKMNx5cuXV//3QlbwyVBQerKaT1b2yD4i0h/dBihGXl5eKFKkiOqq3bt372Mr20ojlr7BMpKli7JEUeafyHBPiRIlVBcwEVmPzNoCf39/9f995MiRDM+XXhdZ8SNCQ0NVgJN+7tmGDRtU74vxgw0R6YvFavFI/oH0PR0nT55UAYZ048qY86JFi1RjJNsHDhzAgAED1HJDGbYRMozz008/qSGbwoUL488//8TAgQNV7oNKlSqZzitDPLL8ULqDpas4IiICCxcuhJOTk0V+biIyb1sgH0DCw8Px2WefqeXKVapUwaxZs/D3339j8eLFpt4UaQdkjsrUqVNx//59tQz57bffVsENEemQpZYPbdy4US37e/DWpUsXtX/SpEman5+f5uLiopUsWVItIUxMTDQdf+bMGa127dpaoUKFNDc3Ny0gIEALDw/X4uLiMnyfunXral5eXmppsSxFXLFiRa7/rESUc22B0dixY9Xz8uTJo4WGhmpbtmzJsP/q1ata+/btNU9PTy1//vxa165dtfj4eL40RDrFYoFERESkO7qfg0JERET2hwEKERER6Q4DFCIiItIdBihERESkOwxQiIiISHcYoBAREZHuMEAhIiIi3WGAQkQ2Q7LKLl261NKXQURmwACFiJ7Zu+++q4IDqRz+oD59+qh98hxzGTFihEppT0S2iwEKEZmFFOGcP38+7ty5Y3rs7t27qmaW1NEhIsoOBihEZBZSIVyCFCnKaSTbEpxUrVrV9FhiYiL69++PokWLwt3dHTVr1sSePXtM+6Ojo1WPy/r16xEcHIw8efKgevXqpmrFkZGRGDlyJPbv36+eJzd5zCg2NhatWrVSxwUGBmLZsmV8hYmsEAMUIjKbbt264YcffjDdnzlzJrp27ZrhOR999BF+/vlnVXH4999/R0BAABo3boxr165leN6nn36KCRMmYO/evXB2dlbnFu3atcPgwYPx4osv4sKFC+omjxlJ8NK2bVtV4VyqnXfs2PGhcxOR/jFAISKz6dSpE7Zu3YrTp0+r27Zt29RjRrdv38aUKVPw5ZdfIiwsDEFBQZg+fTo8PDwwY8aMDOcaPXo06tSpo57z8ccfY/v27WrISJ7r6empghYfHx91k8eMZK5L+/btVeAzZswY3Lp1C7t37+arTGRlnC19AURkO4oUKYJmzZqpIRdN09S2t7e3af/x48dx//591KhRw/SYi4sLXnnlFfz1118ZzlWpUiXTtq+vr/p6+fLlLOezpD8ub968yJ8/vzqOiKwLAxQiMisZiunbt6/anjx58lOfRwIXI5lnIlJSUrJ1nPHYJzmOiPSFQzxEZFZNmjTBvXv3VE+JzC1Jr3Tp0nB1dVVDP0byPJkkK0M5T0rOkZycbNbrJiJ9YQ8KEZmVk5OTabhGttOTIZfevXsjPDwchQoVUsM148ePR0JCArp37/7E38Pf3x8nT55ETEwM/Pz8kC9fPri5ufGVJLIhDFCIyOxk3kdmIiIi1JDLO++8g/j4eLWUePXq1ShYsOATn79NmzZqCXPdunVx48YNtXLInIngiMjyHDSZyUZERESkI5yDQkRERLrDAIWIiIh0hwEKERER6Q4DFCIiItIdBihERESkOwxQiIiISHcYoBAREZHuMEAhIiIi3WGAQkRERLrDAIWIiIh0hwEKERER6Q4DFCIiIoLe/D9Pu6ZkJ7/3iwAAAABJRU5ErkJggg==", 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" ] @@ -690,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "id": "9a96ca55", "metadata": {}, "outputs": [ @@ -700,7 +651,7 @@ "True" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -722,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "id": "ce2fcd82", "metadata": {}, "outputs": [], @@ -740,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "630bb5bc", "metadata": {}, "outputs": [], @@ -761,14 +712,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "id": "c1fddf83", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73a4c844302a4b458ad0780a7e2898f4", + "model_id": "1be22001384a4a0f98955ab22636d9e8", "version_major": 2, "version_minor": 0 }, @@ -782,7 +733,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e339655a9712423887f374b85505eb00", + "model_id": "98511c12d7914f8987a18451fb44a40c", "version_major": 2, "version_minor": 0 }, @@ -799,13 +750,13 @@ "" ] }, - "execution_count": 20, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] From 8f7ef24cd1ff8cf9818ebc6bcf959943c58c6a35 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 30 Jan 2026 17:05:55 +0100 Subject: [PATCH 08/26] test: add unit tests for fine-tuning --- .../models/forecasting/test_foundation.py | 153 ++++++++++++++++++ 1 file changed, 153 insertions(+) diff --git a/darts/tests/models/forecasting/test_foundation.py b/darts/tests/models/forecasting/test_foundation.py index 0e2d55625d..e3b74b127d 100644 --- a/darts/tests/models/forecasting/test_foundation.py +++ b/darts/tests/models/forecasting/test_foundation.py @@ -1,10 +1,12 @@ import logging +import os import shutil from pathlib import Path from unittest.mock import patch import numpy as np import pytest +import torch from darts import TimeSeries, concatenate from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs @@ -17,6 +19,7 @@ ) from darts.models import Chronos2Model +from darts.utils.callbacks.fine_tuning import LayerFreezeCallback, PeftCallback def generate_series(n_variables: int, length: int, prefix: str): @@ -180,3 +183,153 @@ def test_local_dir(self, mock_method, caplog): ) config_path.rmdir() test_local_dir.rmdir() + + @patch( + "darts.models.components.huggingface_connector.hf_hub_download", + side_effect=mock_download, + ) + def test_full_finetuning(self, mock_method, tmpdir): + # 1. Training activation + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning=True, + n_epochs=1, + **tfm_kwargs, + ) + assert model._requires_training is True + + # Capture initial weights + model.fit(self.series) + initial_params = { + n: p.clone() for n, p in model.internal_model.named_parameters() + } + + # 2. Weight update + # We need to actually train for 1 epoch. tfm_kwargs usually has "accelerator": "cpu" + model.fit(self.series, epochs=1) + + # Check if at least some weights changed + any_changed = False + for n, p in model.internal_model.named_parameters(): + if not torch.equal(initial_params[n], p): + any_changed = True + break + assert any_changed, "The weights should be updated after fine-tuning" + + # 3. Persistence (Save/Load) + save_path = os.path.join(tmpdir, "model.pt") + model.save(save_path) + loaded_model = Chronos2Model.load(save_path) + + pred_orig = model.predict(n=6, series=self.series) + pred_loaded = loaded_model.predict(n=6, series=self.series) + assert np.allclose(pred_orig.values(), pred_loaded.values()), ( + "Prediction of the fine-tuned model and the saved/loaded fine-tuned model should be the same" + ) + + @patch( + "darts.models.components.huggingface_connector.hf_hub_download", + side_effect=mock_download, + ) + def test_partial_finetuning(self, mock_method): + # 1. Callback injection + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning=True, + freeze_patterns=["encoder.block.0"], + unfreeze_patterns=["encoder.block.0.layer.0"], # Example unfreeze + **tfm_kwargs, + ) + assert any( + isinstance(c, LayerFreezeCallback) + for c in model.trainer_params["callbacks"] + ) + + # 2. Freezing logic + # We call fit to initialize the model and trigger the callback setup automatically + model.fit(self.series, epochs=1) + + # Check requires_grad status. + found_any = False + for name, param in model.internal_model.named_parameters(): + if name.startswith("encoder.block.0"): + found_any = True + if name.startswith("encoder.block.0.layer.0"): + assert param.requires_grad is True, ( + f"Parameter {name} should be trainable" + ) + else: + assert param.requires_grad is False, ( + f"Parameter {name} should be frozen" + ) + assert found_any, "No parameters matched the freeze patterns, test is invalid" + + @patch( + "darts.models.components.huggingface_connector.hf_hub_download", + side_effect=mock_download, + ) + def test_finetuning_misconfiguration(self, mock_method): + # Warning if freeze_patterns assigned but enable_finetuning is False + with patch( + "darts.models.forecasting.foundation_model.logger.warning" + ) as mock_warning: + _ = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning=False, + freeze_patterns=["some_pattern"], + **tfm_kwargs, + ) + mock_warning.assert_called_once() + assert "enable_finetuning` is False" in mock_warning.call_args[0][0] + + @patch( + "darts.models.components.huggingface_connector.hf_hub_download", + side_effect=mock_download, + ) + def test_lora_callback(self, mock_method, tmpdir): + pytest.importorskip("peft") + from peft import LoraConfig, PeftModel + + lora_config = LoraConfig(target_modules=["q", "v"]) + callback = PeftCallback(peft_config=lora_config) + + # Avoid duplicate pl_trainer_kwargs + kwargs = {k: v for k, v in tfm_kwargs.items() if k != "pl_trainer_kwargs"} + pl_trainer_kwargs = tfm_kwargs.get("pl_trainer_kwargs", {}).copy() + pl_trainer_kwargs["callbacks"] = [callback] + + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning=True, + pl_trainer_kwargs=pl_trainer_kwargs, + **kwargs, + ) + + # 1. Initialize and fit + model.fit(self.series, epochs=1) + + # Verify transformation happened + assert isinstance(model.internal_model, PeftModel), ( + "Internal model should be a PeftModel after fit" + ) + + # 2. Checkpoint merging test (via save/load) + save_path = os.path.join(tmpdir, "lora_model.pt") + model.save(save_path) + + # Loading back should yield a standard model (weights merged) + loaded_model = Chronos2Model.load(save_path) + assert not isinstance(loaded_model.internal_model, PeftModel), ( + "Loaded model should have merged weights and not be a PeftModel" + ) + + # Verify predictions match + pred_orig = model.predict(n=6, series=self.series) + pred_loaded = loaded_model.predict(n=6, series=self.series) + assert np.allclose(pred_orig.values(), pred_loaded.values()), ( + "Prediction of the fine-tuned model and the saved/loaded fine-tuned model should be the same" + ) From 260de97871993e67881f6e71a59beee826372416 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 30 Jan 2026 17:25:37 +0100 Subject: [PATCH 09/26] documentation: update example notebook on finetuning --- .../26-Chronos-2-finetuning-examples.ipynb | 235 ++++++++++++++---- 1 file changed, 187 insertions(+), 48 deletions(-) diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index 8c3f9d92d2..5b7246820c 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -6,14 +6,14 @@ "metadata": {}, "source": [ "# Chronos-2 Foundation Model Fine-Tuning\n", - "This example notebook presents how fine-tuning can be applied to the Chronos-2 model.\n", + "This example notebook presents how fine-tuning can be applied to the Chronos-2 model using both built-in Darts features and external libraries.\n", "\n", "The following fine-tuning methods will be shown:\n", - "1) Full fine-tuning : all the models weights will be retrained\n", - "2) Partial fine-tuning : some layers of the model will be frozen\n", - "3) PEFT fine-tuning : the HuggingFace peft library will be used to apply LoRA fine-tuning (requires `pip install peft` since it is not a darts dependency)\n", + "1) **Full fine-tuning**: All model weights are retrained. This is natively supported by setting `enable_finetuning=True`.\n", + "2) **Partial fine-tuning**: Specific layers are frozen via name patterns. This is natively supported using `freeze_patterns` and `unfreeze_patterns`.\n", + "3) **PEFT fine-tuning**: The HuggingFace `peft` library is used via a custom Darts callback (`PeftCallback`) to apply LoRA. This shows how to extend Darts with external specialized libraries.\n", "\n", - "To be useful, a fine-tuned model should be easily saved and loaded. For each fine-tuning method, how to save and load the model will be shown with an example (straightforward for (1) and (2), slightly different for (3))" + "To be useful, a fine-tuned model should be easily saved and loaded. For each method, we will demonstrate how to persist the model weights.\n" ] }, { @@ -105,7 +105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8547492bdce140e5a935ba4db13a6b21", + "model_id": "04eebab1f5554fedaf9084d8557b30d1", "version_major": 2, "version_minor": 0 }, @@ -159,9 +159,9 @@ "source": [ "# 1. Full fine-tuning\n", "\n", - "In this method, all the model weights are retrained. This is done with `enable_finetuning=True` in the model constructor.\n", + "In this method, all the model weights are retrained. This is simply enabled by passing `enable_finetuning=True` to the model constructor. \n", "\n", - "The model is saved and loaded with the usual `save` and `load` methods, so the behavior is the same as other darts models." + "When fine-tuning is enabled, Darts will treat the foundation model like a standard trainable model during `fit()`. Saving and loading follows the standard Darts API via the `save()` and `load()` methods.\n" ] }, { @@ -173,7 +173,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16d446f7866f4d0ca54ded638d7e66e3", + "model_id": "17fd61ce4c33441ea964bb758bf15730", "version_major": 2, "version_minor": 0 }, @@ -217,7 +217,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30691d9dda1d4f998b5ed5001809a6b5", + "model_id": "ce88ef8f24694408ba71cd8203e5a052", "version_major": 2, "version_minor": 0 }, @@ -231,7 +231,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9cba0f4cc07645f69c14457c61b59d40", + "model_id": "b3d8ba35afd3483d80d3004824d6f640", "version_major": 2, "version_minor": 0 }, @@ -254,7 +254,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -316,19 +316,26 @@ "metadata": {}, "source": [ "# 2. Partial fine-tuning with layer freezing\n", - "With this method, top layers of the model will be frozen. That means that their weights won't be updated during the fine-tuning. " + "\n", + "Partial fine-tuning allows you to update only a subset of the model's parameters, which is useful for preserving general knowledge while adapting to specific patterns. \n", + "\n", + "Darts foundation models natively support this via:\n", + "- `freeze_patterns`: A list of parameter name prefixes to freeze (`requires_grad=False`).\n", + "- `unfreeze_patterns`: A list of prefixes to unfreeze (applied after freezing).\n", + "\n", + "This mechanism automatically injects a `LayerFreezeCallback` into the training process." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "33fa7fc4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a38e2f67955406d8c399db728eb96e2", + "model_id": "7f7cc6aed7a04945bd449ef829d0559c", "version_major": 2, "version_minor": 0 }, @@ -359,14 +366,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "50830283", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c6375a46aa043aba1397eb195e2b3fe", + "model_id": "3df3477b4b4f44a68eee8c816515a008", "version_major": 2, "version_minor": 0 }, @@ -380,7 +387,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5117f22a8ec94ad5842ea8709e74fd4d", + "model_id": "2fbdbaa04f094966b1a0f251cf6994c3", "version_major": 2, "version_minor": 0 }, @@ -397,13 +404,13 @@ "" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -468,24 +475,25 @@ "id": "b0d126dc", "metadata": {}, "source": [ - "# 3. LoRA fine-tuning\n", - "This fine-tuning method uses HuggingFace `peft` library. This library makes it easy to use **P**arameter **E**fficient **F**ine-**T**uning methods such as LoRA which greatly reduces the number of fine-tuned parameters.\n", + "# 3. LoRA fine-tuning (PEFT)\n", "\n", - "More information about peft can be found in the [official documentation](https://github.com/huggingface/peft)\n", + "This method uses the HuggingFace `peft` library for **P**arameter **E**fficient **F**ine-**T**uning. \n", "\n", - "To use LoRA fine-tuning, the `PeftCallback` is used. A `peft_config` is required. In this example, a `LoraConfig` is used with the same parameters used in the [official Chronos-2 implementation](https://github.com/amazon-science/chronos-forecasting/blob/f889ae66477b53f6beb130f5c7b13590b29a1035/src/chronos/chronos2/pipeline.py#L202)\n" + "Darts provides a `PeftCallback` that wraps the internal model with adapters (like LoRA) before training. One major advantage of this callback is that it automatically handles **weight merging** during checkpointing, allowing the saved model to be loaded back as a standard model without needing the `peft` library at inference time.\n", + "\n", + "More information about peft can be found in the [official documentation](https://github.com/huggingface/peft)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "id": "6981052c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd7cd86f70dc4759ab3ed10c73fd7afe", + "model_id": "8dba495f46e248b29d33d99b40675881", "version_major": 2, "version_minor": 0 }, @@ -499,10 +507,10 @@ { "data": { "text/plain": [ - "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=50, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=100, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" ] }, - "execution_count": 8, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -513,8 +521,8 @@ "from darts.utils.callbacks.fine_tuning import PeftCallback\n", "\n", "lora_config = LoraConfig(\n", - " r=8,\n", - " lora_alpha=16,\n", + " r=32,\n", + " lora_alpha=64,\n", " target_modules=[\n", " \"q\",\n", " \"v\",\n", @@ -546,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "id": "49b2c2e8", "metadata": {}, "outputs": [], @@ -558,14 +566,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "id": "41e8a82f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80fe22ec79024e3e8d10bc5165d2f93e", + "model_id": "34b9699f0deb4cb4bca42eb3b663a3ed", "version_major": 2, "version_minor": 0 }, @@ -579,7 +587,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1588da65b96e44559a7b0086ac5b3796", + "model_id": "3955fb726ade4ff9b17f2660cea15057", "version_major": 2, "version_minor": 0 }, @@ -596,13 +604,13 @@ "" ] }, - "execution_count": 10, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -641,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "id": "9a96ca55", "metadata": {}, "outputs": [ @@ -651,7 +659,7 @@ "True" ] }, - "execution_count": 11, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -666,14 +674,15 @@ "metadata": {}, "source": [ "## 3.2 Adapter saving\n", - "Another method is to save only the adapter. This results in a light-weight folder containing only the LoRA weights which can be plugged to the original model.\n", "\n", - "For that, we need to access the internal chronos model with the `internal_model` attribute to save only the adapter." + "Alternatively, you may want to save *only* the lightweight adapters rather than the full model weights.\n", + "\n", + "Foundation models in Darts provide an `internal_model` property that gives direct access to the underlying PyTorch `nn.Module`. We can use this to interact with the `peft` API directly for saving and loading.\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 26, "id": "ce2fcd82", "metadata": {}, "outputs": [], @@ -691,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 27, "id": "630bb5bc", "metadata": {}, "outputs": [], @@ -712,14 +721,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "c1fddf83", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1be22001384a4a0f98955ab22636d9e8", + "model_id": "71e7d2b9d04f427eae8f9e8ea576d7c6", "version_major": 2, "version_minor": 0 }, @@ -733,7 +742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "98511c12d7914f8987a18451fb44a40c", + "model_id": "f523a392cf9c420d98cb2b28510021f1", "version_major": 2, "version_minor": 0 }, @@ -750,13 +759,13 @@ "" ] }, - "execution_count": 14, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -785,10 +794,140 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "022127ca", + "metadata": {}, + "source": [ + "# 4. Performance Evaluation\n", + "\n", + "Finally, let's compare the performance of all four models (Base, Full Fine-tuning, Partial Fine-tuning, and LoRA) on the validation set using standard metrics like **MAPE** (Mean Absolute Percentage Error) and **MAE** (Mean Absolute Error).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b81f2e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelMAPE (%)MAE
0Base Model15.25451870.704819
1Full Fine-tuning3.23726213.835668
2Partial Fine-tuning2.89984712.568367
3LoRA (PEFT)5.95182926.501753
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" + ], + "text/plain": [ + " Model MAPE (%) MAE\n", + "0 Base Model 15.254518 70.704819\n", + "1 Full Fine-tuning 3.237262 13.835668\n", + "2 Partial Fine-tuning 2.899847 12.568367\n", + "3 LoRA (PEFT) 5.951829 26.501753" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "from darts.metrics import mae, mape\n", + "\n", + "results = []\n", + "all_predictions = {\n", + " \"Base Model\": prediction,\n", + " \"Full Fine-tuning\": pred_full_finetuned,\n", + " \"Partial Fine-tuning\": pred_partial_finetuned,\n", + " \"LoRA (PEFT)\": pred_lora_trained,\n", + "}\n", + "\n", + "for name, pred in all_predictions.items():\n", + " results.append({\n", + " \"Model\": name,\n", + " \"MAPE (%)\": mape(val_passengers, pred),\n", + " \"MAE\": mae(val_passengers, pred),\n", + " })\n", + "\n", + "df_results = pd.DataFrame(results)\n", + "df_results" + ] + }, + { + "cell_type": "markdown", + "id": "996456e0", + "metadata": {}, + "source": [ + "### Observations\n", + "\n", + "While the results on this small \"toy\" dataset (Air Passengers) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility of the fine-tuning API.\n", + "\n", + "In real-world scenarios with larger datasets:\n", + "- **Full Fine-tuning** offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", + "- **Partial Fine-tuning** provides a good middle ground by updating only the most relevant layers (like the output head).\n", + "- **LoRA (PEFT)** is often the most effective strategy. It typically matches or exceeds full fine-tuning performance while only training a tiny fraction (often <1%) of the parameters. This makes it faster, more memory-efficient, and allows for much easier deployment of multiple task-specific \"adapters\" on top of a single base model.\n", + "\n", + "### Summary\n", + "In this notebook, we have seen:\n", + "1. How to enable **native full fine-tuning** in Darts foundation models.\n", + "2. How to use **layer freezing patterns** to perform partial fine-tuning without manual weight manipulation.\n", + "3. How to extend Darts foundation models with **custom callbacks** to leverage external libraries like `peft`.\n", + "4. How to use the `internal_model` property to gain low-level access to the underlying PyTorch module for advanced operations.\n" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "6f74bc02", + "id": "2286828a", "metadata": {}, "outputs": [], "source": [] From a20c94311e17ad04a7268de92d314c68d034f53e Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 30 Jan 2026 17:37:11 +0100 Subject: [PATCH 10/26] documentation: update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0217f5bd80..26df25012b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -22,6 +22,7 @@ but cannot always guarantee backwards compatibility. Changes that may **break co - Includes automatic downsampling for large series (configurable via `downsample_threshold` parameter) to avoid crashes when plotting large series - Integrates seamlessly with `plotting.use_darts_style` which now affects both `TimeSeries.plot()` and `TimeSeries.plotly()` - Plotly remains an optional dependency and can be installed with `pip install plotly` +- Added support for full and partial fine-tuning of foundation models with integrated layer freezing and `PeftCallback` for LoRA integration. [#2964](https://github.com/unit8co/darts/issues/2964) by [Alain Gysi](https://github.com/Kurokabe) **Fixed** From a568a3d14d6d78f3bb400463b948e3ffe761636d Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Fri, 30 Jan 2026 18:20:56 +0100 Subject: [PATCH 11/26] fix: update on_save_checkpoint of lora callback to avoid potential OOM error --- .../models/forecasting/test_foundation.py | 6 +- darts/utils/callbacks/fine_tuning.py | 40 +++++----- .../26-Chronos-2-finetuning-examples.ipynb | 78 +++++++++---------- 3 files changed, 63 insertions(+), 61 deletions(-) diff --git a/darts/tests/models/forecasting/test_foundation.py b/darts/tests/models/forecasting/test_foundation.py index e3b74b127d..c3f4a43c22 100644 --- a/darts/tests/models/forecasting/test_foundation.py +++ b/darts/tests/models/forecasting/test_foundation.py @@ -194,7 +194,7 @@ def test_full_finetuning(self, mock_method, tmpdir): input_chunk_length=12, output_chunk_length=6, enable_finetuning=True, - n_epochs=1, + n_epochs=5, **tfm_kwargs, ) assert model._requires_training is True @@ -249,7 +249,7 @@ def test_partial_finetuning(self, mock_method): # 2. Freezing logic # We call fit to initialize the model and trigger the callback setup automatically - model.fit(self.series, epochs=1) + model.fit(self.series, epochs=5) # Check requires_grad status. found_any = False @@ -310,7 +310,7 @@ def test_lora_callback(self, mock_method, tmpdir): ) # 1. Initialize and fit - model.fit(self.series, epochs=1) + model.fit(self.series, epochs=5) # Verify transformation happened assert isinstance(model.internal_model, PeftModel), ( diff --git a/darts/utils/callbacks/fine_tuning.py b/darts/utils/callbacks/fine_tuning.py index 8583191b94..3c904f3824 100644 --- a/darts/utils/callbacks/fine_tuning.py +++ b/darts/utils/callbacks/fine_tuning.py @@ -1,4 +1,3 @@ -from copy import deepcopy from functools import partial from typing import Any, Callable, Optional @@ -210,28 +209,31 @@ def on_save_checkpoint(self, trainer, pl_module, checkpoint): return if isinstance(peft_model, PeftModel): - # Merge adapters into the base model weights - # TODO: This might be inefficient for large models, think about a better way - model_copy = deepcopy(peft_model) - setattr(pl_module, self.model_attribute, peft_model.merge_and_unload()) + # In-place merge of adapters into the base model weights. + # This is memory-efficient as it avoids a full deepcopy and works on GPU. + peft_model.merge_adapter() try: - # Get the state dict of the base model - # This returns the weights including the merged adapters - # base_state_dict = peft_model.get_base_model().state_dict() - - # We need to prepend the model attribute name to the keys - # because the PL module expects keys to start with `model.` (or `model_attribute.`) + # Obtain the state_dict of the base model (which now has merged weights). + # We filter out the adapter-specific keys (e.g. lora_A, lora_B) + # and restore the original key names by removing PEFT wrapper prefixes (e.g. base_layer). + # This allows the model to be loaded back as a standard (non-PEFT) model. prefix = self.model_attribute + "." - new_state_dict = { - prefix + k: v - for k, v in getattr(pl_module, self.model_attribute) - .state_dict() - .items() - } + new_state_dict = {} + # IMPORTANT: We move merged weights to CPU. This avoids GPU OOM + # (holding two copies of the parameters on GPU) and ensures we have + # a 'snapshot' that won't be changed by the subsequent unmerge. + for k, v in peft_model.get_base_model().state_dict().items(): + if any(sub in k for sub in ["lora_", "modules_to_save"]): + continue + + # PEFT wraps layers and adds a ".base_layer" to the key path + # We only replace if it's followed by a dot to avoid partial matches + clean_key = k.replace(".base_layer.", ".") + new_state_dict[prefix + clean_key] = v.cpu().clone() # Update the checkpoint checkpoint["state_dict"] = new_state_dict finally: - # Unmerge adapters to keep the current model in PEFT mode - setattr(pl_module, self.model_attribute, model_copy) + # Restore the adapters (unmerge from base weights) to allow training to continue. + peft_model.unmerge_adapter() diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb index 5b7246820c..09554b97d5 100644 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ b/examples/26-Chronos-2-finetuning-examples.ipynb @@ -105,7 +105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04eebab1f5554fedaf9084d8557b30d1", + "model_id": "d269fe29e6ab4b0faa9ca063fc607f74", "version_major": 2, "version_minor": 0 }, @@ -173,7 +173,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17fd61ce4c33441ea964bb758bf15730", + "model_id": "45bbf43f117543b7a171e399527dce2d", "version_major": 2, "version_minor": 0 }, @@ -217,7 +217,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce88ef8f24694408ba71cd8203e5a052", + "model_id": "5c258dd1f466442ba7f0d7ffa12fe5fb", "version_major": 2, "version_minor": 0 }, @@ -231,7 +231,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3d8ba35afd3483d80d3004824d6f640", + "model_id": "cd4bb0d55fd94fb48bffd7428f22e10c", "version_major": 2, "version_minor": 0 }, @@ -254,7 +254,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -335,7 +335,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f7cc6aed7a04945bd449ef829d0559c", + "model_id": "50dfcff23af64611b005b9875ed3209f", "version_major": 2, "version_minor": 0 }, @@ -373,7 +373,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3df3477b4b4f44a68eee8c816515a008", + "model_id": "aaec2192041d44fa99aedae592c73a9d", "version_major": 2, "version_minor": 0 }, @@ -387,7 +387,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2fbdbaa04f094966b1a0f251cf6994c3", + "model_id": "72100e6de88e44fe904f1ced2c4189dc", "version_major": 2, "version_minor": 0 }, @@ -410,7 +410,7 @@ }, { "data": { - "image/png": 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", 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E5lCdpMvRkH42ZnsMw7RdUuzsAP37S1jwAHRzC8KvwQPx5Jq7cExdhXK1Ci8n/41jS47g2elL4eDobukuM42AnWStBHI0raqqEqabX3/9VZhSSKOycOFCnDx58rIFsp555hnMnz9fRPKcOXMGH3/8MZYvX46nn35a0cIMHTpURODQtrds2YKXX3650f0kR9wff/wR8+bNw+nTp4W/R1xcXL3fiYiIEJqMVatWITMzE0VFRXWu25B+NmZ7DMO0XZJV8kuKt6M3XN2CRDsoqC/mz9qKGx1DlfV+LU/GXYtHIz298S9ljOVgAcVKoPDgQ4cO4YorrsALL7yAPn36CGHliy++EELGm2++We/3r7vuOuFvQk6x0dHR+Oabb4QQQaG8BkiwICGI/D4otJccXBvLzTffjP/7v/8Toby0nfPnz+PBBx+s9zuUw+X1118XDrcUnkyhxfVxuX42dnsMw7Q9KrQVyCiR/cvyC92xdF+Ssow0Ja/c8g/eCLsKDnpz8VF1Feb+ORM4t9VifWYah0oyNfbbCAUFBfD09ER+fj48PDyqLaNoknPnziEyMlJEnTAMwzC2TW339cT8RFz9h5wfqjK/N8pSZ+GekZF48aoe0KiNaRmOn/wDT+z8P1RIWvyamo4Ackm74jVg+KPkpGaxY2qvFNTz/K4Ja1AYhmEYmyOl0Kgx0VXKqQd+2H4O9y7Yh8Iyo2N9z+7X4dfpf+Er+44IIL81SQus+z9g2Z1AeaFF+s40DBZQGIZhGJsjOW6Z0u5cZaxVtulUJq7/aicuZBvneXlHosfslcDoZ5R5BSf/xKMLR+LcuaalSGBaHhZQGIZhGJvWoDhWuWPeXYPg5SKnGTiTUYTrvtqBvedyjF9Qa4DxLwO3/AKdowde9PfDZjsdZm5+FBt2cBiyNcICCsMwDGNzJJVkKm0Pj54Y1y0Afzw0Ap385TQNOcUVuPX73Vi63yjICLpfhbw7/kSKk7xesVqFx88uwqcHPoVWx6kLrAkWUBiGYRib4/9ytfg7KRVfp2UgMlzOrN3RzxXLHxqBUV38xHSlVsKzvx3FO6tPQKszxoP4hPTHops3YIpG9l0hfoj9AQ+sfwA5ZSZaF8aisIDCMAzD2BwuBUnoUFWFjqVu6NcxUJnv6WyPeXcOwp3DjXV4vt2agDk/7a/mPOvi4of3Zm3CcwOfhp1Kzlm6O203bl51M2LPyxXeGcvCAgrDMAxjW5TmwkkrR+Bc0AWgfwfvaovtNGq8dk003rouRgk53nAyAzd8vQtJOUbnWZVajdnRd+D7Sd/D18lXzEsvTsftmx7B7+ueatVDYi6FBRSGYRjGptDlGKueZ9oFIcz70nplxOyhEfjp7sHwcJI1JKcuFuK6L3dgX2J1M86AwAFYevVS9PPuIaYrVSq8lroWO/d92aLHwdQPCygMwzCMTbH19CZ84eWJFW6uyPEJUIqa1saIzn744+ERiPKTnWKzyXn2uz347UBytfUCXALww6R5mOUcIaYna7wxbED9WbKZloUFFMYmSE9PFwUVqZCil5dXg7+XmJgobl5NKYxobVDZAkr931A2b94sjj0vL69R40rf+eOPP9CWobpVjbmOWpKmXKONvRbaGjtTD+Bbb0+84u+Li76Xzxge5e+GFQ+NwMjOsvNshVaHp5cdwdw11Z1n7R1d8cJNq/Bp51vx+nW/CRMQYzl49K2EO++8U9ykav6dPXsWtoo5HwKffPKJqHBMN3EqUljXGFJNIlunLsGCij9eriaTOcaVpqdMmWLW/XTs2BGffvqpWbfJtF/SSlKVdreQmAZ9x9PFXuRKuW2orCEhvtmSgPt/PoCi8qpq604Y8Txc3ALM2GOmKbCAYkVMnjxZPBxM/6j2RFOoqKhAWyI+Pl4UD+zSpQsCAtrujaOy0hhlUBMfHx+4u7u3+LgGBQXB0dHRrPthGHOSpc1X2gO7DW/w9+w1arx5XQzeuDZacZ5df+Iibvh6J5Jzjc6zjHXAAooVQQ8FejiY/mk0GrFsy5YtGDx4sFgnODhYVPKlir+mKl+q6ktqXz8/P0yaNEnMj42NFW/Dbm5uovLvbbfdhqysLOV7Op0O77//Pjp37iy23aFDB7z99tvK8ueeew5du3aFi4sLoqKiRCVj04fokSNHMG7cOPHgpMJP9LDbv3+/0ALcddddoiCUQRv02muv1XnsX3/9tajo7ODggG7duuHnn3+u9vb9+++/46effhLbIU1JTWjbCxYswJ9//qnsj/pgICEhQfSTjoMqRe/atava97dv345Ro0bB2dkZ4eHh+M9//oPi4uI6+0v769u3r6gaTevTdm+66SZxvAb27dsnzCd0Pqg41pgxY3Dw4MFq26F+0rFfc801wsxy3333iX4S3t7e1Y63plqfxogqXtPY07Uya9YsZGTI1V0bQl3jamriMZgfSHvT1PGjflPV6yeeeEI5N6ZjaAppWahfNbViVKWbrntfX188/PDD1a7B8vJyUfGbqlzTGA4ZMqTauTdo8+japv5Pnz4d2dnZ9Y6N4biXLl2qHNegQYOElonOK407/abot5WZmVnt9/TGG28gLCxM/J7o+P75559q2967dy/69esnit7RdqiKeU0u97ttz1CocK5GfgFz0kkIC5QdWxvD7cM6Yv5dg+Cud549mS47zx44n6uso62qwMlTf2HhmgdwLpHDji2CZIPk5+eT0VB81qS0tFQ6fvy4+LQl7rjjDunaa6+tdVlycrLk4uIiPfTQQ9KJEyekFStWSH5+ftKrr76qrDNmzBjJzc1NeuaZZ6STJ0+Kv9zcXMnf31964YUXxPcOHjwoXXnlldK4ceOU7z377LOSt7e3NH/+fOns2bPStm3bpO+++05Z/uabb0o7duyQzp07J/31119SYGCg9N577ynLo6OjpdmzZ4vtnz59Wlq6dKl0+PBhqby8XPr0008lDw8PKS0tTfwVFhbWenzLly+X7O3tpS+//FI6deqU9NFHH0kajUbauHGjWJ6RkSFNnjxZuummm8R28vLyLtkGbZuW03qG/VEfqN90rXTv3l1atWqV2P4NN9wgRURESJWVleK7dNyurq7SJ598Io6Bjrdfv37SnXfeWef5orGn74wfP146dOiQtGXLFqlz587SrFmzlHU2bNgg/fzzz2Js6Jq85557xPgVFBQo61DfAgICpB9//FGKj4+XEhMTpd9//13Mp76aHi+d48cee0z57g8//CCtXr1afG/Xrl3SsGHDpClTpijLN23aJLZD10Ft1DWu9B26xghzjF92drYUFhYmvfHGG8q5MYxhnz59qvWJtkHbNv1d0DX0wAMPiHFcuXKl+C18++23yjr33nuvNHz4cGnr1q2iLx988IHk6Ogo+kLs3r1bUqvV4rql/n/22WeSl5eX5OnpWef5NT3uf/75R5y/oUOHSgMGDJDGjh0rbd++Xfye6JxT3wx8/PHHor+//PKL+A3S74uubUNf6Dql3yRdJ7GxseJ4oqKixL7oOiIa8ruteS20dUzv61tPpUn95kVLMfNjpGnfxTRru2cuFkpj3t8oRTy3Svx1eXG19PuBJLFs6b+Pi33Q348r7zbTkTD59Ty/a9K+BJQdX0jSh92b/5ewtfp2adqwjPbRBOhGTA9lutEb/uhBQLz44otSt27dJJ1Op6xPD3MSSLRarXLDooeCKSRcTJw4sdq8pKQk5eFHD0q6kZsKJJeDbv50kzbg7u4uhJvamDdvXr0PAQP0cLnvvvuqzbvxxhulq666Spkm4Y3GqLFCnuFB8/333yvz4uLixDy6+RMkOMyZM6fa90hQo4daXYIuPVzpfJHwaGDNmjXiO4YHcE3oXNF40UPJAPXj8ccfr7ZeXYLF5R5K+/btE98zCIKXE1DqGtfaBJTmjh8JHSR8mNJQAYWmq6qqql0bN998s2ifP39enIeUlJRq25kwYYJ4wBMzZ86sdi0R9P2GCCimx01CB80jwdPA3LlzxW/TQEhIiPT2229X29agQYPEywXxzTffSL6+vtWuq6+//rqagHK53217F1A+W7lSERzu+WFQs7edW1wu3fLNLkVIob/31pyQTp9dq+zn4QVDzXIcjNQoAUXWb7UXqLR2odG5qsloyy+dNmy3GeW7SYVO6n4DpK4mTpw4gWHDhlULpRsxYgSKioqQnJwsVNcEmVdMIfPLpk2bhJq4Nt8DcsIk9fiECRPq7NOvv/6Kzz//XKxP+yOzEplyDDz55JO49957hbnhiiuuwI033ihMNY2Bjm/OnDnV5tHxffbZZzAXvXv3VtpkKiDIHNK9e3cxTkePHsWiRYuUdeg5Ter6c+fOoUeP2lXINO5kVjBA54i+c+rUKWFyuXjxIl5++WVhbqB9abValJSU4MKFC9W2Q2r+pnDgwAFhJqH+5+bmin0TtP2ePXvCnLTE+DWU6OhoxdRp2P+xY8dEmz5pXMkMaQpd12QOMlxfZNYxhc5VTdPL5Y6bTC1Er169qs0zmNUKCgqQmpoqrl1TaJrGyNAX2iaZd0z70pjfbc1jbW+cSz0I6F2kwp3kqJzm4OXigJ/uGYxX/4rD4j3yb/OrzfE4c9Ef3joJuWoVDmoLhclHY+fQ7P0xDad9CSiO7oB7SPO3o3G8dNqwXdpHEyGBhHxBmvN9U0iguPrqq/Hee+9dsi7d5Mkvoz7Iz+DWW2/F66+/LnxayI9iyZIl+Oijj5R16AFJvg9///031qxZg1dffVWsU/OBYGns7eUqp4RB0DM80Gmc7r//fuE3UROD8NcU7rjjDuHrQIJWRESE8Emgh1FNB+aa560hkH8HnRP6I8HA399fCCY03RIO0i0xfmq1Wggyl3MSNt23Yf+m+ybhhYQ1UyGGqO0Bb47jrjnP0BdzcbnfbXuGrpecgrOAvzwd6RVulu2S8+zb18Wga4Ab3lh1HBR5vO5EJsZ0dEGucykK1Sqcjl+DHt2uNcv+mIbRvgSU4Y/If+YmchTw1Am0FPQGSs6M9OM03CR37NghnCPJGa8u+vfvL75HTod2dpeeaorcIOe/DRs2CC1ITXbu3CkerC+99JIyj5wda0JvdPRHTpAzZ87EvHnzhIBCDq/0dtuQ46PjoQe6AZpurBagofurbZyOHz/eaOGQBAJ6Yw4JkYXT3bt3i4cuOfkajuGrr77CVVddJaaTkpIa5OhIx0HUdywnT54Uws+7774rnFIJck62BA0Zv9rODQlVlIfF9LpubL4acjal7ZIWg5xZ67q+9uzZU20enStzQ5pFuhbovJNDtAGaJgd3Q19I21hWVqZoUWr25XK/2/ZMSl4pJHW6Mh3m1ThtbX0IR/ERkaLg4KOLD6GwvArawlDAWU71cCCBBZTWhqN4bICHHnpIPNweffRR8WCiSBXSVJB5hR6IdUHRDjk5OUJooMgDUg//+++/IrqGbup0g6QonWeffVZEctByuln+8MMPigBDD2HSiNAyMvWsWLFC2X5paamIHCITBgkudCOm/RhU+nSDpbdBEoDowUzmjdp45plnRJQFmbfOnDmDjz/+WESNUGRGY6D9kamBTCy0v/pCdk2hMSBhjI6FHpDUBxpjmq4PGj8Sqkglv23bNqFBoEgeMu8Yxo8eRqTWpwckaaNIILwcJBTSzXLVqlUiQoTGsDbNBD30v/jiC6EJ++uvv8yeI6WhNGT86Nxs3boVKSkpipBG0T10fBRFRtfXl19+KbRwjYEEYxrX22+/XVwzZFKiKJm5c+cKrR5B54XMORQJRH3773//2yDzTlOga5k0H2QapeuQou1oTB577DGxnLSNdG4pWouEutWrV4t+NeZ32545nlqAyko/dC+xQ1iVhPAAownOXIztFoD/u1p+OUor7qfM358lmxWZ1oMFFBuA/BzoRkY3XgrxfOCBB3DPPfcI/4b6MLzN0U1t4sSJwnZOYaqUPM0g2FDY8FNPPYVXXnlFCBY333yzYlOn0FfSitCDhsIl6SFE6xsglTq9xdPDgR4U9HCm0EgyCRHDhw8XfaVt0tsyPYhqg8JIyQxCN2ryN6DQXdLC0AOsMdBNn7QX5NNB+6NjbwjkE0Bh3BRCSm/h9FZO42HQjNQFaQyuv/56oSGh8aXtkMbEAAl65BtCb8QUJkoPyobkcKHzTWNIDzfycahNUKLjI6Fu2bJlQtNEmpSaD7rWoiHjR6G3FLpL/knUd4KuNxovEkzouqbru7FCKUHXCl2DdB3T+afriR7sBvPS0KFD8d1334lrjPazdu3ay/52mgqdY3pxoL7Q740EIRIeSVg1mJ1WrlwpfGdonEg7WdOU05DfbXvlRFoBduXein3n38JLozejS+fJLbKfPmFygsmEsj5w18omvANV+dBpqyd0Y1oWFXnKNuYL9AZEb0z0pkNvxHSTphuEwdGPNkdv93RDICdMchCjN2PDD5SgtwPSBtAPlX5wM2bMEDePhtqMyRmN/CEo54SpwyZBqlN6i6IEZ6aOaAxjTsj3hnKFtIUU+gxj7Rju6+9uz8aWeMqtBBx9dSLcnar7J5mLKq0O0a/+i/IqHYZHvIpjLnJgxPKRH6JLJznHFNM06nt+16RR4ji9DZLAQU5iJKCQipIcJimhlAF6SyZTwP/+9z+h1iYHQHLcowvMAKlk4+LisG7dOqHGJtVvzSgOhmEYhjGgkyQkZMnJ/7oGuLeYcELYadToESw/PNXFRk3ggTOr+IS0Io3ywCJVJDnkkcbEgGkqdtKeUCZIUp9ee63s7Uy+DaSmprfNW265RdjjSe1pyMZIkB2d1OSkor6cWp1hGIZpf1RqdUrEVL8OLV/oMSbUA4eT8pBR3Be+3mcxoKwM4Y4pLb5fpokaFLKlklBBuS7Ilk42VDLlGCAVHHnlUz4MA6TKodTThtTY9Em2VNPcD7Q+mXpqetqb5jQgtZDpH8NY2sTD5h2GaT0qqnTo7rIdoV2fxdm82/D35ldadH8xIZ7iM750AP5NzcNHmdkYkRxLb+Itul+miQIKRQsY/EnIq/zBBx8UTmFUA4Ug4cQ0oZEBmjYso8+ajoIUSkeF0Azr1IQ88knQMfwZwioZhmGY9iOgONtnokCjxik7Hcoq666VZQ5iQmUBRQs7JDhFyzOLLgLZ8S26X6aJAgqp1ygi4Z133hHaE/IbocgJ8jdpSV544QXhUGP4o5BbhmEYpn1A7gMVWglldsYs3qE+LZtRt0ugG+w1+rxTVd2NC843LDqQaWUBhbIY1kyeRaGChtTdhvwPlOLbFJo2LKPPmhVXKX06RfYY1qkJZeAkb1/TP4ZhGKb9+J9odRLi7YyRnqGdJrboPh3tNOgaKGcGX1MYJT7LVCokJKxr0f0yTRRQKIKHkg+ZQrkPKLGUwWGWhAxKzGWA/EXIt8RQb4I+KfyYUlMb2Lhxo9DOkK8KwzAMw5hSViknqFPb5YtPjUqDII+6s2ib2w/lqC4Sc4KCMCwiDPcXHoZk5vIGjBkEFEraRZlGycRz9uxZLF68GN9++63IfEhQhkRKKPTWW28Jh1pKRkQJlCgyh5InGTQukydPFqYhSsxECYkoERVF+HAED8MwDFOTskpZIFDb54nPINcg2KtbLszYNJKHqIQ9Su3dUKVSIV2jQkrqPj5J1iagDBo0SKQ6/+WXXxATEyNSa1NYMeU1MUBp0ykJG/mn0PqUppvCik2TplFxM6qCSlV0Kbx45MiRQtBhGIZhmJqUCg2KDiq1XAgzzK3ltSemjrJEoNYYnHEgeRufpFag0XmTp02bJjQjlHiNcpqQJsQU0qJQWmuKyKF11q9ff0l5cIrYIe1LYWGhcHr98ccfzVJ5lGm70PV05ZVXisR/FKbeUCi9Ol2T5g4JpjBjSv/f0phjPy01Bq3dpzvvvFPRxNYF5Vui7NZUhoG0uVQOoDHXi61CZSHoeK2Bxl6zl7sWdDpJZHS1Vxlra4W2Uk0iStamUcuOsmfKjaU39mtlUxPTsrTvwg5WBN186Uda849MabaKOR8On3zyCdLS0sRNjPyemvoAY1qf2s4LpQqg80maWHNy//3344YbbhCRfqThpTpQdV0vTYWKY9Jvk3zpmNbRnlAUj1plFEpCq1qnJo6TvQad/eWX57j0SDio5Urj+9MtUzm8vcECihVBvjl00zb9M83U2xgqKmRVaFuBKroOGDBA5OBpSME9xvJQsTtD5s+akIaDHOopB5K5IHMyRQhSaQ3yZ3N3dxfVo/l6sW1KKmTBRGUioIR5ylE1rUG03g+lSmuHKA+5UntyUTLSi2vP28WYDxZQrAgKp6abtukf3cgJqhY7ePBgsQ6Fe1OlWwrPNlXxkrMxqXn9/PzETZqIjY0VFYbJhEYJ86iqrqHcPUEPEKqfRGpx2jZVgH377beV5VQYkkx0Li4uiIqKEtWMKyuNqtYjR45g3Lhx4mFA4d8kROzfv1+8ZVJ5eDLhGbRBpPqtC0oASJVuHRwcREXan3/+WVnWsWNH/P7776JsAm2H3shrQtumhIF//vmnsj/qg2mSQeonHQdVtDVkNjawfft2UYmXHmj0dk8JCIuLG54IisaRTJthYWFiHEnFTb5XplxuLAmqSkznicaTKlab1rAy8P333wtnc/LrIl8u0wrKBDmfU54iWk4Zmw8dOnTZ/tMYk8Zh5syZwoxGFZWpyrApH3/8saisS8tpjB566CEhFNTUmJGDPKUjoHG4++67az0vNdX6JMzQ8ZJATueArgEqINpQaJs0ZsT48eOV/dTU4hnMD3R90TFT4kdy0Cdzs+m5pOSQhr7Q9fLbb7+JZdRvuo4IqkFmej3S9sgnzxTal+l1T+vT+Zs+fbq4DkjgpvEy5XK/WbouKfiAltO9gOqhXQ7DcZM5nX7j9F06fzTu9Punew0Jcqa/fYJSSFDZElqfft9UsbxmGglzXLP1UVIh3+ccdcAL2Tl4JjsXvYIHo7UwRPIQPhpZQCEOsBal5ZFskPz8fMo1LD5rUlpaKh0/flx81mR+7Hxp/NLxl/17ZP0jl3yX5jXku7SPpnDHHXdI1157ba3LkpOTJRcXF+mhhx6STpw4Ia1YsULy8/OTXn31VWWdMWPGSG5ubtIzzzwjnTx5Uvzl5uZK/v7+0gsvvCC+d/DgQenKK6+Uxo0bp3zv2Weflby9vaX58+dLZ8+elbZt2yZ99913yvI333xT2rFjh3Tu3Dnpr7/+kgIDA6X33ntPWR4dHS3Nnj1bbP/06dPS0qVLpcOHD0vl5eXSp59+Knl4eEhpaWnir7CwsNbjW758uWRvby99+eWX0qlTp6SPPvpI0mg00saNG8XyjIwMafLkydJNN90ktpOXl3fJNmjbtJzWM+yP+kD9pmule/fu0qpVq8T2b7jhBikiIkKqrKwU36XjdnV1lT755BNxDHS8/fr1k+688846zxeNfZ8+fZTpjz/+WBzrL7/8IsaexpWOibbX0LH89ddfJUdHR+n7778X23jppZckd3f3avtZuHChFBwcLP3+++9SQkKC+PTx8RHnzzAOdM5nzZolxcbGSitXrpSioqLEGBw6dKjO46HxoH3NnTtXjNHnn38uzsHatWuVdWh86JxQ/zds2CB169ZNevDBB5Xl8+bNE8c8fPhwcZx0DPQbre+8GPpUUVEhvfLKK9K+ffvEcdFx0jVPY9KQ3whtk/pN26QxMeyH+uTp6VntvNHv5Prrr5eOHTsmbd26VQoKCpJefPFFZZ233npLXC///POPFB8fL7ZB52Xz5s1SVVWV2D7th/Znej3SGNIYmULnzvR3St8LCwuTFi9eLJ05c0b6z3/+I/qTnZ0tljfkN0tj3qFDB2n9+vXS0aNHpWnTpolz99hjj9V5fg3HTdd+XFycuP4cHBykSZMmSY8++qg4Vz/++KPo3+7du8V3tFqt1LdvX2nkyJHS/v37xfwBAwaIe405r9ma10JNjqfmS4fPXZQO7twolX4QLUmvekhSYYbUWuxJyJYinlsl/h7+5VspZn6M+HttyeRW60Nbor7nd03alYDy5aEvlYurvr9Zf8+65Ls0ryHfpX00Bbr50gOBHpSGP7qZEHTzpIeBTqczHsuXX4obDt1ECLpp0EPVFHogTpw4sdq8pKQk5eZaUFAgbi6mAsnl+OCDD8RNygDdjAw3mprUfDjUBT3Q7rvvvmrzbrzxRumqq65SpunBRGNUH7U9wAw3P7qBGqAbNM2jBwBxzz33SHPmzKn2PRLU1Gp1rddRbQJKSEiI9Pbbb1dbZ9CgQUKobOhYDhs27JL1hwwZUm0/nTp1Eg+3mueZvkt88803kq+vb7V+f/311w0SUEiIMOXmm2+WpkyZUud3li1bJvZler5pPySgNvS81Nenhx9+WJoxY0a92zGFHu60zU2bNlXrU00BhQQfuvYNkFBP40yUlZWJ5Tt37qy2bbpGZs6cKdq0fdoP7c+UhgooL7/8sjJdVFQk5q1Zs6ZBv1kSQEmwoBcBAyTcODs7X1ZAqXncJJx07NhRuYcQdJ8hIZUg4ZTuSRcuXLjkt7N3716zXbP1XQvllVrpSFKuEFCO7VwvCyhvBUmSyb2wpSksq1QElBmfr5L6zosW9/qrf+jVan1orwKK+QzANoCrvSsCXC7vv+Dj6FPrvIZ8l/bRVEh1TKYOZVuu8rYoWooS3JF62DRpHqnXk5OThcqWIPOKKWR+2bRpU60RUuTTQU5+VIiRwr3r4tdff8Xnn38u1qf9kVnJNJPvk08+iXvvvVeozKnoIxWSJFNNY6Djo7B0U+j4GqPivxy9e/dW2qQWJ8hfgdTNNE5Hjx4V4e8G6FlCqn4qgEmq6fqgZISpqamizzWPgbbd0LGkcXjggQeqbYPOO51Dg2qfvktqdNPoOdoOmSoM26BjNQ3rNyRJvBw116NpU5MFReSR6ePkyZPimGm/pM4vKSkR5gqCTHSmY90YyKREJggyK5SWlgo/qpaIlCJTjMEcZLgeDNmtySmdjocixkyhvpDZzByYjg/9xukaMOz/cr9Zw7iYJrWkqEgyiTX2uMksQyZkKtRqOs/QF7qWyJRnWvuMTHdkMqNllEbCHNdsfZTqzTuEGnofFO+OZCtDa+HmaIcoP1ckZBXj6EU1Brk54Kg+oqi0JAfOLpc+Lxjz0K4ElDui7xB/TeGLCV+gpaGbFfmCNOf7ptBD8Oqrr8Z77713ybp0Uya/jPogPw3KcfP6668Lnxa6oSxZsqSazZts27NmzcLff/+NNWvW4NVXXxXrkI3dmrC3NyZ1Mgh6BgdOGieK/iC/k5oYhL/m0pCxvBwGfw+qIF4z67LBV6mlIN8LSjFABULJT4EeiuS3Qw8eemAaBBTy2TAVpBsKjcXTTz8txoMecPQg/eCDD+qscG6ua4Gg/ppeCwRdz+SHYwr51NQHPehlJYmRmj5GDdl/fb/Z5kT11bbf+vpiDpp7zRocZO1QJdLMn7a3R4RnGC4v2piX6FBPIaBQwcJ7Yl5C78ho+PmZ1OdhWoR2JaDYKvQGT06idPMz3PwpAy/dxMkpsy6osCN9j96caouWIAc9eqBQaQLSgtRk586doozBSy+9pMw7f/78JeuR4yf9UaZhcrKcN2+eEFDobZqc8BpyfHQ8d9xhFB5pumbdp8vR0P3VNk7Hjx9vsnBIb8AUNUJ9HjNmjDKfpsmxuaFjSeNAD2RygDRAmZtN325pPyRYmiZHrLkN0maRZsOgRTHdRn3UXI+mDdojKk1BDy4SIAxv3EuXLjXbeaGxGj58uHDcNEBv3q2NwbmXtDim57Lm8RA1j8nf319E3hkgLRNp4BrD5X6zpJ0koYKuE4PwnJubK0Kp6+pvU6FzT+Ha9GfQotDvhDSvht+mOa7Z+ijWCygadQVyNWq8F+CPCZoyPIfWJSbEAyuPpIp2tmow/PyMWiWm5eAoHhuAbtp0k6AMvaRep4gI0lSQecVUPVsTKkFARRhJaNi3b5+44f/7778iuoZurvQAo8gSyv5LETK0nG4uP/zwgyLA0I2a3m5pGZknKJOwAVI3U+QQRUvQw5YeMrQfw0ONbrL0BkUCEEUhkOq8Np555hkRbUHmrTNnzohokeXLl4s36sZA+yNTDdWLov3V9vZaGzQGJEDQsVBUCfWBxpimGwodA731khmH9k9RVrStxx57rEFjSdC6ZOIgAY8eOHSO4+Liqq1DGhgys9D3aR1Kmkjr05gRpM0iIZbU6fQwWb16NT788MMGHQOdP4rooO2SuWXZsmVK/0l4o/H84osvxMOGhKCGVjFvyHmh8aHoL7o+af8U4UTXUmtDQj9ddyRsU/QRnauDBw+K46ZpggRNGuNVq1YhMzNT0RJQ9BCNy7Zt28R5IYG7sZqty/1myfRDWiu63qiGGUX8UBRRffeBpkImW4raIsGCxoCiw0gQIUGIosPMdc3WhU6S9BlkAXu10dQT6l5ds9XaGWXjUgtaff/tFqkdOclaM5dzAKQIAnK6JAc5ijp47rnnlCgUg5NsbU5yFEUyffp0ycvLSzjSUXTC448/rjjckoMcRS2Qgx9FYFB0wDvvvFPNgZAcIckhl5wmyQnQ4HRIURK33HKLFB4eLvpFjqKPPPJItbF/4IEHxPfpfJk6C9bkq6++EtEm1IeuXbtKP/30U7XlDXGSpWgfinigvhqcJWtzwKvNmZKc/gzfJQfl3r17X+L0Wp+TLI3ja6+9JoWGhopjoGUGx8eGjKUB2idFaNE6dLwUDWS6H2LRokUiuoLGnCKwRo8eLSKhDOzatUt8h5bTeoaok8s5yb7++uvCOZmcKeka++yzz6qtQ5FKFI1B1xE5WNI5MnUWrcspuiHnhZxTKWqKvk/XKkWqPP/889WO3VxOsjXHk84DHb8B+m1QBBo5jNK5pKgaOt4tW7Yo67zxxhtijFQqlXJd0v2IzitFc9FvgpzHa3OSpSg8U6h/1M+G/mbJUZYi5+g8USTY+++/X+fvv77jrm08a27n/Pnz0jXXXCN+E+QQT9dHenq6Wa/Zupxki8srhYMs/SWknJDW710vXbn4SmnTzg+k1ia3uNzoKPvVjlbff3t1klXRP9gYpDolGz7l2DB1MiRItU1qVcphYOooyDBM/VoOyqFjLenSGSarqBypeaViINw0qTiXnIb3zr6HLwc9iS6dJ7f6AI18byOSc0vh4qDBspllWHHkG+wvScF/us/G+OHP8gkzw/O7JmziYRiGYayOknKjj48WRsfd0KD+FumPIWEbOe6eTDmOX8uTEa+RsC9lu0X60x5gAYVhGIaxOgwZZNUqFaogK/o9dRJc3CxT6iJGn/KekFwnQKU3PhwoumCR/rQHOIqHYRgRRsww1kKlVocKraw1cbFXoVivTAlUWe6RRaHGBk7luKOLpMFplQ6nVFUoLEiBu0frO++2dViDwjAMw1gVhvwnhLOdsfBpoN2lCewsUZMnNqUAA13lUGOdSoVDJ5ZZrF9tmTYroNig7y/DMAxjYt4h7NWVIAuPBAmBTt4WGx9/d0cEeciBF7Gp+egfPFRZtj95q8X61ZZpcwKKITNiXTk3GIZhGNvRoNipJagqJFTpquDnbDkBxdQPpbCsCiEhVyvzDxQ0LiEf0059UCgxEtWKMNSToBTcTUm9zTAMw1hG+11cUio+7dUqaCsdoSm1R3RwV/j1kjMzW4roEE+sPyE/Wy4UByBKq0KCRsJxVSVKijIs5sDbVmlzAgoRFBQkPg1CCsMwDGMbUL2bjMJy0Xa210Dl5iBeOum+bumXTdOMsmTmGeASgoTyFFSpVDh88jcMH2gs1cA0nzYpoNBFTIW1AgICGpzunGEYhrE8fx5OweebkkT7wbGdMaxrZIsXw2xKqHFsSj5mdh6CZYnLxfT+C5tZQDEzbVJAMUAXtbVc2AzDMMzl2XO+ECmFsg9KdLivVd3DyUnW19UB2cUVoibPoGk34P5D8zCwrAx9tMGW7l6bo805yTIMwzC2y6GkPPHpoFEjzDUfd84fiJcWT8DKTcZK4JbUzhvyoeQUV6DSsTMeUflgaFk5nFMOAZVyan7GPLCAwjAMw1gF9NA/l1Us2tGhHsjIPIgDqnL8VZmB/el7YQ3EhFQ386DjCHlCWwEk77dcx9ogLKAwDMMwVsHhpFyl3b+DN1KyTynTYa7WYUKp7ihbAESMNC48v8MynWqjsIDCMAzDWAWHLsjmHaJfBy8kewYq06GdJsIaMM0oG5eSD6nDMMTb22Gpuxu+Of+3RfvW1mABhWEYhrEKDl4walD6kQalMEWZDgvoDWsg3McZ7k52SqixyqcjHgwOxpt+Pvhel4vKctlExTQfFlAYhmEYi6PVSTiSlC/agR6OCPF0QkqRUUAJdbOOYnzkKGvQolwsKEdGYRkGOMoJ2srUKsSeWmHhHrYdWEBhGIZhLM7ZjCIUlcs1ePqFewtBILkoWUw72znDx8kH1oJpPhQKNx4YbMxwe6AszUK9anuwgMIwDMNYlXmnf4QXdNoqpBZcENOhDl4WzyJbl6Ms+aEMHPigMr0/P95CvWp7sIDCMAzDWJxDNfxPMjJjUUlljMn/pLQI1gTV5DEQm1KADu4d4O/sL6YPZRwShQ2Z5sMCCsMwDGNxDuojeOzUKvQK9URy+mFlWZiTL6yJSD9XuDhojI6yKhUGBA4Q0yVVJTiZc9LCPWwbsIDCMAzDWJT80krhg0L0DPGAk70GgeXFeDQnD9MLizDAu7tVnSGNWoVofcK25NxS5JVUYGDgQGX5/jN/WbB3bQcWUBiGYRiLckSf3p7oF+4lPsNL8jEnvwBvZOXgig7jYW2YmnmEo6xbB2V6/5mVFupV24IFFMYm2brnEzywYAj+3vyKpbvCMIxZHWS95UbueeMKXhFWN8bVMsqm5CMqZAi8dbLPzEFtIbRVFRbsXdugTVczZtoub8X9gDSNCjvOr8DRrRV4euSbsFfbW7pbDMM0N4NsuF5AyTMRULytUUAxhhofS8mHSq3GeKcQ5FYVY4B/NKqqSqGxc7BoH20dFlAYmyM/L1EIJwYWn/sbZ0sz8eGYD+HtpL+5MQxjE+h0khLB4+vqIDK1EucKEhGkUsHZxQ9wcIW10dnfDY52apRX6YSJh3ht5lpLd6tNwSYexuZwyUvGwtR09C0rV+btTd+LmX/PxKkcY3ExhmGsn4SsYhSU6RO0dZATtJWX5eMab3sM7hiOB/2MphRrwk6jRvdgWYtCFZgLyyot3aU2BwsojM1hnx2PPuUV+DntIn7uMAO++hBESot926pbsHbbW816m6uo0pmxtwzDNDz/iewgm5p2UJnnbu9mtQMYo4/kIY7rtSiM+WABhbE9sk4rzb4dxmDJtCWI9o0W06VSFZ5K+BVfrpjZqE1WaXX4aVciBr29HkPnbhBvRAzDtF7+E1MBJTnzmDIvzMVY0diqHWVNBJSK8kIcPPITsjJPWKhnbQMWUBjbI9PEjOPfDUGuQZg/eT6mOQQps13sXRq8uW1nMnHV59vwyp9xyC6uQE5xBVYclGuAMAzTOhoUtQroEyYLKCk5Z5TlYR7G8F1rw1A00JDynvhn6+sYvngY7jj8ATYc/NqCvbN9WEBhbI5lBaewzdkJac4egHuwmOdk54R3bv4XT/sNwzX2Abjzqu8uux3Skty7YB9u+2EvTl+snkqbvPIZhmlZqDjg6YuFot0tyAOujnLcRnJhkrJOqE9Xqz0NXYPcROZbQ0ZZItw/BuX6efszjdlwmcbDUTyMTVFakoM3XSRIrgHopbPDYpMCYhTmd8fUbyHpdKJtSnraIQQF91OyVv534xnM35mISq2ct4DoE+6FhMwiFJZVCQFFkiSrKlDGMG2No0l50KcOQX+9eYdIKctS2mGBfWGtONpp0DXQHcfTCkQm3NIKLbp1ngrX3a+iWK3C/oqcWu9HTMPgUWNsisSkbZD0QkOkY+3l12veDHbu+xJT/r0N81bdi4W7zmH8h5vx3bZzinAS6OGIT27ugxUPDkdffRbLrKIKpBeUtfjxMEx75pBpBtkOxhQByVWyVkUjSQgM6A1rxpAPhQStE+kFsLN3Ql+1HBadpVHhQtIOC/fQdmEBhbEpEtIOKO0o98vbptPTD+OZ2K9RpVLh4+w9WLP/FhSVyDZvymHwn/GdsenpsZjeLwxqtaqa09uxZDbzMExLcvD8pRE8kCSkQCuaQTqVeOBbM6b3DIMfykCfHsq8/af/sEi/2gIsoDA2RUKu0UE2yk+O3KmPMk0UBlZ0VKaPepSgW8c3MaNHETY8NQZPTuwGFwejpZOqqJqmr2YYpmUgE6pBg+LpbI8oP1nrkJ9/AYV6H44wjXULJzVr8sSmyJE8Azteqczbn2EMmWZaUEB57bXXhE3e9K97d2OVybFjx16y/IEHHqi2jQsXLmDq1KlwcXFBQEAAnnnmGVRVyUl6GOZynCtKUdpU+6I+57v3/jmJiZ/uwJ/nHkS3lCFw1sn5Tc45AQe0byIrecUl3zMVUNhRlmFajgs5JSJizqA9Mfh7paQfUtYJc7D+zNA9gt1FBJKpo2x01+vgpHeuOVCeacnutS8n2ejoaKxfv964Abvqm7jvvvvwxhtvKNMkiBjQarVCOAkKCsLOnTuRlpaG22+/Hfb29njnnXeafhRMuyGhsgDQAPaShNCQQbUmWvvtYDI++PcUMguNmWbPS7fgoeDRWJL6AVI1QLZGhbsOzMX/pR/C9Vd+qKwX5u0MLxd75JVU4lhKATvKMkxrFAg08T/p0fUabA/qi+S0A3C2gdIVpIHt5O+GMxlFIiKpvEoLR0dX9FE7Yw/KRFmOlJS9CA0dbOmutn0BhQQSEjDqggSSupavXbsWx48fFwJOYGAg+vbtizfffBPPPfec0M44OHBhJaZuKitLcF5NWhAVOkqaS2zT+xJz8PrKOEXNSjho1LhnVCQeHtcZbo52uC5nBJ7+62bsVZULv5RXU//FyV9P4Jnrl8He3kW8xZEWZduZLGQVleNiQTmCPK1fzcwwNl0g0CSCh5zcPb06ij9bgfxQSEAhx/szF4vE9ADPrtiTf1Qs339qOQsoreGDcubMGYSEhCAqKgq33nqrMNmYsmjRIvj5+SEmJgYvvPACSkpKlGW7du1Cr169hHBiYNKkSSgoKEBcXFyd+ywvLxfrmP4x7Y+k5N1CqCCi7I2mmOTcEjy8+CBu/N+uasLJ5OggrH9yDJ6b3F0IJ4S3Tyf879atuNXZePP7syQRaYumA8VyaGM1R1n2Q2GYFtWg0E+aQvxtmWoZZQ2Osh0niM+wykrosoyJ55gW0qAMGTIE8+fPR7du3YR55vXXX8eoUaMQGxsLd3d3zJo1CxEREUKAOXr0qNCMnDp1CsuXLxffT09PryacEIZpWlYXc+fOFfti2jfnUvcq7SjXUJRUVOHrzfH4dmuCqChqoHuQO165uieGd/KrdTukKXn+ppXotv4ZvJW8BnMzs9GhJBn4dhwwczF6hfpVE1Cu7Gm9qbYZxhahfCEn0uRQ4i4BbvBwsoctY1qTx+CH0rf7jVi3+gUEVZQB3qyFbXEBZcqUKUq7d+/eQmAhgWTp0qW45557MGfOHGU5aUqCg4MxYcIExMfHo1OnTmgqpIl58sknlWnSoISHhzd5e4xtolKp0V2nxjlo4WofifEfbqmWq4RKtT81sRtuHhQOjcFrrR6mX/EBRp69Ev5/PEK3TCD/AvDDRAya8AmVKBPrcCQPw5gfEvy1eifSfuHV/Uy+WjETrg5uiPTridFDnrCJ4e9pKqDotbj2Tu4ICuoPXNgJ5J4DClIBjxAL9rKdhRl7eXmha9euOHv2bK3LSYAhDMvJN+XixYvV1jFM1+fX4ujoCA8Pj2p/TPtj/PBnseyuI9hz+yF8f2yUIpzYa1S4b1QkNj0zFrOGdGiQcGLAv/NEYM5mIKS/mJYqS/D1vucxwFNOrnQ0Wc4oyzBMyzjImvqfaKsq8H3+MXyYtRufHp9vM0Pu7mSPSH2Y9Im0AlF8VNBxhHGlRE7Y1qoCSlFRkdCOkKakNg4flusQGJYPGzYMx44dQ0ZGhrLOunXrhMDRs2fP5nSFaUecyy5HUr58A+gW6I61T4zBS1N7Nl1NTG81d61BUa8b8Ku7G5Z5uMPPW76ZGBxlGYYxf4FAon+EUYOSmRmHSr2fWahGfuDbCtF6LQqZm+Mz9dXQI4wCSmXiNkt1rX0IKE8//TS2bNmCxMREESY8ffp0aDQazJw5UwgqFJFz4MABsfyvv/4SIcSjR48W5iBi4sSJQhC57bbbcOTIEfz77794+eWX8fDDDwstCcM0hN0J2Up7ev9Q5c2lWdg7QT3lPbzvK98sz9sb98GOsgxjPkgjeVAfwePuaIfO/m7KsmSd0WQbRuYRG6I253opbBA+8vHBjSFBuDFrswV71w4ElOTkZCGMkJPsTTfdBF9fX+zevRv+/v4iRJjCh0kIoeRtTz31FGbMmIGVK1cq3ydhZtWqVeKTtCmzZ88WQoxp3hSGuRy7TASUYVG+ZhswFxc/9IHszJZip4KLfZpos4DCMOYjJa9UyVHUt4OXKDFhILnU6AIQFj7MpoY9plpGWVlAUTm64aC7N046OiBeIyEr66QFe9jGnWSXLFlS5zJyWiXtyuUgp9rVq1c3ZrcMg537v8I7R79GpJ078tIHUoC6CB02qFXNxdDuM7D/1GLRrnRNAvKC2VGWYVoq/0mN8OIUk0zRYW5hNjXupveiOH0kDzHQIxJHi06L9sHTf2GinzH7OlM/XIuHsQnOZhzGeQ2wWSqETpJTRw+O9IGdxryX8NBOVyltJ48E8ckaFIZpKQfZ6hE8yYXJSjvULdSmht3b1QGhXs6iHZdaILJaEwO6TVfW2a/hsi6NgQUUxiZIKDXWs8grixSfQ6N8zL6faN9ouNnLNnGNC0Wf6YQ6+qJJODPDMObRoPStqUHJkDOvEiFutheSGxMqa1FKKrQ4ly07yvbveh3UKvlRu//ifov2z9ZgAYWxCc65GNWn8eWy0/WwqNoTsTUHO7UdBgaRCQnQqorg4CirnI8lc2VjhmkuVKfmeKqcJ4SqF5PWwZSU/PPi01crwcXeWMfNlv1Q3Bzc0N1HNuucyT2D/HK+lzQUFlAYmyAhXza3oMoDFTp3uDvZVUuOZE6Gukcp7cHusr8Um3kYpvlQErMKfY6QmuadstJcZGj0IcYq26zLZhrJQ2YeAwMCByjtAxcPtHq/bBUWUBirJ6csB3nlslq4qtxffA6J9GlUQrbGMMxP1tAQFS5J4pMFFIYxb/4T0wRtRFlOPCYXFaNXWTl6OFh/FePaiNabeGpmoR7o20tpHzj8Q6v3q91UM2aY1iY+L15p68oDxOdQM4YX1yQyYhwCtBIKVYC9+InoWEBhmBasYEx4Fefgg0x9CoFRt9nkeAe4OyHA3REZheVCQKGcL1QhfYBPD6gkCZJKhf3ZdRfGZarDGhTG6jl34nel7VPh0OICCpV7/2H8l9gxaw/g8IX4mbCjLMOYT4Pi4qARWaCrkZtobHtH2OxwG8w8BWVVSM4tFW1Pr47ootOI9mlVFUpK5MrpTP2wgMJYPQmZxjcOvwoHeDrbo2dwy9Zj6thxDOwdXatnh2RHWYZpMun5ZUjNl6Pheod5XpoiIE92kBV4d2wblY1NzDyP9LgNX/W4D1uv/1ckhWQuDwsojNWTYJJdMq2sm8h/Ypp9siXpVUv6aoZhmll/p4aDLCHlnDNOeNmuBiXa5J4Ra5KwbdywpzFq8H/g7mFb+V0sCfugMFbPf/KKcGVVHs7aOeMbbYhZ09s3RECxQ/klb0MMwzSOQ0mm/ieXCiizSo8jLywYEVVafO0egtZ5BWnZlxqKWmKaDgsojHVTXoiY3BTEkPe7LgTfQN2i/iem7Dv8IxYc/Q6+3QoQljoKx1JuaJX9MkxbZPuZrDodZCWdDomqKhTZ2wNqe6jsmliZ3AoI9nSCj6sDcoorqjnKMo2HTTyMdZMl17AgzupC4eVij+5BNZzrWojislxskYpQolbDxe248MzP4IyyDNOkAoHH0woU/xM/t+rV6wsKLqBIb7YN08jp4m0VEkYMdXmyiytwsUDWwBLJybvx27on8dovExEfv86CvbQNWEBhrJtMEwFFChH5T1rL/2Rg9CxoJLmeRpZLjvhkPxSGaTwbThj9yK7oEXjJ8uQ0Y/KyUIfq2hVbxNS53tQ0vOnoPLyeug6/V6ThQPzfFuqd7cACCmPVHE7Zid1OjsjQaHBaal3/Ezf3YMRIclhzsoMK/nbnWUBhmCaw7vhlBJSs40o7zAZr8NSb8t7EUbZX+Gjj/OzYVu+XrcECCmPV/Jh9EPcFB2JCh1CcsXPHsE6tG5431Kur0u7ktoNDjRmmkRSWVWJ3gpyALcTTCT2CLzXRJuclGAUUT7kYqC1jKBpY01G2W6cpilY2tsxYAJWpHRZQGKsmoUr+cTvqJJTZdUOXALnScGsxNHKy0rZzjWcNCsM0km1nslCplR/KV/QMrNVhNKU4TWmH+va0+THu4OMi6oXVNPE4u/igiyQnbItXaTlh22VgAYWxWirLi5Gslm9swZUqDO0U0Gr+Jwb69LgRzjq5D8kuhcgoLGVHWYZpBOsvY94Rv61y2ceLCAvub/Pja+oom15QJjJRG4h2ksdAp1LhxBn2Q6kPFlAYq+V88g5o9W9bHhXOrRZebAplkx2glrU22XZqRDjGsRaFYRpIlVaHjacyRNvN0Q5DonxqXS9FK6eEd9FJ8PKyfRNPTT+UOFM/FD9j4cDY5O2t3i9bggUUxmpJSN2rtDXlPq3qIGvKUJMbSrjbXhZQGKaBHDifi7ySStEe09UfjnayecMUbVUFUvWa0jBoRC2stoBpJE9cqtEPJabjeKV9LPdUq/fLluBEbYzVEp99wjghdUDnVvY/MTC0y7VA1m7RdnC6wBllGaaBrDcNL+4pVyKvje/6Po2UnJOw11TPj9J2HGWNGpROHSfAabuEMrUKsRXG9P/MpbCAwlgtp/IvKO1A334Wy8bYpdNkvPLvkxicnw3vSntMLjDayxmGqR3KoGoIL9aoVRjXrXYBRWPngIF978TANjaQkX5ucLbXoLRSWy3U2M7eCT1VjjiICqRogJycePj4dLJoX62VtqFLY9okiVXyj5rC8np1HWuxfqg1drgxeCQiqqrgoSpFUBFllZWrsjIMUzvxmcVIzC4R7YER3vBykXMKtRdIKOupd5RNyilFvt7URYxwi8S44hL8JycPqvSjFuyldcMCCmOVkF06Wa0T7aBKYHiXcMt2KMooII1Qx7KZh2GamT22PRCjF1BqOsrO6XYLPs/Iwn35BfDOYD+UumABhbFKsnMTEVAF2EkSfCud0Mnf1XoEFE0cjiZzZWOGabj/Sd0Cyu4D32D73i+QmLgFOm1VmxrUaNOU9yYCCkJNQqlTDrZyr2wH9kFhrJKUUn8cj38PGlQgINrR8tVAvTrggE9HbJdysccpF+5J5wEYs8wyDGMku6hcRPAQ9HIR6Vf3C8aXx77BYVUlcALYe+MWkcysrVAt5b1JRll4RwLO3kBpLpBygBx2KHmKZTppxbAGhbFKdulTY2vhgEFdY2AN/B0QjO+9PHHMyQHF2ZxgiWHqYtOpTOjzG9arPSFSdBXi01cntSnhhOgS6AYHO/WlGhQSRkL6g7xSTlTloyDLJGKRUWABhbFKdicYI2WG1pHcqbUZFjZKaavtjrCjLMM0IHvslfX4n5RVliJTI2sOwpxat85Wa2CvUaNHkFx76FxWMYrKjSasZZ6eGBYRhptCg7Hr5O8W7KX1wgIKY3VUanXYnygLKIEejvWqh1uTwTG3QqV/KzzsrGZHWYaphbJKLbaekQvh+bg6oF8H7zrHKdW0Bk/okDY5ngY/FLLinEgzmnkCAnqhXJ+ULvYi+6HUBgsojNVx5EIuuoY+i2Hhr2K033eW9z/R4+nVEcEuXURbcsrA3vPGPC0MwxjNsyUVWtEe3z1AhNvWRXJRstIOcw9rk0NY3Q/FaOaJ7nyVcX4lJ2yrDRZQGKtj94n9iHeSEOtWjkxNPKyJYSFDlfaedGMqfoZhagsvrjt7LJFcaCKguLVRAaVaRlmjBsXPrzuCnf1FO05XAq1OFuoYIyygMFbH8Qt7lHaUi3XlT5gYafRDSSw+bNG+MIw1Zo9df1wuDuigUWNUF/kBXBfJWXFKO9QtFG2RroHusNNrkUxzoRAxAX3FZ2lVKc7ln7NI/6wZFlAYq6KiSofNhUZHsu7dr4Y10T+wH1SSHJ3v5HAQGflyFVaGYeSieOkFcpbl4Z194epYfyaLlMTNSjusjWa9cLLXoEug7Ch7JqNI+OgYiPEzRigeyzpmkf5ZMyygMFbF0eQ8aO3SlemoYOuq0OFk54RelfLPpsi+ArtiN1q6SwxjNRhq7zQ0e2xKVZH4pISMgf7WkU6gJTPKanUSTqYXKvN7mVRKj82KtUjfrBkWUBirYld8NtSOcgQAEeUVBWsj2sWYoO1I/HKL9oVhrDV77ITL+J9QWItTVQWcdToE6VSiaGBbJcYko+wxE0fZnj49YHAhjj31hwV6Zt20TZ0aY9MRAGoH+SbnYe8JHyfryIFiyqgeN+HEtmSUFndHatB0S3eHYayC1LxSYeIheoV6ItjTuf4vlOZiYUoqKHK/KHI02jKmjrJxJgKKq4MborQqxGsknEY5ysvy4ehkFGbaO6xBYayG8iotTibFQ20vq0A7O1tn4qYRfa/D4ZyXsb/gOhxOd7R0dxjGKthwUnaObXBxwNxE8UEaBHdK/d6G6RHsoWSyr5ZRljSyTrIjsY8OSL/IlY1NYQ0KYzUcvpCHQE0cDIGHUeXW6YCqVqsQHeohst2SQ2BmYTn83VlQYdo3ptljr+h5GfMOkUf1rPR4d0RbxsXBDp383XA2owin0gtFMIAhBf5DY+biMTsXBAS2XR+cpsIaFMZqoAe+l5Mx1C7Kw3pvWqTCri35EsO0RyiFO/mPESGeTugZbDRpXE6DIvCOQFvH4ChbqZVw+qLRUTY0dDALJ3XAAgpjNexKyEKfskq8mJWDWwoK0S94iFU7vflpkjHEcwk27fmPpbvDMBZl2+lMVGh1oj2hR2CDsj9/k7oZjwb44T0fL2S7Wp+vWUs6ytbMh8LUDpt4GKuAcgMcvJCHe1VZuKJQDj1E5JWwZg2KZ+TnOG6vxllJwouluXCi8ukM0w5ZZ5o99jLViw0cKE3DLlcX0Z7j2QFtnehqKe8LcPMgi3bHJmANCmMVHLqQJ+yynVWp8gx7V8DTelNfd/R1RWiZrLKtUKlw+PhSS3eJYSxClVaHTXoHWVcHTYOrj6doZR8zV50EL6+27SRLkN+agd0J2SLrroFjccswd+k03DqvP/Yd+sFCPbQ+WEBhrAL6wTqiAuEqfSSAXxcobu9W6ijrrTImWdoev9ai/WEYS0Gaz9ySStEe080fjnaay35HW1WBVLX8gA6FBip9Vd+2jIeTPQZEeCsZZQ+cNxYIPHfxEBaXnsdRdSWOJG21YC+ti7Z/VTA2k/8kwi4BB50dkK1WQ/LrBmsnKPg6pb230LqKGjKMZYoDNsy8k5FxDFX6F5AwjRvaC7cOMZqyFu0xVkOPiZygtGPzz7Z6v6wVFlAYq/A/oRBjL9djuDs4EGMjwvCTg/xGZs30jOqDDuXyW+BJTRXy8403HIZpb/4nVA9vXLcGhBdTkcCMI0o71ErzHbUEV/UKhpeLvWj/fSwNOcUVot2xwxhh6iKOVbIDrQEWUBiLc/B8rogAcHLU+5/QW5Vvd1g75CjrWyLfXCWVCvuOLbJ0lximVYnPLEJCZrFoD4zwgbdrw9LVJ2efbPNVjOsqHHjjANm3jnzufjuQJNpqjR2iVU6inaFRIeMi1+UR42LBc8UwinmHqHIw2mSjQqw3xNjUUba8zJhcaXcy246ZdmzeaUhyNj0pBUZtY7hPF7QnZg0x5nxZvOcCdHrNSYy7cX5s/GqL9M2mBZTXXntNxLeb/nXvbnzTLSsrw8MPPwxfX1+4ublhxowZuHjReAETFy5cwNSpU+Hi4oKAgAA888wzqKqqMt8RMTbpIEvkOZQqlU3DQ4fC2iFHWTvPqaK/xK4SQw5chmkfrD/eyPT2epJLjM+FUL/2lUE10s8VIzvLmtfE7BLsiM8S7V6Bxsrtsen7LdY/m9agREdHIy0tTfnbvn27suyJJ57AypUrsWzZMmzZsgWpqam4/vrrleVarVYIJxUVFdi5cycWLFiA+fPn45VXXjHfETE2RWmFFoeT8qBBBdL12uEInRp29rK609rpER6ByDI5auGCBkhL5RsL0z4g/4n953NEO8rfFVH+DXd2TTHxswgJHoD2RjVn2d2yNimm02RlXmyhSZbddkyjE7XZ2dkhKCjokvn5+fn44YcfsHjxYowfP17MmzdvHnr06IHdu3dj6NChWLt2LY4fP47169cjMDAQffv2xZtvvonnnntOaGccHNpuuW2mdijUjlI/hzucRZ7eqz/KvgFpsq2EXmGeOHsyBANxFkPLymCftB8IMb4JMUxbhXKf6K0TuLIR2hPipogp6Jt9HHkVhXB2aftZZGtCyewC3B2RUVgunIwvFpQhMLAP/LQSsjQqxOpKIOl07SL8uj4affRnzpxBSEgIoqKicOuttwqTDXHgwAFUVlbiiiuuUNYl80+HDh2wa9cuMU2fvXr1EsKJgUmTJqGgoABxcXF17rO8vFysY/rHtJ309oS/kzG0LsqGnOYofXVh9pWYl56B+/MK4JfMGhSmfbDhZOOzxxq4etzbeOqGFXhz1nq0R+w1atwyKFy0tToJS/YmCWEkxk5+OStUq3AhaQfaO40SUIYMGSJMMv/88w++/vprnDt3DqNGjUJhYSHS09OFBsTLy6vad0gYoWUEfZoKJ4blhmV1MXfuXHh6eip/4eHyiWXaRoFAwtXB6L8R5WP9ETwGIn1dEe/QDUWS3iSVsBkwyRDJMG2R8iottpzKFG1vF3v078BlHhrLzYM7iNBsYsm+CyIj7xTfvpiTm4/PL2bCP8ek2nM7pVECypQpU3DjjTeid+/eQvOxevVq5OXlYenSlk3z/cILLwgTkuEvKUkOzWJsm+LyKhxJyhNtjYssqBBRQbZjkyZH2W4hPtit6yHPKM4EMo5bulsM0+IvFsUVWtEe3z0QGsOTlmkwoV7OGN9djnxKyy/DplOZuKrb9Xg0Lx/jSkrhks6hxs0ycJG2pGvXrjh79qzwSyHnVxJYTKEoHoPPCn3WjOoxTNfm12LA0dERHh4e1f6YtuF/UqU3YmvsJWgkCSpJQscOo2BLUD6UHboYUC3XUw72iDu+zNJdYpgWZf1x0+yxDQ8vJrIyT+DixaPQaTl689ahxtDihbvPAyH9jQOVcgDtnWYJKEVFRYiPj0dwcDAGDBgAe3t7bNiwQVl+6tQp4aMybNgwMU2fx44dQ0aGMTRt3bp1QuDo2bNnc7rC2HD+E+K6wcuxd+Yu/Dn2vzZXFZgcZbeoIzGuQyhuCA3Gl0n/WLpLDNNiUJG79fr8Jw4aNUZ19W/U93/c/Dyu+OdWDPq5L47E/Yr2zOgu/gjzdhbtrWcycaHUCfCJkhemHwW01p9R22oElKefflqEDycmJoow4enTp0Oj0WDmzJnCN+See+7Bk08+iU2bNgmn2bvuuksIJRTBQ0ycOFEIIrfddhuOHDmCf//9Fy+//LLInUJaEqZ95j8hhkT5wMHRHZEdx8LWIEfZhMqulE5WTO/XFqGyXM6uyTBtjbjUAmGSIIZ18oWbY+OCQVPKspQq4P7endCeIdPYzMFyyDG5ri3eewG64H5IsLfDX04apJ5v38kfGyWgJCcnC2GkW7duuOmmm0RCNgoh9veXJehPPvkE06ZNEwnaRo8eLcw2y5cvV75PwsyqVavEJwkus2fPxu2334433njD/EfGWDVF5VU4miznQugc4IYAd9vIe1KXo6ybowPCSuU8EKVqFY6m7rR0tximRTBoT5oSvUMk28kCjZ0EBAb0RnvnpoHhsNfILzfL9idhoYs9rg0LwUv+vth+dhXaM40SfZcsWVLvcicnJ3z55Zfiry4iIiKEcy3TvtmXmCPC64hhUb6wZchRtmeIB+JyxgHuf4t5u/NPYwCutHTXGMbsbDiR0WT/EzIPpUjloh3sEQ6NHee+8nd3xKToIKw6mobs4gpI6l4A5BecuOz27SjbvrPAMFZh3nEpeQvPLByNr/+Yhfw828ygSI6y+cV0Y5HZk7bHov1hmJYgLb8Ux1JkzWdMqAeCPWX/iYaSV56H4sridlck8HLMNnGW/fd8FxEwQByDXO24vcICCmMRdscbBZRT5WfwjzYXX+UfA1RqmxVQpCovaMtlc+fRzKMoqiiydLcYpgW1J40376QUpSjtMHe5qi8DDIn0QSd/VzEUOxOrEO4hF1CMr8xDSWVJux0i23waMDZNYVml8hbWNdAN5yCrfH11Ejw9jTUqbM1RltAWd5Y/JS0OcHVjpi37nzRBQEkuMCYfYw2KESq8e6tJlWOUy/dBnaTDiZwTaK+wgMJYxP/EUMNjQEdH5OiTPEV5d7PZsxHl5wpXBw28SuQqpcTu/V9btE8MY+7EijvPyprPYE8nRIc0Ph9VcpwxR1BYmVy9nJGZ0T8MTvbyIzkxxeiXF5vVfv1QWEBhLJbenugQZDSDRAX1s2lH2ehQT5QUd4Nabz/eXcSpqpm2w7YzmajQUjpCYEKPAPHW31hSitOUdphrsFn7Z+t4utjj6t4hol1caByb2IsH0V5hAYVpdXaZ+J+4uhvbUZ76BEU2CvmhFOr80LFcDS+tDp0dvFDZju3HTNti3fHm+Z8QqeW5Sjss2HZKWrR2ZlldeSDsdbIAeCxxI9orLKAwrUp+aSXiUmX/k+5B7kgvNWoZorxsX0Ahki48ips6/IYPZm+Fvb2LpbvFMM2GUgJsOiULKGTKpARtTeHLvHKsSUrB95mF8PQ08blgBH3CPEV0FKBBhOyahxQNkJsT3y5HiAUUplXZd87ofzI0yhcJ54xvB1Hutn3DMjjK5mhDEJvGmWSZtsOhC7nIKZZDXkd39YejnabxG9FpYZefjLAqLYa4hkGl5sdPfc6y7mU+8NDqMAIuKCwymsbaE3yFMBbLf0ICyjl92KGbToK/S9PUxtbmKEvEphRYujsMYzbWNTN6R1CQAuj0BQK9bftlpCW5pk8I3B3tsOfiQ8g59wHev3k7OnQYifYICyiMRQoEkn9dnyAdUvUvYlGwt/k3KuEoGyJrUVLySsUbZ0lJFkqKjLZ7hrHl6sUUcDeue+OyxyrkmjiNe3c0U8/aHq6OdpjePxTlkitKKyWsOGjMHdPesO0nAmNT5JVU4HiarFnoEeQBj5LzeC47FzcWFGK8S9tI2mQw8/Rw3oHHfh2KEUvH4s/tXGuKsV0SMosQnymbLAdEeMPHtWnp6fclbcXn3p743c0V6a4+Zu5l28I0J8rC3edFiYD2SOPKUDJMM9h7LkdU7DSYd1zzYjG7oFCe0W9CmxjbXmFybogqyRGHNVQqXYU9mYcw09IdYxgLZY81sDv7KL7zkgX4L+01COIzUifdgtwxqKM39iXm4kxGEfYkZGJAB7d253TPGhSm1c07hIgCyDxlXOhvu0naaovkOVvWF576nBF7q/KhrWrfNTWYNuJ/0oTqxQYSTNPcBxjrVjF1a1H6uK3FwIiX8PjWcdi4+6N2N1QsoDCtnqCN/E8Gd/QBskwEFL+ubeJMRPq5wcVBAwl2iCyTa2sUqlU4fupPS3eNYRpNbnEFDpzPVZzAO/m7NWkU09MPY7M2T7Q9dRLCQ4by2bgMk2OC4OZQhVMuWhSr1TicdqDdjRkLKEyr3ehO6P1PKEU2ZU08kX0S+eR1p3FsM05zGuEoK5t5tEXGY9odv9KCvWKYprH5dIbIgdJc7cni7a+jSp959mavaNg7ysI7UzdO9hr06nyVMn204EK7Gy4WUJhWYc85Y3r7oZG+IsPqLJcyjIwIxz2hIYC6CXkVrJReoV7iM7HI+Ja4O+e4BXvEME1jvRmyxxYVpmFZ4RnRtpckzBz1Op+OBnLL6LEIqJRNxWc15ais0GdvayewgMK0ev4T8j9JSt6tvFF5OzS+6JgtOMqmVnRDcJX89nkIZSgtMQppDGPtlFdpseV0pmh7u9ijfwdZ8G4sv299BUX6gqDXOIbAz6+7WfvZluno54pwraxtKlGr8feev9CeYAGFaVUBhe5TgyJ9cC51r7IsyjW0TZ0Fg6MsEVUlh1NWqlQ4FPeLBXvFMI1jT0IOisrlxGqU+8RO0/jHBWlKf764S5m+fejzfBoaSQ+PLkp7+/E17Wr8WEBhWpzsonKcTC9U8oR4ONkjIdto8ojybVtvVAZHWaKi2Hhsu8+vt2CvGKZxrDdD9th/tr+DixpZezJW5Y6oyPF8GhrJmK5jlXZG+Wmk5Ze2mzFkAYVplfwnBij/CZFQmKTMiwwZ1KbOgqmj7MHsIcr8lHyTTJoMY8VQYjBD/hMHjVrU32kK8TknodInP7qj9xyz9rG9ENN1mjKGeY75WLLXeO9s67CAwrRu/hO9gBJfIYcuqiUJHcPbXp0JQ0bZPG0Q3i9yxMYLyfgoKR5gPxTGBjiRVijKNRBDO/nCzbFpOT0fn/EbVo3/Hx7zGYABvW83cy/bB27uweiokx/VSY4Sft97AlX6HEttHRZQmBZnV3y2olkY2NEbOm0VEiHbtsN0Kjg6ti0n2Zp+KMEOg+FvuKFcMNrjGcYWzDtX9mhi7R09VOju3qvn23ytLUvSy0nWYFFggVvVXqw3ye7bluErhmlRsorKRapmg1bB3cke6RcPo1Tv1R9l594mz4CpgLJLa+Jjk7jdMh1imCYKKBOakd6eMQ83hIzGBxlZWJOUgkEVBVi0p32Yi1lAYVovvNjgf5Js1CJEurbNihxR/kZH2T9zqfCXLJCxgMJYe2jxN1vicTQ5X0yTL1WIl3Ojt3Po6EIRwcOYh35dpmFycQnCqrToq07AtjNZOJ8tF3Bsy7CAwrQoq4+lKe3hVH8HwMX8RMXpK8rLGELXliBzVs9g2XR1Mt8O24O64i1fb0zXZCA/v/04uTG24xS7Ni4dEz/ZirlrTirzp/YObvS20tMO4e6D72LKz4OxbO0TZu5pOyUwBtDIVaT7qBLE5+I9bT+zLAsoTIuRX1KpZKL0c3OQCwQCmHHlR9h78zb8NmwuxvRru579vcKMZp5/XH3xq4c7zjo44NCJpRbtF8OYcjK9ALN/2IM5Px/A+WxZ60E5FGcN6YD7RkU1erAW7pDT2lN48cVi4wsK0wzsHGUhBUBndSrcUYKl+5OExqst0zTXbIZpACuPpqJC7xx6TZ9Q2JskenJy9ka3rtPa9Dia+qF42PUGKpJFe3/SNowd+pQFe8YwQE5xBT5ed0q8ievL7QiGRPrglat7IjrEeP02lMKKQvxWJlctduC09mYlOzgGB/JOINbREZ3z9uFQyRisOZaO6/q1rUSXprCAwrQYyw/KD2RixoC2+yNqiICSXEGh1KtFe59j26k7xNgelVodftp1Hp+tP42CMjmajgjzdsZLV/UQVXRV+jIUjeW307+hWFsm2tdEXQ1fv25m63d7Z7urK14OlKN5+lbGAaVjhLMsCygM00jOZRXj4AW5vHr3IHfFH6M9YXCULanQ4mC6K7r06IIzuWdwMu+seNN0d2ibEUyM9bLpZAbe/Ps4EjKNDpZ0jT48rjPuGRkpKug2lUptJRaeWCjaKqhwe5+2a761BL0irwSS/pYnXDOALGBfYi5OpReiW1DbvJewDwrT4tqT6/uHKm9kx0/+gRcWjcN3f92OU6dXtenRN3WUTc4tRS+f/qKtk3Q4lHHIwr1j2hNnMwpxx497cdf8fdWEkxsGhGHz02OFgNIc4YRYk7gGGSWyz9nY8LGI9Ixsdr8ZIx07jIGrJN9Hz7kaz1VbDjlmAYUxOzqdhOUHZTs0pTu5rq/RvBN7fhNWVWXh89xDOHxubZsffUNGWcLXrofS3p++30I9YtoTeSUVeO2vOEz6dJtSmZgYGOGNvx4ZgQ9v7IMAD6dm70fS6TB/59vK9J3RdzZ7m0x11Bo7RAfLZUHypWI4O8n5peheW6wv6tjWYAGFMTt7E3OUNNmjuvhXuwEm5MshckRUQJ925YdSWdxRae8/u9JCPWLaA5QKfcHORIz9cDPm70yEVu8FG+LphM9n9sOyB4ahd5iX2fa388BXOKOTI4B6q1zQL6Cf2bbNGInxkyN5iCHdZU0YVZxeeSQVbRF2kmXMzu8Hqpt3TEnwDARKE0U7sh1UNjUNNY5PBzppVYjXSDhelonionS4urXNRHWM5dh6OhNvrjquZHAmnO01eGBMJ8wZHQVnfQJBc/LT8Z+U9p1R1zbZyZZpuIASFpwNHJaz/C7ccx43Dwpvc+POGhTGrJRWaJXkbO6OdpgUXf0BnFAgCyceDh7w9TRqFNoqnfzdxMOBoOycA13DRFurUuHQ8WUW7h3TlkjILMI98/fh9h/3VhNOrusbgo1Pj8FjV3RpEeEEaUfwctJZzMovRFctMH7o0+bfByPo5ddLbgDIyD+A3voXoNiUAiX7b1uCNSiMWVl7PB3FFXLyoKt6BVdzvCuqKMLFErnGR5RnVJuT9utylI0J9RDe9uQoO2DENHhmbsHADuPQt8dNlu4e0wbIL63EFxvOYMGuRFRqjQlN+oR74dWre6J/B++W7cDO/yK8SosXcnKhG/Ii1HZyxlPG/AS6BMJXUiFbJSE2Oxb/GRSmCCYLd58X57wtwQIKY1Z+q8e8cy7/nNLu5NWp3Yz8lT0DhYBCnC2/Eo9Of8jSXWLaCDvjs/DI4kMi6ZqBQA9HPD+lO67tEwq1vihni5GXBMT+LredfaDuN7tl99fOUalU6KV2w2apEIVqFfr5JcLdyQ6FZVUiMebLU3vC08UebQU28TBmIz2/DDvOZol2uI8zBnX0qbY8IcEYtRPp3LwS7rbE1N4hSptuIgxjDiqqdHjy1yOKcOJop8Z/xnfGpqfHYnq/sJYXTih6Z/fXgKRPtz74PsDBpcX32d4Z6BuNAZIj7nTtDHdnN8zoL5uNyyp1+DcuHW0J1qAwZuPPwylKyuzabpAJyTuVdlRl2wyLq41QL2cMiPDGgfO5OH2xqE0nVmJaj7+OpCK9QM7a2r+Dl4jOCfNuPQGhID8Js5P/wHRPd9xQUgX3Qfe12r7bM3dM/Q53mExP650jIrWIbWezcNOgcLQVWIPCmK0a6u+mydlqqQ+RYFI4LCp0aLsa+atNqsKuPJyMhHMbsXTt4/hn6xsW7Rdju7+377YaQ/ZfmtqjVYUT4retr+KcvQYf+3jji079ADc5DTvTuvQJ9xIBCQRpsCkPVVuBBRTGLMSlFgjtgCEJVEc/10vWGV9cgusKizCgvBLBQXJW1fYCOQwbfIK3xe7FtVsfw5tpG7A44U9Ld42xQSjp2qmLhYr2ZEBEdXNqS1NZXoxFmXtFWyVJmDX0+VbdP2OEirAO1VeKJ3Pf8bQCtBVYQGHMQjXtid4mWo2qckzPOI83s3IwX+cvsiK2JyhZ3dBI+SZyJMsPHfRm+2MoR2lJjmU7x9gc35poT+aMbn2H89U73kSGRpa4x2k80bHjmFbvQ3tH0umQkrIX+XmJGNXFT5m/Xe8H2BZgAYUxS3XUvw7Lzp8OdmpMNTFnKGTH0y9Kbvu3zwqn0/oYxyVKJwsrVSoVjpzgfChMw4lNycfO+GzRjvRzFVFirYlIa39+jTJ9V98HW3X/DLBhx7sYu6A3Jq+/B2v3foaRnU0ElDMsoDCMwpZTmcjWRxLQzdLTuZYwt6xTxnY7LcE+JSZY5EUhSgqMhdT2X9howV4xtsY3JtqTe0dFKtdUa7Fj/5c4q5ZfNvro7NG3F4cWtzY+7mHI0Z93yodCgio54xtKjZRV6lW0Ng5rUJhms/yQ0bwzo0buEwNZ6UdQbHDC8O/aLkfdx9VBedM5ljNQmb8v/6wFe8XYEkk5JUqmZl9XByXEtDWZf8IkrX2XG1t9/wzQvfNV0EiyM+yxsgyRH8Vwb6Hw832JbcNszAIK0+xqqeuPyyXW/dwcRHHA2ng/bROGdgzHFeEhSHNtXYc+a2Ka3vyVUdURoVX6GwzKUVYqJ3JjmPr4ccc5pfDf7cM6VsvU3BocP/kH9kAObSY/qnFDn2rV/TMyzi4+6CzJ5z5epUVJSRZGmvihbGsjZp5mCSjvvvuukNwef/xxZd7YsWPFPNO/Bx54oNr3Lly4gKlTp8LFxQUBAQF45plnUFXVfvJitCVWHU1DhVZW917bN1R4lNdGQmWe+MzSaODXziJ4TJkYHQQH/RgFlXqIz0qVCsdO6rNxMkwd5JdU4td9SaLtZK/GbcMiWn2s5u//RGnfHjoOGk5rbzFinGTfI51KhRNn/saIzn7GSMH2LqDs27cP33zzDXr37n3Jsvvuuw9paWnK3/vvv68s02q1QjipqKjAzp07sWDBAsyfPx+vvPJK04+CsRjLq0Xv1G7e0VZVIBGyTTRcp4a946UhyO0F8s8Z3VXWMpUVRSnz9yVusGCvGFuAKtaW6Otc3TQwXJgMW9s5NtDJB846Cd46CdeMeq1V98/UXdk4NnmHuB6iQ+SXnhNpBcgsLEe7FFCKiopw66234rvvvoO396WFqEgzEhQUpPx5eMiDRqxduxbHjx/HwoUL0bdvX0yZMgVvvvkmvvzySyG0MLZVPfXgBVkz0j3IHT2DjefZlNS0/SjXO3RF2XMG1av10TyJRUOUMdqff6ZVzhljm5DT47wdcrZQ+indO9Io3LYWKrUaT92wAuuuX41P+j8jzAyM5egVMV5px+aeFJ8jO/tXq9PULgWUhx9+WGhBrrjiilqXL1q0CH5+foiJicELL7yAkpISZdmuXbvQq1cvBAYaQ+MmTZqEgoICxMXF1bq98vJysdz0j7E8Kw6lKG1y1qurOnFCym6lHeVqrEvTXrmiR6BQ0adXRaFzuQ5ji0swMTcTqJRt+wxTWxmJrKJyJRqsg6/lat54enbAgD6mydYZS9Ap8go46f2RYitkHzbTfChtwczT6GxZS5YswcGDB4WJpzZmzZqFiIgIhISE4OjRo3juuedw6tQpLF++XCxPT0+vJpwQhmlaVhtz587F66+/3tiuMi0IpVNefjBFeaO7tm/dgkdCllHwjPJunyHGprg62mFC90D8fSwNsy5E4Ua7rfKClANAxxGW7h5jhb8108Rs941ufe0JY33Y2Tuhh8oRh1CBZA2QmxOPARHkOK0WhQMpHwqVRKjrxbHNaVCSkpLw2GOPCQ2Jk5NTrevMmTNHaERIS0JmoJ9++gkrVqxAfHx8kztJWpj8/Hzlj/rBWJY953KQklcq2hS5Q5lS6yKh4LzSjgpuvw6ytZl59kg9jDMTt1uuQ4zVsvFkBuIzi0V7cKQP+oZ7ter+K8oL8dPqOaI4IGNdxLjKYeYuOh0uJG4UUV2D9RmrqZBkfKZcfqRdCCgHDhxARkYG+vfvDzs7O/G3ZcsWfP7556JNDrA1GTJEtrOfPSvneiCflIsXL1ZbxzBNy2rD0dFR+LGY/jHW4xw7Y0D9uRgSyo0x+ZEdRrdov2yFsd0C4OqgwW5dT+PM8yygMJdiqj253wLak9Xb38IHmbtw5fIp+HMj19yxJmZGTsOK5DTsPJ+MPgXyfXZU57Zj5mmUgDJhwgQcO3YMhw8fVv4GDhwoNCXU1mgujcmn+URwsPzGOGzYMLENEnQMrFu3TggdPXua3KwZq6W0Qqski6IqmhPrSbVNnv/nIDs/B2sluLgYfzztGXrToZDjZMkfyZIfKgEcunhIvK0yjIFDF3JFZlCic4AbxnULaNXBod/vggv/iHaJWoWIgEujNhnLER41AZ0rK6ExmIjJUda0Lo+NCyiN8kFxd3cXjq+muLq6wtfXV8wnM87ixYtx1VVXiXnkg/LEE09g9OjRSjjyxIkThSBy2223ifBj8jt5+eWXheMtaUoY6+ffuHQU68MdqUpvfcmisnNOo9AQwaNpv+HFdZl5yNH4I48QbPdxQplajQUnV6B/n9st3TXGSvhum4nvyahIqFs5rf321B1KWvt+cELfmFmtun/mMnhHAs7eACV6TD0ISJKIqPRzcxRO1bsTskWttLryU1k7Zu21g4MD1q9fL4SQ7t2746mnnsKMGTOwcuVKZR3SsqxatUp8kjZl9uzZuP322/HGG2+YsytMK1Uuvpx5x8+vO3bPWIdfBr2CRwc/y+fFBAoJpLwo6VVhQjgh9p9fx2PECM5nF+OfWDlwwN/dEdf1qz3PUEsyP26+0r5j7Lt8ZqwNlQoI0fv1FWcC+Un6tPeyHwq9SB7Sp4KwRZpd837z5s1KOzw8XPikXA6K8lm9enVzd81YgPT8MuzQl/MO93HGwIhL8+DUxNUtCDE9uWZHTajy8+ToIGw/OogCBcW8faXpmNMC542xPb7fdg76KFLcObwjHO1aN619XHYc9qbvFe0IjwiM6zCuVffPNIxzAZ2xJns/jjk64KZjP2PcqJcwsos//tBXmN9+JlM4V9sitqn3YSzGH4dTlJvm9H5hra5ybmtM6xOMlIouUFfK5q8jVfmo1JFHCtOeySmuwLIDctSMi4MGs4e0flr7BbFG7cntPW+HWsWPC2skzbcjvvb2xHYXZ2xM2iTmGQoHEltt2A+FrzimwVBM/e8HLl+52EBW1knhZMfUzbAoX/i6OqG8pIuYLq0qRZxJ3himffLTrkSRy4K4ZVAHeLrYt+r+U1L2Yu052TnWx9EL13S6plX3zzScATG3ivIDxI6ydHHPDfJ0QpcANzHvaHKeqONki7CAwjSYuNQCnMmQ4+rJtBPhW7fTK/1I7lt5M25a0A9rtrzGgkod2GnUwtFYW2IMH91/cT9fle08rf1Pu+TcQRq1CneP7NjqfVi48y1o9crRW5wj4GRXd54jxrI4OnlikD4fSqZGhdN5Z6pF85DssivBNrUoLKAwDeY3E+3J9f3rd47duu8z4f1/Uq3DwoS/eJTrYVrvYFQVmwgoabVnaWbaz++MTDzE1F7BCPNu3bT2lamH8G+hnFiTUqnfMoqzeFs7I3oZI/+2pWxrM2nvWUBhGgSFqv11JFVx7pzaW85rUxc/pBqdpe/tPksUGmNqZ1BHH/g7h8CpSq5OeyhlByorjfWrmPaDVifhe5PQ4jmtnZjt+J+wn3cV/khOwZM5ubjbrTO8fTq1bh+YRjMqdJTS3pGyQ3wOifSFvUZWg23XBzbYGvzUYBrEllOZylvdlT0DRXhsXRy8eBCHCuQ3sE4uwRgz+HEe5fp+hGoVpvUKRddSjZIQ68RpY2g+035YdzwdidmycDqisy9iQj1bZ8fkK7ZpLrD0dqCyBB46CXe5dcWDU39snf0zzSLcIxwd3DuI9uGMwyiqKBI1v/p3kKMsz2eXICnH9l56WEBhGp/75DLOsd8f+15p393/Eag1zY5mbxdJ26QS2dcguEpCXlHthTOZtu2E/o1JWvs5o1tHc1FSlIFnF43Bwd2fGGf2vgW4czXgYpvhqe2RkSHDxWeVVIU9R35sE2YeFlCYy5JXUoENJ+TSBH5uDhjdxb/OdU/lnFJsoMGuwZgSOYVHuAFQAbhsaQZcz8xBfPz7iI5+gMetnXHgfK6SVIuygY42ebi0ZLTObcuuxBpdHp4I9EOanT0w8S1g+v8Ae3aMtSVGqOWoHWLb2VXik/KhGNh+NhO2BgsozGVZdTQNFVo55PHavqEi8qQuftxsLCZ2R/QdsFe3bnikrULZH8f17oP0qijhh7BGn0GUaT+Yak/uGxUlromWZN/hHzFz7d04rU9lX6FSI3nym8DwR+UMpYxNMSjmNjhIcrjxztJUETnZK9RTMcfvOJst7i22BAsoTCPNO3VH7yQl7cI/BXKImzc0uL7L9Ty6jTTzGFipd0hm2gfxmUVYf0Jf1d3DCVf3CWnR/S399zHMOfwxcvWJFjtqgUXjvsCggQ+26H6ZlsPZxQcP+Q7COx2uxS/XLBeBCRSmPryTnPY+v7QSsSn5NnUK2DmAqZeEzKJqaueeIR51rrtg5xvQ6d+8bvWKgbOdM49uI+gZ7IEoP1ckZBWLCrapuQUI8a57vJm2A0Xu6F9+Rd4TipRrCSg67L3fZ+DX8mRFSzICLnhvxm/w9AxvkX0yrcc9V8+7ZB7lQzFoZCmap0+4l82cEtagMPVC1XYboj1BUQamJx7FxKJiuOl0uGXsOzyyjYRU+tOiXTE66APERD6PV5dfyWPYDsgsLMfvB+XfmZujHW4ZLEdjmJvcnHjcv2i0LJzoucOlE/5761YWTtowozob/VC2nbG8H8ofJs+Uy8ECClMnOp2E5fobJ2mCr+1Xj9p599eILi3CR5nZ+DdoKjw9W+Ym29aZ1rcrEjwykegIxKqLoK2SQ7uZtp3WvqJK9gOZNaQDPJzM77dVVVmGu/6Yjn2qcjFtL0l4O/xqPH3jH7CzczT7/hjroYOvCzr4uCiO2MXlVRbrS3JuCV79q+GlPFhAYepkz7kcpOSVivborv4IcK/Dq78sH9inDy1W28NjBOc9aSpdgz0RVS6XEChSq7HryJ98hbZhSiqq8PNuOa29nVqFu0a0TFp7O3snzOk0XbT9tBLmDXwZ14xnLWdbpKQoA5t2fYA3l0zGnoPfVkt7X6mVsPdcjsX69uP2xEY56rKAwtTJ8oMNTG2/fx5QXiC3+9wCeLSsg19bp7NrD6W9Me4Pi/aFaVmW7ktCnr6Q2zV9QxDs2XJ+W1eNeR3/FzQeS6YuRp+YW1psP4xl2XPsJ/zn9E9YWp6CdadXiHmjOls+HwoVLFyy70KjvsMCClPnm93qY2mi7e5oh4k9A2tdr7wsH/ee+A4r3VxQCRXA2pNmM6HXdUr7dMlpvkLbKFVaHb7ffq5F0tqXlGRh5aaXL5l/06TPEBjY22z7YayPIb1uh53e43p7SbIINx7eyU+Y6S2ZD2XR3vMoqdA26jssoDC1sjbuIor1FxPV3XGyl9Ow1+TPra9ij4MaL/r7YW7nvoBfZx7RZjKs99Xw0OedSXAoQXyGbYUGMg3jn7h0JOcaTajdg8wTsZWauh+3/zoBL174E39uNOYlYtoHLm4BGKCSNXEpGiDxwlZ4utijd5gcvXP6YhEuFpS1ap/Kq7SYtyNRtBuTYocFFOayuU/qMu+Q49285A3K9IyB/+HRNANUGqCn5C7ahRo1Vm5fyuPaBtPaf2uSmO1+M2lPDlw8gJkbH8YpffK1j8+vEj4JTPtipG8fpb3jxJJL0t5vb2Uzz5+HUkW0GnFFj4AGf48FFOYS0vPLsENf/TLcxxmDOsoFp2qybte7SNYrVobBGdHdOTGbuRjsZ1TDn0xey1dpG2N3Qg6OJsuasegQDyWZVnP47fRvuHftvcjRykXhOmiBeWM/E2/UTPtiRPcblPb2jEPic6SJH0prVjemaNBvTSp03zG84Y7gLKAwl/DH4RQYHK2v7xdWa8ptsmv+EC87YBH39LqXR9KMjOpxtdLO15zDqfRCHt82xLdb5WrfBt+T5qS1r9RW4J097+D1Xa+jSieHkA4NGoLF169CVOQEs/SXsS06R01EoFa+ie+TilFamot+Hbzh4qBRBBTS4rUGm05l4GxGkWjTy27f8NpfeGuDBRSmGnTR/n7A1LxTe+XiHfu/VNTIMTo7DO7LAoo56dppiuKHkuxcipWHk/hKbSOcvliITadkR8VQL2dM7WUscdBY0tMO4cGFI/DLyV+UebN7zMbXV/4Pnl4RZukvY3uo1GqMdJajKStUKuw/9rPITjw0StbUkbnl1MXWeen5thkVullAYaoRl1qAMybSboSvnJOjJj8cX6C07+16s/hBMOb1Q3nCLgxfpmdgdVIK4o7sabU3HqZl+c7khn33yMh6i2/WCiXvi/sDx3+ehkn/3oY9kB0e7VQavDH8DTw3+DnYqbmKSXtnRPhYpb09ce2lZp5W8EM5kpQn8mkRnfxdMaF748yN/FRhqvHbgcs7xx6OXYz9+oyUHbUqjBv6NI9iC3BDl2swurQM7pKE8IJDQnhkbBuKniATKuHhZIdbBjW8/k1p6iHgnxeBj7sDy+5At/htCNTKkXY+Ogk/9nsW07vIydgYZmjvO6DRv9TsKDp/iaNsa+RD+bZGhW61Ida5gbCYzShUanX4S19Fl9SBV9Whev7h4H+V9t0Rk8XbPtMCRIxQmkPUJ0SF45hQTx5qG4ZCLSmbJzF7aARcHev/7RQWpOCf3R9gRcoWuJQX4ft0Y0QOeRPcWW6HPN9euGnkK/Dz697i/WdsB3ePUFwHN3jlpWJEaRmk7Hh0DohCoIcjLhaUY8+5bBH+62hXewqJ5nIhuwRrYuVcWn5ujriuX+3uAvXBTxZGYcupTOQUy7VfKDGbp/OlNUGoNoybxhHqygL464BpI1/lEWwpgnpB5+gBdXkBhqhP4p0jqXh+SvdmOVQylqOovAqL9shvsg4aNe6sI5qBHND3H52PP+IWYm1FBsrorZN03c5OSLbTIEyyA3pMA/rdhlmRYwA2rzJ18Fqnm4ANr8sT8RsB304Y2dlfpJEoq9ThQGIuhpuYfczJ99sTlGCLO4dH1JlLqz5YQGFqzX1SV+VijZ0D5t66CQ8l7UJq5jHYO9buo8KYAbUGGR0GYVfabux3soN9xj4cvNAfAyIa7gXPWA9L9l5AYZkcZTO9XygCPKrXtrp48Sj+2vMhVmQdQpLhXm6iEu+hUyNn5OMIG/wI4OLTqn1nbJTOVxgFlDPrgMH3CTOP4V6/7WxWiwgoucUVWLpfduynyCHSFjYFFlAYwc74LKw9flFRx5naKmsjPHyY+GNaln+9fPFhlex537tkP1YeuZoFFBs1n/5oktb+vtGRxmWVJXjilwnYpiuEjrRjJi+aHjoJU10iML3vHPTodm1rd5uxdYJ6AW6BQNFFIHEbUFmGETUcZZ+bbP7dUgFM0tAQNw0Mh5eLQ5O2w06yjCiB/cjiQ0qVSaqo2ujIAqZFGBhlvHtILudFfaTGVANlrIOfd51Har4cbUORDJ0D5EzBhL29C8qkKlk40TMUzni/4wxsnLkDL978NwsnTNOga6rzFUiys8NiJzWOH/8V/u6O6BEsl1WITc0X2g5zUlapxYKdiYoC8J6RRmG8sfBTqJ1DF9MDCw8ovidjuvrjgTGXxqrn5JzF8vVPo6KcE4a1Jt06T4UrFWEEcNwZyCgss2i5dGvjfHaxkkLbWqEkVe/9cxJu6lwM91kAR/xH+HKZMj1iCoK1Eh70iME/V/yA7+7YiyljXoOjEztFM81jk18YrgoPwVw/H/x9ermYZ9CQU5DPjnjzRvOQ+Shb/zyhQItwH5cmb4sFlHYM5dV4YfkxxKbI4asRvi74/JZ+0NQSCrZoy0t4NeVfTFk0DPsO/WCB3rZP7Oyd0D90pGhr7cqgdsjEqqNypFV7D9d9aNEBjPlgM8Z/tBlxqflWW7H4qWVH4K45Du/Oc3Es8AS2qYqw59B31dabNOJFrLn9IB6a/gtCQwdbrL9M26N/zCyoDdWNC8+1aD4U0u5+v81oyry/kYnZasICSjtm/s5ErDiUojgyfXvbQFH1sibFxZn4JS9OtHPUQHjwgFbva3tmYNBApa1xOYc1senCp6E9QjfAn3YlYsJHW7D6WLqYR46njy4+hOJy2QHVmvh6czyOpiajNOwP5JmYTXedX3+JIEoO6Axjbjy9OqI3HEU7QSMhtSAJgyN9RCoJQz4UcyWBXH/iIs5lFYv2sChf9AprngaQBZR2yq74bLz19wll+oMb+qBbkNEubspviatRqNeqTHMMQlBQ31brJwMMDDQVUBKEOW5nfHa7G5rjqQW4/uudeOXPOBGya0pCVjH+789YWBOxKfn4bMNJOIUuhtohT8yLdu+IX4e8gSdnyKp2hmkNRpiUItmetkuE/BqKwKbklSIxWy4wada09mOaX6GbBZR2CF2Qjyw+qDhbks/J1N61J2Wr0Fbgp7ifRFsFFe6a/HWr9pUBevj2gIudbMd1cz1J9UGxSp9Qrz1QUlGFuatP4Or/bhepsw3cPDAcfzw8Am76ZGfLD6ZUqyNlad+up5YegcZ/Fexc5Zu2n7MfPp/8A3p2n865bJhWZVTYaKW9I2WH+KR8KAa2n5FrQzWHA+dzcOB8rmh3DXTD2K7G7TcVFlDao1PszwcUJyZylnpmUrc6118ZvxIZpXL2yvEdxiPKu0ur9ZWRsVfbox8l5wJQbleODg4n8U9cusgC2dbZdDIDV368Fd9sTVAEaqrp8eucoXjvht7oG+6Ft6fHKOuTFiU+U64lZUk+WXcaXuXfwMFnp3IOPxn7CQJcGleLhGHM9ZLj4yTnztmdthuV2kqzp73/Zkv1tPbmSCjJAko7guyML644hmMpskNhuI8zvphZu1Msoa2qxLzYecr0PTH3tFpfmeoM9OistMPc9gu/i22nW76WhqXIKCjDw4sP4q75+4TGjyCb+ZNXdsXqx0ZhiL4qK3Ft31ChTSFKKrQiZJ4EcUuxLzEHW/cvxJmgI8q8l4a8iL4BbBplLINapcbwkOGiXVJVgsMJ/6BnsAd8XB0Ukz85dDeVhMwirDsh59GiVPr0mzRLv82yFcYm+GnXeaEGJ5ztZafY+hLobNj1Hs4Xyqm5hwQNRi//Xq3WV6Y6QztPwwi44DaHXkgulp2UV7bBaB6dThJJnsgJ9u+jch0Pg8PdP4+Nwn8mdKm1dsir1/RE5wA30T6RVoB3Vhv9q1oTctR9+9eNcA9cgUr9G+TNTuGY0fUGi/SHYQyMdDKa8bcdnScK9w3vJAv6heVVOJLc9Ei477efEyHLxF0jIhUH3ObCAko7YU9CNt5cdVyZfv+G3kqynrrqgXx/9jdl+m6/QS3eR6ZuYnreiP/dsQeP37gQ+Wr5TXzd8YsorWg7Zp6T6QWY8b+d+L8/YsUNk/B2scdHN/bB4vuGIMpfFkBqw8XBDl/O6g9H/Y2RhPF/YuUon9bkvVVH8ErxXHx1MQ39y8owQHLAc9cbf0cMYymGd5oKlSHcuCBefFY38zTNDyWrqBy/6X2/yB9s1pAOMBcsoLQDUvNK8dCig6jS2/DvHx2Fq/uE1Pud+NjFOKOSHxI9dBoM6zenVfrK1A+9mUyODlLMGZtOGavb2iokZL275iSmfb4dhy4YnWBvGBCGDU+NxYwBYQ2yZ1MU2qtXRyvTz/52RGRJbi02n7yInofeQH/1WfjpdPhfoR0+u2aZyBTLMJbG26cTBsEJY1XuuDlkjHgJHdnF1FG2aSbjn3YmoqJKNg/dMigcHk6XpqpoKlyLp41DtvgHF1Z3in128uXLsnc+uARrklKxwNMdg/rfDxVXTLUaSLj8VV+Ia+WRVJGt0VbZfCpDOLYm5ch+JkSUnyvent4Lw/Tq58Ywc3A4dpzNwt/H0lBQVoX//HIIv94/DPYtXLohv6QSe5d9gGftNovpKrUjnGcthrNP80MtGcZcfH/73mr38lAvZ0T5uyIhsxiHkvJQWFYJ90YIGBRh99Nu2Q3ATq3C3c1Ia18brEFp406xL/8Rq9gWw7yd68wUW43k/aKwVJBWi+fgi/HDn2udDjOXpaqyDO4VGzHC518xvfFkxiU5QWwBStn/6C+HcOe8fYpw4qBR4/ErumDN46OaJJwQpGmZO6OXcAAnDl7IExE1Lc27S97DOb8/ka+/+Wuu/QII6dfi+2WYxlDbi+YofVZZipLbndC4MhrL9icjr6RSeXEK8ZJ/d+aCBZQ2zMLd5xXboJO9WjjFeuu9tutl+yfG9ojHAPWlTolM66PTVuHKnwdi9v43cdF3o8iHUl6lw7rjre9r0Rwn2MV7LuCKj7YI7Y+BIZE+Ijrn8Su61uoE2xhIxfzFzP7ijY74ekt8k+3rDeG37WuxHb9gu4sTbgkJxLl+d0LV5+YW2x/DmJPqZp6G/04o6uf77dVDi80NCygtrMF4acUxTPlsm0jPbbDTtQZUUO71lUan2Pdm9EbPkLqdYg3kpuxD2am/5Qn3YKDPLS3ZTaYRqDV26KSR31Au2qkQ4iA7ui3ZK5t7rJ1T6YW48ZtdItSdzC+El4s9PrihN5bMGapE4ZgDyo/ynN6USX6BT/x6WGhtzM2FjFT8dPwpJY19oMoN4Ve9Z/b9MIw5qSwvxt5D3yMpaQeGRvkoWvVtZxvuh/Jv3EVF+0muAw15vjQWFlBakIMXcrFozwUR9kjpucd9uBnL9ic1K968IaTlk1PsAcUp9r5RkQ2OS/9oywuYFB6C7z09UDJkDmAn13BgrIOBXsaker18D4rPPedyxDVmzfyw/Rymfr5NyTRJXN8/FBueHIMbB4a3SGZVKvM+tpv8dphVVIEnfz0iNDjmQqfV4vUVM3BO/xMJrpLw6YzfRF0dhrFW9hz8FiMXD8E9Rz/Din2fCZ+TfuFeYhn5olBQRUNevr/dKr8gEXNGt4yvFQsoLcj6E9UjLCjh1DO/HcWkT7eKHA/mvFkaoOyiDyw8KG7IxIjOvsqb5OVITzuEvyvSkaPR4EcvT0h9Z5m9f0zzGBhxhdJ2cped0wjS0FkrZy4WihB3g8Ac6eeKxfcOwcc39YWvW8sJwJTngUKUKXEUsf1sljD3mIt3l9yBvU5y1lpnnQ7vDpkLL6+OZts+w7QEncJHoUSvMdmeL/tnjTQJN25INA+9FBl8Gynhm2l1ZHPCAkoLskGfWY9eDk1PYHxmsciSSbVFKJW3uSpJ0nZe+SNOqVdCHtrCFt/ACIYF215Blf5NdqZXNFzd5HBWxnro1X06HPUP+pPIUerQUFXqvBJZKLU2ftxhLL9OORLWPDYKw1vohlYTEoA+vbmf+A0SH687jf2JjXMErI3VWz/Fr5WHlek5PtPRP+bqZm+XYVoaP/8eInUEcUKtRVbmyer5UBpg5qlWFHB0I9LaV5UDlWWtI6C8++67omOPP/64Mq+srAwPP/wwfH194ebmhhkzZuDiRflBbeDChQuYOnUqXFxcEBAQgGeeeQZVVbYXiVAfSTklOH1Rfrsi9dnCe4eI+iGGCpJEXGqBSOV94/92iURqzYXMSYbwU3KK/ea2AUoq4/qgePjv/rodi0rkB4mTTsKtY95udn8Y8+Po5IneKtmEkKoBbo4uFO2ySh2W6s+9NZFdVI7f9dmL3R3t8MKU7qKSamtCEUGPju+iRCpQ6HFzhLnz53fgrbPfQae/KU8sj8C9175ltv4yTEsz0sNYU23nsZ/QJ8xL/D4JCtOvT7tPGlGKHiRCPJ3qLDRbK+tfA+ZPa3kBZd++ffjmm2/Qu3fvavOfeOIJrFy5EsuWLcOWLVuQmpqK66+/Xlmu1WqFcFJRUYGdO3diwYIFmD9/Pl555RW0Re0JMaFHoPik+iFL7x+GeXcNQrSJQ9H+87m4+dvduO2HPTiabExU1RjorfD1lXHVnGJjQj0v+72Sogw8tWg0Ps89BEl/w73Nsyd8fIy1XxjrYqCn8eYS5b5XaVP2VENBPWuB0tYrSZwGhzcqx4I5+c/4zhgcKRdLS80vw7O/HW2S5pJ+L4+ufwCFeq1krxIHvDJ7mdn7yzAtyYjORiFhe+oOoWUfqg/tzymuwPF6fNq+22bUnlDekwbnGDq7Htj9FZBFFdlbUEApKirCrbfeiu+++w7e3kaNQH5+Pn744Qd8/PHHGD9+PAYMGIB58+YJQWT37t1inbVr1+L48eNYuHAh+vbtiylTpuDNN9/El19+KYSWtsIGvYRJTOhhrGBKGqdx3QKw8pGR+OrW/qIyq2lFyWv+uwP3/7wfpy/Kb8YNIT2/TPidVGolxTmwIU6x5MF967IrsU4n2xIpDfKj3n3xyLWLG7xvpvUZ1HGC0j6VdxBj9GXNk3NLlTcba0kS+PMu2U+GogTuGG45/wy6AX92S1+ROp9Ye/yiEOgay/ncKniWyL/Z0AoJc0b9BE8X8+Z+YJiWpk/Pm+Guf5nZWZkNbVVFNTMP+WvVVcTzj0NyegB3JzvcMrhhae1zc+JxbNXDje5nkwQUMuGQFuSKK4wOe8SBAwdQWVlZbX737t3RoUMH7Nq1S0zTZ69evRAYKGsViEmTJqGgoABxcUYNgCnl5eViuemfNUOJs3brTTbkB9It0L1WBz7KAPrv46Px4Y19RBI10/AtcqR98tfDuJBdclmn2AcXHRD1EAgq/kRq9MsRe3wZbl5/P86q5bdbN52E//a4B3Ou+VmEszLWS6/uM2Cvf/vfX3oRd5o8+BfstB5n2T8PpygZjKfEBCHM27Ip34M9ncVvzcDbf59ArL6yd0Og6Lvn/0zAtpSX0DujC0Z6PIOxMcbU+gxjK9jZO2GonRy5k69WIfbk8mp+knXlDZpHae31Uai3DolQfOAu50Lw6qrbcJuXPb719IC24+iWE1CWLFmCgwcPYu7cuZcsS09Ph4ODA7y85AM3QMIILTOsYyqcGJYbltUG7cvT01P5Cw+XS6tbK9tOZyrajCt6BNTrQERvdlRzZONTY/HmtdHwd5cjDuj5s/xQCsZ/tFnkUiEtSW289lecUr9Edort1yCn2MgOo+AvyetFalVYPO6/GD3kiSYdL9O6ODl7oxccEVBVhd6lxRgeUIyOvi7Kmw/ZiC0NmU++32Z0jr23BZI4NQUyt5KGkaAbLWWzbWgm3q82x+sjF9RIU/8HT18/u4V7yzAtx8jgoUp7x5k/RXQdPUOIfYm5QgNqCv1OKPknYa9R4a4RDdOILlv/JDZJhdCqVFjo5Yn8K19rGQElKSkJjz32GBYtWgQnp9aL9X/hhReE+cjwR/2wFfPOeL3/SUOKwN02rCO2PjMOz0/pLhJYERSaSc6vYz7YhLf/Pi7sgwYoI+cv+iRdVMWVnGIbGrZJETqfjf8C0+z8sPjGfxHZcWwjj5KxJF+GTcP6pFS8m5kNx+R94toxsMAKQo63nsnCmQzZSXxghLdInGYtUNh9L71/1rmsYrzyR2y9659L3IxNB/7G5xvOKOYqCpFubWdfhjEnw2OMAvb2vJPiRdpg5iG/MUr2acqSvRdQqE+weF3fUAR6XF4GSDi3ER+krFem3+xxF3z8uraMgEImnIyMDPTv3x92dnbijxxhP//8c9EmTQj5keTlVXf0pCieoCA5ZJU+a0b1GKYN69TE0dERHh4e1f6sFXJSpNBhwtVBI7L0NQZnBw0eGNMJW58dh/9M6CK2QVBK8++2ncOo9zaKUMktpzPx6l/GG+vc63vV6xSblLRL5DkxpWPHMZh76ya4UcZYxqZwixoHRS93dgNuHBgGF/21svxgCgrK5PoYluJ7E0e6e0eZt4BYc6GXgf/O6qeop0lTaSgJUZOC/CQ8svFRvHD0WfRyXSXmPTS2k1UJXAzTFIKC+qKLTo2wykr0KsyFriizej4UEz+USq0O83YYX3zua0BiNspW+/zmJ1Gmz7lys2MYxgx9qlF9bJSAMmHCBBw7dgyHDx9W/gYOHCgcZg1te3t7bNiwQfnOqVOnRFjxsGHDxDR90jZI0DGwbt06IXT07NkTts7hpDyTysH+Ta4rQvVEnryyqxBUKBMsaUiI4gqteJO748e9ihmJVG3X9w+rc1s793+FW9bfh8f+uQtlpcZMnowN02EY4KgX1E+thoedTmRmJUoqtKKIlyVT2pPDt+imjwuu7Gl9+XQifF3xzvW9lOn/+yMWZ/UaHwNanRbPr74DFzRAsVqNKv9tiAl2VkKWGcbWmRc8GauT0/BCTi7UCZsxopOfkjPI8BsmVh9LE4lGiXHd/NG1Fr/Kmnyx6jaRZ4WI0qrw1DWLGt2/Rgko7u7uiImJqfbn6uoqcp5Qm/xD7rnnHjz55JPYtGmT0LjcddddQigZOlS2d02cOFEIIrfddhuOHDmCf//9Fy+//LJwvCVNia2z8aRROzTeJHqnqZDJ5qWpPbHlmXG4dUgHpQCaAdLQvHhVjzr9AOYd/R4Pxn6NArUKx9VafLP6vmb3ibECqARBt6tEM6uyCClxv+MOEzPPz7sSWyRTcUP4waSA2N0jOl6+eraFuKZPCG4ZJPuzlVZq8cjig9Xs7v89/F9sq5CdBT21OhSk3IuPbh4kNDAM0xbw7DbVRBO7ThSTjQmRNfFUPiOzsFw8R77ZYpqYrVOD0unPL5Sz1NpJEt4b8RacXRpnTSDM/kv75JNPMG3aNJGgbfTo0cJss3z5cmW5RqPBqlWrxCcJLrNnz8btt9+ON954A22BDfr09iSFju/efAHFQJCnE96e3gsbnhqD6/uFgu753YPc8d9Z/WuNQy+pLMFzW5/Dx4c+g05/BY5VuePuKz8zW58Yy5LWeQzuCQrAhA6h+DL2e3QJdBelDYjE7BJhBmxt6IZmGoZIdXasmVevjkYXfZHCk+mFIrKH+CfxH3x/7HvRliQ1qlJuxKzx09At6PJvjgxjM4QPBRz0RTrPbqBy49XMPDvjs7DjbLaSF6V3mOdl3Rbycs/hxcOfK3m1Hvcfhu7drmlS91SSufKstyIUZkzaGnKYtSZ/lOTcEox8b5No9+vghRUPjWixfdGbHr2Z1iacJBcm4/FNj+NU7ill3oOdZuCBYS9zCHEboqK8EGMWD0ORWiXCxLfM2oXNZ4sx5+cDYjnlR1lw9+BW7RP5RxmcSe8fE4UXptSu3bMmyCR1zX+3Cz8v4v+me+Kbs0+itEpWaZelT0Nfz6uxZM4wq9UGMUyT+WUWcOpvZKnVUN+2Aie1MZj1/R6xiCJMLxaUKeYe8t2a1juk3s09u3AM1mhlB9uhcMY3s3dWe+405vnNukozYpoka4IZtSe1QREEtQknuw78D7csn6YIJ672rvh03Kd4aORrLJy0MRwc3THOQb7OSEjZGf+3CKM15NQhDUpCZnW/ipaEhGZDGCKZIk3zs1gzpBV57Ro5n4kGFVh09FFFOKnM6w/7YjlXEQsnTFvkWGg0bgoJwriIMCw7+gMGdPQWpVKIf2LTFeGE7iuToy/vT3b3oKeEz4mnTsLbk39s1nOHBZQWql5sSG/fakgSFvx9Hx449l/kQ7ajd/ToiMVXLcaEDsbMo0zbYmLfe5X22uyj4iF629AIZV5TsqU2FYoeMoTBU30OSoxmK5AvCvV5kNdvyHSQQykdy3xRlj4dL17VUzjVMkxbxKfTlTjhKNds25EbJwI7BkfKpmLTHEH3joxsUI4tMucsuWUzvh32FgICY5rVNxZQzJk9Nj5bKaBE/iGtRkUJsPw+ZJ1dqxQwG61yw+JJ8xHlZR0JspiWYXj3G+BmL9uQNyVtQoW2AjcPClfegCh8tqGJyJoDOeSaOscakqHZCpQD4s1ruiDH74gyL+jiQIzuEiKc0xmmrRIaOlgk6ySOoBL5JdkYVaPaOOXluknvUN4QyCG2Z/frmt03FlDMxPYzWUoKYNKeNLj8dHPJuwD8OBE4tgyP5eZheEkp7vfoiS9u3QZ3F1kKZtouDhoHjAsfJ9pFlUXYmboTXi4OIpGSmFdeheUHWz7kmMxJ8ZnFok1F+XqH2V6eEM/TK/BwXo7IC9GtRIMLuivx/ozerfdbZhgLMTJyovikgIpdGfuqOcoSpJV1cajbVLNz35ci74m5YQGlRaoXt6z/iYGck6uAb8YA6cfEtJ2DG74c/QEemf4r+5u0IyZ2lG8uxNozf4rPO2rU52lpX/jvTbQnpAq2ObSV0Gz9ENcWFeOv5DTEaO7Dj3cOEtFzDNPWGdn1eqW9I2WHsAAE6MuuUFj97SYpDGqy//B8PBD3NWYvHiGyLpsTFlDMpN7edEr2P6FsnkOjWlZzQWFc5G8yeddziNXqnSB9ooB718MuenqL7puxPoaHDIebWi6NsOn8OhHd0yPYQ2gyCNJs1FWd1BwcTy0QoYgE1QRqdf8rc3B4MZCnrzPSaTxem/MwBnZsfN4GhrFFBgQNgJPGSRFQDNnJB0R4CwdxQ424mhSU5uKFQx+JkGLKs7Xx2AKz9ovL1pqBI8l5yCqSnQOpIqS5anSUluQg4fwmnE7dg7M5p3CmJA1ntCXI0uhVzmo1Hgv0w28O3eB9wzzA2dss+2Vs0Myj9sJKXaYczXPoW4wd+pSIojHU0yAtCmU2bgl+2G4sCnj3yEjbi3apqgC2fmCcHvuiJXvDMK2Oo8YRg4IGYVvKNmSWZuJ09nFM6BFd78sGaWXf3PsO0vVqjoGSI+6c8o1Z+8UCihmTsxFXNOHtsaqyDCqVGho72ZOaWLr2cbyVul5JdqNgEE70XOMdAw9KIWzyXab9MbHTVKw6OQ/94AgHOzl6ZmLPQAR7OiEtv0wUsLyQXYIO+qrH5iKjoAx/HUkRbU9ne5E3wdb4beNz2O1Qggft7dApYhwQPsjSXWKYVmekT08hoBDbdn2AblfPr3f9lQkrRUJDwt3eHXOv+LbaM8wcsIBiBtab+J+M7e5fr8R5seQizuadxZncMzgT/4/QjMSrqrBg8KuI6Xmjsm6wVxSktEvfRL10ErqonNDFJQgTulyHwf2MYaZM+2VEvwewPnJStbA+CgmcPTQCH/x7iqLQ8fPuRFE2wZxQGLOhJtSsIR3qdaSzRsgc9k3yOqS7uWKdqwtWDbkH1p37lmFahpH+/ZT29uxY1PdkSSpIwtu731amXxn+CoICmhdSXBu2dTexQqiAEqXIJvqEeyHAvbpTXezxZfjj6I84U5aBMw6OKKyU11UQ6jEVzqTtqyagdI0Yi+i4H9HF0Q9dvKLQOaAvukaMg69vN6jU7DrEVMfe0bXWnAOU3+OzDWdE+fRf9yXhiSu7mk2IKK3QYuEeY2I201pAtsLyTS8iXa+VHKV2R3iXKZbuEsNYhA7hI9BBC6SqJdir1NBpq2oNtiCN//P/3I2SqhIxfW2nazG54+QW6RMLKM1ko4n25Ioa2WMPH56POw5/KOcmoXtgpeynYopakhChU0Otqi50BAb2xpK7Dje3e0w7h4pNXt07BL8fTEZBWZWok0OaDnNA28wrqVQK79laxEt5WT6+S92kmE0fHPyMpbvEMBbl41HvIjSoP9zcg+tc59tVd+Foabpoh7kE4YUhL7RYf1hAMWP22GrViwvS0H3dW7jOWYvl7nIirUB7d3QJ6IMuXl3QxbsLunhEItI9HI5OcvVIhjEH9OZz9MRS9OxytUiHT86yJEwYnGVnDg5vdm4Pilz7sYZzrK3x26YXkKEXTsapPBDd3RhqyTDtkW5dpta7PC52Cb7JPyaq4WokCe/2uEeUU2kpWEBpBsXlVdhlkj22Z7C+8JG2Elh2J5yKM/F6MTDUoxOGXfM9vLxsTwXO2Bart7yKj+N/x0WNCv8tSseYoU+iV5gn+nfwwsELeTh1sRC7E3IwrJNvs+tOJWTJiZmGRfkiJtS2hOyy0lz8kLZV0Z48NOQ5S3eJYaybsnx0Xf827lQVYJ6nBx7w7os+Mbe06C7ZmaEZUG4JQ/ZY0p4ob6XrXwOSdsttjzBMmfErCydMq+Di6CGEE2Jtwiplfs3EbWZNzDbKBrUnm19Apn6crlB7NrkcPMO0ZU1seVm+ccbqZ2CfdwFP5OZjoS4A9039scX7wAJKM9hoWhywuxxevGPnB0jc9z/96NoDNy0AXDnlPNM6DO83B246OapmU3mGiFIhpsQEK8mW1h5PF87dTSU2JV9oYYgoP1eM69Y6mZPNBeUX+j5tuzL9wNCWs6EzjK1xLnEzXlw0HuN+6oslG56WZx5dBhz9VW47eqD39HlmDymuDRZQmmGDp9wShLO9RqjMExO34KlTC3BLaBDWuTgDk+cCYQPNeb4Ypl7I52ScgywwFKpV2HXoO3m+nVopekfyy8Ld582WmE1tY4nZlm56Htl67cmVaq/L2t0Zpr2xsioTOWoVtmUdRmrKPpz6Vy+oEFM/BryNFdNbEhZQmsjRlHxkFZWL9ojOftBV5uKJTf9BsVqFYrUa60O6A4M4RwljmaRtBkzNPBS9Y69/MC/ZewFlldpGbzs9vwwrj6QqFU5n9Le9xGwdfbujk1YFlSThwWEvWbo7DGNVdOwwGqH6W8NBqRTPrL0fM/3d8bOHO3QxNwK9jekwWhoWUMwQXjyhmx/eXHEjzqplfxS6+b06fZnwdGYYazHzUI6eq3rJ4YO5JZX4Sy9oNIYFuxJRpd/27CERcHYwT1mH1oQch3+/fT8W9H8eXTq3TP4GhrFVVGo1RrrILx6VKhWOqivF5yIvb5ROerNV+8ICihnCiyuyP8KqKrkYm4tOwsfjPoWLa8vUPWGYhph5xtZi5mlulWOKWlukNw2RJub2Ya2j5m0JyH7er/dsS3eDYaySERETLsnXNXfgc3CtJz9KS8ACShNIzSvF8bQC0Z4QchSfZW5Slr3R+WZERY433xlimCYwMeqqWs08/cK90DtMDgmOSy3AgfO5Dd6mIdkbcU2fUAR42FZiNoZhGsbgXrNhZ/LyMsezl0UEehZQmpgDgvDUXMQF10VC/UXMdu6ISaP+z7xniGGawPD+tZt5KBTeNCX9/AaGHGtrJGa7x8YSsxUVpuHZhWMQd3K5pbvCMFaPq1sQrnUMEe0hcML90+ZZpB8soDSBDScuQoUqdA/9Aul2snDSR2ePJ69dYu7zwzBNwtHRA2Md/OGh1eKK4mIUnflXWTatTzB8XeUQwX9i03GxoKxB13xitlx7Y0RnX/QM0ScltBEWbXwWa7Q5uGXPq/h59f2W7g7DWD0v3fAHlo/4AN/cugN29pbRlrKA0khKKqqwIz4bPV124ISLXIfERyfhwyk/ioJtDGMtPNPnIWy+kII3snLgc2a9Mt/RToOZg+WQY3J4NfiV1Mf3JtqTe0dGwZYoLC/Agly5rhWl5x4dzb4nDHM57O1dhBN5a+Q7qQsWUBrJjrPZojJsXMkYTFfNgK9Wwnu9H0VQUN+WOUMM00R8ul8De0e9puPkaqBKDosnbh3aARp9/pLFey+gvKrukOOjyXnYe05OzNbJ3xVjutqWA/jCE4tQqA+om+YSgYiIUZbuEsMwDYAFlEZCqm4DY4Y8hNW3bMbQAawyZqwQO0eg2xS5XZ4PxBuduYM9nTE5Oki0s4oqsPpYWp2b+X6bifZkVJRNJWYrqCjAz8d/Fm2NSoP7J+uzPDMMY/WwgNLE7LFO9moM7+QHFxe/ljo3DNN8el4nPgrUKhw8+lO1RaYhx/N3nq8zYu1vvfDi4+qA6f1CbeqskHBSWCk7CF/b+VqEe4RbuksMwzQQFlAawVtLrkE3hy9IVMHIzn5wsre9JFVMO6PTeDwXGIQxHcLwSNFRJZqHGNTRGz30FbiPJOXhcFLeJV+nXCkUwUPMHhrRpGueio69sWQyJv4Yg/mr7hXTrUF+3nn8HLdAtO1UdpjTe06r7JdhGPPAAkoD+Wfr61hWmYgjAQkYEfYWxnezLTs8006xd4LaIwRVKtUlSdso5PjO4RF1VjkuKq8S/imEg0aN24Y2LTHbknWPYVl5CtI0KnyUvQePLhqFvFyj2ailWLDxGRRXyUURr424EqFutqX9YZj2DgsoDSAhLwGvJv6hTEslEZjQU7bfM4y1MynKpDZP1qFqy67tGypq6hCrjqYis9DoSLtsfxIK9YnZrusXolRDbizeztWF+a1SEW5acTWOxLZcWH5uTjwW5R8XbUo4NafLTS22L4ZhWgYWUC5DSWUJntj8BEok+Ubds8ADBU6PIJCzaDI2wvD+98PNzlm0NxXEo0JboSwjk83Ng2S/jEqthF/0GhORmG2HaWK2pocWTxnzGjZdtRSTNN7w1puLSJty5/63sGD1HEg6uYaVOcnZ8yUiK+XjvN4pHCEhXFWcYWwNFlDqgeqUvLbrNSTkJ4hpbVkQ9qQ+jQk9WHvC2Fhtng5ybQ1yGN2dtrvacjLdGAJzFu05j0qtDuuOpyMpRzaPjOrih25B7s3qg59/D3w4eyuWXbUY/SU5rwKZnT7M3IXHFo5Cfr4sGJmF4ix0OrgEv6RexH8zcnHf2Lnm2zbDMK0GCyj1sOTYj1hzbo08UJITSlNuBSQHXNEjsLXOD8OYhUkdJyntfxONWWWJMG8X5Zq+WFAussvWDC1uLJXlxbXODwzsjR9u3YF73Lop8zZJBXh96VQg5QDMwo7PgMpikMw1Jnom5yhiGBuFBZQ6IPv4+wc/UabL026EVOGPQA9HxITaVppvhhkeMhxu9m5iIDad34CK8qJqg3KnScjx3NUnsF9fRLBLgBtGd2lcKD2ZbJ749Qq8tHg88vMurfVDabMfn/Ebvux+Lzx1Ety1Ojx5MQX4YRKw5xtSXTb9hBVlAHv1jsAaR2Dkk03fFsMwFoUFlDoc7J7e+5ZQQRNTXfqjLD9atMd3DxDRDwxjSzhoHDDWs6toF1YVY/dhYzQPMayTL7oGygJMar6xNs+9oyIbfb3/vfVVbJGK8FdlJu5bcV2dPiajhzyGZZMW4FPJF2GUyVZXCax5Flh6O1CW34SjBJatewL77fT7G3g34NG65eEZhjEfLKDURKdF3spH4aCTnWLJXq5SPawsntCdzTuMbTLJp5fS/jd+VbVlJITcblLlmKCCghTl0xiySrPwbrLRhPRAt1lQqeu+zQSHDMDgOzcAwx5R5pWcXImHF47E8ZPGyLmGkJkRh/fyjuCu4EA8EhQIacRjjfo+wzDWBQsoNdnyHiLP7cCSlHRcX1qF96cswIZTBWKRo50aIzpz5ljGNhnebw7cdBJ66jTo5dPzkuWUJdbdyU6Zvm1Y4xOzvbPnHeRrZefaKe6dMX74s5f/ksYemPQ2cMsvkJw88aafD7baA7N3v4wlax8TzuoN4cfNz6Nc7+0b4dsDKtaeMIxNwwKKKVTxdcv7oukOFV6f9C0yKsORoc8NQcKJswNnj2VsEwcnD6y5biV+veswbplMGZGr4+pop1QqJu0JZY5tDGsT12Ld+XWi7e3ojeev+qFxHex+FUrvWYvzLp5islKlwttpG/Hs1mdRVFHdZ6YmFy8ew9IS2bHXSSfhrnHvNW7fDMNYHTYtoNRX4KyxZF88isrl95KLnzxjwitA5ChsOGksDjihR4DZ9scwlsDLO7Le5f+Z0Bk/3zMYfzw8An5uDU/Mlleajbf3vK1MvzjkRfg4+TS6fy7+3bFg1nbMdjb285/Ef3DL37fgVM6pOr/3w+bnUaH3lbnFvQv8/Lo3et8Mw1gXNi2gPPf7USze0/z8CVQb5Kk1d+MuLwekazRAt6nAiMfFsg0n5OKABPufMG0d8kUZ1cUf4T4ujfree8tvRE5ZjmiPDx///+3dCVhUVRsH8D8gO4IiIC6YKe7iEqRh5poLkplZlmJufblk6pdGaloupZlmaV+m5ZJm5pKmopVruCSaYuKC5oI7brmiIIsw3/OeccYZlUSF5s7M//c883Bn7p3LHQYO75z3nPeYTWt+UM6unhjUPgYTG0xAYWd9/ZXjKcfR8eeO+HHbZ3cNuj17JgGLbugXO3SX3pMm4x/6exORdlh1gCKp6feW7ME3G5Me6TwL1vwXOxwysMvNFQNLloKuzWRpqXEuJR17kvWzCaqV9Eagj1s+XTmRZck/+b8OxODI0XWPfK6Nf3yOFTf/VtuFdcCwOu/ly0y3po83x4LWC1C1mH68TGZOJkbt/xaD5zVBmkwnvmX6hiEqHSQ6eFeCr2/wI39vIrI8qw5QDMb88hc+XXUgz4PpTCUnb8PnZ9Yb7/d/oj8cPIqq7d/+Muk9YXE2shFHj61H61k18fLWoZixZcyjnSz9KlYnTDfeHRQUCX+v/JvpFlQ4CHMi5qBDcDvjY7/cvIjlmz9S26dPx2Nx+km17ZGjQ9fG+jFkRGT9rDpA6dvk9ielL2MPY0RMInJurfWR10+RI9a+hRu3Rv63dy2FOrVlHIreuv23x588y/EnZCNKBtbGBQf930lsxrm7irY9kNXv48MzpzDq74uIgBeeb/yIAU8uNVzee3oEJpR7Rc1CesbBEy8/+5naN23jUGO9oiifKijqWz7fvz8RWYZVByg9G5bHyOf1BdTE7C3H8c6iXbiZnbfFx5b89i62Qj8lMjBbh7cjvzXuS8/Kxu+HL6htWcW1ekn9zAIia+fq5oNGLvoVhq85OtxVtC3PkmKBP2erkvJtMx0wrs3Cf6x58qiaPzMMC5pOxejn5sLRqZAaO3YqQ1/x1jNHhy4ce0JkU6w6QBFd6pXFhJdrGhc7++nPZLw5909kSGXKf3Du3G6MP7nSeH949Z7wKny76mRc0gWkZ+kDnaaVA+Bo+AZENqBFuchci7blifS6LO93+36zUUAR/arIBalMmfrGXhIJUr55bSumh/TF4DKR8CliXmiOiKyb1Qcool1oaXwVFQoXJ/3LWb3vHF6fFY+0TH012Huldkat6onrt4KONs7FUb9OX7Nj1prM3pHy9kS2pF7tN1Svw8Omeb6J6YzNGbf+Rso+A4R2gyVIj03dJ3rghaase0Jk1wHKlClTUKNGDXh7e6tbeHg4fv1Vv9qvaNSokRq9b3rr1auX2TlOnDiByMhIeHh4ICAgANHR0bh5896BxINoWT0QM7qGwf1W5UtJz3Sa/geupmXddeyKDR9go07fIPtl6xAdOdNsvwy2/e1WgOJSyBH1H3CxNCJbTvPs2DUb/0s7hF6BARjt5w88/wVQgKkdIrJPD9SqlC5dGmPHjsWOHTsQHx+PJk2aoE2bNkhMTDQe88Ybb+DMmTPG27hxt0fVZ2dnq+AkMzMTcXFxmD17NmbNmoUPPvggX16M1G/4/j914H2rXPefJ67g1Wlb8fetSrAGBy7sNW6/X7kzfHzKmO1PPJ2Csyn6BdOeLl8MHi63y38T2XOaJ/3GZQzf8anxfplyTQFfffVZIiKLBSitW7dGq1atUKFCBVSsWBGjR4+Gl5cXtm7dajxGekYCAwONN+lpMVi9ejX27duH77//HrVq1UJERAQ+/PBDTJ48WQUt+SH0MV/M7xEOPy8XdX//mRS0/3oLkq/oB8OKd15eiq+r9kJnj3L3XCvEtDhbE04vJhv1MGmer37ujuO3VnuomeOMjs3vLplPRJQfHrpfVnpD5s+fj9TUVJXqMZg7dy78/PxQvXp1DBkyBGlpacZ9W7ZsQUhICIoXv10noUWLFkhJSTHrhXlUVUt6Y2HPcJS8VVjt6IVUvDwlDkf+vt0A13uyD6JfXnbP55uVt+f4E7JRD5rm2bvvR8y+fkhtu+h0GNXoUzgV0n8QICKyeICyZ88e1Wvi6uqqxpcsWbIEVavqKz127NhR9Y7Exsaq4GTOnDno1KmT8blnz541C06E4b7sy01GRoYKYkxv91PO3ws/9q6Hx/081f3TV9NVT0riaX1l2NycT0nH7lP6Y6qW8EbJIu73/V5E1pzmcc/JQcT1VBQ7uSPX4zIzruH9rR8i51bNkd7FQlHu8Sb/4pUSkb154MEVlSpVQkJCAq5evYpFixahS5cu2LBhgwpSevToYTxOekpKlCiBpk2bIikpCeXLP3wBpY8//hgjR4584OeVKuKuelI6z9wGP7yHm+ml8Oo3NzGrW12VCroX8+qxnL1Dtq3+E72wMXYi3DKvASkbgZuZwD16Rab90gOHnfTpoCo5TujacqoFrpaI7MkD96C4uLggODgYoaGhKnCoWbMmJk2adM9j69atq74ePnxYfZUxKefO3U6fCMN92Zcb6Y2RgMhwO3lSX9o6L6TI2n+f2IqEIlewNzARIX4foNP0bdh0SL92yJ1MpxezvD3ZOmdXL7hVitDfybgKHIm965gDB1dg+tU9aruQTocPn/4IhZzZs0hEBeuR5wbm5OSoFMy9SE+LkJ4UIWNVJEV0/vztIGDNmjVqIK0hTXQvkk4yTG023PIqJTMF444tNN7XpVbAjaxsVSdl5V7ztJJUj918q3qsLDVfoxSrx5IdqNb29nbi0rt2j4kbbiwn/x+fEFSq+Ny/eXVEZKceKECRnoyNGzfi2LFjKtCQ++vXr0dUVJRK48iMHJmCLPtjYmLQuXNnNGjQQNVOEc2bN1eByGuvvYZdu3Zh1apVGDZsGPr06aOCkILwWfxnOJ9za8qwU1EUDvyv2s7MzsGbc3dg0Y5TxmO3JF1UwYtoUtmf1WPJPpRvArgURpqDA1YeW42sjFSz3R81/RJ1dK4IznFEj1a3FwYkItLMGBTp+ZCgQ+qb+Pj4qMBDgoxmzZqptMvatWsxceJENbMnKCgI7dq1UwGIgZOTE1asWIHevXur3hRPT081hmXUqFEF8doQdzoOiw8tVtuezp4Y/vwC+LsXx7uLd6uS+DLD8p0fd+F6eha6Pv041posDsj0DtkNZzfMfbw2JqYfQbqjIyYnTEODuvpAXgQFhWN65224eOkgnF31g86JiAqag07KploZmcUjAZKMR8kt3ZOWlYa2y9ridOppdf/9p95H+0rt1baseDxyeaJaXNBgYLOK+GHbCZy5mq6qx+58vxk8XVmgjexD7JZP0e/gbLX9vHMARndcZ+lLIiIblJf/3wY2W5964rIoY3BSJ7AOXqr4knGfLPw34vlq6Nsk2PjYhDUHVXAiwssVY3BC9lu07eZlbN4xFWnXb48VIyL6t9lkgCJrhcxL1c8ccocjRoQPh6OD+UuVdYIGNq+E91pVvuv5z3J6Mdlh0bbGQY3V9jVdFnrtnYwXFzbF1h1fW/rSiMhO2V6AkpmG/8XfXiukX0A4grzN19ox1aNBeXz8YghuTVJQGrN6LNmh5hVfNLuf7ATEJi232PUQkX2zvUEW68dgUvIpjPMtghNeRdGh2Rf3fUqHOmVQ1MMZ//vtMCKqB6J0UY9/5VKJtKReqXpqMHlqln4WT6lsoH8r85W+iYj+LbYVoJyKB7ZMho8uB6MvpyL9pZg8rxXSsnoJdSOyV65Ormj2WDMsPayvhTKiwRh4eLGaMhFZhu0EKDczgGV9AF2O/n7jIXALDLH0VRFZlegno1HUtShq+NfAU489a+nLISI7ZjMByq8r+yLs0kGotVlL1ALC+1r6koisjreLNwaEDbD0ZRAR2cYg2f0HlmHIhTi0KVUSywt7Ay98BTjZTOxFRERkd6w+QMnKSsP7cR8g28EB15wccar8M0Dxapa+LCIiIrLnAGXmLz1xwFE/7qRCjiP+EzHN0pdERERE9hygJB1Zh6mXd6ptJ1kGPnw41wohIiKyAVYdoJguA9+1cGVUq2xeaIqIiIisk1UHKImON9XXstkO6B05w9KXQ0RERPnEqgMU4SCpnScHqbVEiIiIyDZYfYAS5VkOtUKiLH0ZRERElI+sOkCRtUL6RnKtECIiIltj1QHK2Hoj4eHhZ+nLICIionxm1QFKhfItLH0JREREVACsOkAhIiIi28QAhYiIiDSHAQoRERFpDgMUIiIi0hwGKERERKQ5DFCIiIhIcxigEBERkeYwQCEiIiLNYYBCREREmsMAhYiIiDSHAQoRERFpDgMUIiIi0hwGKERERKQ5DFCIiIhIcwrBCul0OvU1JSXF0pdCREREeWT4v234P25zAcrFixfV16CgIEtfChERET2ga9euwcfHx/YCFF9fX/X1xIkT932BD+rJJ5/E9u3bNX/Ogjovr5U/A2v63ZJPY/JB5eTJk/D29rbLv4OCOi+vlT/Xgvg9kJ6T0NBQlCxZ8r7HWmWA4uioHzojwUl+NkrCycnJKs5ZUOfltfJnYG2/W0LOm5/ntqa/g4I6L6+VP9eC+j1wcXEx/h//Jxwke4c+ffpYxTkL6ry8Vv4MrO13qyBY099BQZ2X18qfq6V/txx0eRmpojHSrSu9J1evXi2wT2REpH1sC4hsl1X2oLi6umL48OHqKxHZL7YFRLbLKntQiIiIyLZZZQ8KUW4cHBywdOlS/oCI7BzbAuvHAEWjtmzZokZPR0ZGwp517doVL7zwAuyRTJ3t3r27mo4no94fe+wx9O/f31gH6H7Wr1+vGukrV64U+LVSwWFboMe2oLvdtQUMUDRqxowZ6Nu3LzZu3IjTp08/0rmys7ORk5OTb9dGBe/IkSMICwvDoUOHMG/ePBw+fBhTp07FunXrEB4ejkuXLvFtsBNsC+zbETtuCxigaND169exYMEC9O7dW/WgzJo1665I+Oeff0aNGjXg5uaGp556Cnv37jUeI8cXKVIEMTExqFq1qhpIKEXtrF3ZsmUxceJEs8dq1aqFESNGwNbINDz5pLR69Wo0bNgQZcqUQUREBNauXYvk5GQMHTpUHZeRkYFBgwapYmXyPgcHB6t/aMeOHUPjxo3VMUWLFlW/M/IJlKwL24J7Y1sQYRdtgSYDFHvuyhMLFy5E5cqVUalSJXTq1AkzZ868a92C6OhoTJgwQVX48/f3R+vWrZGVlWXcn5aWhk8++QTTp09HYmIiAgICLPBK6GHIJ6JVq1bhzTffhLu7u9m+wMBAREVFqQBWfic6d+6sPlV98cUX2L9/P77++mt4eXmpRmrx4sXqOQcOHMCZM2cwadIkq3tD2BawLbBnl+y8LbDKSrK2TqJeCUxEy5YtVb2XDRs2oFGjRsZjZJp1s2bN1Pbs2bNRunRpLFmyBO3bt1ePSbDy1VdfoWbNmhZ6FfSwpCtXGpwqVarcc788fvnyZRWcSjC7Zs0aPPvss2pfuXLl7loSQoJT6VEj68O2wL4dsvO2QJM9KKZWrlyJ+vXrqx9qsWLF8NxzzyEpKcm4X7qvpMvqp59+Ut1YHh4e6p+yDCyzRhLhbtu2DR06dFD3CxUqhFdeeUU1VKYk92j6yye9LRI1G0h6QFJAZL3uVwFAfvdlILWkgOwB2wK2BfZKZ6dtgeYDlNTUVAwYMADx8fFqUJDU72/btu1dgz4lD/fOO+8gISEBFStWVP/gb968CWsjgYhct4zWluBEblOmTFFddNKTklfSHSiBmy2R9/7OP1TTtJatkNyxvHemAacpeVxyyXd2+do6tgVsCwzYFthHW6D5AKVdu3Z48cUXVaMtAyJlPMaePXuwb98+s+MkOJEBpRKcjBw5EsePH1ejna2JBCbfffedGlsigZbhtmvXLhWwSH7RYOvWrcZt6eI7ePBgrt2AtkLG2kj+1LTM+dGjR2FrpKdQ0neSortx44bZvrNnz2Lu3LmqVy0kJEQF6pL+uxfpRTPM4rIFbAvYFhiwLYBdtAWO1pCDk94QyafJujsyelvcOSvFNJ1RokQJ9fX8+fOwJitWrFDBxuuvv47q1aub3aRxNk3zjBo1SvUoyewdGUjo5+dn8wOLmzRpgjlz5mDTpk0qSO3SpYvq1rRFX375pRqV36JFCzXVXGqiSIpDApdSpUph9OjR6m9BfgZSK0WK00mwJrO8JBctpFaC9MTI79Xff/+tZoRYM7YFbAsM2BastIu2QPMBisxOkZHM06ZNwx9//KFuIjMz0+w4Z2dn47YhtWFttT8kAJEBTrIQ4p0kQJE01+7du9X9sWPHqkI9oaGhKpJevny5MUq2JfIeSppLDBkyROVYZRyS9JZJQFa+fHnYogoVKqj3WwJzGfgsr7NHjx5qnJWMrzIMepP030svvaRG+cvMrzfeeEOlQoQ0XtKbOHjwYBQvXhxvvfUWrBnbAj22BWwLethLW6DToC5duujatGmju3Dhggw40G3cuNG4b9OmTeqxJUuWqPtHjx5V93fu3Gk85vLly+qx2NhYna2R1ySvTV6jPWjRooWuT58+lr4MshC2BbljW0C2TtPTjGXwj+Tjv/nmG5W2kbSORIBk+yTVtXnzZtVN2atXL0tfDlkY2wL7xbbAfhXScre+jNSeP38++vXrp8ZhyFRaKUJjWg+EbJPkUmVu/8CBA9GmTRtLXw5ZCNsCYltgvxykGwUaI8XJZNaODBQkIvvFtoDIfjlqrStPRhlLt76hGh4R2R+2BUSkqRQPu/KIiG0BEWk2xUNERET2TVMpHiIiIiLBAIWIiIg0x2IBipTvlsqQssaMVH6V8rymzp07p0q4y35ZoVhG80upa1My3Viea3q7s2aGlIOvV68eChcujMDAQAwaNMgqFxEkslX50RYIqaopJdA9PT3VshgNGjQwW8tIKlJHRUWpfbI6uiwpYS0lv4nskcUCFCnBW7NmTUyePPmufTIsRsqYHzlyBMuWLcPOnTvVWgIys8dQutdAyvnKAnKG27hx44z7ZJG9Vq1aqQZNzrFgwQLExMSw2BuRhuRHWyDBifydN2/eHNu2bVM1dKSct9RSMpDgJDExEWvWrFGzBSUwkpLhRKRROg0wLV0vDhw4oB7bu3ev8bHs7Gydv7+/btq0acbHGjZsqOvfv3+u5x0yZIguLCzM7LGYmBidm5ubLiUlJd9fBxFZpi2oW7eubtiwYbmed9++feo827dvNz7266+/6hwcHHTJycl824g0SJNjUGQVV+Hm5mZ8TD4Jubq64vfffzc7VpablpV8pdKsLCaXlpZmdh7Tcwh3d3ekp6djx44dBf46iKjg2wJZtVwWEQ0ICFDpXFkMTRaVNG0rpIdF0jphYWHGx6QXRs5lWICUiLRFkwGKrMRYpkwZFXBIwSZZufiTTz7BqVOnVBrHoGPHjvj+++8RGxurjp0zZw46depk3C9L1cfFxWHevHnIzs5GcnIyRo0apfaZnoeItCkvbYGkf8SIESNUynflypV44okn0LRpU+NYFVnxWwIYU7KchqwEK/uISHs0GaA4Ozvjp59+wsGDB1UDIgPjJAiJiIgwyylL/liCkJCQEJVf/u6777BkyRIkJSWp/ZKPHj9+vBo4K5+4KlasqMakCNPzEJE25aUtkPV6RM+ePdGtWzfUrl0bn3/+uVq7a+bMmRZ+BUT0sDT7Xzo0NBQJCQm4cuWK+qQkn4ouXryIcuXK5fqcunXrqq+HDx82PjZgwAB1DlkJ+cKFC8aF5/7pPERkPW2BrHQuqlatava8KlWqqL97ITP4JBVkSmbzycwe2UdE2qPZAMXAx8cH/v7+qqs2Pj7+H1e2lUbMtMEykKmLMkVRxp9IuicoKEh1AROR9citLShbtqz6+z5w4IDZ8dLrIjN+RHh4uApwTMee/fbbb6r3xfDBhoi0xWJr8Uj9AdOejqNHj6oAQ7pxJef8448/qsZItvfs2YP+/fur6YaSthGSxvnhhx9UyqZYsWLYvXs33n77bVX7oEaNGsbzSopHph9Kd7B0FY8dOxYLFy6Ek5OTRV43EeVvWyAfQKKjozF8+HA1XblWrVqYPXs2/vrrLyxatMjYmyLtgIxRmTp1KrKystQ05FdffVUFN0SkQZaaPhQbG6um/d1569Kli9o/adIkXenSpXXOzs66MmXKqCmEGRkZxuefOHFC16BBA52vr6/O1dVVFxwcrIuOjtZdvXrV7Ps0btxY5+Pjo6YWy1TEX3755V9/rURUcG2Bwccff6yO8/Dw0IWHh+s2bdpktv/ixYu6Dh066Ly8vHTe3t66bt266a5du8a3hkijuFggERERaY7mx6AQERGR/WGAQkRERJrDAIWIiIg0hwEKERERaQ4DFCIiItIcBihERESkOQxQiIiISHMYoBCRzZCqskuXLrX0ZRBRPmCAQkSPrGvXrio4kJXD79SnTx+1T47JLyNGjFAl7YnIdjFAIaJ8IYtwzp8/Hzdu3DA+lp6ertbMknV0iIgeBAMUIsoXskK4BCmyKKeBbEtwUrt2beNjGRkZ6NevHwICAuDm5ob69etj+/btxv3r169XPS7r1q1DWFgYPDw8UK9ePeNqxbNmzcLIkSOxa9cudZzc5DGDCxcuoG3btup5FSpUQExMDN9hIivEAIWI8k337t3x7bffGu/PnDkT3bp1Mzvm3XffxeLFi9WKw3/++SeCg4PRokULXLp0yey4oUOHYsKECYiPj0ehQoXUucUrr7yCgQMHolq1ajhz5oy6yWMGEry0b99erXAuq51HRUXddW4i0j4GKESUbzp16oTff/8dx48fV7fNmzerxwxSU1MxZcoUjB8/HhEREahatSqmTZsGd3d3zJgxw+xco0ePRsOGDdUxgwcPRlxcnEoZybFeXl4qaAkMDFQ3ecxAxrp06NBBBT5jxozB9evXsW3bNr7LRFamkKUvgIhsh7+/PyIjI1XKRafTqW0/Pz/j/qSkJGRlZeHpp582Pubs7Iw6depg//79ZueqUaOGcbtEiRLq6/nz5+87nsX0eZ6envD29lbPIyLrwgCFiPKVpGLeeusttT158uSHPo8ELgYyzkTk5OQ80PMMz83L84hIW5jiIaJ81bJlS2RmZqqeEhlbYqp8+fJwcXFRqR8DOU4GyUoqJ6/kHNnZ2fl63USkLexBIaJ85eTkZEzXyLYpSbn07t0b0dHR8PX1VemacePGIS0tDa+//nqev0fZsmVx9OhRJCQkoHTp0ihcuDBcXV35ThLZEAYoRJTvZNxHbsaOHatSLq+99hquXbumphKvWrUKRYsWzfP527Vrp6YwN27cGFeuXFEzh/KzEBwRWZ6DTkayEREREWkIx6AQERGR5jBAISIiIs1hgEJERESawwCFiIiINIcBChEREWkOAxQiIiLSHAYoREREpDkMUIiIiEhzGKAQERGR5jBAISIiIs1hgEJERESawwCFiIiIoDX/Byo2+KBRTPAMAAAAAElFTkSuQmCC", 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" ] @@ -486,14 +486,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "id": "6981052c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8dba495f46e248b29d33d99b40675881", + "model_id": "2fb40bbee4d246c4aac9d8c63d6dcec6", "version_major": 2, "version_minor": 0 }, @@ -507,10 +507,10 @@ { "data": { "text/plain": [ - "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=100, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" + "Chronos2Model(output_chunk_shift=0, likelihood=None, hub_model_name=amazon/chronos-2, hub_model_revision=None, local_dir=None, input_chunk_length=24, output_chunk_length=6, enable_finetuning=True, n_epochs=100, pl_trainer_kwargs={'accelerator': 'gpu', 'callbacks': []})" ] }, - "execution_count": 22, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "id": "49b2c2e8", "metadata": {}, "outputs": [], @@ -566,14 +566,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "id": "41e8a82f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34b9699f0deb4cb4bca42eb3b663a3ed", + "model_id": "32bc596de3864391bc0544b8850eef53", "version_major": 2, "version_minor": 0 }, @@ -587,7 +587,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3955fb726ade4ff9b17f2660cea15057", + "model_id": "67cf8731f2b149be8e37ac2a426256f8", "version_major": 2, "version_minor": 0 }, @@ -604,13 +604,13 @@ "" ] }, - "execution_count": 24, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -649,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "id": "9a96ca55", "metadata": {}, "outputs": [ @@ -659,7 +659,7 @@ "True" ] }, - "execution_count": 25, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "id": "ce2fcd82", "metadata": {}, "outputs": [], @@ -700,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "id": "630bb5bc", "metadata": {}, "outputs": [], @@ -721,14 +721,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "id": "c1fddf83", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71e7d2b9d04f427eae8f9e8ea576d7c6", + "model_id": "975912059afa49ffb2f73ed18de29fc1", "version_major": 2, "version_minor": 0 }, @@ -742,7 +742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f523a392cf9c420d98cb2b28510021f1", + "model_id": "7e4af30c12834bf29a072214857d1bc1", "version_major": 2, "version_minor": 0 }, @@ -759,13 +759,13 @@ "" ] }, - "execution_count": 28, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -806,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "3b81f2e2", "metadata": {}, "outputs": [ @@ -846,20 +846,20 @@ " \n", " 1\n", " Full Fine-tuning\n", - " 3.237262\n", - " 13.835668\n", + " 4.550051\n", + " 20.350664\n", " \n", " \n", " 2\n", " Partial Fine-tuning\n", - " 2.899847\n", - " 12.568367\n", + " 4.891376\n", + " 21.527288\n", " \n", " \n", " 3\n", " LoRA (PEFT)\n", - " 5.951829\n", - " 26.501753\n", + " 5.457223\n", + " 23.800879\n", " \n", " \n", "\n", @@ -868,12 +868,12 @@ "text/plain": [ " Model MAPE (%) MAE\n", "0 Base Model 15.254518 70.704819\n", - "1 Full Fine-tuning 3.237262 13.835668\n", - "2 Partial Fine-tuning 2.899847 12.568367\n", - "3 LoRA (PEFT) 5.951829 26.501753" + "1 Full Fine-tuning 4.550051 20.350664\n", + "2 Partial Fine-tuning 4.891376 21.527288\n", + "3 LoRA (PEFT) 5.457223 23.800879" ] }, - "execution_count": 31, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } From 6fb2138e345fcae37b758ee7fd1f4129d653442f Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Mon, 23 Feb 2026 06:49:16 +0100 Subject: [PATCH 12/26] remove FoundationPLModule and enable finetuning for TorchForecastingModel --- .../components/huggingface_connector.py | 8 +- darts/models/forecasting/chronos2_model.py | 157 +-- darts/models/forecasting/foundation_model.py | 117 +-- .../forecasting/torch_forecasting_model.py | 77 +- darts/utils/callbacks/fine_tuning.py | 239 ----- .../26-Chronos-2-finetuning-examples.ipynb | 957 ------------------ ...oundation-Model-Fine-Tuning-examples.ipynb | 629 ++++++++++++ 7 files changed, 757 insertions(+), 1427 deletions(-) delete mode 100644 darts/utils/callbacks/fine_tuning.py delete mode 100644 examples/26-Chronos-2-finetuning-examples.ipynb create mode 100644 examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb diff --git a/darts/models/components/huggingface_connector.py b/darts/models/components/huggingface_connector.py index 4006ec6085..91e60db038 100644 --- a/darts/models/components/huggingface_connector.py +++ b/darts/models/components/huggingface_connector.py @@ -17,8 +17,6 @@ logger = get_logger(__name__) -from darts.models.forecasting.foundation_model import FoundationPLModule - class HuggingFaceConnector: def __init__( @@ -109,10 +107,10 @@ def load_model_weights( def load_model( self, - module_class: type[FoundationPLModule], + module_class: type[PLForecastingModule], pl_module_params: dict, additional_params: Optional[dict] = None, - ) -> FoundationPLModule: + ) -> PLForecastingModule: """Load the model by creating an instance of the given module class and loading the weights. Some configuration files might contain external parameters that are not part of the module class constructor like `architectures`. They are filtered @@ -140,7 +138,7 @@ def load_model( **pl_module_params, **additional_params, ) - self.load_model_weights(module.model) + self.load_model_weights(module) return module def _get_file_path( diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index 04630f0ffe..39f18ce5ea 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -11,7 +11,7 @@ import math import os from dataclasses import dataclass -from typing import Any, Literal, Optional, Union, cast +from typing import Any, Literal, cast import torch from torch import nn @@ -24,10 +24,8 @@ _ResidualBlock, ) from darts.models.components.huggingface_connector import HuggingFaceConnector -from darts.models.forecasting.foundation_model import ( - FoundationModel, - FoundationPLModule, -) +from darts.models.forecasting.foundation_model import FoundationModel +from darts.models.forecasting.pl_forecasting_module import PLForecastingModule from darts.utils.data.torch_datasets.utils import PLModuleInput, TorchTrainingSample from darts.utils.likelihood_models.torch import QuantileRegression @@ -47,7 +45,7 @@ class _Chronos2ForecastingConfig: time_encoding_scale: int | None = None -class _Chronos2Module(nn.Module): +class _Chronos2Module(PLForecastingModule): def __init__( self, d_model: int = 512, @@ -60,11 +58,12 @@ def __init__( feed_forward_proj: str = "relu", rope_theta: float = 10000.0, attn_implementation: Literal["eager", "sdpa"] | None = None, - chronos_config: Optional[dict[str, Any]] = None, - quantiles: list[float] = None, + chronos_config: dict[str, Any] | None = None, **kwargs, ): - """Core Chronos-2 model containing all the modules and forward logic. + """PyTorch module implementing the Chronos-2 model, ported from + `amazon-science/chronos-forecasting `_ and + adapted for Darts :class:`PLForecastingModule` interface. Parameters ---------- @@ -90,12 +89,12 @@ def __init__( Attention implementation to use. If None, defaults to "sdpa". chronos_config Configuration parameters for Chronos-2 model. See :class:`_Chronos2ForecastingConfig` for details. - quantiles - List of quantiles for probabilistic forecasting. **kwargs - Additional keyword arguments. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. """ - super().__init__() + + super().__init__(**kwargs) self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff @@ -182,11 +181,19 @@ def __init__( num_layers=self.num_layers, ) - quantiles = quantiles or self.chronos_config.quantiles + quantiles = self.chronos_config.quantiles self.num_quantiles = len(quantiles) quantiles_tensor = torch.tensor(quantiles) self.register_buffer("quantiles", quantiles_tensor, persistent=False) + # gather indices of user-specified quantiles + user_quantiles: list[float] = ( + self.likelihood.quantiles + if isinstance(self.likelihood, QuantileRegression) + else [0.5] + ) + self.user_quantile_indices = [quantiles.index(q) for q in user_quantiles] + self.output_patch_embedding = _ResidualBlock( in_dim=self.d_model, h_dim=self.d_ff, @@ -195,14 +202,6 @@ def __init__( dropout_p=self.dropout_rate, ) - @property - def device(self) -> torch.device: - return next(self.parameters()).device - - @property - def dtype(self) -> torch.dtype: - return next(self.parameters()).dtype - def _prepare_patched_context( self, context: torch.Tensor, @@ -329,14 +328,14 @@ def _prepare_patched_future( return patched_future, patched_future_covariates_mask - def forward( + def _forward( self, context: torch.Tensor, group_ids: torch.Tensor, future_covariates: torch.Tensor, num_output_patches: int = 1, ) -> torch.Tensor: - """Forward pass of the Chronos-2 model. + """Original forward pass of the Chronos-2 model. Parameters ---------- @@ -449,86 +448,6 @@ def forward( return quantile_preds - -class _Chronos2PLModule(FoundationPLModule): - def __init__( - self, - d_model: int = 512, - d_kv: int = 64, - d_ff: int = 2048, - num_layers: int = 6, - num_heads: int = 8, - dropout_rate: float = 0.1, - layer_norm_epsilon: float = 1e-6, - feed_forward_proj: str = "relu", - rope_theta: float = 10000.0, - attn_implementation: Literal["eager", "sdpa"] | None = None, - chronos_config: Optional[dict[str, Any]] = None, - **kwargs, - ): - """PyTorch Lightning module wrapper for the Chronos-2 model, adapted for - Darts :class:`PLForecastingModule` interface. - - Parameters - ---------- - d_model - Dimension of the model embeddings, also called "model size" in Transformer. - d_kv - Dimension of the key and value projections in multi-head attention. - d_ff - Dimension of the feed-forward network hidden layer. - num_layers - Number of Chronos-2 encoder layers. - num_heads - Number of attention heads in each encoder block. - dropout_rate - Dropout rate of the model. - layer_norm_epsilon - Epsilon value for layer normalization layers. - feed_forward_proj - Activation of feed-forward network. - rope_theta - Base period for Rotary Position Embeddings (RoPE). - attn_implementation - Attention implementation to use. If None, defaults to "sdpa". - chronos_config - Configuration parameters for Chronos-2 model. See :class:`_Chronos2ForecastingConfig` for details. - **kwargs - all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` - base class. - """ - - super().__init__(**kwargs) - - # Get quantiles for model initialization - chronos_config = chronos_config or {} - chronos_config_obj = _Chronos2ForecastingConfig(**chronos_config) - quantiles = chronos_config_obj.quantiles - - # gather indices of user-specified quantiles - user_quantiles: list[float] = ( - self.likelihood.quantiles - if isinstance(self.likelihood, QuantileRegression) - else [0.5] - ) - self.user_quantile_indices = [quantiles.index(q) for q in user_quantiles] - - # Create the core Chronos-2 model - self.model = _Chronos2Module( - d_model=d_model, - d_kv=d_kv, - d_ff=d_ff, - num_layers=num_layers, - num_heads=num_heads, - dropout_rate=dropout_rate, - layer_norm_epsilon=layer_norm_epsilon, - feed_forward_proj=feed_forward_proj, - rope_theta=rope_theta, - attn_implementation=attn_implementation, - chronos_config=chronos_config, - quantiles=quantiles, - ) - # TODO: fine-tuning support w/ normalized loss # Currently, Darts own `RINorm` is not used as Chronos-2 has its own implementation. Major differences # 1. Chronos-2 `RINorm` normalizes both target and covariates, while Darts normalizes target only. @@ -583,16 +502,16 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: # determine minimum number of patches to cover future_length num_output_patches = math.ceil( - future_length / self.model.chronos_config.output_patch_size + future_length / self.chronos_config.output_patch_size ) - # call the core model's forward pass + # call original Chronos-2 forward pass # Unlike the original, we remove `context_mask`, `future_covariates_mask`, `future_target`, # `future_target_mask`, and `output_attentions` parameters. They are not needed for Darts' # implementation. # We also remove `einops` rearrange operation at the end so the raw output tensor is returned, # in shape of `(batch, vars * patches * quantiles * patch_size)` - quantile_preds = self.model.forward( + quantile_preds = self._forward( context=context, group_ids=group_ids, future_covariates=future_covariates, @@ -607,15 +526,15 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: batch_size, n_variables, num_output_patches, - self.model.num_quantiles, - self.model.chronos_config.output_patch_size, + self.num_quantiles, + self.chronos_config.output_patch_size, ) # permute and reshape to (batch, time, vars, quantiles) quantile_preds = quantile_preds.permute(0, 2, 4, 1, 3).reshape( batch_size, - num_output_patches * self.model.chronos_config.output_patch_size, + num_output_patches * self.chronos_config.output_patch_size, n_variables, - self.model.num_quantiles, + self.num_quantiles, ) # truncate to output_chunk_length @@ -633,17 +552,17 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: class Chronos2Model(FoundationModel): # Fine-tuning is turned off for now pending proper fine-tuning support # and configuration. - _allows_finetuning = True + _allows_finetuning = False def __init__( self, input_chunk_length: int, output_chunk_length: int, output_chunk_shift: int = 0, - likelihood: Optional[QuantileRegression] = None, + likelihood: QuantileRegression | None = None, hub_model_name: str = "amazon/chronos-2", - hub_model_revision: Optional[str] = None, - local_dir: Optional[Union[str, os.PathLike]] = None, + hub_model_revision: str | None = None, + local_dir: str | os.PathLike | None = None, **kwargs, ): """Chronos-2 Model for zero-shot forecasting. @@ -958,11 +877,9 @@ def encode_year(idx): self.hf_connector = hf_connector super().__init__(**kwargs) - def _create_model(self, train_sample: TorchTrainingSample) -> FoundationPLModule: + def _create_model(self, train_sample: TorchTrainingSample) -> PLForecastingModule: pl_module_params = self.pl_module_params or {} - model = self.hf_connector.load_model( - module_class=_Chronos2PLModule, + return self.hf_connector.load_model( + module_class=_Chronos2Module, pl_module_params=pl_module_params, ) - - return model diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index 7e4cd6afec..ed7b53a390 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -9,26 +9,16 @@ """ from abc import ABC -from typing import Any -from torch import nn - -from darts.logging import get_logger, raise_log -from darts.models.forecasting.pl_forecasting_module import PLForecastingModule +from darts.logging import get_logger from darts.models.forecasting.torch_forecasting_model import MixedCovariatesTorchModel -from darts.utils.callbacks.fine_tuning import LayerFreezeCallback logger = get_logger(__name__) class FoundationModel(MixedCovariatesTorchModel, ABC): - _allows_finetuning: bool = False - def __init__( self, - enable_finetuning: bool = False, - freeze_patterns: list[str] | None = None, - unfreeze_patterns: list[str] | None = None, **kwargs, ): """Foundation Forecasting Model with PyTorch Lightning backend. @@ -53,16 +43,6 @@ def __init__( Parameters ---------- - enable_finetuning - Whether to enable fine-tuning of the foundation model. If set to ``True``, calling :func:`fit()` will - update the model weights. Default: ``False``. - freeze_patterns - A list of strings. Parameters whose names start with any of these patterns will be frozen - (``requires_grad=False``). This is only used if ``enable_finetuning=True``. Default: ``None``. - unfreeze_patterns - A list of strings. Parameters whose names start with any of these patterns will be unfrozen - (``requires_grad=True``). This is applied after ``freeze_patterns``. This is only used if - ``enable_finetuning=True``. Default: ``None``. batch_size Number of time series (input and output sequences) used in each fine-tuning pass. Default: ``32``. n_epochs @@ -168,94 +148,21 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. - """ - # validate fine-tuning flag - if enable_finetuning and not self._allows_finetuning: - raise_log( - ValueError( - f"Fine-tuning is not supported for {self.__class__.__name__}." - " Please set `enable_finetuning=False`." - ), - logger, - ) - - if not enable_finetuning and (freeze_patterns or unfreeze_patterns): - logger.warning( - "`freeze_patterns` or `unfreeze_patterns` are specified, but `enable_finetuning` is False. " - "These patterns will be ignored." - ) - - if enable_finetuning and (freeze_patterns or unfreeze_patterns): - pl_trainer_kwargs = self.model_params.get("pl_trainer_kwargs") - if pl_trainer_kwargs is None: - pl_trainer_kwargs = {} - else: - pl_trainer_kwargs = dict(pl_trainer_kwargs) - - callbacks = pl_trainer_kwargs.get("callbacks") - if callbacks is None: - callbacks = [] - else: - callbacks = list(callbacks) + enable_finetuning + Enables model fine-tuning. Only effective, if not `None`. + If a bool, specifies whether to perform full fine tuning / training (all parameters are updated) + or keep all parameters frozen. + If a dict, specifies which parameters to fine tune. Must only contain one key-value record. Can be used to: - callbacks.append( - LayerFreezeCallback( - freeze_patterns=freeze_patterns or [], - unfreeze_patterns=unfreeze_patterns or [], - ) - ) - pl_trainer_kwargs["callbacks"] = callbacks - # we must update model_params to be picked up by super().__init__() - self.model_params["pl_trainer_kwargs"] = pl_trainer_kwargs + - Unfreeze specific parameters, while keeping everything else frozen: `{"unfreeze": ["patterns.to.freeze"]}` + - Freeze specific parameters, while keeping everything else unfrozen: `{"freeze": ["patterns.to.freeze"]}` + """ + # Set default fine-tuning to False for foundation models + if "enable_finetuning" not in self.model_params: + self.model_params["enable_finetuning"] = False # initialize `TorchForecastingModel` base class super().__init__(**self._extract_torch_model_params(**self.model_params)) # extract pytorch lightning module kwargs self.pl_module_params = self._extract_pl_module_params(**self.model_params) - - self._enable_finetuning = enable_finetuning - - @property - def _requires_training(self) -> bool: - return self._enable_finetuning - - @property - def internal_model(self) -> Any: - """ - Returns the underlying PyTorch model (nn.Module). - This gives access to the actual internal mechanics of the model, which can be useful - for advanced usage like accessing PEFT adapters, inspecting weights or custom saving/loading. - - If the model has not been initialized yet, returns None. - """ - if hasattr(self, "model") and hasattr(self.model, "model"): - return self.model.model - return None - - @internal_model.setter - def internal_model(self, model: nn.Module): - """ - Sets the underlying PyTorch model (nn.Module). - This allows replacing the internal model, which can be useful for advanced usage like loading PEFT adapters. - - Parameters - ---------- - model - The new PyTorch nn.Module to set as the internal model. - """ - if hasattr(self, "model"): - self.model.model = model - else: - raise_log( - AttributeError( - "The internal model cannot be set because the outer model is not initialized yet." - ), - logger, - ) - - -class FoundationPLModule(PLForecastingModule): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.model: nn.Module diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index 56e5fff00b..7c8d1b0caf 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -145,6 +145,7 @@ def __init__( random_state: Optional[int] = None, pl_trainer_kwargs: Optional[dict] = None, show_warnings: bool = False, + enable_finetuning: Optional[Union[bool, dict[str, list[str]]]] = None, ): """Pytorch Lightning (PL)-based Forecasting Model. @@ -265,6 +266,14 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective, if not `None`. + If a bool, specifies whether to perform full fine tuning / training (all parameters are updated) + or keep all parameters frozen. + If a dict, specifies which parameters to fine tune. Must only contain one key-value record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: `{"unfreeze": ["patterns.to.freeze"]}` + - Freeze specific parameters, while keeping everything else unfrozen: `{"freeze": ["patterns.to.freeze"]}` """ super().__init__(add_encoders=add_encoders) suppress_lightning_warnings(suppress_all=not show_warnings) @@ -361,6 +370,8 @@ def encode_year(idx): # pl_module_params must be set in __init__ method of TorchForecastingModel subclass self.pl_module_params: Optional[dict] = None + self.enable_finetuning = enable_finetuning + @classmethod def _validate_model_params(cls, **kwargs): """validate that parameters used at model creation are part of the model cls __init__, @@ -498,8 +509,72 @@ def _init_model(self, trainer: Optional[pl.Trainer] = None) -> PLForecastingModu _get_runs_folder(self.work_dir, self.model_name), INIT_MODEL_NAME ) ) + self._setup_fine_tuning(model) return model + def _setup_fine_tuning(self, model: PLForecastingModule): + """ + Sets up the model for fine-tuning based on `self.enable_finetuning`. + """ + # Default behavior (None): fine-tuning (training) is enabled, all parameters are trainable + # This means we don't need to do anything, as parameters are trainable by default. + if self.enable_finetuning is None: + return + + # Boolean behavior + if isinstance(self.enable_finetuning, bool): + requires_grad = self.enable_finetuning + for param in model.parameters(): + param.requires_grad = requires_grad + return + + # Dict behavior + if isinstance(self.enable_finetuning, dict): + keys = list(self.enable_finetuning.keys()) + if len(keys) != 1 or keys[0] not in ["freeze", "unfreeze"]: + raise_log( + ValueError( + "If `enable_finetuning` is a dict, it must contain exactly one key: 'freeze' or 'unfreeze'." + ), + logger, + ) + + mode = keys[0] + patterns = self.enable_finetuning[mode] + + if not isinstance(patterns, list) or not all( + isinstance(p, str) for p in patterns + ): + raise_log( + ValueError( + "The value of `enable_finetuning` dict must be a list of strings (patterns)." + ), + logger, + ) + + # "unfreeze" mode: freeze all, then unfreeze specific + if mode == "unfreeze": + for param in model.parameters(): + param.requires_grad = False + self._apply_freeze_patterns(model, patterns, freeze=False) + + # "freeze" mode: unfreeze all, then freeze specific + elif mode == "freeze": + for param in model.parameters(): + param.requires_grad = True + self._apply_freeze_patterns(model, patterns, freeze=True) + + @staticmethod + def _apply_freeze_patterns( + model: PLForecastingModule, patterns: list[str], freeze: bool + ): + """ + Applies freezing or unfreezing to parameters matching the given patterns. + """ + for name, param in model.named_parameters(): + if any(name.startswith(p) for p in patterns): + param.requires_grad = not freeze + def _setup_trainer( self, trainer: Optional[pl.Trainer], @@ -2473,7 +2548,7 @@ def min_train_samples(self) -> int: @property def _requires_training(self) -> bool: """Whether the model should be trained when calling a `fit*` method.""" - return True + return False if not self.enable_finetuning else True def _check_optimizable_historical_forecasts( self, diff --git a/darts/utils/callbacks/fine_tuning.py b/darts/utils/callbacks/fine_tuning.py deleted file mode 100644 index 3c904f3824..0000000000 --- a/darts/utils/callbacks/fine_tuning.py +++ /dev/null @@ -1,239 +0,0 @@ -from functools import partial -from typing import Any, Callable, Optional - -import pytorch_lightning as pl -from pytorch_lightning.callbacks import Callback -from torch import nn - -from darts.logging import get_logger - -logger = get_logger(__name__) - - -class ModelTransformCallback(Callback): - def __init__( - self, - transform_fn: Callable[[nn.Module], nn.Module], - model_attribute: str = "model", - verbose: Optional[bool] = None, - ): - """ - A PyTorch Lightning callback that applies a transformation function to an internal model - within a LightningModule. - - This is useful for modifying model architectures (e.g., applying PEFT or freezing layers) - just before the training starts, while ensuring the transformation is correctly handled - during checkpoint saving and loading. - - Parameters - ---------- - transform_fn - A function that takes an ``nn.Module`` and returns a transformed ``nn.Module``. - model_attribute - The attribute name of the model within the LightningModule. Default: ``"model"``. - verbose - Whether to log information about the model transformation, such as the number of - trainable parameters. If ``None``, it will be set to ``True`` if the trainer has a - progress bar callback enabled (e.g. when ``model.fit(..., verbose=True)``). - Default: ``None``. - """ - super().__init__() - self.transform_fn = transform_fn - self.model_attribute = model_attribute - self.verbose = verbose - self._transformed = False - - def _get_inner_model(self, pl_module: pl.LightningModule) -> nn.Module: - """Get the inner model from the Lightning module.""" - return getattr(pl_module, self.model_attribute) - - def _set_inner_model(self, pl_module: pl.LightningModule, model: nn.Module): - """Set the inner model on the Lightning module.""" - setattr(pl_module, self.model_attribute, model) - - def setup(self, trainer: pl.Trainer, pl_module: pl.LightningModule, stage: str): - """Apply transformation before training begins (before optimizer setup).""" - if not self._transformed: - inner_model = self._get_inner_model(pl_module) - transformed_model = self.transform_fn(inner_model) - self._set_inner_model(pl_module, transformed_model) - self._transformed = True - - verbose = self.verbose - if verbose is None: - verbose = trainer.progress_bar_callback is not None - - if verbose: - # Log trainable parameters - trainable = sum( - p.numel() for p in pl_module.parameters() if p.requires_grad - ) - total = sum(p.numel() for p in pl_module.parameters()) - logger.info( - f"Model transformed. Trainable: {trainable:,}/{total:,} ({100 * trainable / total:.2f}%)" - ) - - def on_save_checkpoint( - self, - trainer: pl.Trainer, - pl_module: pl.LightningModule, - checkpoint: dict[str, Any], - ): - """ - Handle checkpoint saving for transformed models. - - For PEFT models, we could optionally save just the adapter weights - or mark the checkpoint as requiring transformation on load. - """ - # Mark that this checkpoint was saved with a transformed model - checkpoint["model_transform_applied"] = True - - def on_load_checkpoint( - self, - trainer: pl.Trainer, - pl_module: pl.LightningModule, - checkpoint: dict[str, Any], - ): - """ - Apply transformation before loading checkpoint weights. - - This ensures the model structure matches the saved weights. - """ - if checkpoint.get("model_transform_applied", False) and not self._transformed: - inner_model = self._get_inner_model(pl_module) - transformed_model = self.transform_fn(inner_model) - self._set_inner_model(pl_module, transformed_model) - self._transformed = True - - -class LayerFreezeCallback(ModelTransformCallback): - @classmethod - def _freeze_layers( - cls, model: nn.Module, freeze_patterns: list[str], unfreeze_patterns: list[str] - ) -> nn.Module: - for name, param in model.named_parameters(): - if any(name.startswith(layer) for layer in freeze_patterns): - param.requires_grad = False - if any(name.startswith(layer) for layer in unfreeze_patterns): - param.requires_grad = True - return model - - def __init__( - self, - freeze_patterns: list[str], - unfreeze_patterns: list[str] = None, - model_attribute: str = "model", - verbose: Optional[bool] = None, - ): - """ - A callback to freeze or unfreeze specific layers of a model based on name patterns. - - Parameters - ---------- - freeze_patterns - A list of strings. Parameters whose names start with any of these patterns will be frozen - (``requires_grad=False``). - unfreeze_patterns - A list of strings. Parameters whose names start with any of these patterns will be unfrozen - (``requires_grad=True``). This is applied after ``freeze_patterns``. Default: ``None``. - model_attribute - The attribute name of the model within the LightningModule. Default: ``"model"``. - verbose - Whether to log the trainable parameter count after freezing. If ``None``, it will be - set to ``True`` if the trainer has a progress bar callback enabled - (e.g. when ``model.fit(..., verbose=True)``). Default: ``None``. - """ - unfreeze_patterns = unfreeze_patterns or [] - - super().__init__( - transform_fn=partial( - self._freeze_layers, - freeze_patterns=freeze_patterns, - unfreeze_patterns=unfreeze_patterns, - ), - model_attribute=model_attribute, - verbose=verbose, - ) - - -class PeftCallback(ModelTransformCallback): - @classmethod - def _apply_peft(cls, model: nn.Module, peft_config) -> nn.Module: - try: - from peft import get_peft_model - except ImportError: - raise ImportError( - "Please install the `peft` package to use PeftCallback: `pip install peft`." - ) - peft_model = get_peft_model(model, peft_config) - return peft_model - - def __init__( - self, - peft_config=None, - model_attribute: str = "model", - verbose: Optional[bool] = None, - ): - """ - A callback to apply Parameter-Efficient Fine-Tuning (PEFT) to a model using the ``peft`` library. - - It wraps the internal model with a PEFT adapter (e.g., LoRA) and manages the merging of - weights during checkpointing so that the saved state can be loaded as a standard model. - - Parameters - ---------- - peft_config - A PEFT configuration object (e.g., ``LoraConfig``) from the ``peft`` library. - model_attribute - The attribute name of the model within the LightningModule. Default: ``"model"``. - verbose - Whether to log the trainable parameter count after applying PEFT. If ``None``, it will be - set to ``True`` if the trainer has a progress bar callback enabled - (e.g. when ``model.fit(..., verbose=True)``). Default: ``None``. - """ - super().__init__( - transform_fn=partial(self._apply_peft, peft_config=peft_config), - model_attribute=model_attribute, - verbose=verbose, - ) - self.peft_config = peft_config - - def on_save_checkpoint(self, trainer, pl_module, checkpoint): - # We replace the state_dict in the checkpoint with the one from the base model - # (with adapters merged), so that the model can be loaded as a regular model. - super().on_save_checkpoint(trainer, pl_module, checkpoint) - peft_model = getattr(pl_module, self.model_attribute, None) - try: - from peft import PeftModel - except ImportError: - return - - if isinstance(peft_model, PeftModel): - # In-place merge of adapters into the base model weights. - # This is memory-efficient as it avoids a full deepcopy and works on GPU. - peft_model.merge_adapter() - try: - # Obtain the state_dict of the base model (which now has merged weights). - # We filter out the adapter-specific keys (e.g. lora_A, lora_B) - # and restore the original key names by removing PEFT wrapper prefixes (e.g. base_layer). - # This allows the model to be loaded back as a standard (non-PEFT) model. - prefix = self.model_attribute + "." - new_state_dict = {} - # IMPORTANT: We move merged weights to CPU. This avoids GPU OOM - # (holding two copies of the parameters on GPU) and ensures we have - # a 'snapshot' that won't be changed by the subsequent unmerge. - for k, v in peft_model.get_base_model().state_dict().items(): - if any(sub in k for sub in ["lora_", "modules_to_save"]): - continue - - # PEFT wraps layers and adds a ".base_layer" to the key path - # We only replace if it's followed by a dot to avoid partial matches - clean_key = k.replace(".base_layer.", ".") - new_state_dict[prefix + clean_key] = v.cpu().clone() - - # Update the checkpoint - checkpoint["state_dict"] = new_state_dict - - finally: - # Restore the adapters (unmerge from base weights) to allow training to continue. - peft_model.unmerge_adapter() diff --git a/examples/26-Chronos-2-finetuning-examples.ipynb b/examples/26-Chronos-2-finetuning-examples.ipynb deleted file mode 100644 index 09554b97d5..0000000000 --- a/examples/26-Chronos-2-finetuning-examples.ipynb +++ /dev/null @@ -1,957 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "da55dd6c", - "metadata": {}, - "source": [ - "# Chronos-2 Foundation Model Fine-Tuning\n", - "This example notebook presents how fine-tuning can be applied to the Chronos-2 model using both built-in Darts features and external libraries.\n", - "\n", - "The following fine-tuning methods will be shown:\n", - "1) **Full fine-tuning**: All model weights are retrained. This is natively supported by setting `enable_finetuning=True`.\n", - "2) **Partial fine-tuning**: Specific layers are frozen via name patterns. This is natively supported using `freeze_patterns` and `unfreeze_patterns`.\n", - "3) **PEFT fine-tuning**: The HuggingFace `peft` library is used via a custom Darts callback (`PeftCallback`) to apply LoRA. This shows how to extend Darts with external specialized libraries.\n", - "\n", - "To be useful, a fine-tuned model should be easily saved and loaded. For each method, we will demonstrate how to persist the model weights.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "bfa59f65", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "310fa52a", - "metadata": {}, - "outputs": [], - "source": [ - "# fix python path if working locally\n", - "from utils import fix_pythonpath_if_working_locally\n", - "\n", - "fix_pythonpath_if_working_locally()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d510b54b", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "import numpy as np\n", - "\n", - "from darts.datasets import AirPassengersDataset\n", - "from darts.models import Chronos2Model\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "import logging\n", - "\n", - "logging.disable(logging.CRITICAL)" - ] - }, - { - "cell_type": "markdown", - "id": "6b82a07a", - "metadata": {}, - "source": [ - "## Data Preparation\n", - "Here we just load an example dataset with 144 samples as a fast demo. The data is split between train and validation, with the 2 last years (24 samples) for validation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2f87bcc5", - "metadata": {}, - "outputs": [], - "source": [ - "# convert to float32 as Chronos-2 works with float32 input\n", - "data = AirPassengersDataset().load().astype(np.float32)\n", - "train_passengers, val_passengers = data.split_before(\n", - " len(data) - 2 * 12\n", - ") # last 2 years for validation" - ] - }, - { - "cell_type": "markdown", - "id": "b9251561", - "metadata": {}, - "source": [ - "# Model prediction out-of-the-box\n", - "Let's see how the model behaves on the validation data without any fine-tuning. For that we:\n", - "- Create the model\n", - "- Call fit to load the model internally (no training is done)\n", - "- Predict on the validation set" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ea8456ae", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d269fe29e6ab4b0faa9ca063fc607f74", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = Chronos2Model(\n", - " input_chunk_length=24,\n", - " output_chunk_length=6,\n", - ")\n", - "model.fit(train_passengers, verbose=True)\n", - "\n", - "prediction = model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - ")\n", - "val_passengers.plot(label=\"Ground truth\")\n", - "prediction.plot(label=\"Forecast\", title=\"Base model (not finetuned yet)\")" - ] - }, - { - "cell_type": "markdown", - "id": "1313019f", - "metadata": {}, - "source": [ - "# 1. Full fine-tuning\n", - "\n", - "In this method, all the model weights are retrained. This is simply enabled by passing `enable_finetuning=True` to the model constructor. \n", - "\n", - "When fine-tuning is enabled, Darts will treat the foundation model like a standard trainable model during `fit()`. Saving and loading follows the standard Darts API via the `save()` and `load()` methods.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "72832dff", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "45bbf43f117543b7a171e399527dce2d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_full_finetuned = full_finetuned_model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - ")\n", - "pred_full_finetuned_loaded = full_finetuned_loaded_model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - ")\n", - "val_passengers.plot(label=\"Ground truth\")\n", - "pred_full_finetuned.plot(label=\"Forecast of the full finetuned model\", linestyle=\"-.\")\n", - "pred_full_finetuned_loaded.plot(\n", - " label=\"Forecast of the loaded full finetuned model\",\n", - " linestyle=\"--\",\n", - " title=\"Full finetuning\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d3dc22f4", - "metadata": {}, - "source": [ - "We can also verify numericaly that the prediction of the trained model is identical to the prediction of the loaded model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "599402d3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(pred_full_finetuned.values(), pred_full_finetuned_loaded.values())" - ] - }, - { - "cell_type": "markdown", - "id": "3cabab8a", - "metadata": {}, - "source": [ - "# 2. Partial fine-tuning with layer freezing\n", - "\n", - "Partial fine-tuning allows you to update only a subset of the model's parameters, which is useful for preserving general knowledge while adapting to specific patterns. \n", - "\n", - "Darts foundation models natively support this via:\n", - "- `freeze_patterns`: A list of parameter name prefixes to freeze (`requires_grad=False`).\n", - "- `unfreeze_patterns`: A list of prefixes to unfreeze (applied after freezing).\n", - "\n", - "This mechanism automatically injects a `LayerFreezeCallback` into the training process." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "33fa7fc4", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "50dfcff23af64611b005b9875ed3209f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_partial_finetuned = partial_finetuned_model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", - "pred_partial_finetuned_loaded = partial_finetuned_loaded_model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", - "val_passengers.plot(label=\"Ground truth\")\n", - "pred_partial_finetuned.plot(\n", - " label=\"Forecast of the partial finetuned model\", linestyle=\"-.\"\n", - ")\n", - "pred_partial_finetuned_loaded.plot(\n", - " label=\"Forecast of the loaded partial finetuned model\",\n", - " linestyle=\"--\",\n", - " title=\"Partial finetuning\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3c01daaa", - "metadata": {}, - "source": [ - "Again, we verify that the prediction of the fine-tuned model is the same as the loaded model to make sure that saving/load works correctly" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "01717b70", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(pred_partial_finetuned.values(), pred_partial_finetuned_loaded.values())" - ] - }, - { - "cell_type": "markdown", - "id": "b0d126dc", - "metadata": {}, - "source": [ - "# 3. LoRA fine-tuning (PEFT)\n", - "\n", - "This method uses the HuggingFace `peft` library for **P**arameter **E**fficient **F**ine-**T**uning. \n", - "\n", - "Darts provides a `PeftCallback` that wraps the internal model with adapters (like LoRA) before training. One major advantage of this callback is that it automatically handles **weight merging** during checkpointing, allowing the saved model to be loaded back as a standard model without needing the `peft` library at inference time.\n", - "\n", - "More information about peft can be found in the [official documentation](https://github.com/huggingface/peft)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6981052c", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2fb40bbee4d246c4aac9d8c63d6dcec6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00]})" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from peft import LoraConfig\n", - "\n", - "from darts.utils.callbacks.fine_tuning import PeftCallback\n", - "\n", - "lora_config = LoraConfig(\n", - " r=32,\n", - " lora_alpha=64,\n", - " target_modules=[\n", - " \"q\",\n", - " \"v\",\n", - " \"k\",\n", - " \"o\",\n", - " \"output_patch_embedding.output_layer\",\n", - " ],\n", - ")\n", - "peft_callback = PeftCallback(peft_config=lora_config)\n", - "\n", - "model_lora = Chronos2Model(\n", - " input_chunk_length=24,\n", - " output_chunk_length=6,\n", - " enable_finetuning=True,\n", - " n_epochs=100,\n", - " pl_trainer_kwargs={\"accelerator\": \"gpu\", \"callbacks\": [peft_callback]},\n", - ")\n", - "model_lora.fit(train_passengers, verbose=True)" - ] - }, - { - "cell_type": "markdown", - "id": "e86085e3", - "metadata": {}, - "source": [ - "## 3.1 Full-model saving\n", - "Darts `save` and `load` methods can be used to save the full model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "49b2c2e8", - "metadata": {}, - "outputs": [], - "source": [ - "# Fully save the model including adapters\n", - "model_lora.save(\"chronos2_lora_finetuned.pt\")\n", - "model_lora_loaded = Chronos2Model.load(\"chronos2_lora_finetuned.pt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "41e8a82f", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "32bc596de3864391bc0544b8850eef53", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_lora_trained = model_lora.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", - "pred_lora_loaded = model_lora_loaded.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", - "val_passengers.plot(label=\"Ground truth\")\n", - "pred_lora_trained.plot(label=\"Forecast of the LoRA trained model\", linestyle=\"-.\")\n", - "pred_lora_loaded.plot(\n", - " label=\"Forecast of the loaded LoRA model\",\n", - " linestyle=\"--\",\n", - " title=\"LoRA finetuning - Save all\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "32cef0b5", - "metadata": {}, - "source": [ - "Again, we verify that the prediction of the fine-tuned model is the same as the loaded model to make sure that saving/load works correctly" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9a96ca55", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(pred_lora_loaded.values(), pred_lora_trained.values())" - ] - }, - { - "cell_type": "markdown", - "id": "c633f2ad", - "metadata": {}, - "source": [ - "## 3.2 Adapter saving\n", - "\n", - "Alternatively, you may want to save *only* the lightweight adapters rather than the full model weights.\n", - "\n", - "Foundation models in Darts provide an `internal_model` property that gives direct access to the underlying PyTorch `nn.Module`. We can use this to interact with the `peft` API directly for saving and loading.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ce2fcd82", - "metadata": {}, - "outputs": [], - "source": [ - "model_lora.internal_model.save_pretrained(\"chronos2_lora_adapters/\")" - ] - }, - { - "cell_type": "markdown", - "id": "6e2f159a", - "metadata": {}, - "source": [ - "Then, a new model can be created, and the internal model can be replaced with the loaded adapter" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "630bb5bc", - "metadata": {}, - "outputs": [], - "source": [ - "from peft import PeftModel\n", - "\n", - "model_new = Chronos2Model(\n", - " input_chunk_length=24,\n", - " output_chunk_length=6,\n", - ")\n", - "model_new.fit(train_passengers) # Initialize model\n", - "\n", - "# Replace _Chronos2Module with PeftModel containing _Chronos2Module + adapters\n", - "model_new.internal_model = PeftModel.from_pretrained(\n", - " model_new.internal_model, \"chronos2_lora_adapters/\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c1fddf83", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "975912059afa49ffb2f73ed18de29fc1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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qsznTzYCcHsmnhIQRmlXRA49mVnTjovBVYyChg1T8dDOnXB00O9T3SdAnNDQUAwYMEEIMhV22FnRTJl8TuqmSSYL8F6gvdPOn2TQJHzTb1gozBPlb0GyeHlw086SHyB133CG0RvSfQmVJWKFwT3NAGgc6h6SpoHBQclqm2Tap8EmQas6Muya0X3IwpvEj4ZPMGjR2ZCIgfxQ691phgMJFSXtGph1S35MAQzPxukJNSZAlnxXKX0PhrDV9mmi86RxRyDuZE2mflIuEHFJpv9SuCxLAtdqDu+66Swin9LAnswcJCVpIuKIQdQoHp/7Tw5nyA2nHUh9a/9JLL4lriBw5KeyXrue6BDwaK/p90fYk/NNYkdaCvosWuuZIQCMtApneyNxIfSY/DPptkbM1jQEJlSQ0kBaDlpPZhq7D+qBrlcaI/HrIB4U0fiSIkalT609SG3R80hTR74AmMOQzRE7MNKEhc5a+Q2xzofGn65cEPfITIg0KY0WYKXqIsWK0IX11vZKSksR2hw8fFmGXbm5ukouLi3TFFVdIu3fvNtiXNmTyt99+q/VY48aNE6GMt956q9hOP2y5JtowWApDNFWYcc1+1RUSfOTIEemaa64Rob4UFkqhnXPmzJE2b95ssN3LL78swkMpbFM/LJJCVinslcJbKRSbPkshqnWFGdfsf80wy/rCjGuGh2u/K/3XQqGjzz77rBQUFCQ5OztL48ePl06fPi2+39133y2ZCgobf+ONN8TYU8gvhTd7e3uL4/3+++8G2+bm5kq33Xab5OfnJ64purbOnDlz2ffUcv78eeWa3LlzZ53Hv++++6SwsDARmkzfd8KECdKXX37ZYN/XrFkj9enTR3JycpI6deokvfnmmyJcvuZ5oJDXF198UXw/Gku6pk+ePHlZvynM+JFHHlG2GzlypAijruva/Pnnn0VIdUBAgNieQoprhmcXFRVJ8+bNEyHW9Bn9kOOKigrR5549e4prlsZ94MCBoq/5+fnKdvQ5GqOa0PmZNGmSOD6FAoeHh0t33XWXlJaW1uDYFRYWivD2kJAQMe4Uqk9h+Pqh2PUdu65ru2YIv3749Z133tlgvxjLQkV/zC0kMQxj+ZDZhUwupB2j2S9jHiiTLGkgSCugXx6CqR3SzJDjPWkltWkCGOuAfVAYhrkMfSfXmn4T+iUBGMbSIdMbmcnIHMZYF+yDwjDMZZDfAjkok88E+S1QlBQ5IJPvRW11cxjG0qCkjpRDhuoRUX0gU/pOMa0DCygMw1wGRWOQYzM5NFOIrtZxlsw7DGMNUAQPCdfkIF0zKzJjHbAPCsMwDMMwFgf7oDAMwzAMY3GwgMIwDMMwjMVhlT4olFKZkjhRmmR2fGIYhmEY64Aym1AGZCoYWVtBWKsXUEg4aaiWC8MwDMMwlglVtKcMxG1OQNEWmKIv2FBNF4ZhGIZhLAOKCiQFg36hyDYloGjNOiScsIDCMAzDMNaFMe4Z7CTLMAzDMIzFwQIKwzAMwzAWBwsoDMMwDMNYHFbpg2Is1dXVqKysNHc3GIZpBezt7WFra8tjzTBtBLu2Gmednp4uysMzDNN+8PLyQlBQEOdHYpg2QJsUULTCSUBAAFxcXPhmxTBtHJqUlJSUIDMzU7wPDg42d5cYhmkmdm3RrKMVTnx9fc3dHYZhWglnZ2fxn4QU+v2zuYdhrJs25ySr9TkhzQnDMO0L7e+efc8YxvppcwKKFq7RwzDtD/7dM0zboc0KKAzDMAzDWC8soDDN5oUXXkC/fv3MPpLjxo3Dgw8+aO5uMAzDMCaABRQLiz5avHgxoqOj4eTkhMDAQIwcORKff/65iFCwVrZu3SpU76YK+zb1/hiGYRjLo81F8VgrFy9eFMII5XF47bXX0Lt3bzg6OuLEiRP48ssvERoaipkzZ9b6WXIIpCRV1k5FRQUcHBzM3Q2GYRjGAmANioVw7733ws7ODgcPHsScOXPQvXt3REZGYtasWfjrr78wY8YMZVvSHpBWhQQWV1dXvPrqq2I5LYuKihIP+a5du+KHH35QPhMfHy8+d/ToUWUZaSBoGWkk9DUTmzdvxqBBg0RExIgRI3D27FmDvr7xxhtCu0PlshcuXIiysrI6vxcd94orrhBtb29vsf9bb71VMcn873//E2YZPz8/TJ48ucF+1rc/Qq1W4/HHH4ePj49I2EXmJ4ZhGMb6YAHFArh06RI2bNiA++67TwgcxkQn0IN39uzZQsNy++23Y/Xq1cI89Mgjj+DkyZO46667cNttt2HLli2N7s/TTz+Nd999VwhLJDTR/rX8+uuv4tik5aH1lBDrs88+q3NfYWFhWLlypWiToJOWloYPP/xQWb9s2TIhUO3atQtffPFFg30zZn80hvv27cNbb72Fl156CRs3bmz0GDAM0/7YvPtNnD23ztzdYNqbiWfGxzuRVVjeqsf0d3fE2vtHNbjdhQsXRCZM0nroQ1oFrXaChJc333xTWTdv3jwhgGi58cYbhSaBNDHEww8/jL179+Kdd95RNA7GQhqZsWPHivb//d//Ydq0aaIf5BfzwQcfCK0JvYhXXnkFmzZtqlOLQsmySJtBUPIsMmHp07lzZyFIaCENSX00tL8+ffrg+eefV/b9ySefCI3QxIkTGzUGDMO0HyS1Gt/8dTs+zDmEoDMSPrOxw/mUPUjNj0eP4CEYMfg+c3exXdJuBBQSTtIL6jZFWCL79+8XJov58+ejvNxQuCITjD6nT5/GnXfeabCMfFr0tQvGQg95LdqU4ZSdMzw8XBzn7rvvNth++PDhTdLUEAMHDoQp0e+7tv/a9OcMwzA1Ka8ux4u7nsPanEPifbqtCh/sfhHbpSLxfm5JJgsoZqLdCCikzbDUY1LUDplwavp6kA+KfgpvfeoyBdWFjY1szSNNjZa6sm3qO9xqTUskKLUENb9HY/pZGzWdhan/LdV3hmGsm+zSbDy45UEcyzqmLLvfux+uGf4Urlg/R7xPKc81Yw/bN+1GQDHG1GIuqGYQmSDIHHH//fc3WvggyKmW/DhuueUWZRm979Gjh2j7+/uL/+Sz0b9/f9HWd0RtzHHIv+Pmm29WlpEpqT60kTlUJ6khjOlnY/bHMAxTG2cvncH9Wx5AWnGaeO9k64TXhj2LidEzhcnHUS2h3EaF1GrrTfFg7bQbAcXSIUdTMsmQ6YacUMlUQdqEAwcO4MyZMw2aQh577DER/UMP9SuvvBJr167FqlWrhH+IVgszbNgwEYETEREhzB7PPPNMo/tJjrjk60L9pP4uX74cMTExiranNjp27Cg0GevWrcPUqVNFX9zc3Grd1ph+NmZ/DMMwNdmy5x08cXYZSjWxBwEuAfh4/Mfo4StP6FQ2NgiRbBAHCakqtRBYaBnTuvCIWwgUHnzkyBEhXDz55JPo27evEAI+/vhjPProo3j55Zfr/fzVV18t/E3IKbZnz55YsmQJvvvuOxHKq+Xbb79FVVWVEHYotJccXBvL3Llz8eyzz4pQXtpPQkIC7rnnnno/QzlcXnzxReFwS+HJFFpcHw31s7H7YxiGEUgSlq1biMVnlyrCSS/fnlgxbYUinGgJsZVN62U2KuTkXOABNAMqSd/YbyUUFBTA09MT+fn58PDwMFhH0SRxcXFi9k1RJwzDtB/498/USVU5sPZB/HJxDV7xkyMBp9j64KVr/4STs2E0IPHyiqvwa3mKaP806Dn07nk9D24LP79rwiYehmEYxmopr6pGZkE5wnxc6t6oOBtYMR9I2ou5lLnb3h7e4SNx18wf6jTdhLgGAxoBJeXSafRuqS/A1AkLKAzDMIxVUlGlxvSPduJ8ZhGuG9gBr87uBUc7W4Nt8pP2w3PlQiAvUV5g54z/G/8eVL2uqXffoZ6dgJyDok35UJjWh31QGIZhGKvkeHKeEE6I3w8l48Yv9xok5Ny29z1ctek2bK7Ikhe4BwO3rW9QOCFC9XxSUjWRPkzrwgIKwzAMY5UcTjTMUXI4MQ+zPtmJk8m5WPbXItx/5lsU2djgSX9fnA/pBSz6DwgdYNS+QwL7IrCqCgPKyhBW0bpZyBkZNvEwDMMwVsnhhDyl7elsj/zSSmTl5+HN1dNw1KOQMjWKdWPsfdBh7p+Ai+wcawy+vp2xKS0XqCoF/LxbpP9M/bAGhWEYhrE6KABVq0Fxd7TDvw+OwfAOJejd6SVZONFwt0cvvH3jVjg3QjgRkHDjFS63yX/F+gJerR4WUBiGYRirIyWvFJkaf5N+4V4oyt6BXJeXcN5ZLm1BmWB7pA7E6dLHUNbUpNNaAYW0KMUaPxam1WATD8MwDGN1kL+Jll7Of2HB9lUotpVNOn7Vajgmz8a+kuHA8TQkXCrBVzcPQpCnU9MEFK0WxS3AZP1nGoY1KAzDMIzVcURj3nG1ycO6spUotpGFk+5qW/w8dTkeue5+uDrIIccnUvIx85OdOJqkE2qMYZs9cEtwAK4MC8G/cetb4Fsw9cECCmMVpKeni4KKVEjRy+vyrI91ER8fL+r2NKUwYmtC9Y2oXEFLQ3We+vXrB0tg69at4tzk5Rn/0OjUqRM++OCDFu0XY10alGK1F17u+T/YShIm2nhi6dxNCArqh4k9ArHy3hHo4C2nrCdz0Jwle/DnUTn5mjGUunjjsJMTMuzskJwX12LfhakdFlAs6AFFN+uarwsXrLcGxNKlSxslTNTH+++/Lyock6Bx7tw5sz7kW+JBT3WUaLwYhmmYsspqnErNF+0of1eMG3oPfhj8PN6ZtxUuLn7Kdt2CPPDnfSMxpJOPktht8YqjePvfM1CrG3Z6DfXtprQ5F0rrwwKKBXHVVVeJh7D+i2oKNYWKigq0JWJjY0XxwM6dOyMgwHrswJWVlUZtR7UpTCXMMUxb52RKPiqrZQFjQLgcAky1cmxsL3er9HVzxI93DMXcQWHKsk+3xOKuHw+huLyq3uOEhA5V2ineHUz4DRhjYAHFgnB0dERQUJDBy9ZWtqFu27YNQ4YMEdsEBweLSr5U8VcLVS2mqr5U/dfPzw+TJ08Wy0+ePIkpU6bAzc1NVP696aabkJ2drXxOrVbjrbfeQnR0tNh3eHg4Xn31VWX9E088gS5dusDFxQWRkZGikrH+Q/fYsWO44oor4O7uLgo/kRBx8OBBob6/7bbbREEorTaItA518fnnn4uKzg4ODujatSt++OEHA7X+ypUr8f3334v9kKakJrTvZcuW4c8//1SOR33QcvHiRdFP+h5UKXrPnj0Gn9+5cydGjx4NZ2dnhIWF4YEHHkBxcXGtfSVNB1VTpu+uPZZW+0Ft+i4zZ84U5igay+rqaixcuFAIm7R/+n6kMalP+0Pnk/pAVaN9fHzEtVBz/Mg0cscdd8Df31+M/fjx40Wf9HnjjTfEeafzQ32gYnrGmF3+/fdf9O/fX/SX9puZmYm///4b3bt3F8eaN28eSkpKlM+Vl5eL/pLwSEU6R40ahQMHDhjse/369eJaon3SuSDzW00acx6Y9ot+grYBHRvOUeJgZ4M3ru2N56b3gMZVBRtPZeDaz3cjOVd3HdfExyMMznayiSi1NNMUXWcag2SF5Ofnk+gs/tektLRUOnXqlPhvTdxyyy3SrFmzal2XnJwsubi4SPfee690+vRpafXq1ZKfn5/0/PPPK9uMHTtWcnNzkx577DHpzJkz4pWbmyv5+/tLTz75pPjc4cOHpYkTJ0pXXHGF8rnHH39c8vb2lpYuXSpduHBB2rFjh/TVV18p619++WVp165dUlxcnLRmzRopMDBQevPNN5X1PXv2lBYsWCD2f+7cOenXX3+Vjh49KpWXl0sffPCB5OHhIaWlpYlXYWFhrd9v1apVkr29vfTpp59KZ8+eld59913J1tZW+u+//8T6zMxM6aqrrpLmzJkj9pOXl3fZPmjftJ620x6P+kD9pmulW7du0rp168T+r7vuOqljx45SZWWl+Cx9b1dXV+n9998X34G+b//+/aVbb7211v6WlJRIjzzyiPju2mPRMoKOFRAQIH377bdSbGyslJCQIFVUVEjPPfecdODAAenixYvSjz/+KM7nL7/8Uuf5p/NJY/fCCy+IPi1btkxSqVTShg0blG2uvPJKacaMGWK/tA31ydfXV7p06ZJYT/t3dHSUvv76a3E9PP3005K7u7vUt29fqS62bNkivsOwYcOknTt3imsmOjpa9GfSpEni/fbt28Vx3njjDeVzDzzwgBQSEiKtX79eiomJEd+HrittXxITE0VfHn74YdEXGgO6luhYdJ0aex7ovNH6urDW3z/TOO76/qDU/6kvpRs+GSh9vmqRdDFui9Gf3Xo2U+r1/D9SxyfWideAlzZI++Pk67Q2Zq2eJfVa2ksa+MNASa1W86lqwed3TdqXgLLrY0l6p1vzXxe3G+6X3mvX0TGaAN3Q6aFMN2jtix6kxFNPPSV17drV4MdBD3MSSKqrq8V7eoDQzVwfEi7ooaJPUlKSGDt6UBcUFIiHhr5A0hBvv/22NHDgQOU9PfBIuKmN7777TvL09GxwnyNGjJAWLVpksOz666+Xpk6dqrynhzeNUWOFPK2AQg9pLfQApWUkVBELFy6U7rzzToPPkaBmY2NT54OOhMPaHvS03wcffFBqiPvuu0+69tpr6+w7nc9Ro0YZfGbw4MHSE088ofSPBJiysjKDbaKioqQlS5aI9vDhw4VQq8/QoUONElA2bdqkLHv99dfFMhK4tNx1113S5MmTRbuoqEgImMuXL1fWk1BGAstbb70l3pOQ3KNHD4Nj0XfRF1CMOQ8soDB0Hxz8ykbp2lcXCsGBXu/+PrtRA3M+o1Aa+9Z/ipAS/dRf0i/7E2vd9p6N9yjHySrJ4hPQigJK+8qDUl4IFKY2fz/V5Ze/1+6XjtFESO1N5gEtZCIgTp8+jeHDhwvVu5aRI0eiqKgIycnJwixDkHlFH1L3b9myRZh3avPpIBMBqeYnTJhQZ59++eUXfPTRR2J7Oh6ZlUjFr+Xhhx8WZgYyyVx55ZW4/vrrhammMdD3u/POOw2W0feraQZpDn369FHaZCIjyGzRrVs3MU7Hjx/H8uXLlW1I1iDzV1xcnDBrNIZBgwZdtuzTTz/Ft99+i8TERJSWlgofoYacbPX7rO039ZmgPtP58PX1NdiG9k3nSjuud999t8F6uo7ommgI/WOTiUhr4tNftn//ftGm45HZj86ZFnt7e2GSpD5o+zJ06NDL+qKPqc8D0zZJzS8TETldgs8rywZ1GN2ofUQHuOGP+0bivp8OY9eFS8Kf5fGVx3EuoxBPTu0OW60diPxQ7HX3u9TUg/CLuspE34RpiPYloDi6A+4hzd+PrePl77X7pWM0ERJIyBekOZ/Xhx5gM2bMwJtvvnnZtvSwI7+M+iA/jfnz5wt/C/JpIUfOFStW4N1331W2Ib8I8kf466+/hI/C888/L7aZPXs2LAl6YGrRCnr04NOO01133SX8HWqiFf6acx5oPB599FExbvRQJn+Qt99+G/v27TO6z9p+6/eZzqG+n40WUzjb1hyv+vpiKkx9Hpi2yeEE2f/ksJMjyEOPfs39u89p9H68XByw9LYheGXdKSzbkyCWfb0zTlRH/nhef3g4ydd8aKEug2xK8l70YQGl1WhfAsqI/8kvUxMxGnhEnim2BDRzJCdRmk1qH667du0SD7oOHer2LB8wYID4HDmZ2tldfqopIoacETdv3iy0IDXZvXs3OnbsiKefflpZlpAg/5D1IcdHej300EO48cYb8d133wkBhRxeyUHUmO9H3+eWW25RltH7Hj105c6Nwdjj1TZOp06dapRw2Jhj0XcZMWIE7r33XmWZVsvRVKjPlBuGziud37rGlYSgm2++WVm2d+9emBqtczN9T7peCNKokJMsOW1r+7JmzRqDz9XsS1POA9NOHWRtSmDjmC7ed/PpDneP0Cbty97WBi/O6oUuQe54/s8YVKklbDuXhdmf7sI3twxGJz9XhHh2Ai7J2sKUgssdu5mWg6N4rAB6sCUlJeH+++/HmTNnRKQKaSrIvGJjU/cpvO+++5CTkyOEBnpY0EORojMouoYerhRtQVE6FClCETK0nh4a33zzjSLAkEmCNAC0jkw9q1evNjAnUOQQzeJJcKEHFB1Hq4qnByfNikkAosgh/agPfR577DERBUPmrfPnz+O9997DqlWrhNahMdDxyERw9uxZcTxjQ3xpDEgYo+9CeVaoDzTG9L6+Y5HZgbanY5GprC5oHCmyicaecrhQJFTNCJfGQuY00sZQ5M+GDRtERAx9BxIm6VjE4sWLhVmJBEY6Ll0zMTExMDWkMbrnnnvEefznn3+EkLFo0SJxvilyiCBTE40rbUPn56effros70tTzgPTPhO02bpQAkY5zHhgoKFpuynMH9oRPywcCi8XWWsSm1WMWZ/uwu4L2egZMRGLfQbirU7XYnK/Rc0+FmM8LKBYAaGhoSJEk2z+FCJLN3u68T/zzDP1fi4kJEQIDSSMTJo0Cb179xYzWjIBaAUbelg+8sgjeO6554RgMXfuXMXPgUJlSStCDwjyl6CHB22vhUKgL126JGbopEGZM2eOCGkmkxBBWgPqK+2TQmEpnLk26CFL/ibvvPMOevbsiSVLloiHKoXaNgZ6KFIIL/mA0PHouxsD+VtQGDc9xCnElcJraTxo/Ori2muvFXlryG+IjvXzzz/XuS2ZLa655hoxDuSHQWOmr01pCqRJo2tizJgxQuCk8b/hhhuEoEj+IQQdj84XCaDkn0TrSJBoCSicmcaEwthJE0IJBkkg8/b2Vkw0pM37448/xDX8xRdf4LXXXmv2eWDaZ4I2OxedJmNQ4OU+X01heJSvSOrWOUD22csvrcQd3x+Eg2c/3DFjKaaMfQFhYTo/K6blUZGnLKyMgoIC4Q9BOTb0HTYJyvNAM1vKOUEaAoZh2g/8+2/bHIzPwXVf7EF0xAvIcJJz+myfux3eTg3nQjGWwrJK3PPjYey8IOeL+vCGfpjVr2kmJKZxz++asAaFYRiGsRr/EyoOmO1YKt5HV6tMKpwQ7k72WDhKl8E7JrXApPtnjIcFFIZhGMYqOJKYh84ue1GtCRYY6NIymo1eoZ5K+0RyPkqKMnHuwt/YuvddFBfJzrmMBQooKSkpWLBggci/QBEg5NegdcojyGJEdmMKgaT15MxHzm76kOMmha+Seof8IcifgpwpGYZhGKY26NlCGpThlVl4MjsHk4qKMaqR+U+Mxd/dEYEecjqJk6n5eP+vW3Htrsdx/9mlOB//H58gSxRQcnNzRTImyolAOS/IW59yO2gd4QhyhKRoD3KCoxBH8vCnHBr6NUBIOKFogo0bN2LdunXYvn37ZYm6GIZhGEY/QVtGQTmukC5iXmER3s26hHF9bmuxAeqt0aIUllXB20FXoDT10lk+KZaYB4USflEBL4qw0KJfbZck3A8++EBEl8yaNUsso/BViiog732KMqCMkhSKSGGW2oybH3/8MaZOnSqiONhjn2EYhqktQZsjKtBXpckh5BMJeMhZoVuCniGe2HRajmi0ge44qfmcC8UiNSiUaImECkpnTlVLKQzwq6++UtZT9AwljyKzjhby1qXQSm31WPpPZh39dOC0PYW91pVZk3JMkOev/othGIZpP5B5p7/NBTiqNFXcO7ZsyK9Wg0Lklet8XVJK2AfFIgUUSo1OybQo8RTlOKCcCpSWmsrcEyScENo8DFrovXYd/SfhRh/Khkkl5bXb1OT1118Xgo72RVochmEYpn0laOvosh8HnBxRrmp5AUXfUfZCgS4XT2q5nGqfsTABhWpvUBImSrBE2hPyG6HkWORv0pI8+eSTImZa+6KsqgzDMEz7StAW63setwcHYnjHMGQHNa4URmMhJ1k/NwfRPpDmAme1nDIstVoOcWYsTEChyJya9VEo+yilQyeCgoLE/4yMDINt6L12Hf3XZirVQhVyKbJHu01NHB0dRcSP/othGIZpH8Sk5gPVpYh3ks07fmrAL6hvix6TsjVrtSj5pdUIkeTHZaqNBHW1xszEWI6AQhE8VEdDH0pLrS0QRg6zJGRQ7RUt5C9CviXa0ur0Py8vD4cOHVK2+e+//4R2pmY5dobRQua/iRMniqiwxlTrpRo1dKOh2i6mhKo4U/r/lsYUx2mpMWgqVMeInOktbawZy+VwQh6iXQ6g3EbOfzLI0b9VjtsrRGfm8YecmbxCpcIljuSxPAGF6rJQMTky8VCtDSr49eWXX4qidATdBKnWyyuvvCIcak+cOCHqtFBkDtVb0WpcqIYJmYaotgzVS6FaLxTh054jeG699VYxfjVfNM7WChWDa4wwUR/vv/8+0tLSxEOWhOK6xlB7nTEM07YcZP1cTijvBwYMaJXj6vuhuEk6zX1K5rFWOX57p1FhxoMHDxbVbMkn5KWXXhIaE5oJUV4TLVSYrLi4WPinkKZk1KhRIqxYvy7O8uXLhVAyYcIEEb1DRcYod0p7hwQ3/RBuggrRNYWKigo4OMj207YAVVOmgnfkoM0wTPtL0NbRK1VZNrDzzFY5dq9QnVAiVXgBdhlwr1ajIC+hVY7f3ml0Jtnp06cLzQglXqOcJqQJ0Ydm/SS8kEqettm0aZOotKoPReyQ9qWwsFA4vVJJeDc3uYJke4Z8bchEpv+iisEEVXkdMmSI2IZ8gf7v//5P+O5oocq/JPSRBsvPz08kxyNOnjwpKgzT+FI0FVWbzc6Wi2ARZFqj5HrR0dFi31R19tVXX1XWP/HEE+L8ubi4IDIyUlTHraysVNYfO3ZMVPR1d3cXvkEkRFBm4a1bt4oqu3R+tdogUtXXBUWHRUVFCaGKKhL/8MMPBiYBqoRLOXVoP6QpqQntm6LJ/vzzT+V41Af9CDTqJ30PqqarDXvXsnPnTlFBl7IfU5QYRaeRoG0sNI503Xfo0EGMI5kkSDDXp6Gx1FYFpvNE40kZlvUTHGr5+uuvhSaShP5u3brhs88+M1hPmklyYqf1FM5/5MiRBvtPY0yaT9J40rVCZlvSgmZlZYmcRrSMqg3rZ40m6LxQBWr6zrQPStyoD/mbzZgxQ4wrTWhoclITmsjccccdQhina2j8+PHiumIYbYK27IJCJDhXiPd+1RI6hrdMBtmahHo5w9vFXrRV2T2wKyEJuxOTMUZqO5O/NqNBsXaWxSzD96e+b3C7Hj498PGEjw2W3b/5fpzKOdXgZ2/ucTNu6XkLTAmVF6BEdvRgpof0mTNnhGBIDyD9hz49oCn0m8xm2hs/3ezp5k8mktLSUvGQnDNnjvD7IUgbRrlsaD1pu8iMQvvXQg9KMtWQ+Y0EUzouLSNNGUHaM3oYkoBBwhSZYCjT8IgRI4R2jcoeaP2W6hJCSSu3ePFisT3lxKHswiTc0MOehApK6kcPTnp4ffjhh+JhV5NHH31UCMzk86TVQpEgnJoqz7qefvppkQiQNDDUvvHGG4X5jELcSTtD2it6QJOwTA9lEvboVVOjVRfUL3o4L1myRIwH7WfmzJkiY7JW69PQWP7666/ifH766afiXJCQRppFEma00AOexvSTTz4RxyHhg/ZDvjm33HKLKBlBkwjy1/nxxx9FbiIaW2Oga4DMtyQ4UZuEWTqPt99+O95++21x7dB5oO9EAiD5kdG1RH2eO3cudu/ejXvvvVeUwdAKkfSfzsGWLVvEdUGCX00necqrROeUslNTGgEaQ9KukimPziHTvqEEbVFOR5BmI8+nBzr4QKVptzRaR9kd57NxvsQfHo5yJA9yWYPSKkhWSH5+Pl0l4n9NSktLpVOnTon/Nfn0yKdSr6W9GnzN+2veZZ+lZcZ8lo7RFG655RbJ1tZWcnV1VV7XXXedWPfUU09JXbt2ldRqte67fPqp5ObmJlVXV4v3Y8eOlfr372+wz5dfflmaNGmSwbKkpCQxdmfPnpUKCgokR0dH6auvvjK6n2+//bY0cOBA5b27u7u0dOnSWrf97rvvJE9Pzwb3OWLECGnRokUGy66//npp6tSpyvtZs2aJMaoPWk/b6RMXFye+79dff60si4mJEctOnz4t3i9cuFC68847DT63Y8cOycbGptbriHj++eelvn37Ku9DQkKkV1991WCbwYMHS/fee6/RYzl8+PDLth86dKjBcaKioqSffvrpsvNMnyWWLFki+fr6GvT7888/F9/3yJEjdfalY8eO0oIFC5T3aWlp4jPPPvussmzPnj1iGa0j5s2bJ02cONFgP4899pjUo0cP0aZrjLbfv3+/sp7GnJa9//77yjh7eHhIZWVlBvuh70nfpbaxro/6fv+MdfLCmpPS/LdmKPfYn//+X6se/42/T0sdn1gn9X3iZ0l63kN+LTO8zzCmeX7XpF1pUFztXRHgYpgkrjZ8HH1qXWbMZ+kYTYW0BaSJUPblKu+LNAMU/UTSvH5EFc2Wk5OThVmGIPOKPqQmp5lrbZoL0hqQhoWy9NJstS5++eUXMYun7el4ZFbSD/N++OGHhYaGZvuk/aDZMJlqGgN9v5q1mOj7kVbCVJB5QguZyAiayZOJhMbp+PHjBuYHsnuT2YY0EGROqQ/S2pCWgPpc8zvomyoaGksah7vvvttgH3Te6RwSZHKiz5LpR9+0SvshzYN2H/Rd9X2+tBF0jRkjbbJFKgZacxmNG5kf6Vjakhb635k0YdXV1WI9aaj0r0sab33HaRofGgvSuuhD2j76rgxDFYydXVKUgRgYPa1VB0UbyZMHN1TYuMBBXQLkyak1mJalXQkoZHppqvmlpsmnJSCBhHxBmvN5fejGT/Z/qqFUE3pIk19GfZCfBplwXnzxReHTQg/BFStWGPgZkHp/3rx5+Ouvv4SK/vnnnxfbzJ49G5YEmRe0aAU9EkC043TXXXcJ80NNtMJfczFmLBtCW/GbTHI1Q/K1vkqmHqP6xs0U0Heia1HfX0iLqSLAGOtO0EY5UIb7eaJDZSaKbIGoCF0pldZAl/JeheXu/ki2K0C6fSE+rq6CjW27eoS2Ojy6VgDN4MkZkWb12ocE+ZmQ/wL5adQFZf2lz5HzIs1ka0K+EWT7p7w1pAWpCfkUkLMk+WxoSUi43PZKjp/0ojB08u0gvw0SUMjhlWbSxnw/+j7kQ6GF3tdMCtgQxh6vtnGiytxNFQ5JC0J+JdTnsWPHKsvpPTk2GzuWNA6UM4j8PLRQWL++BoOOQ4KlfuRczX2QNouca7VaFP19mBLtedOH3tO1QAITaUtIu0O+KhQBSJA/Emnu9MeeHOrp+qTrlGH0IeGkslrC9oxHcF2HDnjhqoBWFwrCfJzh4WSHgrIqbHCyx0kXd7E8O/sMAgJ78QlrQVrH04hpFuR4SOn977//fuHASpEqpKkg8wqFadcF5aehDL0kNJCjKanMqYYSOaDSg5weYOT4SE6a5HxL6+lh9s033ygCDGUJppk+rSPzBDm06qvhyZGUZr/0sKWHEx1HaxKhBw7NkEkAosihkpKSWvv52GOPCedRMm+dP38e7733HlatWiUcXxsDHY9MNfQQpOPVjJCpCxoDEiDou5CTL/WBxpjeGwt9B9JUkRmHjk9RVrQvrYNqQ2NJ0LbkXEsCHjmI0jkmh1R9SANDtano87QNOdvS9jRmBGmzSIglExAJXevXrxfOwS3BI488Is7tyy+/LPpCTtrkvKs9bxSNRc7HpJ0iwYsEFRKE9Z2cySxIJijKX7NhwwaRVI7OBQlyNSOGmPaZoE3LgHBvuLm3XPViYzLK2lXotNQpGZaR+LBNI7UjJ1lLpjYHT322bt0qnC4dHBykoKAg6YknnpAqKyuV9eQku3jx4ss+d+7cOWn27NmSl5eX5OzsLHXr1k168MEHFYdbcrJ95ZVXhJOkvb29FB4eLr322msGTo/kdEkOuXPnzhXOjVrH1/LycumGG26QwsLCRL/IUfR///ufwdjffffd4vN0vsjZsS4+++wzKTIyUvShS5cu0vfff2+w3hgn2czMTOG0SX2l423ZskVxktV3EM3NzVXWayFHTu1nyUG5T58+lzm96lPTcZPG8YUXXpBCQ0PFd6B1f//9t8Fn6htLLXRMPz8/sQ1938cff/wyB9Hly5dL/fr1E2Pu7e0tjRkzRlq1apWBMyt9htbTditXrjTKSVbruKqFPrN69WrlfW1j+fvvvwunWO21Q46/+pBD7bRp04QzNq2n81rzWOSsff/994vrh/ZD19P8+fOlxMTEWse6Pqz198/Uzt0/HBQOqvQ6ndawU2VL8epfp0QfJr21QHHWXRuz3Gz9aS9Osir6AyuDnBLJhk85NmrW5SHVNjk2Us4FfUdBhmHaPvz7bzvQo2noaxtRXpKMartQHHt+Emw1qe5bmzXHUvHAz0dg534Szh1+FMvu738/7uxj6NzPNO/5XRM28TAMwzAWmaDNqfwo1F3eR6cOj+L3jcbl82kJeoXID1J1pbeuf0W6zLZMy8ACCsMwDGNxHEnMRQc3uahsggNQWFFgtr508nWFm6Md1BU6ASWlSBf6zLQMLKAwDMMwFukgm+mkE0oGRUwyW19sbFToQVoUtTOkakexLDWX8/S0NCygMAzDMBbHocQcpLkUirajrSN6djFvbiU5H4oKHavk+lippZmorjIuUpBpGm1WQLFC31+GYZoJ/+7bBpSg7XRmPGzs88X7fgH9YO9weQ2u1kRb2di9StagVKlUyMo2TAPAmJY2l6hNm/mScm7UVlSOYZi2izbXjn4GXMY6E7SpnXSZrgcGGpbxMAfajLL2xWEYapuJPv6hsLPjSNGWpM0JKJTBklJkayumUml7/Ro2DMO0Tc0JCSf0u6ffvylS/zPm9T9xdjmvvB8UOMjspyPCzw3O9rbYcWkRwiRnfH3XeHN3qc3T5gQUggqZETXLujMM07Yh4UT7+2esl8OJuQh2OYw02MAeKvT2NX9KeVuNo+yhhFwk5ZQir6QCXi4O5u5Wm6ZNCiikMaECZAEBAUanO2cYxrohsw5rTtoGcUnHkBYiu0j2luzhZG8Z5noy85CAQsSkFmBktJ+5u9SmaZMCiha6WfENi2EYxnpIzSuFb/UeZEiScEQd6BEFS6GnJmEbcTwxE129suHn182sfWrLtGkBhWEYhrE+886Uskv4KiEZxxwdEDJAVwHc3PTuIDvK9or8PyxJlLA2XoX1t58wd7faLG02zJhhGIaxTgfZITZn4EK1eMqr0LHLdFgK0f5ucLSTH5vVKhXSbCRUV1WYu1ttFhZQGIZhGIvhQnw8utjIaeTVQX0BRzdYCna2Nuge7AG3Sge9XCinzN2tNgsLKAzDMIzFJGhzz9ivvLeLGAlLgxK2OVTqhKbUjONm7U9bhgUUhmEYxmIStFV5b8fj/r741d0NRaHmz39SWySPflXjlJxzZu1PW4YFFIZhGMYiOJKYh1T3LPzt5opXfL2h7mD+DLI16RniibJKf+V9SkGCWfvTlmEBhWEYhrEIjl88iwQHuY5aZ7UtPDzDYGl0CXRHUVW48j61JM2s/WnLsIDCMAzDWAS5WeshaUqTDHKzPOGEcLCzgbuXLrNtSrlc0JAxPSygMAzDMBaRoE1lq6sOPDjE8hxktUR3iIZbtVq0U9Rl5u5Om4UFFIZhGMYiErQVuGQr7wf0uB6WCkXy+FbJj88MzoXSYnAmWYZhGMbsHI69iHhH0kqo0KkK8PGJhqVCkTwFmzwxzOYsQqqqYVOUAXhZpknKmmENCsMwDGN2UpPXiuysxEDnUFgy5CjrVBaK/uUVCKyuhio/ydxdapOwgMIwGo4c/wHzvxuA67/ri+ys0zwuDNNKlFdVo7TqsPJ+SOhQix57J3tbVOg58VZkx5u1P20VFlAYprwIq1beiJuPvIXjNpU4Y6PGn/vf43FhmFbiZEoBCp0zlfcDu11r8WPv6N9JaWcnc7K2loB9UJj2TewWYO0DGFGYAoTr1MoXPQPM2i2GaU8cScxFYfrV6Ot6BAF+RQgM7ANLxzOkE7ZnOSHVzg7lWYdwi7k71AZhAYVpl0gluVBtfBY48oN4HwTgmZxCvOLjLt7H5ceZuYcM074ieM6XDRGvvxeMhjUQ2LET7sqQJzK9K5JZQGkB2MTDtDu27HkH834ahYJjP+oWdhqNuTdvRqibrEW5mH8RkiRntGQYpmU5nJAn/rs62AoHVGtgQHR3JRdKlqrS3N1pk7CAwrQbcnIu4PEfx+KBc8tw0t4G7/l4Aw7uwPT3gZvXAL5RiPCMENsWVxYjqzTL3F1mmHaRoC29QE521jfMC7Y2ciSPpUOOsp7VTqKdaadCUXm5ubvU5mABhWnzSGo1/t72Aq7+82r8XZ2jLM90D0TlPTuBQbcDNvJPIcLeS1kfd3GjWfrLMO2JAxeTMdrvM3R32YH+ofID31qocOot/qtVwL5EjuQxNSygMG2azIyTeODHUXg8fiVyNTMzD7WE18Jn4tObdsPeW+eJvyf2ElJikpX3F5P3mKXPDNOeOHFmDY76JyK5419IzVwMayLMXedYvz/5gln70hZhJ1mmzWpN/vjvCbyd9DcK9VTGE2088dTUr+Dn311ZlnCpGK+tP41/YzLQ0yUIcJWXs6Msw7Q8abk7ADe5PSiwv1UNeVe/jjisqRV4KpM1KKaGBRSmTfLI8jHYqM4HNMKJj1rCM53nYeKop5RtCsoq8el/F/DdrnhUaJzdEsv6oFd6MkJ9++B/Vy8wW/8Zpr0kaNujkn97xIge18Ca6B8SiZ9j5XZSHgsopoYFFKZNMjRgADambxHtGXb+eHzqN/Dylh1gq6rV+OVgEt7bcA6XiiuUz/i7O6Kg1A97chcgVHKGhyfX1mCYluR4ci4qnFJB0wgHeKJTuHWEGGuJtLFV2mEVO1FRpYaDHXtOmAoeSaZNcv2V72GqnS8+7XYHXpv/nyKc7Dyfjekf78TTq08qwgndUP53RTS2PDoOfTvITrIpeaW4VMRe+QzTkmy4cBgqW/l3FuneGypNLR5rIcSni9IusSvFuYxCs/anrcEaFMaqqaosw/f/3Iussmw8cf0aZbmNrR3enL9VeX8xq0j4mWw6rUunTUzvE4z/m9INHbxdxPveHTyxP16O9DmRko9xXTmjLMO0FPvTDirt4SGDrW6g3T1CEVQlwaEacKl0xcmUfPQK9TR3t9oMLKAwVsu5nLN4bs08xKhkTcj4I99gcP+FBtvkl1Tiw83n8f2eeFSpdYnX+nbwxLPTe2BQJ5/Lyqi722QhyuUgtu39Gd6V09G31w2t9I0Ypn1RVrQVkOcGmNp5JKyRl6/Yhhu+3CvaHVM1HrNM65t4XnjhBaGC039169ZNWT9u3LjL1t99990G+0hMTMS0adPg4uKCgIAAPPbYY6iqqjLNt2HaDX9d/Atz/7pBEU5UkoSTKbuV9eRnQkLJ2He24NtdcYpwEuThhPfn9sXqe0deJpxoNSidXfchNmwbfscxbDu3shW/FcO0H1JyC1HkmCLaHtVqdPGOhjXSI8RDaZ9IKTBrX9DeNSg9e/bEpk2bdDuwM9zFokWL8NJLLynvSRDRUl1dLYSToKAg7N69G2lpabj55pthb2+P1157renfgmlXVKmr8Oq+V8V/ItLGBS/1f0jRdGw9m4lX/zqN85lFymec7G1w15go3DU2Ei4OdV/2Eb6uKFR3JW8V8T6uOK3Fvw/DtEe2H16HAlt5jtxN7QobPYdTa8LDyR4Rfq6Iyy7G6bQCVFarYa/5XkwrCygkkJCAURckkNS1fsOGDTh16pQQcAIDA9GvXz+8/PLLeOKJJ4R2xsHBobHdYdohx7OOo7BCdkYbHjwcn0z4BA62DriQWYhX/jqNrWcNU9Rf3S8Ej1/VDSFezg3u28ZGBR//AbgkfY1KlQoXK3lGxDAtQXnCIcwsKcIhJyf0cNdp4q2RniEeQkChKJ4LmUXoHqzTqjBNp9Fi3vnz5xESEoLIyEjMnz9fmGz0Wb58Ofz8/NCrVy88+eSTKCkpUdbt2bMHvXv3FsKJlsmTJ6OgoAAxMTHN+BpMe2JniqzdIKZFTkNxGfD8nycx+YMdBsJJ/3AvrL53BD64ob9RwomWPmF+CNJEHyfaqIUjLsMwpiU64wxezc7BP8mpuH3gfVY7vOcv/IOSkrvQI+oJjPb7XDjKMmbQoAwdOhRLly5F165dhXnmxRdfxOjRo3Hy5Em4u7tj3rx56NixoxBgjh8/LjQjZ8+exapVq8Tn09PTDYQTQvue1tVFeXm5eGkhgYZpv+w6sUxpJyR1wNgft6CgTOfHFOLphP+b2h0z+gQ3KWyRvPBj4p2Q5FiOKpUKySn70KnTWJP1n2HaO+WVVehadhyUAKUYzvCOGAhrRYKEA/Y0iVHBx/ESYlILcL25O9UeBZQpU6Yo7T59+giBhQSSX3/9FQsXLsSdd96prCdNSXBwMCZMmIDY2FhERUU1uZOvv/66EIYY5lL2OZyCrN6IKAfe+1cn2Drb2+LecVFYNCZSVBptKn06eOH3cm/ATd53XNoBFlAYxoScP30MvVSypiHRrQ+621pvQGlIYD+lXWpXKtITMKahWZ48Xl5e6NKlCy5cqL1IEgkwhHY9+aZkZGQYbKN9X59fC5mK8vPzlVdSUlJzus1YMXtOfK+0vYp1OUquHdABWx8bh/sndG6WcEJ09HGBVKUrAhaXfapZ+2MYxpCLMetQqNFuloUMs+rhcXMPhqcmSjDfvhKnUgtQrZfSgDGTgFJUVCS0I6QpqY2jR4+K/9r1w4cPx4kTJ5CZqUuWtXHjRnh4eKBHjx51HsfR0VFso/9i2idXXUrDD6npuDs3H3n5Q4T3/Jr/jcS7c/oi0MM0pdrJUdbDrafy/nw+19hgGFOyrmADRnXsgDkhQaiI7GP1gxuiMUZk26lQUVkqEkMyrSygPProo9i2bRvi4+NFmPDs2bNha2uLG2+8UQgqFJFz6NAhsX7NmjUihHjMmDHCHERMmjRJCCI33XQTjh07hn///RfPPPMM7rvvPiGEMEy9qKthF/sf+pVX4ObcCpwqG4brBnYQJhlT06HDKKUdVy5nlmUYxjTE2uVBTVFy9vbo2+8qqx/WUDu5HHO1SoVAh0Sc5IRtrS+gJCcnC2GEnGTnzJkDX19f7N27F/7+/iJEmMKHSQih5G2PPPIIrr32Wqxdu1b5PAkz69atE/9Jm7JgwQIhxOjnTWGYOkk7CpTKwsIudS9UwQ7Do3xbZMB6d+yIsAo1upVXoEtpCSCxypZhTMGJczuRqSmoF11pDycn+eFuzYQ46e5DvvaJOJHMgRymoFGeSStWrKhzXVhYmNCuNAQ51a5fv74xh2UYmQublZHYpu4DVwdbkZq+JaD9PpHohbG2J+UFxVmAG9flYZjmsu34b0o70q5TmxjQELdQoOSiaLs5pLEGxURwujvGanjz4ios83BHrL0dtlf3weAInxbL2NjR1wVJtmG6BVlnW+Q4DNPeOJMr+yYSPUPaRvh+qLcuStXePls4yqrZUbbZsIDCWAX5eQn4SVWEd3y98b+AYKTAH8MjW8a8Q1D+lHIvXW2QgmSO5GEYU3BedUn8t5ckjB88r00MaoifLshDbV+AovIqxF8qNmuf2gIsoDBWE15MTnWEf7Gf+N9S/idanIJ16bdzEzSmHoZhmkxy2jGk2su/48hyWwT7tg2zaYegAViYl49ns3MwtaBaLON8KM3HerPjMO2KXUk6/6b8or5wd7JDz5CW8T/R4tWxC+6/5Ic4B3v4l+3Bdy16NIZp+2w+rPNjDFPpcg1ZOy5ugXiwzBYozUeyJDvyU0bZWf3aznc0B6xBYSweSa3GrjI5q6ujWsKZ4pEYGuELW5vGp7FvDL2ieuCgkxMS7O2RpOJ6PAzTXI5l7Ffa0X4j29aAeoWLf8G4BDtU4UQyZ5RtLiygMBbPudi/kWUrCyNRpQ4ol1xb3LxDhPu5IqhSzkqbYadCMUXyMAzTZPIri2EjSbCVJIzoM7dNCii2KglBqhwRySNxeoJmwQIKY/HsOrNSadsXy2GJLekgq+8oW23bVXl/OCu5xY/JMG2ZUzmvQn3uCUSkTkefyKbXZ7NEqj3DkGZri4NOjgiyS0BhWRUSc0rM3S2rhgUUxuLZdem40k4oHA5vF3t0C3JvlWN7eeuqrO5KOt0qx2SYtkhafinS8stQqPaBs//VLW6ibW1+QT4mhYfituBA2LvJaQlOpnDCtubAAgpj0RQXpeMwZP+P4EoJSRU9hP8J1ctpDfoEdFHaJzPPt8oxGaYtcjghT2n3Dzd9eQpzE+qlnwtFDqXmSJ7mwQIKY9HsP/EjqjThxYHF3uJ/a/ifaBnZUZffILEwrtWOyzBtjcOJuUp7QLj8W25L6OdCybWTQ41juCZPs+AwY8ai6dN5Jl4sTsfa2B3IKRzQ6gLKwNBIqCQbSCo1VJWnhdMb+aYwDNO4SLyTKfMxKtQRUmk4+odPbHPDFxI+CtgltxMc3RQNCt8zmg5rUBiLxtevC2Ze8RYOpL6EY0WT4OfmgM4BrVdczMHWAR2q1KJdaF+MlFy2KTNMYzkRewynnKpwzKMYas8L8HF1aHOD6OrgBi9H2XRl5yibs/JKKpGSV2rmnlkvLKAwFs/JVDl1NDE00rfVNRghcBH/K1Uq7IvZ0arHZpi2wOaTukKfHez90VYJpaKBAKpUJKDI96yTKZwPpamwgMJYPHtiZYez1govrsnwwBnondEdHRMmI64gsNWPzzDWzvocXaLDzl2moq0S4hYi/ktQQ2UvCyYcydN02AeFsVh+3fAgOnh3xv4Lupo4rel/omXyqIfx8q4tou2bIbX68RnGmskrqUBSURzsNYE7Q7q0jQrGtRFaUa60uzscx6nKKziSpxmwgMJYJGWluXgrZRPK0zYjVGRjfBOBHo6I9HNt9b6EejkLm3lOcQU7vTFMI9lyNhMqhwzlfZReOG5bI0StMz8HuKThVLFs4mFH2abBJh7GIjl44keUa3Kd+Jd6KOYdc0TQ0DF7hcqFCUlISc3nujwMYywbYzJg6ygLKN4O/nB3aJ0ki+Yg1FsWvuwkCZ4usjblUnEFMgp0mhXGeFiDwlgku6pyDaoXm8u8o6WPbzby3DbCzSEVxxP7IdSLq5QyTEOUV1XjeOxhqDrJQn1314A2PWiDet+EDWFj4O/fE+9ujgcSYpVw4yBPJ3N3z+pgDQpjkezMOCA3JBscL5ogmsMj/czWn5OXnkNc2GacCDyNk/ExZusHw1ibg7uv7UnlfXR52w65dXHxQ3DIQNjZO6FXiKx1JTiSp2mwgMJYHMmFyYgviBdtdVk4oHYWfiBhPs5m61OEky40Millt9n6wTDWxKbTGfBwTFDeR3vrSke0dXprzMIECyhNgwUUxuLYnaoTACoL5RvaMDP5n2jp4qNz7CsoOcNl1BmmAcgxdNOpTAwqLcdDObmYWViEnqEj2s240YTKw0n2ojjJKe+bBPugMBbHjpM/KO2q4i5m9z8hIvx7AxnbRFttm47k3FKE+cgJ3BiGuRzK/5FeUIaRDtkYlF+o+SHJ5tq2zO6Dn2FfwmaklmZhSNAd2BTvK5xkMwvLEODOfiiNgTUojEVRWV6MfQVyUT7vajXUZcEWIaBEdhiptMsd8lllyzANsPFUukhZ1kWVLC/w6AA4yRF5bZm98RvxbdE5/FOdi0h3nXkrJoXLZDQWFlAYi+LIqZ9RqgkvDi+hcERbhPu4CB8Uc+Lv3wOuarkmzyWHChzn9NUMUy8bT2ciELnwUJXICwJ0CRfbMiFuHZS2i32a0qZIHqZxsIDCWBR+aTFYkF+AiIpKxf/EHOnta6KysUEnyV60M+1UOJ2UZO4uMYzFkpRTgtNpBejkcA4nHRxQQv5j/u1EQPGOVtqVyFTa7CjbeFhAYSyKyIT9eCInD3+mpONc/lUWYd7REunkI/5LKhUuZe1nR1mGqYPNp+XEbPbux3FjaBCGdgrDP/bto0xEqF8PpZ1TeQlujhpHWdagNBoWUBjLoSANyJBzJlyw64xceFqWgOIWprSdVeeFoyzDMJezUSOgwDFLWRYa2K9dDFVwcH+lnVpZgB4hst8NZaC+VMQZZRsDCyiM5RCrK8n+T3kv8T/S3xWBHpbh+R7h212ksI6uqECQKhfHk9mmzDA1yS+txL6LOaJd4lSkLI/q2HaLBNZM1uajlrVFKepyw4Rtqewo2xhYQGEshr1nViLd1la0t1T1sRj/Ey1jomfiQHwSVqekY2JxGY6n5Jm7SwxjcWw9m4kq8YBWI9W+WiwLqZbg4haI9kIoNP5qNkDPQPmeRrCZp3GwgMJYBFWVZXik7BwmhofipuAQHJOiLMq8Q9j7dYatjWxPjlKl8c2GYWph02nZMTTQLhHFNvIjJtLWrV2NVYi9u+KvFuKUqCxnAaVxsIDCWAQnz6xCgeZmZqv2QDVslQyyFoOtPVTeEaIZqUrFyeRcdpRlGD0qqtTYekYWUDq5n1OWR7vK+YzaCyHOurphUkUsnO3l+xlnlG0cLKAwFsHOC2uUdkVBpPjfJdANfm6OsCj8u4p/TqpKuJenIzFHk+OBYRjsj8tBYXmVGIlwb0rUJhPVjmrwED08ozG6pBQ3FBTCr7xIcZRNyilFXkmFubtnNbCAwlgEu/LOKu3zhWMszv9Ey243Dzwc4IerQ4MQ4HqIHWUZ5rLssTJqB10OkOjgQe1qnK7qOBGfZWTh6Uu56FJWalA4MIYdZY2GBRTG7OTkXECMqlK0IytVyKoKtzj/Ey1ZLp7Y6OqCWAcHuDgmsU2ZYfSKA248pcl/YqtCmqRzIo8Mbx8RPApe8j1MkJeInhoNCsEZZY2HBRTG7Ow5/r1wJiOCyoPEf3o7NMLyBJSIwAFKW+WYzRoUhtFwKq1A5Poghkf5oVTSRvAALm4B7VpA6d1BL9SYE7YZDVczZszOrpSdSjs1p7f43y3IA96uDrA0IsLHAAfl9jF7D0ip+VCrJdho6gcxTHtFqz0hJnYPwE3Dj6OoMA2Z2afR7rB3BlwDIBVnojA/EdH+bnC0s0F5lZoFlEbAGhTGrKirq7CrQrZVu6jVOFM8wmL9Twh39yD4O/uLdrVDLgrLqpDAjrIMg03a7LEAruwh5zxxcw9GZMT4djk6T/h6YnjHDhjlYwt1VTG6B8tmnvhLJSgok03aTP2wgMKYldPn1yBHo33oWeWKSjhZrP+JlkhPOcrIxq4YKttitikz7Z7UvFKcTJGzpPYK9UCwp3mrj1sC1Q4uIg8Mma/T0o+KcdESoxkrpn5YQGHMSnV1JYbDGfaSBIdSuQooyStDIuTCfJZIJ89OStvGIQsnkjmjLNO+0RYHJCZ2l/3I2juhGk0rkXLpTI1IHi6TYQzsg8KYlT495+LLnnORnJmCie9vF8t6hXrC01lOFW2JRLiGKu2OjjE4kaJznGWY9sgGPf+TK3sE4PmfJ0KChM6eUVhw1edQaZIwtidCoycDR+Vkdalu3ujpqhNQOJLHOFhAYSyC4xk2KJU8LNr/REtkhRypQIQ6ncaRlAJ2lGXaLYVlldh78ZJoh3o5o3ugG/4pS0OJjQqh6Rm4qR0KJ0SIXw+lnVqUipmR7oqj7PZzWSirrIaTJsMsUzvt88phLI7dsdlKe5gF+58QEaHDlXaFQz6KyqsQf6nYrH1iGHOx7VwWKqvl6r1Xdg9AeuYxIZwQ0XbtqwaPPiFuIUo7pSgFDnY2uKqXbP7KLanE+hNpZuyddcACCmM2kpP3orpKTvu8J1aegdnaqDC4k+X6nxCBAX2wwLkTrlcNREHWZLGMVbZMe2WTfnhxjyBcqC5S3kd1bGcJ2vQIcQ0x0KAQC4Z1VJb9uDfBLP2yJlhAYcyCpFbjlg13YMwPA/Dkj+MRm1Uolvfp4Ak3R8u2PJI9/Yk5azFm6Fs4XTpKLDuRzE5vTPujslqN/zTFAd0d7YRze2xerLI+qoOcNqA94mTnBF87V9FOvSTnghnU0RvdguRKx4cT89hZtgFYQGHMwoWLG5Bpq0KBjQrp5YXKpWjp/if66HvlH+fskEw75EB8DgrK5OKA47oFCDPGhbwLyvooryi0Z0IrysX/THU5KsoLoVKpMN9Ai5Joxt61MQHlhRdeEAOs/+rWrZuyvqysDPfddx98fX3h5uaGa6+9FhkZOvUfkZiYiGnTpsHFxQUBAQF47LHHUFUlX+BM+2FX9lGlHYjOStuS85/UxNfNUTgFEjEpckZZhmmv2WPJ/4S4mHdR/FdBpeQMaq+E2utyn1AuFGJ2/1C4OsjOsX8eTRFOxoyJNCg9e/ZEWlqa8tq5U5em/KGHHsLatWvx22+/Ydu2bUhNTcU111yjrK+urhbCSUVFBXbv3o1ly5Zh6dKleO655xrbDcbK2Vmgm2Xtz7tKKTA2qKNl+5/om6guZZ/DcN+d6Oy0H8UV1biYzY6yTPuBigNqs8fa2agwrmuAyAwde+mUWBbq5Atnu/adsG1+v3vwabc78Meo9xASMlAsIxP21f3lVAUlFdVYfSTFzL20XBpt7Lezs0NQ0OWJePLz8/HNN9/gp59+wvjxcmrj7777Dt27d8fevXsxbNgwbNiwAadOncKmTZsQGBiIfv364eWXX8YTTzwhtDMODpZXe4UxPSWVJTiccVi0A12CcSFLFkr6hXnBWTOzsHQuxm3G1TsfBhyAPn7OOJ88BCdS8hAd0H6jFpj2xdmMQiTllIr20EgfkbsoJWU/SqEWy6LLZfNGe6ZvrxtqXU7Ossv3JSrOsjcN6ygsEkwzNSjnz59HSEgIIiMjMX/+fGGyIQ4dOoTKykpceeWVyrZk/gkPD8eePXvEe/rfu3dvIZxomTx5MgoKChATE1PnMcvLy8U2+i/Getmfvh+ValmtGebUXyiDrc3/JKzDMNhIskmnwEG+SZ9I5uuSaafRO93le3ps8i5lWZReFAtjCNXlIYdZ4lxGEfbH5fAQNVdAGTp0qDDJ/PPPP/j8888RFxeH0aNHo7CwEOnp6UID4uXlZfAZEkZoHUH/9YUT7Xrturp4/fXX4enpqbzCwsIa023Gwth5YZ3Sri7qajX5T/RxcHRHmFoWrNLt1VChSmhQGKZd+p9oigN2LC7AAzl5mFpUjAEBNPlg6sIg5FijTWGaYeKZMmWK0u7Tp48QWDp27Ihff/0Vzs4tZ2t88skn8fDDDyvvSYPCQor12q13xv0L2NLFp0JsonxjI+//AeHyjMJaiLBzR4JUiDIbGwTZJSAm1RHVaknkcmGYtkxGQRmOaULrSRvQwdtFtDvmp2FRvkaTGD3dnF20GI6eWI7k7NOoVFdg9oS3lOVTegfhpXUOyCmuwD8n05BV2AP+7o5m7WubCjMmbUmXLl1w4cIF4ZdCzq95eYazSIri0fqs0P+aUT3a97X5tWhxdHSEh4eHwYuxThITdyJF42bST+2AlFy5PTDc2+rSPke46K7ZQKfzwuHtYpYuSRXDtFW0zrHERE30jiBTzvcBlQ3g18UMPbM8HjrwOp5M/BMfJ6w3WO5oZ4s5g2RrAGXi/fVgkpl62EYFlKKiIsTGxiI4OBgDBw6Evb09Nm/erKw/e/as8FEZPlxODU7/T5w4gcxMObEPsXHjRiFw9Oihq1vAtF1SY/9FgCasPNq+i1WGF2uJ8JarLxNujvLNhTPKMu3NvEPZYwVqNZAtF8eDdyfAvn1H8GgJUcnBH1m2KpSXGSZ0nD80HFrf2J/2JQoNLNNEAeXRRx8V4cPx8fEiTHj27NmwtbXFjTfeKHxDFi5cKEwxW7ZsEU6zt912mxBKKIKHmDRpkhBEbrrpJhw7dgz//vsvnnnmGZE7hbQkTNtneNpZbEpKxcrkNFRWT7JuASWwn9K2cZBrCR3njLJMG6e4vAq7L8ilKYI8nNArVNZoF2WfwRlVJcrpgevf3cy9tBxC7eXMsfq5ULSE+bhgbBd/0U7JK8XWs7rJO9NIASU5OVkII127dsWcOXNEQjYKIfb3lwf4/fffx/Tp00WCtjFjxgizzapVq5TPkzCzbt068Z8ElwULFuDmm2/GSy+9xOeiPVBZBsTtEDE7nZ388E9yB7HY2d4WfTsYOldbAxFhcpp7othRTtV/kjPKMm0cqsRbUS2HEl/ZI0AJjz108R9cHxqMIR3DsNSZ/bC0hDjLz0ciNevkZeO5YCjX5zGJk+yKFSvqXe/k5IRPP/1UvOqCnGrXrze0xTHthMTdQJUcklvUYSzSj8h5EgZ18hZOstaGp2c4fKslXLJVIcteNlvFpBawoyzTptl4uhbzDpWvyDoh/qtVKvi18xT3+oS6dwCKzop2Sq4uQaWWK7oFiIzUQoNyLgtJOSVCs8JwLR6mFak8v1FpH3UcpLSHWVH+k5pE2jiJfCgeUjXcVbkoraxGLDvKMm2UKr3igJQRdVikLvNzbIGuOm90yGCz9M8SCfXWlfJILbzcEZai/uYNDRdtSq2kTeDGsIDCtCJzUv/CTcGB+MrLE2sKrdtBVstb3oNxID4J65LT0BlZYhn7oTBtlUMJucgrkZMsku8ERaJouVAhJxsjgT0ifIzZ+mhphPj3UtoppfI9oiYUzUOlPgiK5imvqm61/lky1qdXZ6yS1NSDuGAr4aiTI7Z7+WFLgnyTo6JZ+lWBrQ2/wD6U7V4QZZMq/rMfCtM+krPpwoupBk8cZDMnJTB0dLLe37SpCQ7SOdOnVMq+ajWh/CeTe8rmMjkvSt2JS9sTLKAwrcKuk8uV9gDXLsgukv1PBkf4wN7Wii9DP536NkolCyjHkzmjLNM2kyxq/U/ILHFFV52AkpK6D2WaBIVRehV8GQhhzb9aDh9OlSrqHBKqx6OF6vMwLKAwrcSujP1K29VpnNK2pvo7taKXjKq3o3zzPpVWIGz1DNOWuJBZhIRLJaI9uJM3vFx0xV1jk+V6a0SUq1ypl9ERpnJAcFUVIirKUV1eezLHIRE+6BIoFxs9EJ+LM+lc28uKp66MtVBZWYK9VXKCIm+1hKN5/duE/4nAowO+9fbF4/6+WOYvq2XLKtW4wI6yTDuJ3iEuZOvCZ6N9OQdKTb5z64cNSan4Nj0TtoVptY4vhWvP55BjA1hAYVqcYzG/olij/h3h4I+98fLMwN3JDj1DrNxWbWODfzw88LebK/Y7AQ4qOYyaHWWZNp09VlO9uLYInqiQIa3aL2vAxltnvkFe3eab2QNCRV4oYvXhFBSVy3497RUWUJgWZ2esrnpxD6/BwgmMGBrh2yYK60XYy0nmqlUqhDqcF212lGXaEpmFZTiaJPtWdQ10R7ivYZ6OS1XF4r8tR/DUjoGAUncYsYeTPa7uL5vIiiuq8ceRFLRnWEBhWpxdBfJDmyh3mNJ2zDsaIoIHKu1MBzk6iTUoTFviv9OZIkdHzegdLV/esh87Zq7Bj0Oeh4OjLrU7o8FLznPSkIBCLBgWbuAsK2kHvh3CAgrTomRlxuCMjeww2kNti/1p7m3HQVZDRJSuppCbt2ziOZ1WgEp2lGXacnHAGnh5R6BXj+tbsVfWQ76LD57w9xV5oF7N2FHvtmT27h8ua2XPpBeK3DPtFRZQmBbl0JnflfZIzy7Yd1EuMubtYo9uQW1jphXpGam03d3lZFXlVWqcz6jdW59hrImSiirsvCAXwwxwd0QfK85bZC6cfCKx3s1V5IE6VdGwwMH1eWRYQGFalKvGPI8tU3/FJ11vQ7+IO1BQVqX4n9i0Af8TItwjHDYq+ack2elmmuyHwrQFdpzPFgI3MaF7YJv53bYmjo4eCNDkQklB3blQtEzrEwwvF3vRXn8iHZc0eaPaGyygMC2On393jB32MM4WRrc5/xPC0dYRoa7Bol1QSfZl+WZ+PIUTtjHWzyYD887l/idL/rwJL624Csv/vhelJbIGkbmcEA/Zt+SSjQplVWX1DpGTva1If09Q5ejfDiW3yyFlAYVpNfZozDttTUAhIorlPC9lUiUC7GQnuBMpnGiJsW6q1ZJSHJDCX0dE+V22zcac4/itPAVvZ2yHra0862cuJySgt9JOLZazTtfHvCE6Z9nl+xKgVrc/Z1kWUJhWgTKr7o+TZ1d+bg7oHCBnTGwrRDrr8kL09IkX/9lRlrF2jiTm4pImLcCYLn5iZq9PdVUF4lRyYbtwtQ1H8NRDqJsuw25qUcMCSic/V4zuLAuESTml2Ha+9kKDbRk7c3eAabts3vUG9iZvRy//PvALukFJOjQ00ldkTWxLDA6/AkVxpYj06IQT6AtkAhVVapzLKLT+ZHRMu6Wh6J3kkjRUaH7L0QF9WrVv1kaIW4jSTik0Lr/JTcM6Ch8gYvneBIP6R+0BFlCYFmNrwib8UZkBJCXhjjyaPUS2qfBifcYMXSxexNc7LgKnT4v2ieR8FlAYq09vT36x47td/nC8kHdBaUeGDmvVvlkboc7+SjslZR/QbW6DnxnfLQDBnk5Iyy8Tprbk3BJ08DZMkteWYRMP02KcLJdVknaShKO5vZTlI9qY/0lNeuuFYZ5IkX1TGMbaiM0qwsUsOUPsoI4+8HF1uHybvFilHe2lc4JnLidU0pnHUtMOGjVEdrY2uFHji0IuKD/vrz/JW1uDBRSmRSipLMFFze8x2jkQe+PlMLlAD0dE+Lm26VHvGeoJrQWLBRSmbUTvGNbeqU2DEuUV1Sr9slaCAvvCRpMV9myF8RF+NwwOg50mtPuXA0nCdNxeYAGFaRHO5JyBWpJ/SIHeQ1BaWa2Yd9qa/4k+BfmJiD3/G7r6yjeiM2mF7eqGwrRN/5Mr6xBQYpP3iP92Kht08ujUan2zRqgEwHSHQNzn1RefT/rS6M8FeDhhck/Z/ye7qAL/xshV09sD7IPCtAgn9cqvq0vleP62GF6sz1u/zcIPJRdFe7rvLTiT3V3kMCBH2V6cfZOxIigx2KFEOeNpdIBbrVrPqqpyxFFWVJUK4VUS7DnEuEFenbe5Sedj/rBw/HUiTanPM6OvzuG2LcMaFKZFiLkUo7TTsnS5E4ZHXp5Hoa0Q5KqbZXo5xiltLhzIWBubz+gVB+xeu/YkKXkvKjXa0Ch7jlRrSYZH+iLKXxYS98XliElPe4AFFKZFiEncLv472NjjVLzsdR7q5YwwH+c2O+IRfjpH4ErowgjZD4Vpi/4nLvlJuDc3D5OLijHUkx1kWxKVSoX5Qzsq7ynkuD3AAgpjcgryk5BQLRfK61ypRnmVPMsa1sb9TyJChijtzKpMPUdZTnnPWA8Jl4qV3BuUVLFfmFxZtyaBeWm4J68A72RdwtzIGa3cS+smPn4bPl49F2//Nsvoz1w7sAOc7OVH9qrDKSjW5JVqy7CAwpicUxfWK+0gtW+78D8hQoIHwVGTjjq+qgjR/nK23LPphSivkp2EGcZSKSirxOvrT2Pie9sVp3bKw2FbV3HALDnXjyCgeyv10vqpqizDzVvuw5cFp7CiOBaFBcYlbfN0tsesvnI22sLyKqw51nA2WmuHBRTG5ATnp+Lu3HyMKilFcZ4cw29vq8IVXXWJitoiNrZ26AQ5tjrJRo3ewY6iXVktCSGFYSy1DAXVerni7a1Ysv2icOzWpgR4YELnuj+YeUb+b2MH+HCIsbHY2TvhKhfZXENZeDfuf9/ozy4YpjPz/LAnAZLWUaiNwgIKY3I6Zl3EfXn5+DwjC/GX5OySlKLZ101+YLdlIjTOglUqFbp66hxl2Q+FsUR2ns/G9I934unVJ5WaOw52NrjviihsfmRcnVlLSQtwoSAOlfTGNxqwuzyJG1M3M3rdqrTXJm81eqh6d/BE3w7yPeZUWgGOJLVt8zELKIzpST0i/pWrnBArhSj20/ZApJvue7rhrNKmlPcMYylczCrCHcsOYME3+3BGT7s3vU8wNj88Fo9N7gY3x7qzUCQm78bsYD8M6RSGNz3bVuHP1qBX92vRSWP1PagqR2qqcZlla2pRKOS4LcMCCmNairKA/CTRPK7uBDVs4O1i326KXEX4dFXahZUXRQ0TgjUojCWQX1KJl9edwqT3t2PT6UxleZ8Onvjt7uH4ZN4AhPk0XOslNmWvoin0cKs9yoepG5WNDab79lPerzv4odHDNaNviPBHEZ87noZcjearLcICCmNS0uO2INbeDjQ5OFotFwec1S9UqI3bAxFBA8V/lSQhvyRdJLkiyAelTON4yDDm8DP5fk88xr2zBd/sjEOVxpmb/Ezevb4v/rh3JAZ38jF6fxcunVLaUb49WqTPbZ3pg+5X2muzj0FSG5dx2sneFtdpNNKUpfq3Q/KEsC3SPp4aTKuxOvZPXN0hBCM6dsA2J3ex7NoB7cO8Q0SGjcbvKenYl5CMJwor0TtUDtGkBwI7yjLmYOvZTEz5cAee+zMGuSXCawSOdjbCAXbLo+OE+dWmrkidOogt1BWti+4w3OR9bg+Ehg7BQEn2y4u3lRBzZqXRn50/VA4+IJbvS4RaI3C2NVhAYUxKTIGc6r3ExgbxFV3RJdANvUI92s0o2zt7oqtLEJzJuz77PHqHyEIacZwrGzOtyIXMQtz63X7c+t0BnM+U8xIRs/qF4L9Hx+HhiV3g4tC0aiexlflKpfIwFlCazIwO45T2mhPLjP5cpL8bRkXLWbkTLpVg5wU5b01bgwUUxmSQijKmSna4c6tWI7mii1BFtuXkbLXip/FDqSjEAF+dffgkO8oyrQD5JDz/50lM/mAHtp7NUpZTwrVV947Ahzf0F1mdm0plZQniVbK5spPaBvb2DfusMLUzachDcNCECu8sioNUZbw/yYJh4W3eWZaLBTImIyPzOLJtZWGkQ7k9MlQ2uLqfnFioXeHXBbiwUTS72qYJR1nSwLIGhWlJyB/hh70J+HDTORSU6bKMhng64Ykp3TCzb4hJJgtJyXuEcywR5VB7llnGONw9QvE/hw4ITjmGcSWlUMVuBrpOMeqzV3YPFD5EGQXl2HQ6A6l5pQhphuBpibAGhTEZMRf/VdrOZb4Y08VflApvbyR7BOBrTw887eeD7Yn/oEugbOahAl9t2eOeMQ+UrGvz6Qxc9cF2EaGjFU6c7W2FGYfymZCjuqk0mReS9yjtKDddpXKmadw26CFcVVwCJ9KkHFth9OfsbG1ww2BZi0IToBX7dX5BbQUWUBiTEZOui+UvKY1oV86x+mS4eOFDHy+scXfDgUsnhaBGVKsl/BOTbu7uMW0ImjXf/O1+LFx2EBezi5Xl1wwIFQ6w5Ajr7CBnNzYVsXoRPNF+PU2673ZJ1HjARVPl/ezfQKnxydduHBKulCJYcSAJlZoswG0FFlAYk3GiQGcHvVQ9sM4qqG2diPBRSjuuLAsz+sjJ6og1R9t+/Qym9TQn9/x4SCnsRwzq6I0/7xuJ9+b0Q5Bny2gvU0p0+VOiQuVM0UwzsLUHel8nt6vLIcWsNvqjQZ5OmNhdvs9mFpYb+By1BVhAYUzmIHtKkmdw3lVqDO0+QsTrt0d8vKPgpZITKcU5OIoopk6+siPh3rhLyCwoM3MPmbbA7thLOKZxvA5wd8Qn8/qLZGt966g+bCpevmEDtk5biW/6LEZ4hxEteqz2gtRnLrY6O+MRf1/cffLTRn32Or0s3RRS3pZgAYUxCckpe1BgI19OweWOuH5wO7ZNq1SI8O8lmhkVeSipKhEOigSZmSn7I8M0ly+2xSrtZ6f3wPQ+pnGCNSYLqq9fFwzpf4cofMeYgOB+eN8/ABvcXLFbVY6UlH1Gf5SqxFMxVqKthRuzgMKYhDOJJ+CpsX96VgdiQLh3ux7ZCM8IpR2fHy/SU2tZe5zNPEzziEnNV0w7YT7OmNIriIfUiiGhb4Zff+X9uiNLjP6sq6Odcr+lnCgJl3S+SNYOCyiMSThdNgHJ596Ae+wd6BXxQPvLfVKDCA+dgHIx/yI6B7qjW5AczXMkMQ9JOSVm7B1j7Xy5XU6ISCwaHSkiOhjrZtqgB6C9a66tyBQ+RsaidcQn9H2SrB2+qplmQz+kVYeTxeWUVhmNa0fpsiO2VyK95DpERFzqfvGftSiMKUjOLVHMhFSI8/qBrWdOXfPfU3j1l6n45d8HkJNzodWO2x4IDhmIwUFDRDuhMAEnsk8Y/VltVlliJwsoDKPjUEIu4i/JGoHhkb7NylLZVoiAg9KOi98i/utH86w9xn4oTNOgYn8Usk7cMqKTycOI62Nryg6sKEvCK+lbkF9AkxLGlEyPnK6018auNfpzvUI94eUiO+bvis0WxSHbAqxBYZrNSqE9kWmvuU9qEhI0QElhHVdZIP6H+7ooERan0wpErRSGaQyU6G/Ffrl6rZO9DW4e3qlVB1Bbg8eeavBwiLHJmdhxIhxt5QKCf8f/jcpqubhjQ1AulJEaLUphWZUS3WXtsIDCNIuyymokXfgQAzs9hXHB76Kr20keUbph2Dlgkr0/rrYPxDVBulwR2mgeYg1rUZhGQjVXSivlOjhzBoXBx1WnqWtp6GGZaCc/MiJcgjiCpwVwc3DD+NDRop1fno8d+943+rOj26CZp1kCyhtvvCGcIR988EFl2bhx48Qy/dfdd99t8LnExERMmzYNLi4uCAgIwGOPPYaqKl3tCMZ62HAqAyrHczjnrMYhrywUFsWbu0sWw+vzt+DleZtw89QvlWXTegdTFLJg3bHURjnCMe0bmgws3S3/vih56B2jdH5OrUF8QTyqJFk4igoa0KrHbk/McNU52K+LXWP050Z11gkoO85ntW8B5cCBA1iyZAn69Olz2bpFixYhLS1Neb311lvKuurqaiGcVFRUYPfu3Vi2bBmWLl2K5557runfgjEbKw8lI9dJV8q9R/RUPhsNZH4c0slHtCk1eUyqbP5hmAZ/a4eTcUlTy2lq72BhMmxNYvN0eVeivaJb9djtieH974RvtTxxSa0qQrWRFY47eLsg0t9VtI8k5aGwzDjzUJsTUIqKijB//nx89dVX8Pa+PN8FaUaCgoKUl4eHh7Juw4YNOHXqFH788Uf069cPU6ZMwcsvv4xPP/1UCC2M9ZBRUIYd59MRr9Eyhzv5w9NTVwKcqZ2Z/fSdZTknCtMw5BT7lV5o8V1jolp92GLzdQJKlFfrH7+9QMnvnu9+K34f/jpW3HZUmIsba+ah62VP7CW0SwHlvvvuE1qQK6+8stb1y5cvh5+fH3r16oUnn3wSJSW6nA979uxB7969ERioq9MyefJkFBQUICYmptb9lZeXi/X6L8b8rD6SAjhkQGUjm+d6Bg0yd5cskpKiTKSm6gopTukVrBT4onBRtSYig2HqYkNMuhIpNyLKF707eLb6YMXGbVba0e6t65zb3rhi+KPo2kUX0WMsozu3rXwodo39wIoVK3D48GFh4qmNefPmoWPHjggJCcHx48fxxBNP4OzZs1i1apVYn56ebiCcENr3tK42Xn/9dbz44ouN7SrTgpDvBJl3bJ11ETw9ubKpAZXlxZi6fCjSbVXoo7bH8tsOi+Xk2Eh5C7ady0JKXimOJOViYEfZ7MMwtf3W9NPa3zXWPNqLC7kXAFvAUS2hg3s7LmVhwQyL8oWdjQpVaqlNpL1vlAYlKSkJixcvFhoSJ6faazDceeedQiNCWhIyA33//fdYvXo1YmN1P7DGQlqY/Px85UX9YMzLiZR8nM8sgo2TTkDp5SfXn2Fk7B1dlcyQcagQBRVrjebhCsdMPeyLy1HCRikb8Rg9Z8jWoqK8EIk28vUbAdtGmR2Y5pOXG2fUdm56ae/jsoutPmN1owSUQ4cOITMzEwMGDICdnZ14bdu2DR999JFokwNsTYYOHSr+X7ggZx0kn5SMjAyDbbTvaV1tODo6Cj8W/RdjXkh7QoQ7HxX/baBCd5/uZu6V5RFpKzutFdqocCnnnLJ8Ys9AOGhCNv86kdZmEisxLZvW/u6xUWYpI1GRfQYL8wowobgEwx0NNeBMyyCp1fh+/Z249ru+mLd6psEEpz5GG0TzZLcfAWXChAk4ceIEjh49qrwGDRokNCXUtrW9PKMhLSeCg4PF/+HDh4t9kKCjZePGjULo6NGjR/O/EdPiVFSpseZYKhxVxch2LBfLIqsBF/vWjSqwBiKcA5R2XNIupe3hZI8rusr24uyiCjFLZpianE0vxH9n5HtliKcTpvWR76OtjVtOAu7Py8cHmdl4uMMks/ShPRYQ3JF1FOds1EiyBY7F/GzU50br1eXZeSGr/Qgo7u7uwvFV/+Xq6gpfX1/RJjMOReSQpiU+Ph5r1qzBzTffjDFjxijhyJMmTRKCyE033YRjx47h33//xTPPPCMcb0lTwlg+dMPMLalEJ6djqNLM5no46X4UjI4IT72aPJnHDYZmZt9Qpc1mHqYh7cnC0ZGwN1dRwKwzunYAa0pbixlhukCUtTE/GPWZ3qGe8HS2VxK2acsiWCMmvdodHBywadMmIYR069YNjzzyCK699lqsXaurKUBalnXr1on/pE1ZsGCBEGJeeuklU3aFaYXU9v2qcvFORhZuI9Vv4GAe81qICNDlCYrL1z1siPHdAuCiqaPy98k0oZliGC1p+aX482iKaNMD54bBZnRMzTyta/t3M18/2hlXDnkQzhoB45/SZOELZFzae1/RLiirwvHkPLSbKJ6abN26VWmHhYUJn5SGoCif9evXN/fQjBm4VFSOLRqV82j7VEwuKRUvdL2Oz0ctRISNBI5/INoXSw19r6jI28QegfjzaKq4kVD2xwnd2b7PyHy7M05EYxA3DesIV8dm366bTFL2aZBxyc7OCfDmEOPWwsUtABMc/LGuKhsFNipsP/AJrhz1pFHhxutPpCtalP4ax1lrg2vxMI2CfE+0N82hjgmaq8gOCOQIntrw9ekCd814xVVf7lFvWJuHk7YxMvmllfhZUxSQnKmparG5KC/Lx3S3CgzpFIbHgkMBm9arnswAM7roJn9r49YZNSSUxqAtOMqygMI0it810TsuKINfmabuTmBPwL72sPP2Djm6RankkMw0WxWS9BxltTMdDyd5ZrzxVAZKKy6PhGPaHz/tS0RReZVSIdzf3Xz+efGJO6BWqVCpUsHOqfUTxLV3hvZfBH9N6vvt1flGhRyH+bggwk+OIDycmKtcS9YGCyiM0ZxJL1Bqx0wIvoB/XZyQbGcLKbgfj2I9XOHbV/z3qK7GxRM/Gayj2TFlliVKKqqViA2m/VJeVY1vd8kPIfJBXzRaVzzOHMSm6pJyRnlwKYvWxtbOAVPd5dpHFJTwz/73GhVuTBrvvVaa9p4FFKbRuU+IQJ8TeCzAD1PCQvGHCydtqo+ZQx/Be5mXsCUxBWPPbQdq5DPQr82z5pjsFMm0X/44koKsQjl8f3KPIET6u5m1PxdydA6y0X69zdqX9sqMfouU9tpUQy2scWYe6ww3ZgGFMQpKJLb6iOwjYW+rQh502Xy7h43hUawHv4BemBg0DEKMy0sEEncbrB8W6Qs/N1mFv+VsFgraQBVSpmlQXaYl+kUBx+rC1M1FbLFOaI7qMNysfWmvdO08DV3UNnBQSwgqyUOlfth3HQyP8lVqflmrHwoLKIxR0AWeXSTP6iZ0C8TpSjmxGNXliIqsvWgko0e/ebr2UUMzD91EpvWWsyhTqPHGGMNoH6b9sPlMJi5mFYv2kAgfi4i+iK2UzbpOagmhIXJmcKb1eb3jbGxJSsa7WZdgf1KubVcf7k72GBDuJdoXs4uRnGt9ae9ZQGGM4ndN7hNiRncVEjWO/F3hAHvOINsw3aYBjnKJhrwza1BWcqkeMw9H87RXlugXBRxjfu0JRfAk2cgOmhGwg42t+UKd2ztdBt0FD0lT5uDYCqoi2eBnRkXrZZW1Qi0KCyhMg+SXVIoIE8LX1QGe0j5lXS9X3YOVqQd7Z5zueiUeCvDDFUFe2LjP0NGtf5g3Qr2cRZuqkOYUV/BwtjMOxufgYEKuaHcOcMMVXXVlEsxFXMI2EcFDRDtwxW2z4hEMRI6T23kJQOLeBj8yuot1hxuzgMI0yLoTqUqWU5rpn0nXCSg9/XSZUpn6KYuegE2uLsIT/8/EDYY/RBsVpveVo3koNTVllmXaF/q+J3eOiRTXhLm5kMYRPBZFnxvEvzKVCjGHv2x481BPJY3BrljrS3vPAgpjdO4TbU6GmDy5MjXRK2ICj6CR9Os1D+GaNCf7pTKk5RnmM5jRR8/Mc5TNPO2JC5lF2HRa1lIGejhiVj9dnSZzkpCr+61H+XMEj9npPh1v+vnjivBQLMo7IExw9WFna4ORmmievJJKnEypf3tLgwUUpl5is4pwJFGu5dAtyB09QzwQUym/d1FL6Bg2mkewEUnbZnS6SrTJlLw2caPBehrbSE1ypf3xOUjPL+OxbSd8veOi4lKwcFSEyI9jCdw7azk2X/UzlvS8B/27XWvu7jAOrsj3DkeRjQ0KbVTYduDjBsdklCYfijWGG1vGr4CxWFYdNtSe5Fw6LzKiEj1UTiKJEGM8M4c8pLTXxK6BpOfoplKpMEOT+p4W/3WCzTztgcyCMqw6LIfyujva4cYh4RYlVAcE9sKIQffC04tr8FgCM7rNUdprsw81uP2Yzv4W5Yfy20FdioqGYAGFqTcnw2rNjZNCYWf1D0F+YTIGS45wVUvo5WbG6qpWSohbCIYEDRHthIIEHMs6ZrB+hsYPheBonvbBd7vjUVEt+3jNGxYuwkMZpi6G9L0dAQ5yyYGdhfHILZMdq+tLe9/J18Ui0t4n5ZTg5XWnjN6eBRSmTvZcvIRUjZlhbBd/BLg7ITJiPL699SB233wU90z9ikevCcyMmqm0/zzzi8G66AB3dA+Ww5GPJeUh8ZL15S5gjIceFj/uTVASIN4+0rxp7RnLx9bOAdM6XyPaVVIV/on/x2gzT2W1hH0XL5nVlNkYP10WUBijUtuTecfgwrG1g4uLzrbJGM/EjhPhbCPPkv+NXYuy0tw6KxyvPc7Osm2ZFfsTUVgmz2hn9w9FoIflFN3ce2gJ3vx1BlZufATp6UfN3R1Gj+lR05X22ti1aAgqSmpuMw+lTvilEeYdggUUps6Z3d8n00WbwtQmdDd/Toa2gou9CybayhlCydFt64GPDNZP76Mz86zlpG1tFgrd/2ZnnEFosSWxN34jfiyNxwupG3AuYYu5u8Po0cW7C7r5dBPtE9knEJd2qBFp783jKPv9nniUVRrWIWsIFlCYWvn7RBpKK+WYWHLcdLK3RXVVBaQahe6YpjGr+43if3e1LRzt5cgdfZtxf02K6jPphTiXUcjD3AYh4TNNY0K9snuAMO9ZErHFOu1dZOgIs/aFuZzpAbqyA+v2vIn68HCyR78w+Z4Sm1WM1LxStCalFdVYtjtetLWCkjGwgMLUykr96J2Bsnln6773MHZZH9y9bCj2HPqCR64ZDOpzK1aNfBu/3nYUVwx/9LL1BmYe1qK0OSh6a8l2vbT2Y6NgaVxwkasoO6vsEBI80NzdYWowNWIqbDRRgOvyTkNdXb/z62i9cOPWTnv/+6Ek5JbIRVAn9ww0+nMsoDC1elrvvSgXA6S8HP01kvfJ9IPItVFhF0pQUm5dCX8sDfLh6Rwt50SpjWm9g6HJMC4EFP1wZMb62Xo2C+cyikSbCroN6mj+ooD6lFaVIqVYNvFG+nTlGjwWiH9ADwxXuSK0Gpju3QMVFYVG+6Fsb0UzT1W1Gl/t0JkybxtpfLg6V35iLkObk0GrPaH8HIQ2QRvRK2oKj1wLEuDhhGERviKSKv5SCU6mFKB3Bzm0kLF+ampPtL8xSyEuPw4SZKE4ysvytDuMzBszV8DDI8woAbJvB0+4O9kJp+xdF7JFGonWKKfwT0w6EnNKFC1O92Dj72OsQWEMoJn6qiOyeYfumRRZoF0eI8l2Sz9HbwT49+KRMxHk1xNzeiWSkvbUU+FYJzQy1s3RpDwDDeXE7sarvFuL2DydABXtFW3WvjB14+UdYbR2i9Lej4jyFW0yt8SkFqBVTJnbdDWm7hrTOGGXBRTGAKqmmqDJvTEyyg8hmgq7SYVJKKiQL+he/n1Fhkmm+Zw9/xdmL+2HG/a/gB92v2Kw7qqeQbDTzHDWHU8TMx7G+vlST3uyyEKKAtbkwkVdMcsoN8vJbMs0j9Y285AG+ISm/g+V8hgZLQtIxsJPGabu3CcDdQXLYi7FKO0efj141ExESGA/JKvkyKi/SxJQWV6srPN2dVAc2yja41Bi/RkjGcsnPrtYCd/3c3NUNJSWRmzaQaUdrclaylg2aamHsK+BCsf6jrKtEW5soD1pgimTBRRGoayyGn8dl+u/uDrYYnLPIGXdyeyTSruXL5t3TIW7RyjG28uzijwbFbYf/LhuMw9XOLZ6vtIrCkjOghS+b4lcqCpSCoIGBw0wd3eYBkzEtywdiEkbb8Xjxz4S6SDqoqOvK8J95LT3hxJyUVLRcmnvT6cVYNs5WQjq4O2Mqb10zxNjYQGFUfg3Jh2FmjoNU3sHw8VBZ9uMOfW70u7p3ZVHzYTM7HKd0v6zRlbIK7sHwlFT2Xb9iTThEc9YJ9lF5fhdo6GkCcCCoR1hiUjlRZhamI9xxSUYCUc251o4Khsb+NjKpvgcGxVOnNHdq+vToshp72VfqJbgy+067cmi0ZHCB6axsIDCKGhvnvq5TwiSyE+pZdNDSLUEH1fOKmtKhg+4CwHV8rR6hzofOTkXlHVUOG58N3m8LxVXCJsuY518vzse5VWygHnDkHB4ulhmUUBV3DY8kJuPjzOz8Z73MHN3hzGCMSG6RHrbzq022szTUn4oKXmlSrFTLxd7XD/IsFSKsbCAwgj+OJKi1GggddyQTj7KyMQnbEOpxpGvp72cE4UxcfEvzy6iXaVSYf2+d+pM2sZmHuukuLwKy/bIRQHJ8fn2URZaFLA4G1j3kO595Dhz9oYxktF9b4dKYzvcln+u3m2HR/lB65fdUgnbvt0Zh2qNU//NwzsZaOMbAwsoDE4k5+OJlceVkbh/fLRBZEFEQSb+TE7Fq1mXcH0Az6hagln97lHaa9L3Gqy7oluAMAlocwqUV8klCBjr4aPN55FfWqkInKGa6DhL82XI/uMuoChDXhA9EeitMz8yloufXzf0lmSN3HkbNVJTdU7ONfF01qW9P59ZhLR806a9zy+pxM/7E0WbzNO3DG+6KZMFlHYO2cXv+uGgonq+cUgY5gwKM9jGJvUIIiurMLOoGMM766poMqYjKmoieqplIeS0TTXOXfhbWUeOlJM0DsuUZGn7OfNUI7U0KqvV2HgqA8eTdQkELREqb//lDtke72Brg3uvsMzEZ79ufBAzKs5gvasLQJXKr/5MTobEWAVj9YIXth1fWu+2o1qwuvGP+xJQUiFPouhZ4uvm2OR9sYDSzm/w9y4/jFRNwTJKuf3CzJ6Xh4KlHtG1g/u1ci/bDzODRyntNUeW1G3m4do8OBifg+kf7cSi7w9i1qe78N8Zzazfwigsq8Qjvx1TIncemdTF4ooCErGxG/F26n8osrHBEwF+OD3xacCNfc2sibHd5irtbRkH6t12TAvV5aFI0O92yUUBSQl/x+jmmTJZQGnHvLLuFPbHyV7cAe6O+GLBQDja1Qh7pJC19BNy2ycKcGYflJZi6tBH4KZWY0pRMcamnCLvZGXdyGg/4WxGbDqV0aLhgZZMXkkFnlx1HNd9sQdnNVWe6eH/yK/HTK6qNgWvrDuN5Fy5X+TXdcfoSFgaFeWFeGL7oyjXmHVvdApH9/63m7tbTCPpEj0VQRpn+/1SMUqKMuvctm+YF9wdZb+QnZq096Zg9ZEUoZUnpvQKFmHNzYEFlHbKrweSFKc9Ujt/cdNAUf+lJgnxW/CxuxP+c3FGTjDnP2nptNVbnfviraxLGJyXAcT+p6xzsLPBFE0egdLKamw+XffNpy1CKbNXH0nGhHe34ef9ScpyrW8Ope5e/PNRiwrDJvPTLweTlH6+O6dvo0rNtxYfrlmAszbyuEVXq/DwzB/N3SWmieHGY11k83ylSoU9x76rc1t7WxsM06S9zymuwKm05qe9J6fYr/RCi+8c03xhnAWUdsiRxFw884cu8dorV/fCgPDaq6nuv/gPvvT2xOJAf6xzvVyAYUyLY78FujdHlxusm9FOzTwXs4ow/+t9eOiXYyLUmnBztMMLM3pgxxPjFYfT/fE5+GDTeVgCl4rKhaZHy3MzeiBMkyDLkth94FN8X6Lxj5EkvDH6dTg5W1ZlZcZ4xkReBXtJwsiSUrimHat/W4OsstkmEcgvZsvpKIZH+gotTXNhAaWdkVlQhrt/PIQKzUzz5uEdMWewoVOsPvop7nuF6nwkmBai80TZQZE4ux4o1aW3HxrhC3932eFs29ksJSqkrUL27Pc3nsNVH+zA7lhd/pepvYOw6eGxuHVkBHxcHfDRjf0VzcSnWy+0SgrvhrQ9T60+gewiWZi6snvAZY7nlgDl23n6xOfK+4cCR6Fr52lm7RPTPIb3uR07UnPwRUYWhsUdANRqo+ryNPc3Q9f8F9v0K3SbxpTJAko7gsJTSTjJKJBthEMifPDs9Prr6sSUyc6HNpKEbp2ntko/2zW29kCfOShVqfCXkx2273lXt8pGhWm9g0WbBMwNMXJNl7bI7gvZmPrhDny4+bwiTFN+nu9uHYzP5g9EkKdOmzewozcem9xV8Ud56JejyCyUHb/NwcrDKfg3Rv7dkAD1+jV9Gl2DpDVCip9fdzOybeV+jYQL5k/+zNzdYpqJvZM7XCM0uWuKMw0DHGrQ0ddF/KaIg/G5KNVE3jSFA/G5oko30S3IHWO76ISf5sACSjuBJNzn/4zB4UT5IgrxdMJn8wcIW2RdlJXm4oJKvmgjJVu4aGf2TIuS0XUyrggPxf8F+GFJwvq6a/O0QTMPOdiRgDHv632KupgSm909NgobHxorcsLUxp2jIzGuq3xTJM3FgyuOKomiWpPk3BK8sEandXxtdm9F63UxbrOIlrEEftv4ELZKspOxj1rCK1OXckr7tkKXybr2uX/q3IyEZq0WhSYB++IumaRCN/memEogZwGlnbB8XyJWHEhSkucsuWmQqKZaH2dj/xWZTYmejqaRiJmGCYwYh1DIHvbHbSoRF79VWdc/zEvxuSCzh9Zj3tqhKAJK7kROsBQJoK8dWffAKPzflG5w1jjE1gYlFnz3+r4I9HBUxubTLbqSAa31HR797RiKNPWsrh3QAVdpHJtPxPyGa7YtxtU7H8aXa24SGgxzEuwdJQQT4qXut8PPv7tZ+8OYXkCpIg34ecPaXi3hh3I+oxCbNE77NPHV95VrLiygtAMolFh/VvfGtb3Ru0PDJdRPJu1Q2r18+AbWmswMHKq016TtVNo0M9HeAEhD8PdJ6zfznE0vxJwle/DkqhOKXw1lu3z9mt747a7h6BbkYdR+KCHUhzf0V9J4f7DpHPa2Yu2ib3fFYa+m+BoJkc/P1JlPVxz7HNUaYf/j3KN4+depqKo0nxlq9JAHsHLG73g2eALGDnvYbP1gWgD3IHwe1hVjw0Nxg1Mx0tOP1rnpCL209031Q9EvCkglHOrTyjcWFlDaOJQb4t7lh1ClmS3dMSoCs/sbV7jpVO5ppd0zbEyL9ZG5nGmjnoetStYYrE3eimp1da1J29YetV4zD9m83/j7DKZ9tAMHE3TOwLP7h2LzI2Nx45Bwg5ILxjAs0hcPXinXNaJLfvGKIyKipqU5l1GIt/49q7x/+/o+8HCS89aUlGRjU7lhWPhv5Sl4f8VVQIVsxjJXevQ5kz4w2/GZFsS3Mwps5fvH9nrCjalgZZ8OcrTNuYwiZBQ0TmhOzy/DH0dljaeHk50ogmlKWEBp41EQd/1wSIkmGBntK1TlxnKyTFb52UkSukZf1WL9ZC7HzyMUI0NHinZGSQb2p+9X1nUPdkeUv6sSWmuJCcoaYsuZTEx8f5vw/NcKzxF+rlh+x1C8P7dfg+bH+rjvimhxrRPkEP7wr8dMloiqNiqq1MJvhv4TC0dFiJmplq0HPkaJRtDqrrYVv6fAqircFH8cWDYDKGqdqKOMjONmNy0xrcPYbtcr7a0F51vMzPPd7jhUapLDLRjWUYT/mxIWUFoYumllFZabLdTxeHK+eB/m44xPbhwAOyPVb8VF6YjTJG/qLNnBwdHy0nO3dWZFzVLaf8b+WauZx9oqHNMMjTR6ty09oGRYpUSBiyd0xt+LR4uMuc2Fop1kIcdBvN92LkuphdMSfPzfecSkyomuogPclIgiLesS/lXaj/V/AF/0WYzPckoQVF0NpBwCvpkIXNI5GbYEuTmxuPGveXho+Wjk5ca16LEY89O9y0wEOMhm/H1lGSipLDGyLo/xwnJBWSV+2puo/IZvHdkJpoYFlBbWYEz5cDsGv7oJdyw7gFOam1hrQPUQVh2WVW/O9rb48qZB8HaVb9jGUFZWgBucO6KP2h4DXS0vh0N7YFzYOHg4yP4Xm+P+QVFhmrJuVr9QAwdoc0SsNAbq39JdccIJdv0Jnd/MiChf/P3gaDw0sYsoimgqAtyd8MHc/kqtu7f/PYtDCbJ/iCk5nJirOONStNEHc/sZfI9L2eewW10k2sHVEgb2vhlDByxCl5vXA+4aITM3DiXfTsLp06vQEpDW5Ll1NyHLVoXN6gK8tX5hixyHsayssmM6TRTtCnUF9qXtq3Pb/uFeSkbmXY1Ie//zvkQUah3CB4aK35ypYQGlBaGZW2yWbGMmL+epH+3AfT8dxoVM+YbVkjkkXl2v8x955/q+6B5snKOhFl+/Lnhq7l9YftthPDGnfk9wpmVwsHXAFBfZplsmVWPjvveUdWQOGa1RzSbmlGDrWctNfU83PCrq98LaU0qEi6+rA96b01eYdKL83VrkuKM6++G+cdGKgPTAz0dFLR9TQfWQHv7lqPB1IUgL1CvU0Pn8bPwmOGnWT/PsBhtbjQo8sCdwx0bAvzvILfhhNxVu3vsctu7VnWNT8dv535WQYm+1hIcmfmzyYzCWx9gOY5X2tuRtdW5HTq3DNSZJcgc4nV5gVE4tcgonaBLQUjWmWEBpQTafvrzC6l/H0zDp/W2iuFlSTt1qt6ZC+yQhSDujvndcFKb1kZN7MdbHzOirlfafyVsM1t06QqdSXbpbriBqqYL6f2d0AtQNg8OEE+w1Azq0eAKzB6/sLIr0ESl5pXj0t+PC/GkKXl9/BvGX5N9wvzAv3DMu6rJtRgy6F1vmbsfbEdfhmiEPGa707ADc/g9+6tQXu1ycUWajwuIz3+LX/aYTUi7mXcTbB95W3r/U53/wD+hpsv0zlsvQ4KFwtJV9ubYnb4Nar/hoTbSTHWP9UP48mqok/JzUI7DFJhksoLTgrFF7UyYTyzPTuis2cZIdVh5OxhXvbMXTq08IT2hTRUXc+cMhUTiNoMRVj0wytIcz1kXv7teLLJ/3ePTCyxMMZ75XdA0Q2SC1NxXKR2CJfKXn//H+3L5449o+8HIx3tzYHMjn6sMb+8FbWwn6dIZSDr65QtcPe+Vim072NkIbVJd/l7OLD64a8zzCwkbWstILN974F6bYykKUWqXCy6e/w0eHP2q2IFVRXYEndjyBsmr5/jK361yMG3h3s/bJWA/Ods4Y6iELzVml2Th9fo1RAsrOBgQU9WVFAS8XzC1CQHnjjTfEDOjBBx9UlpWVleG+++6Dr68v3NzccO211yIjw1CTkJiYiGnTpsHFxQUBAQF47LHHUFXVtsrHH03OU6JnSNVMKrBtj12Bx6/qKnI8EBS9QP4DY97egpfXnWpW0i26mT2+8jhOa6pSkgmAckI0pXpqeVk+sjJ1eVMY89qSv7hlH+6d/TPCwoYbrKMQ3JuGdVTeL9tjeVqUmNR8pY4OXZOz+up8Z1qLYE9nUUlYy+t/n8bxZDmjclMgM9Fjv+kKsT01tTsimzGDJAf0N27cjNv8dblvvjrxFZ7Z9Qwqq5teb+njHc/gTM4Z0Y70jMQjgx5p8r4Y62Ssk057vu30b3VuR79N/aKb9aW933I2E+c1bgqDO3mLZIoWJ6AcOHAAS5YsQZ8+fQyWP/TQQ1i7di1+++03bNu2DampqbjmmmuU9dXV1UI4qaiowO7du7Fs2TIsXboUzz33HNqqeWdi90Dx39XRDveOi8b2x6/AAxM6K45JFOnzzc44jHlrC97+9wzyNRqQxrBk+0Ws1aQ+p/1+edNARRBqLIdO/Ijxf9+ACd/2wu8bOYmTJXP9oDC4aK6jlYdSLK6A4Nc74gySODU2r4mpGN8tUCn/TmGR//vpiIhCaArP/hmDTE1kHs089YVEfcdUfafmhiDflIenfo3/G/J/UEEeozWxa3Df+psatR8tew59gaUJf4u2vY093hzzpphRM+2LMX1vE/8DqyU41XP+SdEwpouf8jwiIaUulmzTaU/uakHtSZMFlKKiIsyfPx9fffUVvL110lN+fj6++eYbvPfeexg/fjwGDhyI7777Tggie/fuFdts2LABp06dwo8//oh+/fphypQpePnll/Hpp58KoaWtsOmUbN4hE3vN+iEkODw8sYsoFX/XmEihIiZKKqrx6ZZYjHrrP3y8+bziUNgQ5CD55j/yTImgEMvOgU0PC47ReHxn2qrgyDc1i4auJUqpTpRWVuO3g3I5A0uA8rNohWYysVyn6ae5oPBfiljQOhY/ufJEo80oVP9I+50oMdXb1/Wt1Y/mWMzPGPf7RDz24xgcPfmT0fuf330+3h33LhxsZBPYnpwY3Pb7VcjMOGn0PiiM+OljnyjvF/sNRTcf4/MfMW2HoMC++HPMB9h463HcPuPbercdFa0LN95ZR7gxRa1phRcKqR9fR20sswooZMIhLciVV15psPzQoUOorKw0WN6tWzeEh4djz5494j397927NwIDZa0CMXnyZBQUFCAmpnazQnl5uViv/7JkyFH1rMYfoG8HL6VYWE2o0umTU7tj+2NX4JbhHWGvqSxaWFaFdzeeExoVsvVRuHJdxGcX44Gfj4gqrlqnwEk95fofTeWkWpfdsmenCc3aF2M6EhN34pPVN+Dj1XMNlt8ywtDMYykhx0t3xStJ2CiJU321dJoCaRbe/HUGlv21yKgEZBSt8NEN/YVgQfx1Ik2YWI2FfMWeWX1Cef/y1b0Mqirrsy7mR5TbqPBPdS4SM40XLoiJHSfi64lfwlNzGs/YqPHl1seM+7AkQfXP/6F/qey8OxzOuGkSR+20ZyIjJhhVCJKSG2pl7bocZb/U055Qgc6W1og2WkBZsWIFDh8+jNdff/2ydenp6XBwcICXlzxL0ULCCK3TbqMvnGjXa9fVBh3L09NTeYWFhVmPeaeH4XetjQAPJ7w4qxe2PDoOcweFKX4jOcUVIlyYBJUf9sQrmSq1kIblzh8OoqCsSjnWA+M7N7v/MZVycjdXe1d06qgLVWPMR0V5IeZuvhtLCmKwPC8GpSU6FWx0gLvi5JaUU2oQMWMu6Nr8ab8uidNNwy83gzSXl/68AT+WxuOd7L3YuOs1oz4T5uOCt67T+aO8tO6UUfmJSNPy2O/HlN/a9D7BBrlo9CG/kX8q5XPgpJYwYcgDaCz9gwbi+7EfIrQa6CfZ49GZRmphDi+D59l/8E7WJbyaV4ZXpnyrC21mmHogx3Vt2vsz6YXIrJH2/mJWEf49JT+jA9wdMau/6YoCmkRASUpKwuLFi7F8+XI4OZk+KUtdPPnkk8J8pH1RPyyZzXoPiAndjVeBdfB2wZvX9cGmh8diVr8QRZolezfZvSnq59eDSaiqVgtP6kd+PSrqJ2jVbRRJ0FyJNrs0W6RWJ3r49oCNjWlnvUzTHSknaRzeim1U2Lz/fYP1t+llcVxmASHHvxxIEppA4ur+ISZP4rRz/8f4u1onpG07/4fQHhgDVRjWhmiT0P+/nw6juAFz6o97E5RZJd2cX7m6V53b7kjZgXy1bK4eHzISrm5N02hGRozHjzN+xcezVsLJ2QhHxOzzwD9PiibdBWZO/hABAXX3k2l/5ORcEJOduhitl8l55wVDLcpXO+KUnxj5kzna2VqWgEImnMzMTAwYMAB2dnbiRY6wH330kWiTJoT8SPLyDD3kKYonKEj+kdL/mlE92vfabWri6OgIDw8Pg5elUlhWqVRQJa/ork3wBdFG4PyzeAwm99RpYCiPw+O/H8ek97eLsu7/xsjj5u5kJ5xi3TXFyZpDTLbOzNbLl29ulsTMHguU9p966dOJcV10Icd0YzFnyDEJ0N/u1DnHmjqJE2mPXjn5pfJ+WlExXkk8DyTKZmRjeHJqN/QKle8jF7OL8cwfJ+v0R6GZo37iw7euqz9Met3FdUp7ek/dOWsKfv7d4eUdYbAsOXkvfvnXUCtTWV6MrJW3AdqU5oNuB7pNbdaxmbbD9n0fYsF3AzBuzdXYe/TbRudDySwsE6kxCKq3M2+oaYsCmkRAmTBhAk6cOIGjR48qr0GDBgmHWW3b3t4emzdvVj5z9uxZEVY8fLgcIkn/aR8k6GjZuHGjEDp69NCVJ7dWtp/LVoonkcmlOYmouga5Y8lNg7DmfyMxtovOgYluqKuOyGnsafdkV29OmKM+JzMOKe0eftZ/PtoSA3rfhA4ad6R9UgnS044o60hzdstwy0jc9k9MuhCmCbpuuzTDYbs2vvh7EVI0k7fBFdV4PeuSHPey6yOj90GzP6pNpS1utvpICn47JN+AawpbVGywrFI2ry4YFo5xXevWihZUFGBbkpy108fJB8NDDEPDTVFT554Ni/BK+ha89dssJfnWx2sX4Br7HPzn4gz4dQEmvWrS4zLWTVV1OY7ZVEJSqbAtTo7uqo3+4d5KdCkJKFqhnbSyWheD+UPDlUrdFiWguLu7o1evXgYvV1dXkfOE2uQfsnDhQjz88MPYsmWL0LjcdtttQigZNmyY2MekSZOEIHLTTTfh2LFj+Pfff/HMM88Ix1vSlFg7+v4njTHv1AfZBZfdPgS/3T0cQyPkhE5aHp3U9bIooeYQc+pXpd3LzfTFn5imQ45uM33ksH660aw78IHB+usGdVBuLlSHqSnh6s2FbmikCtayyMTak7Pn/8KywrOibS9JeHb8R1B5aHxBzv0NZMnrjKGTnytev6a38v65P09epnn6fGssjibJGuFOvi4i50l9bDrxg6h9QkyNmAo7G9P6f2w9/AXiNcLZDyUX8fjP48Xs+LviC8iztcWjAX5In/Ym4CBr0xiGGNb3NjhohI1tJcl1OpU72NlgWKRcCZzycpEvCpk/f9gjJyWkQI7bRhpq9FoSk2eSff/99zF9+nSRoG3MmDHCbLNqla4Ilq2tLdatWyf+k+CyYMEC3HzzzXjppZdg7dBs6z9NTRSamQ2NkE+0qRjcyQcr7hyGHxcOxcy+IXjiqm4ilb2poIs2plr2afFSSwj16WKyfTOmYebgxUr7z+wjBjcamtVcN1AXckz+Sq3NwYRcHNM80LsFuYvIAFNB2oKXdj6Dao1WcpFXH0REXQkMu0fZJmWHLq27MVBVaK26mrQkVCZCm6TqRHI+Ptwsl6on16735vaDi0P9AsfaE98p7ekdxsHUzL7ybbwQMgm2mofNv9W5uO/M18r6xf7DERQx3uTHZawbF1d/DFbJWvYMWxXOnteZIes382RhxYEkxTmcHMPrilyzSAFl69at+OAD3UyOnGcpp0lOTg6Ki4uFcFLTt6Rjx45Yv349SkpKkJWVhXfeeUf4sFg7hxPzkKeZtVLSG5JGTQ2ZjCgz7Uc39he1P0xZy+RS5kloyxj2tHE1KjSNaV1CQ4dgkCRrGuNtJRzX03gRN+vV5zFHyLF+CmzSnpjy+vx140M4biPfKDtVq7BwyhJ5xYBbsNPDBzcHB2BGwV5kZ51q1H6fm95DCFMEOZ2/sCZGhPY/9OtRJUyaEiwOCK/fUTUt9RAOqsqV/vUIGoyW4NqJ7+Kj7ovgXOPcDqOQ4qs+b5FjMtbPuEDd9bj1TN1ZZUfruRNQROA3eqUqtMkOWwt+ArWQeedKTfZYa8IvJw57E5Lwa0oa/hcwwtzdYepgZphuhrzm5FKDdVS0S+uvlJxbWmvBypYiLrsYGzXHC/RwFNoJU+Jo5wRXzUP5uQEPisgmgZMH9of3xxEnJ1SqVPhpx/ON2q+TvS0+mTdA1MwifjmYhHlf7VWqjvcM8RCZnxsiI+Y3RGmSTU737duiAv6YoYvx7dDn4aMZD9J4vnoVhxQzdTOmzy1Ke3tO3bl5Iv1cEaLRkuy9mINUTa24Cd0CTO5P1hAsoJgQ7c2Z1MFUyM3qSD0Ccn3qXlGJXhGGSfgYy2HS0IeV2fPO4iSoKwyrYt860jzOshS5ow2EuWVEJ5NrEMm8sWbqCjwbNB6D+91usG7+6Bdgpzn4L0UXUKKNZjESCtPXDx0mbShB34EyMzf4XSQJ/c5tweqUdPyWkoZrB9eoXNwC9OpxPX6Z8iMe8hmMZeM+REAgR90xdRMSMghd1PJ1fMKmCtnZuuzj+pDWc3RnnRZFy11jWzatfW2wgGLC2ePFLDkDKxVP8nZtnWqtJiXlsK4d0t+cPWHqgfJq3Occgbcys/Fncgpszv1jsH5sZ38Rqk5Qob6z6S0fcpxbXIHfDsk+L1QbaP4Q0ydmI+ghPGfyh5ctDwzsg6kaB+ICqQqrL6xu9L6vHdhB8eHR8vjkrsbNGtNPAFlnRDRRt8CB8AsZgNYgKKifSGFO2UIZpiHGeur8Cncc/abO7UZr6vJo6RfmJQoDtjYsoJgIazfvZGQcx4lMTYVWSizl0fJZApmmc8uQRzCluAROpDU4tsJgnRxy3LFVtSjL9yUoobhzBoXB08U0YYjGpLDXcuvoF5X2D6d+QJW68RXSX5rVU/FHGRXth9uNjVg4/ouu3WdOo4/LMK3B2C7XKu2tabvr3G5klJ+SKJS4e6xp/cmMhQUUE7HJILzYugQUKi52118LsNDPDbspQ3Akp7e3eDqNAbThtRc2AUVZl2kDdDk+kpFX0nKFOMurqrFME4ZI5k2jH+pG8P6qa/HSiqtQkN9wRFJn784YGTpStFOKUrAxYWOjj0dROivvGYEfFg7Bt7cONiozc3VVBS6cWim/sbEHes5u9HEZpjXo1e0a4bdkI0koL8mGVCHnK6oJWQDGaXzZugd7YGKP5tV3008oWNkI8ysLKCaA8k0ciM9VciVE+cvqdWugpCgT9/1xDWJtJZTa2ODNgABUTWickyFjBsgBs/f1cluqhvrE7warKauw1lxBmg1KPd9S/Hk0FVmFcvTK5J5BCNdktG0up8/+ie+LzuO38hTMWTm13hTdWm7rKZeXJ7479GGjNDBaXB3thA3eWB+afUe/xmwfe1wfEoQtUcMAF8NcRQxjKdjaOeBD937YlpiCL9LSoUrcVee25HtF0aI/3TFUqQ/XXD5bdwvu+GWi0duzgGICtp7LVMI5ybxjDlVYU6Ab/uKV05XQTb9qCZ9M/BJ2nrUXQWMsC6nPXGx3dsJj/r647Ywu9buWm/XMPN/vSWiRkGNKzPbNDtOnta9WV+Ol458rOU9m+/bTRe3Uw5CgIejuItcsOl2cggPH6i8xbwr+OicLh2ccHVDVkaPfGMumX4/r4aUV3M8a+q/pQ+UcKN+WqfwpT55ZjW8Lz+CsjSYdthGwgGICNp3OtDrzDqml/+/XqdgLWcXnrpawZMzbCAszbWpupuVQBfbAJwHB+MfNFYdtqnAxTldigqDyB+O6ympaSj2vb4Y0FdvPZ+OsJvvqgHAv4SBuClacXYGTJXI5h0hbV9x+1RdGfY4mB7fphch/d1yXxKwlKCnJxqbyTOU3NGbQfS16PIZpNlHjZVMkce5fo4tsNoeK6go8e2YZ1I2cvLOA0kwqq9XYqske6+Fkh0Fm8HRuLKT2fvm36diozlNKwn826El0iZ5i7q4xjWRGoFxCglh77HKvfG3VXmLpLtM7y36tl8TJVGnt04vT8fGRj5X3z038FPaOxptNJ474P4RWA6Pgitv63IGWZOuBj1GiUX9PcgqBo5Nnix6PYZoNaSI7jRJNdX4iytOOoqX54tgXuJAfK9pd3Y0vNMgCSjM5EJejlJWnImL2tpY/pB+unoOVFWmiTbkj3u95F/r1nm/ubjFNYMqQh2Arl8rDX1XZUEuGPhdjOvuLxEvEnouXcCa9wGTjfDqtQKl4Gu7jgkk9TeNI9+b+N1FcKYfsX9v5WgwMHNioz9vZO+H36zfg81v2Ykj/lhVQ1ulVlZ7W/YYWPRbDmIqkiBF4wdcHV4aF4IeDhjW9TE1Mdgy+PSmbWqk21TMjjS9kaflPUysy71zZw/LNO8v+WoRviuSCaipJwmsR12HUkPvN3S2mifj5dcWIUHk2lFachkN61aiVkGP99PcmDDn+Ws/3ZOGoCJM40m3Z8w42JW5SqgE/NLBpCc/c3GU/lJbkUvY57FbL2WaDqiUM7H1zix+TYUyBKmIMVnq4IcvODlsvnUBL+jk+s+1RVEuy38ldfe5CZ5+GszJrYQGlmQ6CWru+nY1KSTFuyXg7+yuFxp4OHo8pY18wd5eYZjIjaobSXhO75rL1hiHHKSKpWnPJKCjDmmOyj4insz2uH2SY4KwpFBel49XTutT9j/dcBE9HyzWZ/HPgA8WJd5pnN9jYWn89MaZ90KHDMERXy9fucVUFcowI428KX/y1EBeKkkW7u3c3LOy9sFGfZwGlGVCtjsScEqXSMN2oLZ2Z41/DB91uw2KfgZg7+SNzd4cxAVeEXQFXe9mMQ7k/SmvkGSDhRCtAiJBjE1Q5Jk1MZbUs6M4fGt5glV9jWPnvYlFplRgBZ0ztMc8kzuCbdr6Ou5YNQVGhbNY0FX+l71HaM/rdZdJ9M0xLM6bDGPFfUqmwI0svi7iJiDm9Et8WnFJcCV7usRD2WudcI2EBpaXNO9VVQKnsjGopjBv2CO6YYVhkjrFenOycMDFUTq5Hvhtb97x12Ta3DO+kZIb8YU8Cqqobnx9ES0lFFZbvSxRte1tDE1KTST2CBSc34NnsHPhVV+OZ8e+bpNjeR3/eiIdif8JulGLl9udgKuLit4p6JkR3tS2ioozP7cAwlsC4fjptxrbkbabdeVU5bLe9iYjKSvH2Lq++6Bp9VaN3wwKKydLb11IcsLIUWDIaeLMTsOouVObK2TZbk5gzq7B2y9OtflymdZnpoauxsSb+8twGnfxclQKWzQ05/u1gMvJL5RvPzL6hCPSQK582GRLi1y6GjaTGnMIi/Bt9K8LC5IywzWXWgHuV9g8ZexqVxbI+qqsrcKWNJ+wlCdMChphknwzTmvTx6wMvRy/R3p26G5XV8m/aJGx/G93Sz+KXlHT8X4UTFk77qkm7YQGliVwqKsehxFylEmpH31rCIE/9CWSSiktC8YlfMHPVFHy6+kaRO6E1uBj3H+7Z8xyeSlyD79ff2SrHZMzDwD63IFhjcsmqLhUppesLOf6uiSHHlOztm536idlMkNZ+/5dAmqYOVEAPOIw0XSVgKqI3TiUneCPz0T87XzPJfqOjJuH9m3Ziy+z1uGY0Z15mrA9bG1uMDh2taF4PZhw0zY5TjwA73hNNBxs7zJ/xLeztm5ZdmgWUJrLlbJaS36bO4oCHv1ea33h5INnOFl8UnMT0X8bjj/OrLwsJNSWUS+KuXU8iVxNZsTnzEKoqy1rseIx5IQfNZ3ouwq/DXsFvtxypNW/I6M5+ShmGfXE5OJXa+JDjDTHpit8V7Y/qdDSH9LQjOLTzDd2C6R8Atqb15bq1r84/ZGn82ialv68LT89wuGtrIjGMlTEmeKjS3r7n7WbvjyZGVX/cI8pvyAd4DAjq3eT9sYDSQuadxIQd+Cz/JFLtbAHfaKhDBghHISLLRsKzu5/DDetuwIH0AzA1uWW5uHPjnUivlh8k3dQ2+OSaNSI/BNN2GTN0Mbp3nVWn7wZlWb21mSHHX+klZmt2WnsKc994H24N8MQLfj4o6L8ACNfdME3FgN43obdaduI9Z6PGnkOfm/wYDGONjAwYpDyXtuafb7bw/sX62zHfLgfn7e1lwWT0I83aHwsoTazeuv2cXD3Wx9UB/cMvzx678uAH+NzbE1d1CME/nUfhwev/wOpxn2Kcgy4U+XTOadz+7+14cOO9SEzcCVNAqrp7Nt2DuHxZDR/uHo7Pr/2LZ3mM4JoBHeCuCTn+42gKchoRcnwoIReHE2WH766B7hjT2a9Zo7p595vYIslp8re6uALj/q9FzhIJbLdG68rMLz21rFkzRIoKKi/LN1HvGMZ8uHuEYqDKWbSLVBIuXZJzZDWFU2f+wDf5MTjl6Ij5IYHIm/Jms7WhLKA0gb0Xc1BcIauwqNZJzQRV5Ii3Jl8+0bYUgqypz9Gp01h8fON/+HrS1+jq3VXZfnPqDsz67268/dssFBbIuSWaAt00F/9zB2IuxYj3Ac4B+HLSl/DzaH6OCsb6yM2RU0vXrNQ7Z3CYaJdXqbHigByN09i09gtHRzSrKCaF/L529kfl/f9FXw8PT7lfLcGE4Y8jTKN13oNSnDl7eb4YY9hx6FMRFXTFzyOxatOjpu0kw5iBe/vdj+/7P4GtNx2Gn3/3Jgvuz+x5XskLdJt3X3h1bL6jOwsozTTvTKzF/2THgU+QrcnncIWtF3z9dBEWxNDgofhl+i94acRL8NN4UVepVFhZFIvyioZLytcG+Zc88dtU7Ms5Kd57OHhgycQlCHVj+3h7Y/nf9+L67/ri+j9mQU0RMrVUOdbKFj8aGXKceKkE/8aki7a/uyNm9QtpVh8//nsRMjW/kZFwxeRRz6Kly8zfHDJOeb/04PtN2s86TSK8QhsVAjx01aIZxloZ0Pdm9O+zQPxGmsqS9Qtx3ka+j3RV2+COKU2L2qkJCyhNyR57ShZQHGxtMLqW7LGrL/yhtGd3nVOnB/XszrOxbvrvWOTRA45qCYv8BsPPr1uTbPlv/T4Lm9Wy06MzFf8b8w6ivaMbvy/G6tmTdQRnbNQiauXAMbkGhj4UcTZeE3Kcml+GjZrruT6+3RUHtcYp/JbhHeFIvlVN5OSp3/BzSbxSqPKZCR+YJOdJQ8wa8yK8NF+iND8J6kaG/RdUFGCbJEdH+aiBYf0XtUg/GcaaOHXmD3ydL0+MyZ/llREvN6q4Z32wgNJITqcVips6MTTSR0khriUz4wS2awSFwGoJIwbcXe/+XN0C8cDsX7B28jIsmGhYtCk/Lx4P/zAa5y9cntfCgI3PYXr8YXhWV4sL5IPe96Jv6PDGfjWmjTC9k64q9Zozv9S6za0j9UKOG3CWzS+pxK+a7LNO9jaYP7TpmgPS9L247xWRvZK4x2+ISLvdGji7+OClgDH4IzkVH2ZmwWbfkkZ9fmP8RlRIskZqao/57HTOtEmkRjjKkmnnWT3TziLP3ujWdabJ+sICSnPMO7Vkj12z7x2oNSfrau9eRqvNgkMGXlaq/YsN92OjOg/X7XwUL6+4ShQnu4yd7wO7P0Kf8gosS8vEO9HzMGKQLjkV0/4YN/h+uGs0BZvKM1BaknPZNqOi/UT+HmJ/XA5iUut2+ly+PwElGp+r6weGwdu16arg5RvuF9odoovaBjdd9QlakyvGvYQoSTOpOLysUVme111cp7Sn69U/Yhhrp6w0Fz/+fTcWLRuCp3++0ujPfbn+DhEZpzXtLJr6tUn7xQJKI9HPwDm+m2F4Mdn7V2UfVioFzx7ycLOqQG4vkme2JPD8Wp6C6WuvwbdrbxfrBIeWApt0xf6irnoHE0Y91eRjMm0DJ2dvTHKSfURKbFT4b//l5dTJwdWYKscVVWplHcndVLW4qaSmHsSnmXuU38dzg59scgKnJuPqB/TT1PipKAIOfWfUx9KK0pREVp08OqGHb4+W7CXDtCp2ds74PH0n9qIUWysyjcq4fPrsn/g6/0SLmHa0sIDSCDILynAsWZ5pdgtyRwdvw5vrweNLkaQxzQ9VuSA0tOkpsB0c3bHyxu1Y7D0ALprZcJGNCu/nHMDM5SPw0arr8fauF6Ao4yY8Dwy8tcnHY9oW07vrCu2tTfi31m2u6R8KdydtyHFqrSHHa4+lIqOgXHEIp5T5TaWqqgzdVI6iPcc5HH173QCzMPx/JCKBdEKbDi/RCfz18NdeXX2j6ZHTmxXBxDCWhp29E0Y5+CkO4EdO/tTgZw5f/Fd5/txhYtOOFhZQGsF/ZzLrNe+sO7NCaV8bMc0kM+E7Zi7DXzN+x7UOwbDRJNRJsQW+KjyD7z3d8ayfD6qG3QuMMl16cMb6GdB7AUK0YbVSMbIy5dDzmiHHcweFKZqSn/cnXuYQrp+YbdGY5iVmCw8fhaU37cNLHaZgsYm8/JuEbxR2dhmD6R2C8ZCnPdbvfKVBm/y6xM3K+6nBpqkTxDCWxNgOcsFRYtuFtQ1uP3/KZ1g26ClMsvXCnSY27WhhAaWJ1Ysn1BJe/OSsX/Fa+EyMVblh/NCmm3dqQpE9L9y4Ab+OfBNDYZgN1tYnCraTXpX17wyjl/p+uncPxUS4/sCHtY7NzXpVjn/cm4BKvZDjXRcu4Uy6rF3oG+aFQR0vT0jYlH7NnvCW2RMHuvW5AcmU7ZLMWwl/1+sYePb8OsTaypOD/pIDwgJ6tVo/Gaa1GNnv/9u7D+ioyrQP4P9k0gkJLYFQQg2dEAwCQST0IrAIuCA1AoqyoKyyoKysKIoUD5/CiigIS1VEJQgoJSC9B+k9lAChhpJCSEiZ7zzvZCYTCBBImTsz/985c3Jn7s3NnZnkzTNveZ7B0GV+CN6ckPXB5HGC6vbB1H5b831ox4gBSi4lp6ZjW9QNUx6IwHLZJ7QaVwl0aTkBXw/YqYZo8luNgE6Y3X83vq4xEM1QBAM9A/DR31cVyhJNsj5dGmRNll51fXeOx/iX9EDrmoZg+0pcMtYdzZpjla33JA+J2XLKxWJp0rBKsCGidHpsNesheVCxM5vw+p04+KWloXNZQ3E1Ilvj7e2PBpkfgKN1wPnzmy19SQxQcmt7VCySUw2fsiSHhOMD2WMLiwQjoU3ew8ywXXivxzIudaRHkszFUoPGWa9H+XuJuHfZMIH7QQPNlhzP22EokXDqWgI2Z5ZzKFfMHR3qlHmmV/rQ0Z/Qff5z2Hvg4XwslhYW/E/T9ryTP+Z8UEY6yhxbhRG347Am5ga6NR1TeBdIVMhCfYJM25uPZmV6Nvp+RRjmrXod6Wm5L5GRF/zo/QzDO21ymH9CpEXjq/bExguX8OX1WLgfCc/xmKZVSyIgc8nx3vO3cSQmLlta+0HNKsNJ9/RNhawE+GTP52p4ZNDBL7Ftz3+hJS3r9lUrcoQU7Twa+/A8HZzbDCQaepUcq7eHsyf/9sl2hdbpZ9refGN/tn1SHmLGrX2YenM3Bi0KKZQghQFKLmRk6E35T1ydHFUOCXNSQ2f0olDs/muWJruzyX5VCx4Cb1URCsDhn1WPQI5Vjs16UaauO4nl+y+rbVnl0yuzds/TWnxoTrYcCVrLvOro4IgBdQaY7s87Ou/hgw4tzdoOzDkrNJGtqFQxFP6ZTcRfSEZcnGHifGp6Ksbu/z9VkkU87x2Qp9T4ucUAJReOXI7D9QTDUssXqpWCu0tWmm9JghWeeAar02/h7YPTkZRk6BYn0oQiJYGAdobthCvAuS05HtatQTl4ZS453njyBu5nTpbt08j/oWzJuRGTGINvTixU29KkjWs0VpPDkX+r+jeUcCuhttedX4tLl3aZ9t1LuonvL0YgRtL6u3oDAe0teKVEBU8+rIQWNazWk+ywBw8bhnm+P/w9TqbcVNsBDm5486U5hXA1DFCefnjngdU7648tVuvGRTvXMvAs6pe/7xBRXgX2Ul9SHIAj+3NeDujh4oRXG/lne8zJMXvPSm7J8uQJuybgXto9db93zT6oV+fv0CJXnSv6+BrKQmRAj4U7spYcb9wzHdO8PdChQjnMqdoA0GCARZTf/lazJ/4TewvrL8Sg+bWzOHHrBGYdmqX26Rx0+KzT/AJbtWNTPSg/RxROuXNjcUDRulb27LHGzLGie4PH190hsojqHfB/PqXRskJ5DEo4iKSk2BwP69+kIsznfncO9IOft/tT/7iI6Ahsjdmqtn3dffF2g7ehZb2C3lQFNsXyu+dNr88qswR3gdVfttj1ERWmmrV7oud9HUqnpyP1dATGbvsQaZk1qF6v93qhZlG26gBlxrWtiInZU6A/4/Kdezh2xVD8r145b5T2yvoUFR0fnS39dYOaPQr0WoieibMb4ktWRoLOEfccHbBh98Op70WFEh7ZEhC+/uLTJ2ZLiI/BpM2jTffHNB4DTxfDBFytKla8MnoVDVDJEJe0/BoeHqUQG3sCO/SJar9fuh7B9fpb+jKJCofOGajWWm1+75aBk7cNNeACigfgzcA3UZisOkBJdnTAlA0Fm0F1w4lHD++En85aFdE9oDvTX5Nm/a1WX9P2qpsHHnncxO6BGBBSEdNeDULdHHL9PMm01W/ghkoiD7TwrIzW/oaGTutGvhKukiFWrtRC3V+7d5qpQmsn75oqwRyR3ajeASddnDGrmKENkDyFn4Z8AmcJXgqRVQco4k99PLY84hNhflcvNh/eSUu9h9+iDAGKk4MTurC6KWlYg3r9UM61pNrelXQR15OyAm9zJYq4YHzXuuga9PSZXmOj1mN5UrTaliGTf4eMs9qgfdXVrMmynYMK91MjkcUFtMVhVxd4ZGZYHuxVC3V86hX6ZVh9gCImHp2DlORHl4t/VndT0rAjyjBz2c/bDXXKepn2bY38GrHJhjL2oaXqo5R79qXHRFoiCf4613hFbWfoM/DH2T/y9wekp6JUxDgsvXwVwfeSMcy3KfzKBsManTu/CUccDWPutTJ0qFq1raUviahweZTAztJVEa/Tqb+BNzsVzqodmwpQqt0zXP4lHfBleP7nWNh6Ota03LJVTd9snwaXmQ/vlGyQ7z+bKL+Z9/KtPPvkYmBPZdc3wLUjqJKahv/BF/3aaSspW24lJV5Hn41S7digc+lnr0hOZM0+7R6O2XWHY8GrGwukdIvNByi+CDMVN/ol+Rh+3vJ7gQ3vmGePTb1zEVeTDT0rvul6vBA8NF9/LlFBqOhVEYE+gWr71O1TOHlxR/6c+HY0sHFi5h0HOHSZDp2zK6xR5NEfkWi2lKljwxEWvR4iS5HJ4k2C34Sbe96LhNplgDIx7HW8mGzIO5Li6IBlhz/CkgdKxj+r9Aw9/sycIOvhokNIFcP4vXA+vFR1ZS++fBUfln6xUDLqEeWHLqWbmLZX7p6S5/NJFeDfVr2BlHRDzhM0GgKUs86hHdGs4TBTEcEuTj7w8a1j6UsisltWHaC4OunwWe8FKJVmGIY54nkfSyM+x6wtZ/J87gMX7+DmXUOtAUlt7+acmT1WJg39tVBlxwxMuY9WTd/P888iKiwdqnSGU2av4x/xUXmup7Fu+wSMzbiCHuX8EFm8LNBqLKyZrNb59pXVmB80CuN65Fy7iIgKh1UHKMLb2w+jqhmyVHZJuIuP0tZj6h+HMGXNCZXRMr+HdxC9HbhtqPiKyqFA8afPtElkyZwfzXXeKJOux8vFaiEl+c4zn0vlPDn9k9qOdnZGXMMBgFvWRHJr5eHpi+fqD4Cr29Mvsyai/GMTi/s7vvgRKp7bjjqxhqRtQ51W4KtNLoi7l6qWTOrM02Pm0vrMAEXmxcoEWaOrkbNhKjz/XFahMSJrMb7LYhQtWj7PuT0k50mszvC31cKhKFqFsDeRiCzUgzJz5kwEBgbCy8tL3UJCQrB69WrT/hYtWqiVLua3t97Knv79woUL6NSpEzw8PODr64tRo0YhLS0tz0so67z0X8DR0OAO1a2Ev8M1LN59Af/86QDuZw4B5dbFW0k4dc2QRTKoQjGU8jRM+IuPu4DOiX+hV9nSWFncB6jZOU/XTWQJ3sUq5Tk4OXhkCZbeu5CV86TNf9XfIRFRfnmqFqV8+fKYNGkS9u3bh8jISLRq1Qpdu3bF0aNHTce88cYbuHLliuk2ZUrWRLz09HQVnNy/fx87duzA/PnzMW/ePHz00Ud5fya+NYEm/1Cbrg6pGOMipdMzsPLgZQxZGIl79x8uM/+k3pMHs8f+sfMLNRn3mKsrjvrVYvEwskupqUn4ZO9E6DOX3Q8vbb05T4jIRgKULl264KWXXkJAQACqV6+OCRMmwNPTE7t2ZWVdlJ6RMmXKmG7S02K0bt06HDt2DIsWLUJQUBA6duyITz/9FDNmzFBBS56FjkZG0bII9yyCiRVj0cjbsOx408kbGDB3txryyUuAsuyKoQCa6MbCgGQDLl+OxMqNTzexdeGa4TjtaOiVlCROfdpOL6CrIyJ79sx9stIbsmTJEty9e1cN9RgtXrwYpUqVQt26dTFmzBgkJSWZ9u3cuRP16tVD6dJZ//Tbt2+P+Pj4bL0wD0pJSVHHmN9y5FoUO5q8ho98SuK2Toc7pbehpJth+ePe87fRe9YuxCamPPZ5xSenYvdZQ4bY8sXdUb20odDZsRPLcdzR0AtTN8MJNQI65e6FItKoD39ojfYRA/Fh9HJcu3YoV99z6dIuzMyc6+Wo12Nck//AyTmrgCYRkcUClMOHD6teE1dXVzW/JDw8HLVrG8ov9+nTR/WObNy4UQUnCxcuRL9+/Uzfe/Xq1WzBiTDel32PMnHiRHh7e5tuFSpUeOSxL4SMQmMYGswrOgd0bbJX1RcRUpW457c7EXMnM2dDDracuoG0zNLr0ntizB677OAs0zHdy4U+4VUi0r5y7obJ3zJU8/ver3KV8+SzDSNUkU7R26My6tRiBW8i0kiAUqNGDRw4cAC7d+/G0KFDERYWpoZtxJAhQ1SPiPSS9O3bFwsWLFABzJkzectLIsFOXFyc6Xbx4sVHHisT9f7dfDKcVKYSYPmNCHzVrzzKehuClrOxd/HKzB2Ium6YBPugDccfrl6cfO82/kjKmhDYkasVyAZ0Dh5m2l55Y58KQB4nIyMNQcUC4KLXqwzKwztmBe1ERBYPUFxcXFCtWjUEBwerno369etj2rRpOR7buHFj9TUqKkp9lTkp165lze8Qxvuy71Gkt8a4csh4e5wqlVshrO4gtZ2akYofoqZh6VshqFKqiHrsSlwyen63E4cvZS8wmJaeYcoeW9TVCY0ql1DbEbumIiHzU2M719LwLGrIXktkzfz9myEow1A+PcoxAydPr3rs8ZIx+a2ui/BrixmY/Ny/+HdARAUqz+sCMzIy1ByRnEhPi/DzM/xDl7kqMkR0/XpWL0VERIQKOIzDRPllSOAQlCliCHq2X96Ok/E7VZBS288Q3Ny6ex+9Z+/CrrOGmjpiX/Rt00Ta5tV94OJkeHmWRa8xHdO9Tli+XieRJXUp+6Jpe+Wh3FUsrVQpFA2DXivAqyIiesoARYZatmzZgvPnz6tAQ+5v2rRJDefIMI6syJElyLJ/xYoVGDBgAJo3b65yp4h27dqpQKR///44ePAg1q5di7Fjx2LYsGGqlyQ/eTh7YPTzo033J2/5AB6OCVjyZhM8X8lQ/CgxJQ1hc/eYssZuyOw9EW1qG8bno6O3ItLBEIBVSndAg3pZc2qIrF37xu/COTPj8u8JZ5CWmvzQMTk9RkSkqQBFej4k6JB5KK1bt8bevXtVkNG2bVs19LN+/XoVhNSsWRMjR45Ejx49sHJlVll3nU6HVatWqa/SmyITaOV848ePL4jnhjb+bdDUO0BtX8lIxvdrhsLLzRkLBjVGixo+6vGUtAwMWbgPy/fHYP0xQ6AiozktqhsClPSM+2jj6K3ql3T3fZ7JqMjmkrZJ6ntxU+eAXftnP5TzpO+iJpi+7O9qLhYRUWFx0OelYI2FyDJjWc0jE2afNB/l/PnN6LZpGNIcHFAqXY8/Xt0Cd48SKrvsyJ8PqkRuD2pUqYQaDjIXG3sCLs5F4OX96BVERNZow47J+OfpRWr7JaeSmNx3k2nf3JWD8OWtvWpbAvUv+2+z2HUSkfV7mv/fNp+bWsbLB3rVRg8XPyx7+TcVnAiZX/JVryD0bez/0Pe0rpVVe8eoVKmaDE7IJjUPHg7vzKX1f96Pxd1Ew5L/SwmXMPP2X6acJ683fNei10lE9sUmigU+ydsvL8lxaEaKCH72cl14uzvjm01ncq5eTGTjnF2L4CUPf5y9fRpdEu9Cd3IN9M+F4bPdnyFZb0hO2LtkA+Y8IaJCZRcByuOKmEkittEdaqqCgNP/PI2Odcugqo8nYm8cx74Tv6Blo3/CxbVooV4vUWH7IGQcHOe2M9w58ivWlCyD7THb1V1fD18Mbz+TbwoRFSqbn4OSk5uxp/DXiV/Qttm/sz0uL4Uxc+z3K1/DtFv7UCxDj8/rDMGLjd7Jt+sn0hxpBv4bDNw6g3hHR3StXg+xKYZJsV+1+AqtK7a29BUSkQ3gHJTH+CXiPXRZ2R2jo37A2XMbsu0zBieSUTP8xj61fcfRAZX8ni/I94vI8uR3P7CX2pxe3NsUnLSo0AKt/FtZ+OKIyB7Z/CTZB11JjFFZYWVVz+dbPsgxvXfktX24oDNsN3bwQIUK2Vf0ENmkwJ6IcnbGT16GIU13PfDv5z8wBe5ERIXJ7gKU19vPRFnDvD/sRjLWbvvsoWPCo8JN292bfVSYl0dkOSUq4+tylU13h/uEwK9oOb4jRGQRdhegyDLj92v2N93/ImqpaVmliL8fj3XR69S2l4sXWldsY5HrJLKEwc+Phn860MXJB33aTeebQEQWY3cBimjZ5F940cFQOPC6zgHfrRlq2rf67GqkpBtS23eu0hmuuvxNwU+kZfXq/B2/DzqMz/v+CSdnQwVwIiJLsMsARZYdj2k5VZWNFwsTT+PMmQi1/WtkVmXm7gHdLXaNRERE9swuAxRRocILGORdT22rCbNbx+D4ieU4np6gHqujd0GNEjUsfJVERET2yW4DFDG4wzcolzlhdo9DCobs/I9pX3e/Zpa7MCIiIjtn1wGKm3txjKk9UG2XSE9H2J07GHwnDuXT0tExZLSlL4+IiMhu2UWq+8cJbfIe/nN6DTpE74FXZsG0ERU6wsGLyyuJiIgsxa57UIx6dp4FL8es1ToOwWEWvR4iIiJ7xwBFFK8EdJgky3uAmp0Bf2aOJSIisiS7H+IxaTgQaNAP0Dlb8v0gIiIi9qA8gMEJERGRJnCIh4iIiDSHAQoRERFpDgMUIiIi0hwGKERERKQ5DFCIiIhIcxigEBERkeYwQCEiIiLNYYBCREREmsMAhYiIiDSHAQoRERFpDgMUIiIi0hwGKERERKQ5DFCIiIhIc5xghfR6vfoaHx9v6UshIiKiXDL+3zb+H7e5AOXmzZvqa4UKFSx9KURERPSUEhIS4O3tbXsBSokSJdTXCxcuPPEJPq3nn38ee/fu1fw5C+q8vFa+Btb0uyWfxuSDysWLF+Hl5WWXfwcFdV5eK1/Xgvg9kJ6T4OBglC1b9onHWmWA4uhomDojwUl+NkpCp9NZxTkL6ry8Vr4G1va7JeS8+Xlua/o7KKjz8lr5uhbU74GLi4vp//jjcJLsA4YNG2YV5yyo8/Ja+RpY2+9WQbCmv4OCOi+vla+rpX+3HPS5mamiMdKtK70ncXFxBfaJjIi0j20Bke2yyh4UV1dXjBs3Tn0lIvvFtoDIdlllDwoRERHZNqvsQSF6FAcHByxfvpwvEJGdY1tg/RigaNTOnTvV7OlOnTrBnr322mt4+eWXYY9k6eygQYPUcjyZ9V6xYkWMGDHClAfoSTZt2qQa6Tt37hT4tVLBYVtgwLZgkN21BQxQNGrOnDl4++23sWXLFly+fDlP50pPT0dGRka+XRsVvLNnz6Jhw4Y4ffo0fvzxR0RFReHbb7/Fhg0bEBISglu3bvFtsBNsC+zbWTtuCxigaFBiYiJ++uknDB06VPWgzJs376FI+Pfff0dgYCDc3NzQpEkTHDlyxHSMHF+sWDGsWLECtWvXVhMJJamdtatUqRK++uqrbI8FBQXh448/hq2RZXjySWndunUIDQ2Fv78/OnbsiPXr1yMmJgYffvihOi4lJQXvv/++SlYm73O1atXUP7Tz58+jZcuW6pjixYur3xn5BErWhW1BztgWdLSLtkCTAYo9d+WJpUuXombNmqhRowb69euHuXPnPlS3YNSoUZg6darK8Ofj44MuXbogNTXVtD8pKQmTJ0/G999/j6NHj8LX19cCz4SehXwiWrt2Lf7xj3/A3d09274yZcqgb9++KoCV34kBAwaoT1XTp0/H8ePH8d1338HT01M1Ur/++qv6npMnT+LKlSuYNm2a1b0hbAvYFtizW3beFlhlJllbJ1GvBCaiQ4cOKt/L5s2b0aJFC9Mxssy6bdu2anv+/PkoX748wsPD0bNnT/WYBCvffPMN6tevb6FnQc9KunKlwalVq1aO++Xx27dvq+BUgtmIiAi0adNG7atSpcpDJSEkOJUeNbI+bAvs22k7bws02YNibs2aNWjWrJl6UUuWLInOnTvjzJkzpv3SfSVdVsuWLVPdWB4eHuqfskwss0YS4e7Zswe9e/dW952cnNCrVy/VUJmTsUfzXz7pbZGo2UiGB2QIiKzXkzIAyO++TKSWISB7wLaAbYG90ttpW6D5AOXu3bt47733EBkZqSYFSf7+bt26PTTpU8bh/vWvf+HAgQOoXr26+geflpYGayOBiFy3zNaW4ERuM2fOVF100pOSW9IdKIGbLZH3/sE/VPNhLVshY8fy3pkHnObkcRlLfrDL19axLWBbYMS2wD7aAs0HKD169ED37t1Voy0TImU+xuHDh3Hs2LFsx0lwIhNKJTj55JNPEB0drWY7WxMJTBYsWKDmlkigZbwdPHhQBSwyvmi0a9cu07Z08Z06deqR3YC2QubayPipeZrzc+fOwdZIT6EM38kQ3b1797Ltu3r1KhYvXqx61erVq6cCdRn+y4n0ohlXcdkCtgVsC4zYFsAu2gJHaxiDk94QGU+Tujsye1s8uCrFfDjDz89Pfb1+/TqsyapVq1SwMXjwYNStWzfbTRpn82Ge8ePHqx4lWb0jEwlLlSpl8xOLW7VqhYULF2Lr1q0qSA0LC1Pdmrbo66+/VrPy27dvr5aaS04UGeKQwKVcuXKYMGGC+luQ10BypUhyOgnWZJWXjEULyZUgPTHye3Xjxg21IsSasS1gW2DEtmCNXbQFmg9QZHWKzGSePXs2du/erW7i/v372Y5zdnY2bRuHNqwt94cEIDLBSQohPkgCFBnmOnTokLo/adIklagnODhYRdIrV640Rcm2RN5DGeYSY8aMUWOsMg9JesskIKtatSpsUUBAgHq/JTCXic/yPIcMGaLmWcn8KuOkNxn+e+WVV9Qsf1n59cYbb6ihECGNl/QmfvDBByhdujSGDx8Oa8a2wIBtAduCIfbSFug1KCwsTN+1a1d9bGysTDjQb9myxbRv69at6rHw8HB1/9y5c+r+/v37Tcfcvn1bPbZx40a9rZHnJM9NnqM9aN++vX7YsGGWvgyyELYFj8a2gGydppcZy+QfGY+fNWuWGraRYR2JAMn2yVDX9u3bVTflW2+9ZenLIQtjW2C/2BbYLyctd+vLTO0lS5bgnXfeUfMwZCmtJKExzwdCtknGUmVt/8iRI9G1a1dLXw5ZCNsCYltgvxykGwUaI8nJZNWOTBQkIvvFtoDIfjlqrStPZhlLt74xGx4R2R+2BUSkqSEeduUREdsCItLsEA8RERHZN00N8RAREREJBihERESkORYLUCR9t2SGlBozkvlV0vOau3btmkrhLvulQrHM5pdU1+ZkubF8r/ntwZwZkg6+adOmKFq0KMqUKYP333/fKosIEtmq/GgLhGTVlBToRYoUUWUxmjdvnq2WkWSk7tu3r9on1dGlpIS1pPwmskcWC1AkBW/9+vUxY8aMh/bJtBhJY3727Fn89ttv2L9/v6olICt7jKl7jSSdrxSQM96mTJli2idF9l566SXVoMk5fvrpJ6xYsYLJ3og0JD/aAglO5O+8Xbt22LNnj8qhI+m8JZeSkQQnR48eRUREhFotKIGRpAwnIo3Sa4B56npx8uRJ9diRI0dMj6Wnp+t9fHz0s2fPNj0WGhqqHzFixCPPO2bMGH3Dhg2zPbZixQq9m5ubPj4+Pt+fBxFZpi1o3LixfuzYsY8877Fjx9R59u7da3ps9erVegcHB31MTAzfNiIN0uQcFKniKtzc3EyPySchV1dXbNu2LduxUm5aKvlKplkpJpeUlJTtPObnEO7u7khOTsa+ffsK/HkQUcG3BVK1XIqI+vr6quFcKYYmRSXN2wrpYZFhnYYNG5oek14YOZexACkRaYsmAxSpxOjv768CDknYJJWLJ0+ejEuXLqlhHKM+ffpg0aJF2Lhxozp24cKF6Nevn2m/lKrfsWMHfvzxR6SnpyMmJgbjx49X+8zPQ0TalJu2QIZ/xMcff6yGfNesWYPnnnsOrVu3Ns1VkYrfEsCYk3IaUglW9hGR9mgyQHF2dsayZctw6tQp1YDIxDgJQjp27JhtTFnGjyUIqVevnhpfXrBgAcLDw3HmzBm1X8ajv/jiCzVxVj5xVa9eXc1JEebnISJtyk1bIPV6xJtvvomBAweiQYMG+PLLL1Xtrrlz51r4GRDRs9Lsf+ng4GAcOHAAd+7cUZ+U5FPRzZs3UaVKlUd+T+PGjdXXqKgo02PvvfeeOodUQo6NjTUVnnvceYjIetoCqXQuateune37atWqpf7uhazgk6Egc7KaT1b2yD4i0h7NBihG3t7e8PHxUV21kZGRj61sK42YeYNlJEsXZYmizD+R4Z4KFSqoLmAish6PagsqVaqk/r5PnjyZ7XjpdZEVPyIkJEQFOOZzz/7880/V+2L8YENE2mKxWjySf8C8p+PcuXMqwJBuXBlz/vnnn1VjJNuHDx/GiBEj1HJDGbYRMozzww8/qCGbkiVL4tChQ3j33XdV7oPAwEDTeWWIR5YfSnewdBVPmjQJS5cuhU6ns8jzJqL8bQvkA8ioUaMwbtw4tVw5KCgI8+fPx4kTJ/DLL7+YelOkHZA5Kt9++y1SU1PVMuRXX31VBTdEpEGWWj60ceNGtezvwVtYWJjaP23aNH358uX1zs7Oen9/f7WEMCUlxfT9Fy5c0Ddv3lxfokQJvaurq75atWr6UaNG6ePi4rL9nJYtW+q9vb3V0mJZivjHH38U+nMlooJrC4wmTpyojvPw8NCHhITot27dmm3/zZs39b1799Z7enrqvby89AMHDtQnJCTwrSHSKBYLJCIiIs3R/BwUIiIisj8MUIiIiEhzGKAQERGR5jBAISIiIs1hgEJERESawwC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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pred_lora_trained = model_lora.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", - "pred_new = model_new.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", - "val_passengers.plot(label=\"Ground truth\")\n", - "pred_lora_trained.plot(label=\"Forecast of the trained model\", linestyle=\"-.\")\n", - "pred_new.plot(\n", - " label=\"Forecast of the loaded model\",\n", - " linestyle=\"--\",\n", - " title=\"LoRA finetuning - Save adapters only\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "022127ca", - "metadata": {}, - "source": [ - "# 4. Performance Evaluation\n", - "\n", - "Finally, let's compare the performance of all four models (Base, Full Fine-tuning, Partial Fine-tuning, and LoRA) on the validation set using standard metrics like **MAPE** (Mean Absolute Percentage Error) and **MAE** (Mean Absolute Error).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3b81f2e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ModelMAPE (%)MAE
0Base Model15.25451870.704819
1Full Fine-tuning4.55005120.350664
2Partial Fine-tuning4.89137621.527288
3LoRA (PEFT)5.45722323.800879
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" - ], - "text/plain": [ - " Model MAPE (%) MAE\n", - "0 Base Model 15.254518 70.704819\n", - "1 Full Fine-tuning 4.550051 20.350664\n", - "2 Partial Fine-tuning 4.891376 21.527288\n", - "3 LoRA (PEFT) 5.457223 23.800879" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "from darts.metrics import mae, mape\n", - "\n", - "results = []\n", - "all_predictions = {\n", - " \"Base Model\": prediction,\n", - " \"Full Fine-tuning\": pred_full_finetuned,\n", - " \"Partial Fine-tuning\": pred_partial_finetuned,\n", - " \"LoRA (PEFT)\": pred_lora_trained,\n", - "}\n", - "\n", - "for name, pred in all_predictions.items():\n", - " results.append({\n", - " \"Model\": name,\n", - " \"MAPE (%)\": mape(val_passengers, pred),\n", - " \"MAE\": mae(val_passengers, pred),\n", - " })\n", - "\n", - "df_results = pd.DataFrame(results)\n", - "df_results" - ] - }, - { - "cell_type": "markdown", - "id": "996456e0", - "metadata": {}, - "source": [ - "### Observations\n", - "\n", - "While the results on this small \"toy\" dataset (Air Passengers) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility of the fine-tuning API.\n", - "\n", - "In real-world scenarios with larger datasets:\n", - "- **Full Fine-tuning** offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", - "- **Partial Fine-tuning** provides a good middle ground by updating only the most relevant layers (like the output head).\n", - "- **LoRA (PEFT)** is often the most effective strategy. It typically matches or exceeds full fine-tuning performance while only training a tiny fraction (often <1%) of the parameters. This makes it faster, more memory-efficient, and allows for much easier deployment of multiple task-specific \"adapters\" on top of a single base model.\n", - "\n", - "### Summary\n", - "In this notebook, we have seen:\n", - "1. How to enable **native full fine-tuning** in Darts foundation models.\n", - "2. How to use **layer freezing patterns** to perform partial fine-tuning without manual weight manipulation.\n", - "3. How to extend Darts foundation models with **custom callbacks** to leverage external libraries like `peft`.\n", - "4. How to use the `internal_model` property to gain low-level access to the underlying PyTorch module for advanced operations.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2286828a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "darts", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb new file mode 100644 index 0000000000..ad753ca698 --- /dev/null +++ b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -0,0 +1,629 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da55dd6c", + "metadata": {}, + "source": [ + "# Chronos-2 Foundation Model Fine-Tuning\n", + "This example notebook presents how fine-tuning can be applied to the Chronos-2 model using built-in Darts features.\n", + "\n", + "The following fine-tuning methods will be shown:\n", + "1) **Full fine-tuning**: All model weights are retrained. This is natively supported by setting `enable_finetuning=True`.\n", + "2) **Partial fine-tuning**: Specific layers are frozen via name patterns. This is natively supported using the `enable_finetuning` parameter with a dictionary configuration.\n", + "\n", + "To be useful, a fine-tuned model should be easily saved and loaded. For each method, we will demonstrate how to persist the model weights." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bfa59f65", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "310fa52a", + "metadata": {}, + "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d510b54b", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import numpy as np\n", + "\n", + "from darts.datasets import AirPassengersDataset\n", + "from darts.models import Chronos2Model\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "import logging\n", + "\n", + "logging.disable(logging.CRITICAL)" + ] + }, + { + "cell_type": "markdown", + "id": "6b82a07a", + "metadata": {}, + "source": [ + "## Data Preparation\n", + "Here we just load an example dataset with 144 samples as a fast demo. The data is split between train and validation, with the 2 last years (24 samples) for validation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f87bcc5", + "metadata": {}, + "outputs": [], + "source": [ + "# convert to float32 as Chronos-2 works with float32 input\n", + "data = AirPassengersDataset().load().astype(np.float32)\n", + "train_passengers, val_passengers = data.split_before(\n", + " len(data) - 2 * 12\n", + ") # last 2 years for validation" + ] + }, + { + "cell_type": "markdown", + "id": "b9251561", + "metadata": {}, + "source": [ + "# Model prediction out-of-the-box\n", + "Let's see how the model behaves on the validation data without any fine-tuning. For that we:\n", + "- Create the model\n", + "- Call fit to load the model internally (no training is done)\n", + "- Predict on the validation set" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea8456ae", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ce4f4174dac540ff94f2a9f23ddf2d60", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from darts.utils.likelihood_models.torch import QuantileRegression\n", + "\n", + "model = Chronos2Model(\n", + " input_chunk_length=24,\n", + " output_chunk_length=12,\n", + " likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", + " enable_finetuning=False,\n", + " random_state=42,\n", + ")\n", + "\n", + "model.fit(train_passengers, verbose=True)\n", + "\n", + "prediction = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "prediction.plot(label=\"Forecast\", title=\"Base model (not finetuned yet)\")" + ] + }, + { + "cell_type": "markdown", + "id": "1313019f", + "metadata": {}, + "source": [ + "# 1. Full fine-tuning\n", + "\n", + "In this method, all the model weights are retrained. This is simply enabled by passing `enable_finetuning=True` to the model constructor. \n", + "\n", + "When fine-tuning is enabled, Darts will treat the foundation model like a standard trainable model during `fit()`. Saving and loading follows the standard Darts API via the `save()` and `load()` methods.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "72832dff", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6a7240f650574baea33dffef644c445b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred_full_finetuned = full_finetuned_model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "pred_full_finetuned_loaded = full_finetuned_loaded_model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "pred_full_finetuned.plot(label=\"Forecast of the full finetuned model\", linestyle=\"-.\")\n", + "pred_full_finetuned_loaded.plot(\n", + " label=\"Forecast of the loaded full finetuned model\",\n", + " linestyle=\"--\",\n", + " title=\"Full finetuning\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d3dc22f4", + "metadata": {}, + "source": [ + "We can also verify numericaly that the prediction of the trained model is identical to the prediction of the loaded model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "599402d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(pred_full_finetuned.values(), pred_full_finetuned_loaded.values())" + ] + }, + { + "cell_type": "markdown", + "id": "3cabab8a", + "metadata": {}, + "source": [ + "# 2. Partial fine-tuning with layer freezing\n", + "\n", + "Partial fine-tuning allows you to update only a subset of the model's parameters, which is useful for preserving general knowledge while adapting to specific patterns. \n", + "\n", + "Darts foundation models natively support this via the `enable_finetuning` parameter. You can pass a dictionary with:\n", + "- `{'freeze': ['pattern1', 'pattern2']}`: To freeze specific layers (keeping others trainable).\n", + "- `{'unfreeze': ['pattern1', 'pattern2']}`: To freeze everything *except* the matching layers.\n", + "\n", + "In this example, we will unfreeze only the output head of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "33fa7fc4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f3ae6034d5c34f78a9db426821509270", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pred_partial_finetuned = partial_finetuned_model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "pred_partial_finetuned_loaded = partial_finetuned_loaded_model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "pred_partial_finetuned.plot(\n", + " label=\"Forecast of the partial finetuned model\", linestyle=\"-.\"\n", + ")\n", + "pred_partial_finetuned_loaded.plot(\n", + " label=\"Forecast of the loaded partial finetuned model\",\n", + " linestyle=\"--\",\n", + " title=\"Partial finetuning\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3c01daaa", + "metadata": {}, + "source": [ + "Again, we verify that the prediction of the fine-tuned model is the same as the loaded model to make sure that saving/load works correctly" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "01717b70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.allclose(pred_partial_finetuned.values(), pred_partial_finetuned_loaded.values())" + ] + }, + { + "cell_type": "markdown", + "id": "022127ca", + "metadata": {}, + "source": [ + "# 3. Performance Evaluation\n", + "\n", + "Finally, let's compare the performance of the three models (Base, Full Fine-tuning, and Partial Fine-tuning) on the validation set using standard metrics like **MAPE** (Mean Absolute Percentage Error) and **MAE** (Mean Absolute Error)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3b81f2e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelMAPE (%)MAE
0Base Model15.20693170.817078
1Full Fine-tuning3.12440713.061282
2Partial Fine-tuning14.17313063.913483
\n", + "
" + ], + "text/plain": [ + " Model MAPE (%) MAE\n", + "0 Base Model 15.206931 70.817078\n", + "1 Full Fine-tuning 3.124407 13.061282\n", + "2 Partial Fine-tuning 14.173130 63.913483" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "from darts.metrics import mae, mape\n", + "\n", + "results = []\n", + "all_predictions = {\n", + " \"Base Model\": prediction,\n", + " \"Full Fine-tuning\": pred_full_finetuned,\n", + " \"Partial Fine-tuning\": pred_partial_finetuned,\n", + "}\n", + "\n", + "for name, pred in all_predictions.items():\n", + " results.append({\n", + " \"Model\": name,\n", + " \"MAPE (%)\": mape(val_passengers, pred),\n", + " \"MAE\": mae(val_passengers, pred),\n", + " })\n", + "\n", + "df_results = pd.DataFrame(results)\n", + "df_results" + ] + }, + { + "cell_type": "markdown", + "id": "996456e0", + "metadata": {}, + "source": [ + "### Observations\n", + "\n", + "While the results on this small \"toy\" dataset (Air Passengers) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility of the fine-tuning API.\n", + "\n", + "In real-world scenarios with larger datasets:\n", + "- **Full Fine-tuning** offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", + "- **Partial Fine-tuning** provides a good middle ground by updating only the most relevant layers (like the output head).\n", + "\n", + "### Summary\n", + "In this notebook, we have seen:\n", + "1. How to enable **native full fine-tuning** in Darts foundation models.\n", + "2. How to use **layer freezing patterns** to perform partial fine-tuning without manual weight manipulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2286828a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "darts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 01706ee6b4c173006ee628c77752e161d554e167 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Tue, 24 Feb 2026 13:30:33 +0100 Subject: [PATCH 13/26] feat: use fnmatch instead of startswith for finetuning arguments --- .../forecasting/torch_forecasting_model.py | 11 +- ...oundation-Model-Fine-Tuning-examples.ipynb | 290 +++++++++++------- 2 files changed, 181 insertions(+), 120 deletions(-) diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index b84d20ba0d..81f2d241aa 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -19,6 +19,7 @@ import copy import datetime +import fnmatch import inspect import os import shutil @@ -573,7 +574,7 @@ def _apply_freeze_patterns( Applies freezing or unfreezing to parameters matching the given patterns. """ for name, param in model.named_parameters(): - if any(name.startswith(p) for p in patterns): + if any(fnmatch.fnmatch(name, p) for p in patterns): param.requires_grad = not freeze def _setup_trainer( @@ -2543,7 +2544,13 @@ def min_train_samples(self) -> int: @property def _requires_training(self) -> bool: """Whether the model should be trained when calling a `fit*` method.""" - return False if not self.enable_finetuning else True + # Multiple cases, enable_finetuning is None, we retrain the model + # If enable_finetuning is a dictionary, we retrain the model (not necessarily all weights) + # Otherwise, it's a boolean so we return the value + if self.enable_finetuning is None or self.enable_finetuning is dict: + return True + + return self.enable_finetuning def _check_optimizable_historical_forecasts( self, diff --git a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb index ad753ca698..09f5a4ac18 100644 --- a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb +++ b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -5,14 +5,14 @@ "id": "da55dd6c", "metadata": {}, "source": [ - "# Chronos-2 Foundation Model Fine-Tuning\n", - "This example notebook presents how fine-tuning can be applied to the Chronos-2 model using built-in Darts features.\n", + "# Full Model Fine-Tuning and partial fine-tuning\n", + "This example notebook presents how fine-tuning can be applied to the a pytorch model using built-in Darts features.\n", "\n", "The following fine-tuning methods will be shown:\n", "1) **Full fine-tuning**: All model weights are retrained. This is natively supported by setting `enable_finetuning=True`.\n", "2) **Partial fine-tuning**: Specific layers are frozen via name patterns. This is natively supported using the `enable_finetuning` parameter with a dictionary configuration.\n", "\n", - "To be useful, a fine-tuned model should be easily saved and loaded. For each method, we will demonstrate how to persist the model weights." + "To be useful, a fine-tuned model should be easily saved and loaded. We will also demonstrate how to persist the model weights." ] }, { @@ -52,7 +52,8 @@ "import numpy as np\n", "\n", "from darts.datasets import AirPassengersDataset\n", - "from darts.models import Chronos2Model\n", + "from darts.models import Chronos2Model, TiDEModel\n", + "from darts.utils.likelihood_models.torch import QuantileRegression\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", @@ -83,6 +84,145 @@ ") # last 2 years for validation" ] }, + { + "cell_type": "markdown", + "id": "477c039b", + "metadata": {}, + "source": [ + "## Regular PyTorch Model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7ddac1ed", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "95bc1c1b4f204ace85ac6675f3486185", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = TiDEModel(\n", + " input_chunk_length=24,\n", + " output_chunk_length=12,\n", + " use_reversible_instance_norm=True,\n", + " likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", + " random_state=42,\n", + " n_epochs=100,\n", + " model_name=\"my_model\",\n", + " save_checkpoints=True,\n", + " force_reset=True,\n", + ")\n", + "\n", + "model.fit(train_passengers, verbose=True)\n", + "prediction = model.predict(\n", + " n=len(val_passengers), series=train_passengers, random_state=42\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "prediction.plot(label=\"Forecast\", title=\"Trained TiDE model\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5e414418", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fc3bf9eb0f15480a9c00635a1f3f8a11", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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9S3FNQmTFv4bhvm/2CYFCQuXuJXvx5LjOeGhkFJyd2TnaluAiWY1AhaYVFRUidfPjjz+KVApFVJYuXYrjx483OMhu3rx5WLx4sejkOXXqFP773//it99+w1NPPaVGYQYNGiQ6cGjbmzdvxvPPP2/yflIh7ldffYWvv/4aJ0+eFPUeR44cqfc1kZGRIpKxatUqXLx4EQUFBXU+15j9NGV7DMPYJ/vk9A7RP1ISKASldJbPHYqJ3cPEbQrGvrP2JB78bj8KSiussq9M42CBohGoPfjgwYMYO3Ys5s+fj169egmx8tFHHwmR8eqrr9b7+htuuEHUm1BRbLdu3fDZZ58JEUGtvAokLEgEUd0HtfZSgaup3Hrrrfj3v/8tWnlpO+fOncODDz5Y72vIw+Xll18WBbfUnkytxfXR0H6auj2GYexboPTTEygEFcd+MqMv5k3oIupTiDVHMnDDwu1IusgnNLaCU7UN9mPl5eUhICAAubm58Pf3N3iMuknOnDmDDh06iK4ThqkP/nthGNuDUju9X16L/NIKBPl6YO9zY+oc/EneKI/+cFC0IRN+Hq54/7beGNM1tJn3mmno+7smHEFhGIZhbIqTGflCnCjpnfqmko/qItWldA71Fbfpdfd8sw8fbjiFKrmGhdEmLFAYhmEY260/kQtk66M91aU8NBTX9tDVpfx33Uk8sHQ/8kuM7xJkmhcWKAzDMIxNsf9sdp31J3Xh4+GKhbf3xdPX6OpS1h6V6lJOc12KJmGBwjAMw9hkBMXD1RndWgcY/TpKBVG78dd3XgV/T8ll4/TFQtzw8XasP5phsf1lGgcLFIZhGMZmyMgrwYXLxWLdq20LuLua/jU2skuI8MbqEupnUJfy/vqTXJeiIVigMAzDMDY5f0ff/8RUIgN98NtDQzCppzTOg3h//Snc9y3XpWgFFigMwzCMzbDvXLZJBbIN1aV8PL0Pnp0YA8Vkdv2xDExZuB2JmVyXYm1YoDAMwzA2wwG9Dp6+7ZomUJS6lAdGdMLiuwYgwEua5ZVEdSkLuS7F2rBAYRiGYWyC4rJKHEmVBgRGh/iihbc0l8scDO8cjJX/GoaYMKkuhWzx535/AGm5Ur0L0/ywQGEYhmFsgrjzOaiQi1ibmt6pjXaB3qIuZYw8/bi0ogo7T2eZ/X0Y42CBoiHuvPNOEW6seUlMTIQtQsMLW7RoYe3dYBjGTtivV3/SL7KVRd7D290VD3fJxXr3p/Cp23tIOK97T0bjAiUlJQUzZ85EYGCgmDzbo0cP7Nu3T32cRvu88MILCA8PF4/T8DuarqtPdnY2ZsyYIXz46Qtszpw5PJFW5pprrkFaWprBhebEmEpZWZlF9o9hGEZrE4zNSl4aem57EFHOqbjGZS+ckzZa5n0Y8wqUy5cvY+jQoXBzc8Nff/2Fo0eP4t1330XLlro/lLfeegsffvghPv30U+zevRs+Pj6YMGGCGMqmQOLkyJEjWLduHVatWoUtW7bgvvvuM2VX7BYPDw+EhYUZXFxcXLB582YMGDBAPE7ijyb50sRfBZpaTFN9afpvUFCQ+MyJw4cPY+LEifD19RWTf2fNmoVLly6pr6uqqhK/s6ioKLHtdu3a4bXXXlMff+aZZ9C5c2d4e3ujY8eOYpJxebmuBS8+Ph6jRo2Cn5+fEJw0gZgE66ZNm3DXXXeJgVBKJOill15qts+RYRj7gvxJlALZIF93RAZ6m/9NKsqAn2fDuTBTvSvw8kFUVFaZ/72YBpGs9IzkzTffRNu2bfH111+r9+mf3VP05P3338fzzz+PKVOmiPu++eYb8cX4+++/47bbbsOxY8fw999/Y+/evejfv794zkcffYRrr70W77zzDlq3bm3KLjkEFLWiz4dSQPR5Hj9+HPfee6+Yvqv/pb9kyRI8+OCD2L59u7idk5OD0aNH45577sF7772H4uJiITimTZuGf/75Rzxn/vz5+OKLL8Tjw4YNExEb2r4CCQ9K1dDvJSEhQbwv3ff000+rYrNPnz5YtGiREFJxcXFCwA4ZMkT8LVA07cSJE+K5JJIYhmEaw6nMAnUicb8GBgQ2mnX/Bs7vNrirT/UJnMwoQGzr+ifvMlYWKCtWrBBn5rfccos4o2/Tpg0eeugh8aVF0Nj69PR0kdZRoLHKAwcOxM6dO4VAoWtK6yjihKDnOzs7i4jLjTfeeMX7lpaWiov+uGaT+WwEUKBTxc2Cbwhw/2aTXkIRJf0vcop+UASDhOHHH38s/iljYmKQmpoqxAYJAPrsiOjoaBENUfjPf/4jxMPrr7+u3vfVV1+JbZ08eVJEYj744AOx3dmzZ4vHO3XqJISKAolNhfbt2+Opp57CsmXLVIGSnJyMefPmiX1S9kH/d0/7S1EghmEYc/mfGDt/xyQO/Qzs/lRau7ijxMkTnhV56O2ciJXnL7JA0bpASUpKEmfKTzzxBP7v//5PREEeeeQRuLu7iy84EicERUz0odvKY3QdEhJiuBOurmjVqpX6nJosWLAAL7/8MpoEiZP8VGgdSpfQZ6xAKbK5c+di8ODBBmcMlGqjup0LFy6ItAxB6RV9KP2ycePGWiMXp0+fFhEWEn5jxoypc39+/PFHkbKj59P7UVqJUjkK9LdAEZpvv/1WCE0SryRyGIZhzMl+PQdZsxfIph8GVjysuz3xLRQc2QjPM3/Ay6kMWYn7gIF8XNO0QKF6BYp8KGfkdHZONQ5Ub6KcgVsCSkPQF6F+BIWiACZHM5qbRrwnCRKqB2kM9Fp9SFBcd911IjVXE4qekOCsD4p2UQqHxCFFzigiQtETqjtSoBTT7bffjj///FPUJb344oviObVFwhiGYZpaIEuzd7q3MWM0ozgH+GkWUCH7nfSZCfS7E75Ua3fmD3GXW8oeALea7z0Z8wsU+lKLjY01uK9r16749ddfxVoJ5WdkZIjnKtDt3r17q8/JzDRMtdBZOXX21JUKoOJNujQJE1MtWkL5jKnGR4miUJ0J1YJERETU+bq+ffuK11FqhqJUNaF0DHVabdiwQURBarJjxw5ERkbiueeeU+87d+7cFc+jFBRdHn/8cUyfPl3UKJFAochaZWVlE35yhmEYIDO/BMnZRWLdKyIAHq4u5tlwVRWw/AEgWz5ZC+8FXPsO2cvCs+NQ9WltCw6htKLSfO/LmL+Lh9IKSsGjAtUy0JeYUjBLIoO+8PSjHVRbQikKgq4ptbB//371OVSwSdEZqlVhroTqfM6fP4+HH35YFLD+8ccfIlJBUSWl/qQ2KDVEwo9EA6XjKE2zZs0a0V1DwoGKbKmOhepJqPiWHt+1axe+/PJLVcBQjQlFROgxSvUsX75c3T4V3VLnEHXskHAh0UTvQ4KKIGFEURz6e6DOoaIi6QDDMAyjifTOtneBk39Ja6+WwLRvATcv6XZwVxQ7S+nxvk4ncCKtEbWPTPMJFDpDpi8wSvGQedj333+Pzz//XHwREnR2T22uVJxJBbXU9XHHHXeIDpAbbrhBPIe+vMjrgwpr9+zZI77U6EuOCmi5g6d2qBh59erV4vPq1asXHnjgAeEdo1/AWhv0edLnS2Jk/PjxwrOGfj9UpKwIG2obfvLJJ0WxLf1ubr31VjXCdf3114vfOf1+KAJGERV6vgJ17WRlZYnfMUVQqDuIinqVeiHq5KF9pW0GBwcbFPAyDMNY1f8kcQPwj2Kp4ARM/R/QUjrZFjg7I6tlL7EMdspF0snD5nlfxmicqilvYGKXCdWEkPkaRUzoLF7p4iFoc3R2T8KFIiXUEfLJJ5+ILzAFOqunL72VK1eKL8qpU6eKs3Nj21ApKkP1EOSxoV+wSZDfCnUT0b5RhIBh6oP/XhhG+9B04fjzOWJ98N/j0NKniTN4Lp8DPh8BFMvCZ9TzwIh5VzwtZcWraHPgHbH+sc183Hrvs017Xwb1fX83qQaFmDx5srjUBUVRXnnlFXGpC+rYoegLwzAMwzQ4IDAlV6w7Bfs0XZyUl0hFsYo46TwRuPrJWp8aFDsCkAWK/0VdWQLTPPAsHoZhGEazxF/QGxBojvqT1U8BafHSumUH4MZPRTqnNjza9UcFpMLYqNIjQiwxzQcLFIZhGEaz7NerP+nX1AnG+5cAB7+V1q5ewK1LAa96Bpq6eyPVq4tYRjul4MSZs017f8YkWKAwDMMwmmXf2WzzFMim7JeiJwrXfwiEdW/wZYWhOtfzS8e2Nv79GZOxW4FiYu0v46Dw3wnDaHtAoBJBCfRxR4cgQzNKoynMAn68A6iUp7wPuB/oOc2ol3pH6UZ/OJ3f1bj3ZxqF3QkUGlRHsOcGYwzK34nyd8MwjHZIvKgbENi3sQMCqyqBX+8G8i5It9sOBMb/x+iXh3cfoa5DcuJMf3+m0ZjcxaN1yJuDfD4ULw9vb2/LTL1kbD5yQuKE/k7o74X+bhiG0W79SaPTO//8B0jaJK19QoBblgCuxncCubcIQ4pza7SpSkXnilPIL8iHn69f4/aFcWyBQiiW+TUt9RmmJiROeNoyw2iTfXoOsv0bUyB7bBWw7b/S2skFuGUx4K8bw2IsGS16o012KjycKnAqYQe6D55g+r4wJmOXAoUiJjQLiKYml9PAJ4apBUrrcOSEYbTL/nPZegMCA0x78aVE4PcHdbfHvwq0183XMYXKNgOB7NViXZi4DWCB0izYpUBRoC8f/gJiGIaxPS7ml+JsllQj1rONiQMCSwuAH2cCpfL8nG43AYMeavS+BMYOBxKktXf6vkZvh3HwIlmGYRjGzvxPTKk/oc68FQ8DF49Jt4NjgOs/EhOKG0u76F64XC3VnbQrTJCmIDMWhwUKwzAMo9n0jskCZfenwJHfpLW7n2TG5mHcnLe6cHV1wSnPbmIdgHzkpRxt0vYY42CBwjAMw2h6grHRAuXcDmCt3pT3GxcBQdFm2Z/coH7qOuPwRrNsk6kfFigMwzCMpigpr8RheUBgxyAfBPp6NPyi/HTg5zuBKsk3BcMeB7peZ7Z9cu84WF1XnGXDtuaABQrDMAyjKQ5dyEV5ZbXx0ZPKcuCn2UBBhnS7wwhglF4kxQy0iR2C0mrJ0DEw64BZt83UDgsUhmEYRlPs06s/Mcr/hNI6ig29fwRw81eAi3mbVDuGtsJhdBLrkIpUIF8WQ4zFYIHCMAzDaIr9egZt/SJb1f/kQz9LhbGEizsw7RvAJ8js++Ts7IQLfj3V27kneXCgpWGBwjAMw2hrQGCyJFBaeruhU7BP/X4nqx7X3Z74FhChK2Y1N6XhA9Q1CxTLwwKFYRiG0QxJlwqQU1Su1p/UO0stZR9Qli+tu14P9LvTovvm31nnROuessei78WwQGEYhmE0On+nwfROyn7dOmZSk8zYjCG2Y3ucrGoj1kEFJ4CyQou+n6PDAoVhGIbRpP9JgwWyKXrdNG0sl9pRaNvKC4ecY8TaFZWovsC295aEBQrDMAyjOYt7dxdn9GhoQKASQfEIAFpJHTaWhNJNl1r1VW8XJG63+Hs6MixQGIZhGE1wqaAUZy5JaZPubfzh6VbPgMDcFCA/TVq36UNtNs2yjy6Rg9R16WkWKJaEBQrDMAyjuQGB/dubUH/SDOkdhbYduyGzuoVY+106CFRVNtt7OxosUBiGYRjbm2BsJYHSs20L7KvqLNYelYVAJg8OtBQsUBiGYRhNYAsCJTzAE8fcpMnGRHUyz+WxFCxQGIZhGE0MCEy4IA0I7BDkg6D6BgRSWiU1Tmdt7xfWTHspFcoWhuoEUREXyloMFigMwzCM1aHpxWWVVWLdt10D0ZNLp3QGbW10XTXNRUD7fiiqlgSUkzIDiDE7LFAYhmEYG/M/sU56R6FHZCDiqqS2Zu/iNCD3QrPvgyPAAoVhGIbRlINs/wbrT/ZZVaB0bxOAvdVddHdwHYpFYIHCMAzDWJXq6mockAcEBnjRgEBfIyMoTkDr3mhuQvw8ccaru3qbC2UtAwsUhmEYxqokXSpEdmGZ2r3j7FzPTJ3yYiDjiLQOjgE8/GANqiOuQmW1tJ9lZ3ZYZR/sHRYoDMMwjFXZbzAgsIH0TnoCUFUhrSOaP72j0LldG5yobifW7peOASV5VtsXe4UFCsMwDGNV9p3LNqH+xLoFsgo0J2ivbNjmhCrgwl6r7Yu9wgKFYRiG0UQHj5uLE3q1lWzktS5QekYEYH8VF8paEhYoDMMwjNWg2pOki9KAwG6tA+ofEKgvUFw9gZBYWIsW3u5I9e+l3q5igWJ2WKAwDMMw2hgQ2FB6pygbyE6S1uG9ABc3WJPQdlFIqQ6UblCKp7Lcqvtjb7BAYRg9J8sHl+7Hvd/sQ24xH2gYptnrTxo0aDugifSOQs82Adgnp3mcK4qlAl7GbLBAYRyeC5eL8Niyg5j80Tb8dTgd645m4Me9ydbeLYZxwA6eVjZRf6LQM0I32VjAaR6zwgKFcVhyi8rx+upjGP3OZvwel2rw2MHkHKvtF8M4CqUVlTiUIg0IjAz0RrBfPQMCNShQurfxVyMoAp7LY1Zczbs5hrGNg+K3O8/ho38SDVI5LbzdUFxWidKKKhySp6oyDGPhAYEVVcb5n1RX6wSKVyugZXtYGz9PN5QHdkFevhf8nYqFo6wT7adTPUZzjNFwBIVxGKqqqrEiPhVj/7sZ//nzmCpO3F2dcf+Ijtg8bxR6yy2OKTnFuJhfauU9ZhhHmr/TQHonJxkouqSLnmhEBPRoG4iDVdFi7VSQAVw+a+1dshs4gsI4BLuSsrBg9THE60VG6Ph2Y582eHJ8F7Rp4SXuIw+G3Wekor1DF3Iwpmuo1faZYewdW5pgXJ9h275DnTHC5ZCuDqVVB2vvll3AAoWxa05l5OPNv49j/bFMg/uHRQXh2YkxYippTfMlhfjzLFAYxqIDAmWB4u/piiijBwRqS6DQMePd6hp1KL2nW3OXHDPF89JLL8HJycngEhMToz4+cuTIKx5/4IEHDLaRnJyMSZMmwdvbGyEhIZg3bx4qKuS5CgxjJjLzSjD/t0OY8P4WA3ESE+aHJXcPwLdzBlwhToheEToXS/1oC8Mw5uXMpUJkyQMC+zY0IPAKgdIXWoHM5Q5Vd0JFtfx1yp081ougdOvWDevXr9dtwNVwE/feey9eeeUV9TYJEYXKykohTsLCwrBjxw6kpaXhjjvugJubG15//fXG/xQMI1NYWoHPtyThi61JKCqrVO8P8/fEE+M7Y2rfCLjUcyCMaOmFVj7uwt2SUjx0lkdCm2EYKxq0VVYAqXHSukUk4BMEreDl7oK2ocE4nN0evZ2SgIvHJUM57wZqahjzCxQSJCQw6oIESV2Pr127FkePHhUCJzQ0FL1798arr76KZ555RkRn3N3dTd0dhhFUVFbhx33n8d66U7hUoCtu9fVwxYMjO+HuoR3EgaQhSIz0igjAxhMXcbmoHOezi9EuUCeyGYYxv0Bp0P/k4jGAjNA0lt7Rr0PZf6kLejvLLrfn9wBdrrH2bjleF8+pU6fQunVrdOzYETNmzBApG32+++47BAUFoXv37pg/fz6KiorUx3bu3IkePXoIcaIwYcIE5OXl4ciRI3W+Z2lpqXiO/oVhCIpwkLEapXKeW35YFSeuzk6YPTgSm+eNxNxRUUaJE33zJYX4C+yHwjCWLJCl/1Wle86o9E5Ef2iNnm1bYC/7oVg3gjJw4EAsXrwYXbp0EemZl19+GVdffTUOHz4MPz8/3H777YiMjBQC5tChQyIycuLECfz222/i9enp6QbihFBu02N1sWDBAvFeDKMPFbG+tvoY9shdNwoTu4fh6Wti0CHIp1Hb1T9Y0ntc16t1k/eVYRgdOUVlSMwsEOturf0bPoHQaIGsvuX9h+woa12BMnHiRHXds2dPIVhIkPz000+YM2cO7rvvPvVxipSEh4djzJgxOH36NDp16tTonaRIzBNPPKHepghK27ZtG709xvb5evsZvLzyqMF9ZPT0f9d2bdjwqQH0O3nYsI1hrJze0Z/B4+QChPWE1ogJ90OOS0ucrQpFe+cMaX8rSgHXBpxxGcsZtbVo0QKdO3dGYmJirY+TgCGUx6k2JSMjw+A5yu366lo8PDzg7+9vcGEc2wn2rb9PqLcpUvLpzL745YHBTRYnRKCvh+qLkpCSK+pbGIaxkv9JWSGQKZ+MhMYC7tqrCfNwdUFMmD/2V8tRlMpSXVEvYx2BUlBQIKIjFCmpjbg46RekPD548GAkJCQgM1PX9rlu3TohOGJjY5uyK4wDQXNyisulDp2xXUOw9vHhuKZ7uFm7bZQ0D71P4kUpFM0wjPkHBDbYwUNf9NVVmk3vKPSICOA6FGsKlKeeegqbN2/G2bNnRZvwjTfeCBcXF0yfPl0IFerI2b9/v3h8xYoVooV4+PDhIh1EjB8/XgiRWbNmIT4+HmvWrMHzzz+PuXPniigJwxjDjsRLevUm4XBzMf/EBoM0z3lO8zCMuaDZO0rxedtWXgjx97Tp+hP9OhSebGxeTDqyX7hwQYgRKpKdNm0aAgMDsWvXLgQHB4sWYWofJhFC5m1PPvkkpk6dipUrV6qvJzGzatUqcU3RlJkzZwoRo++bwjANsU1PoAyNsowfAlneK8RxJw/DmI3DqbliIKdR83dsSaBEtMDp6ta4XO2rEyg0OJBpniLZZcuW1fkYFa1SdKUhqKh29erVprwtw6jkl5SrDq+dgn0QFtDA2VcjIZdZyhjR8YUM2xiGMX96x6iaMaVA1s0HCNY5l2uN6FBfuLu6Yn9VNMa6HASKs4FLp4BgvagKYxI8zZixKailuLKq2qLRE8XgTZkNcjwtHyVyzQvDME1j37ls4wtkCzKBXNlrq3UfwNl4P6PmhlLNsa39sY/rUMwGCxTGpmiO9E7NNE9FVTWOprE5IMOYw1hRaTH283RF5xA/46InGpu/Uxc0y4vrUMwHCxTGptiRmCWuaZzOoI6BFn0vsrxXOHSe0zwM01TOZRXhUoE8ILCdqQMCtVt/om95n1DdEaXVcvUEC5QmwQKFsRky80twIiNfrHtEtECAl5tF30+/UJYnGzOMmf1PjKo/2WdTAoW6/0rhLkSKIPu0lKZiGgULFMZm2Hlaip4QQztZNnpCkPGSu9zCzDN5GKbp7NerP+nXUP0JVagrERSfECAgAlqnY7AvvN1datSh7LbmLtk0LFAYm2HbKV39yTAL158Q7q7O6Bou5ciTLhYit7jc4u/JMPbMPrmDx8WYAYHZSUBJri56YkYjRktBPxd1AHIdinlggcLYTHHddrlA1sPVGX3NYGlvaprncAqneRimKQMCT8kDAmPD/eHt7mrCBGPtp3f0Dduo1ViFBUqjYYHC2ARns4qQmlsi1le1bwVPN5dmM19SiONCWYZpNAeSTfU/sa0CWX3L+8vwR2KVPAU9LR4oK7L2btkkLFAYm0CJnhBDoixff6LQu63+ZGMWKAzT1PSOUf4nNQUKeaDYCNRqTKhzearKgVS9dmnGaFigMDYnUJqj/kShY5CvMG0jDnEnD8OYxcOoQYv7ijIg7ZC0DowCvJonpWsOIgO9hceLOtmY4DRPo2CBwmgeco7dmSR18Ph7uqJba11Uw9KQTwN5GxBpuSXIzJPSTAzDGE9KTrEq8Kn+pMERFRmHgcpSm0vvEDRVndqNuVC26bBAYTTP0dQ85BRJHTRDOgWJSvnmpKdemof9UBjGdNYeSVfX13QPs9v6E/3atbPVYbhY7S/dcX4PUCUNSGSMhwUKo3m2n9a3t2+++pOaOWUingtlGcZk1ugJlAndjBEoB2xboIioqxP2K3UopbnAxWPW3i2bgwUKY2MFss1Xf1K7oywLFIYxhayCUjHkk2gf6I3OodIQTqMiKM5uQFgP2BrUyUPs5TRPk2CBwmia0opK7D0rHdzCAzzRMcin2fehdYAngnzdxZry6OTJwjCMcWw4lgl5ADkmdA8TNRr1QuZsl05KaxInrh6wNdq08EKgj7sugkKwQDEZFiiMpjlwLgcl5VVq/UmDBzcLQO+ppHnITZYGnjEMYxx/m5reSY0ja0abTe8oxwyKohypbo/iaunkBudZoJgKCxTGdtqLo5u//qQ2wzZO8zCMcRSUVqgjKkL9PdBb7//IXgtk9etQyuGK+OpO0h05yUBeqrV3y6ZggcLYTIEsRVCshUEnz3nu5GEYY9h0IhNllVIEdHxsmGjbdxiBUtOwjeA0j0mwQGE0S15Judo1Ex3ii1D/BrwTzMHls8DFE/V28rCjLMMYx9+HTUzv6AsUD3/JpM1GUQpl93OhbKNhgcJolt1J2Wpx3VBLd+9Q4euOj4APegMLBwDndho83MrHHW1beYn14dRcVMhnhQzD1E5JeSU2Hs8U6wAvNwzs2IB7LEEpkPw0nb29s+1+RdEJFaW1DlRFo6pajhxxHYpJ2O5vn3Go+hOLChSy1f7jX8Da53XFeUf/qDOKQkW7JzOkqawMw9TOjtOXUFhWKdZjuobAzcXZYdI7+mmePPjgRHWEdEd6AlCab+3dshlYoDCaFyiUtjbq7KsxFF4CvpkCxC01vL+W4V6c5mEY41lzOENdX2NqeoeI6A9bRzJsA/YpdSjVVcCFfdbdKRuCBQqjSTLySnAqs0A1SvP3dLPAmxwFvhgFJO+Qbrt6Ap5yMSwNKquU7PUVaL6GAnfyMEz987PWHZMEipebC4Z3DjbuhXYWQVHqUHguT+NggcJoNjysMNQS3Tsn/ga+HCe1/hG+YcBdq4FOY6TbFcVApqE1dfc2ASKaQ3AnD8PUDZkrZheWifWIzsHwdHNp+EU0qybloLT2bwP4GRl1sYFOHjWCQnAditGwQGE0ybZT0vRis9efUDHs9g+BH24DyuQ6kvDewH0bpTM2/bO2GmkeHw9XRIf4ifWJjHwUy/l1hmHqnr1j1HBAgtxjy+T6jDZ9YQ9QcX1ESy+kIAjp1XKa+vxeoLLC2rtmE7BAYTQHWckrERRPN2f0jTTC3MkYKkqBP+YC6/6tK4aNvQG46y/Av/WVB0b9gWUyvWQ/FAphH03jKArD1Pb/u/aIlN5xdXbCqJgQh0zvGKaGnXRzecoLgYzD1t4tm4AFCqM5ki4VIi23RKyvat8KHq5GhIcbouCiXAz7ne6+kfOBWxYD7t66+8J7AU7OdQoUfUfZOE7zMMwVHE7JQ0pOsTrck1qMHVmg9GhTS5qH61CMggUKozl2mLu9OOMI8MVoIHmnrhj25q+Bkc/S0AzD57r7AMEx0jrzKFBmOHent95kY+7kYZj60zsTuoUa/0JVoDhJaVc7oZdaKKsvUAx9lpjaYYHCaI5t+vN3mipQTvwFfDkeyJWLYf3CpZRO95vqfo2S5qmulHwL9OgS5gd3V2d1sjHDMLUPByTtPy7WSIFSXqJLe9AJgqc/7IVucqvx8eq2KHSSo7VnNgNVXMPWECxQGE1BtR07T0sFsi283RAb7t/4Ytht7wM/TNcVw5Iz5b3/NFyA17pv7WFnQJhNKft05lIhcosMW5EZxpE5fbEAibI9QL92LRHiZ+R4CjoRqKqwu/QOQSmuDkE+qIQLtlT2kO4svgxc2GvtXdM8LFAYTXE4JRd5JdKBakinQOOGi9VWDPv7Q8D6F3XFsN1uBO5crSuGrQ99AVOLYZtBmieF0zwMU3t6x4Q2YYP6E/vo4KnNQ2l9RR/dnSfXWG+HbAQWKIx9TS+mYtgl1wPx3+vuG/l/Us2JfjFsfYR0A1w86imU1Z9szAKFYRTWNGY4IJGi565qZxEUooec5tlU1QvVVGNDsEBpEBYojKbYkZjV+PqT9MNSMaxihOTqJXXpjHzmymLY+nB1B8LkUGz2aSkcqwc52yrEcx0KwwhSc4rV/4eu4f5oF2jkCYF+BIVODEK7wd5Quv+yEIDz3l2lOzOPADnnrbtjGocFCqOp6afkQEm0aeGFSFMOcMdXX1kMe/dfUmqnMRikeWR3S5kOgT7w83AVa46gMIzE2sZ27xRlA9lJujZ/FwuMtbAy3Vr7qy7Um6r1ji2nOIpSHyxQGM1w4NxllFZUqfUnTsZEPUQx7HvAstslAySlyPXejVJRbGPRDzPXSPNQXYwyYyMzvxTpsmcLwzgya2RzNpPcY2vWedlhekdxoY4K8RXrX/JidQ9wmqdeWKAw2mwvjg4yshj2QWD9S7pi2O5TpZk6/uFN25nWdUdQrkzzcBSFcWxo7s4eOfpJkc8uodJICKPQPwGwgwnGDRm2HaqMRJm3HGE6s+UKryVGBwsURjNsl9uLicGdAo0ohr0OiP9Bd9+o54CpXwJuXk3fmcAowMO/1lZjffMlgtM8jKOz/liGsAggrukWZlz000E6eGqOySAjurOthknLihJJpDC1wgKF0QS5xeVIkCMRdPbVoH/Cb/cA53frimGnfQOMeNq0Ytj6cHYGWstulvlpQF5anREUNmxjHB39+pPxpnTvUIpWESheLYGWHWCvKJ08xDYnvVTWyb+ts0M2AAsURhPsSsqCfAKGIVFGRE+SNklrnxDg7r+B2Cnm3ymDNI9hHUqYvyeC/TzUFE+VsvMM42AUlFZgyykpPRvi54E+euK9QXKSgcKLuvoTc51gaBDqbHJzkX6+r1LboVqxMji1VhJqzBWwQGE0wXZT7O3JJlqh93RdpMPcGEw2NkzzUAi7l9w6mF9SgbNZcoEuwzgYm09cRJlc3D6+W6hp5op2OiCwNjzdXDA+VoouXSh0RkbgVdIDeSk83bgOWKAwmhIoLs5OGNChVf1PVqInRMeRltupejp5atahcJqHcVQa7R7rYAKFmD2kvbr+o0j2WiI4zVMrLFAYq0NtuqcvFqo28n6e9fggUCg0SY6gUIi03WDL7Zh/GymFpKR4aoRhe+qFsuO4UJZxQEorKvHP8Uyx9vd0xaCODaRna6Iv/B1AoFzVvqVI9RDfZslT04mTa623UxqGBQqjqfTO0Ia6dy6f0ZmxtRtono6duqB8uJLmKcnVmUnVGkFhgcI4HjtOZ4kaFGJs11AxTNNoKiuAtDhp3SIS8Gni5HIbgFLDdw6JFOsL1cFI85CLgmlwYKHuOMg0QqC89NJL4gPWv8TE6FRgSUkJ5s6di8DAQPj6+mLq1KnIyNCZ9xDJycmYNGkSvL29ERISgnnz5qGiQp5iyTgkBgKlofoT/fROhxGwOAaTjQ3TPC283VW32yOpeSivlPLwDOOIs3dM6t4hLh4HyoscJnqiMKV3GzGpnVihpnmqgVPrrLpfdhFB6datG9LS0tTLtm3b1Mcef/xxrFy5Ej///DM2b96M1NRU3HTTTerjlZWVQpyUlZVhx44dWLJkCRYvXowXXnjBfD8RY1NUV1erAwK93FzQp13L+l+gpHeIjqMsvHc1Dpy1TDZWCmXJAfdEer7l94dhNAL5nqw7Kp2Aero5Y0TnYNM24GD1J/rFsrde1Vas11XoFfiz7X3TBYqrqyvCwsLUS1CQdMabm5uLL7/8Ev/9738xevRo9OvXD19//bUQIrt2ScPb1q5di6NHj2Lp0qXo3bs3Jk6ciFdffRULFy4UooVxPE5fLEBGXqlYU3Gsu2s9f5JVVboOHo8Ay3Xv6KNvl1+LYZvBZGNO8zAOxP5zl5FVKB23SZx4ubuYtgE7n2BcH7MGRYrZPAero5ELyQIfiRuAynJr75ptC5RTp06hdevW6NixI2bMmCFSNsT+/ftRXl6OsWPHqs+l9E+7du2wc+dOcZuue/TogdBQ3SCpCRMmIC8vD0eOHKnzPUtLS8Vz9C+MfbBdb3rx0Ib8T9IP6SYLd7gacDbxgNgYfAKl/DiRdkjKm9dl2HaeO3kYx+Hvw03o3tFPmTq5AOE94UhEtPTGuNhQVMIFGyvln700D0iWviuZRgiUgQMHipTM33//jUWLFuHMmTO4+uqrkZ+fj/T0dLi7u6NFC0OTHhIj9BhB1/riRHlceawuFixYgICAAPXStq0UHmMcuP7Eku3FNVHO7iqKgYvHrphSSq3RBEdQGEdKzSrtxa7OThgTY8L0YqKsEMg8Kq1DYgF3HzgaSsvxP5V6UVoeHth4gUIpmVtuuQU9e/YUkY/Vq1cjJycHP/30EyzJ/PnzRQpJuZw/f96i78c0DxWVVdiZJEVQWvm4o2uYPPvGGIO2ZhUodRfKeru7IlqeUnoyIx9FZVzwzdg/VBSeklOszs0KkIs+jSYtHqiusvv5O/UxuGOgGOuxuaoXKqtlczsWKOZrM6ZoSefOnZGYmCjqUaiOhASLPtTFQ48RdF2zq0e5rTynNjw8PODv729wYWyfw6l5woVVOcjV60BZXgKck8Offq2lYX7NhUEnz5V1KOTdQpDbPR24GcbeaZI5mwMXyOpDXbB3DIkUNSj7qrtId2adArJOW3vX7EOgFBQU4PTp0wgPDxdFsW5ubtiwYYP6+IkTJ0SNyuDBkpkWXSckJCAzUzL2IdatWycER2xsbFN2hbF5/5MG0jsX9kgpFiV60pwzO8J7AU7OdXby9JQ7eQiebMw4kkChf8PxsSamd2oKlIj+cFRu7NNGGNxtrNTv5mHTtkYJlKeeekq0D589e1Z059x4441wcXHB9OnTRW3InDlz8MQTT2Djxo2iaPauu+4SomTQoEHi9ePHjxdCZNasWYiPj8eaNWvw/PPPC+8UipIwjoVJ83eSrJTeITx8gWDZ7yfjKFAuC6VaO3m4UJaxb5IuFuBkRoFY02DAEP8GJo/XJ1DcfHT/Ww4IpYin9W+LDVV6UVq2vW+cQLlw4YIQI126dMG0adOEIRu1EAcHS/3v7733HiZPniwM2oYPHy7SNr/99pv6ehIzq1atEtckXGbOnIk77rgDr7zyiim7wdgBJeWV2HdO6siJaOmFdrLhmXEFss1g0FZXmqe6Uurm0aNLmB885PZodpRl7J01R3Rp+mu6NyK9Q9PIaYoxQVYBzdGNp2HuGNweiWiD81XS92j12e1AKXsqEa6mfJDLli2r93FPT0/haUKXuoiMjBTFtYxjs+/sZXUCaoPRk+IcXWqFzrb8GnFQbCpUyBe3VFrTvpDNvgzZe1M3z4HkHJzLKsLlwjK09HFv/n1kGJurP3HMAll96ORsdJdQ/JPYG7Od18Gpqhw4vRGIvR6ODs/iYazCNr30zpCGBMq57bqK/+ZO7xjRyXOFH0oKp3kY+x3sqQzGjAnzQ2RgI9qDuUC21pbjfwzSPNzNQ7BAYazCDtnenhjS0IBAa/mf6BPSDXBxr7OTR7G8J7hQlrFX1h5tYvSEYIFyBVdHByGjVX8UVUu1mBUn1kjO2Q4OCxSm2ckpKkOCHGWgs7AgXw/jBAo5TkYOhVVwdQfC5MFe2aeltFNdERSuQ2HslCa7x1ZX6wSKTzAQwKabSsvx7UM7Y3tVd3HbtfgikHYQjg4LFKbZ2ZWUJY5TRrnH5qUCl07qzrY8reiBYzA40PDg0T7QW7QLEnHnc4XTJsPYE1RbtftMtli3a+WNruF+pm8kOwkokQV8m/7NaxegcW7qG4FtzrpjTMmRv+DosEBhrFp/oun2YhMM2+gMSPFDuVRQirTckubeO4axKBuOZ4oJxsSEbqHib95k9Ou3OL1jgK+HK/x7TFJv5yesgqPDAoVpdnbIAwJphgdNMNZ0e3FdhbI1IihEr7Y6PxRO8zD2nN5pVHvxFROMuYOnJjeO6I/DVdKMnuD8Y6jMTYMjwwKFaVZSc4qRdKlQrPu0awEfj3o63SlNoggUN28g4ipYlcBowN2vzk4efUdZSvMwjL1QWFqBracuinWwnwf6tG3ZuA3pRx5b6w3JYwQdg32R2EJXZ3d86y9wZFigMFZzjx3SkL091Z4UyGdtkUMAVyu7DTs7S8ZSRH4qkJdW60wegiMojD2x+eRFlMq+ReNiQ+ufm1UXFWU6k8NWnQDvBqKnDkrbgTeo66LDju0ZxgKFaVZ2nJbSO8Sw6CDttxfXm+YxjKKE+nsi1F8SUQkXclEl5+sZxp7M2a5pbHtx5hGgslRac/1JnfQZNAaXIaWLY4v3IzFVd1LnaLBAYZoN6mxRCmS93V0MvEMaFCgdrFx/oqB/YK0nzZNfWqGmshjGliHH53+OSQNe/TxdMahjA75FdcH+J0bh7OKCi+HS8c7HqRRb1/8BR4UFCtNsJGYW4GK+dAY1sEMruMvza2qlsgI4u01aewcCoZI/gNXR7+SpZbKxfpqHDdsYezFVJMFNjO0aWv//bX1c4AnGxhKhl+ZxTVyL3OJyOCIsUBirtBc36H9CXTKlebroCdV/aIGACMlgSomg1PA70Z9szHUojL0NB6T24kahX/Du6qmdEw6N4h0zDpWQhiiOwH78vFceruhgaOSozzgC2+X2YqMEipbai/Uh7wclPE2GU2Q8pUfPNnoRlAvcycPYNuR7sk62t6eJ3cM7y+LcVC6ekArLCXKDdvM0417aIZ7+KG0zWCzbOV/Elp3bHbKmjQUK0yxUVFZhd5IkUAJ93NEl1M/2CmRrTfMY+qEEeLuhQ5A0QO1oap46sZlhbJEDyZdxqaBMrEd0Doa3ez22APVx+h/dOmqMmfbOvvHufq26jsnbgU0npTogR4IFCtMs0IRfJY9N04vrbVMsKwQu7JHWLdtLFy1hMNn4ysGBSpqnrLIKJ9Lzm3PPGMasrGnq7J3aBEqn0U3cKweh8zXqcrRLHBbvOAdHgwUK0yxsP6VXf9LQ9OLknUBlmTajJ1dY3l9ZKKvfnRTHdSiMDXfd/S23F5Pr85iuIY3bUEWpruDdLxwIjjHjXtoxgZ1QHRgllv2dTiDu5BmcvlgAR4IFCtMsbD9tQoGsFtuL9fEJBFpESuu0eKnjqC7Le+7kYWyUo2l5uHC5WKyptbiFt3vjNpS8C6go1kVPeECg0ThFTxDXrk5VGOF8CN/udKwoCgsUxuIUl1XiwLkcdQpq21bexg8I1KJA0U/z0IH34jGDh7q1DoCLnMI6xIWyjF2kdxrZvUNweqfxdJYECjHKJQ4/7zuP/BLHaTlmgcJYnL1ns0U9hlHRk8IsIF22ww7rKUUrtEg9aR5PNxe1CPhUZr6YY8IwttxePL5J9Scb5IWTNlO2WqbdYMDDXyxHOcehuKwcv+6/AEeBBQrTrPN3hkY1IDjObNZme3FN9J0wazFs6yUbtlFn4OEUjqIwtsWZS4U4kZGvDvWkMQ6NoiATSE+Q1uG9AJ8GTlAYQ1zdgU6jxLKlUwH6OJ3CNzvPOUzLMQsUplnrTxocEKjl9mJ96GDr5FxnJ08vPcO2eC6UZRxx9k7N/2dO75ihm+egGKGxVe+kz55hgcJYlMuFZTiSKjnCxob7o5WPu3ERFBd3KbypVTx8gaAu0jrjKFAuFwHWmMlDsGEbY8sChduLrUzUOCk9RgLFWfJdWrLjLBwBFiiMxacXK27wDU4vzj4DXJb/8SIGAO6S4Znm0zzVlbowtkznUF94ukn/XjyTh7GlgvaP/zmFg8nS3yzVUrWXjQdNhv7xFYHi5gO0HWDGPXUgfIPVY01X5/NojUvYeCIT57LsfxgpCxTGoqyIT1HXwxoqkDWoP9FwekehTR/dukaax9XFGd1bS2keatXMKpDHzDOMRi3tf9p7HiPf2Yh31p5U77++d+vGbzTzKFAgF9q2Hwa4ephhTx2UzoZpHtJ+VIti77BAYSwGTS7eII9pD/HzwJCGDNqSbEygNGTYpjfZmJx0GUaLZmx0Nn7tB1vx9K+HkJEnCWlqk581KBL3D+/Y+I1zesd8dB6vLse6xInrn/adt/sOwUYOVmCYhvn9YAoq5Grzqf0iRFShTqqqdBEUaqtrrRed0Co0kZVqZcj1tpZOHv3JxpTmGdWlkU6cDGMBqLtswV/HDIZ4EmO7huLZiV0QFdLAvKyGSFTai1mgNJmwnpILb34ahrocgSdKkV8CLD+YgpmDZNNIO4QjKIzFzsx+3HdevT2tf9v6X5BxGCjK0oWDXVxtowUwrIe0zkoEinPqtLxnwzZGK1y4XITHf4zD5I+2GYgT6jxbdt8g/G92/6aLEyoaP7dDWge0BYKim7jXDo6Tk2ra5lZdhiHOR9RiWTrW2issUBiLcCA5B4mZ0tyIAe1bqRN+7ab+xIjJxpGB3gjwclMjKPZ8IGG0T25xORasPobR724WZ94KbVt54aPpffD73KHC0t4skDiplOuuyMeD7e2bTrTOVfa2gKPi+lRmgWhEsFdYoDAWgSyZFaZd1UD0xJb8T0wwbHNyclLTPFmFZUjJMWxFZpjmoLSiEv/bmoQRb2/EZ1uSUFYhuTq38HbDvyfHYv0TI3Bdr9bi79VscP2J+ek4AnCRCo2vBh1rpBOexXbccmwDcXTG1qDCrZXxqWLt6+GKa3uENTztVAkH+4YBQZ1hMygzeeoolO3dtgW2ypOcKc0T0bKBOUQMYyYoYrfyUBreXnMc57N14tjd1Rl3DW2Ph0ZGqRE+s3N6o7xw0u48LVvD3QfoMBxIXAev4nQM803HtoJwbDiWgfPZRQ3POLNBOILCmJ3VCWkoLKsU6+t6hcPbvQEdfGEvUF6ki57YUjg4MBpw96tToBgYtrEfCtNM7E7Kwg0Lt+ORHw6q4oT+rW7q0wb/PDkC8yd2tZw4yU8HMo/oBLx3K8u8j4MPD3yoTaK4pj6Epbvss+WYIyiM2aH2N6OLY22xvVgfZ2egdW/g7FYgP1U6OPvpIkZsec80J4mZ+Xjjr+NYL7f368/AIlHSvY3u79FicHrHckTr2o2vKtsLd5cRYhDrsr3n8djYzvByd4E9wREUxqycvliAvWcvi3V0iK9IcZhWf2KD4eB60jwh/p4ID5AGrSVcyBX1AAxjbjLzSzD/twSMf2+LgTiJCfPD4ruuwtI5A5tHnBAsUCxHy0ggJFYs3dL2Y1qsl1oA/UecrvDZXmCBwpiVn/fpRoHfelXbhgvvSvJ0LqxUe+LfBOdKTRi2XTk4UOmMoLTX5hMXm3PPGDunpLwS7607iZFvb8IPe5JFuJ8I9ffAWzf3xJ+PXI2RXULMWwBbH+RnpNSfUOoz4qrmeV+HjKJU477wJPVuKpa1t05BFiiM2aiorMKvBySB4urshBv7tGn4Ree2S7NsbDG9U1sEpRbDNn278N/t8CyHsR5P/RyPDzacQpFc80VF6fMmdMGmp0aJ9Co5wjYrGQlAkTxplwo6XSxU5+LIdNbZ3rfL2qJGqY+n52PfOSl6bS+wQGHMxsYTF4W9veJGGejrYb/txfqQEZVPsC7FU+Ms5uqoIATKU5wp/J5XUm6NvWTsjCOpuVh1KE09IZg9OBKb543E3FFR1qtFMEjvjLLOPtg7EVcBnnLqPHEDZg3QnQCtPybPPrITWKAwFimOpfSOUSgCxckZiBwKm4TC50qapyQHyNaFXQmy+CefCYI8KP5O0I2yZ5jGsmjTaXX9/KSueHlKd+NOCiwJ159YHhdXIHqctC7Nw1ifM2rjo72lkFmgMGYr0vvnuFScF+bvieGd5YhCfVDHy8Xj0pq+4L2MKKi1iTSPoaMscYNeukvfxZNhGkPSxQL8mSBFT4J83XHbgHbW3iWgrBBI3iWtW0QCrZowaJAxOs0TcH4DesoF0JTmSc8tgb3AAoUxC8sPpIiR7cTUfm2My33bcntxfY6ytU02jghQ7f53nclCWi67yjKN59PNp9VM4t3DOsDTTQPtpWe3S4MzleiJLfkZ2Rqd6POVv75PrsEIvRPCLSftJ4rCAoUx+2DAW/qZmN6x1fbiOmfyXClQqItiilwsS18sK+Ikp12GMRUamfDbASkK5+fpillamWarn96JGmPNPbF/vFsBbQdJ66xTGB9eqD60mQUKw+jYf+4yki5K/yADO7RC+4YGAyrf0opAcfUCIgbApvEJBFrIYfbUOKCy4oqn3NBbl+b5nQUK00i+2JKECjlaeeeQ9vDzdNOWQHFyAdpfbe29cShX2a75u1Rn4K2nLoqOSnuABQpjneLYrETJeZWIHAy4SWZmdpHmqSjW1dboQcJNaQk8lpaH4+l5zb2HjI1zqaAUy/Ymi7WXmwvuGtoBmiD3AnDphLSO6G/b9WQ2KFBcEtdgWHSQWOeVVCDOTsZqsEBhmkRBaYXa6ujn4YqJ3cONe6E9tBebmOYh9L1hfj/IURTGNL7efgYl5dLZ8fQB7dBKbl+3OupwQO7eaTaCY3RR27PbMaajt92leZokUN544w2RW3/sscfU+0aOHCnu07888MADBq9LTk7GpEmT4O3tjZCQEMybNw8VFVeGxBnts/pQmmoSdV3v1sb7L+gLFHuZdtqmfkdZYnLPcLWAeEVcCqoU60+GaQDyz/lmhzQUzs3FCfcO10j0hOD24ubHyUnXzVNVjtFu8oBGFijA3r178dlnn6Fnz55XPHbvvfciLS1Nvbz11lvqY5WVlUKclJWVYceOHViyZAkWL16MF154ofE/BWM19ItjbzVmMCBRVQmc2SqtvVoCYVf+Ddkk4b11lfW1dPIQ5FMxXA7FpuaWYM/Z7ObcQ8aG+XbnOeSXSidyU/tGIDxAmsNidej/OUmOoHgEGEYSGcsSrUvztLjwD7qG+4v1oQu5Ih3okAKloKAAM2bMwBdffIGWLVte8ThFRsLCwtSLv7/0oRFr167F0aNHsXTpUvTu3RsTJ07Eq6++ioULFwrRwtjW5FQqkCW6hPqhp97k3nqhItLSXF30hCYC2wMevkBQF2mdeRQor72VWN8TxR4HfDHmp7isEl9tOyPWFIC7f0QnaIa0OKBYtljvSPb2rtbeI8eh/TDATU7tnFyLEdHS3C+lWNbWadQ3w9y5c0UUZOzYsbU+/t133yEoKAjdu3fH/PnzUVRUpD62c+dO9OjRA6Ghoep9EyZMQF5eHo4c0YWo9CktLRWP618YbQ0GnGbMYEAF5WzLHtqL60rzVFUA6Qm1PmVcbCi85VTYn4fSeMIx0yBUGJtVKJ3ATerZWvXU0V56h9uLmxU3T6CjPFKgMBOTAnUu1fbgKmuyQFm2bBkOHDiABQsW1Pr47bffLqIjGzduFOLk22+/xcyZM9XH09PTDcQJodymx2qD3isgIEC9tG1rZCqBsRjleoMBKR9u1GBAhTN2ZNBWk9Z9dOs60jze7q64pluYWnG/8bjtH0gYy0HjET7fohuf8NBIDUVPriiQ5fk71uzmic3ZCB/55GfLqUs2X+NmkkA5f/48Hn30UREh8fSsvS30vvvuExERipJQGuibb77B8uXLcfq0bm6EqZDQyc3NVS+0H4x1IVv7SwVlakTA6G6CsiI9O+x2QEsNFfqZ21G2jk4eYopBNw+neZi6ob+PNNm+fExMiFpnoAlK84Hzu6V1q05Ay/bW3iPHI2Yy4Cyl1VyOLsfQTlKaJ7uwDIdT5VS6IwiU/fv3IzMzE3379oWrq6u4bN68GR9++KFYUwFsTQYOHCiuExMTxTXVpGRkGE5cVG7TY7Xh4eEh6lj0L4x1+VmvOJbGuhvN+V06O2yqP7E3O+zQ7oCLe72dPAQdRILkwW4k9nKLecIxcyU0PmLRZt3J3UOjoqApzm6T0pkEd+9YzySyoxyJzj2PqaGS7QOxycbTPCYJlDFjxiAhIQFxcXHqpX///iJSQmsXlytbTOl+Ijxc8scYPHiw2AYJHYV169YJ0REbG9v0n4ixOJl5Jdgo/+GHB3ji6mgjBgPas/+JPq7ukkhRzOiKazdMognH1ysTjiur8Jc8+I1h9PnrcBrOXJJcmgd3DES/yCubEqwKtxdrg+5T1eWQks3abTcmB/HlD1pGoPj5+YnCV/2Lj48PAgMDxZrSONSRQ5GWs2fPYsWKFbjjjjswfPhwtR15/PjxQojMmjUL8fHxWLNmDZ5//nlReEuREkb7/Ko3GPDmfhHGDQasbUCgvfif1JfmoQ6HOrihjyRQCJ5wzNQ242rhRl30ZK7WoidE4gbpmlIM1FHCWIeYSWrk1i9xFaKDpBKMg8mXkVukoegspQOPrzT66Wbt73R3d8f69euFCImJicGTTz6JqVOnYuVK3Q5RlGXVqlXimqIpVEBLIuaVV14x564wFjxo/tyYwYBEUTaQFi+tQ3sAviZEXuzMsI3o0SYAHYOlbozdZ7LFEDiG0Q/P00gEZRr20ChdC6kmuHwWyJYFFM3S8uTUu9XwDACix0vrgnTMbC1FZOk8clviJWiGuO9NenqTG9Y3bdKF7Km7hmpSGiIyMhKrV69u6lszVmDv2ctI0gs5twvU2Ss3yJktJHHss71YH32jqjo6eQhqy6YBgv9dd1LcpgnHD2qtQ4Ox2onAxxuluj2l9sToNn5rdO9EcXrH6nS/CTi+SizHVW3Di7hOrDefzMSknkaOILEk5At1ZLlJL7EThyxG04MBHaG9WJ+gaMDdT1qnHqz3qfoTjtm0jVHYcyZbNUGMDvHFuK6G1gyagOtPtEXna1TTtvCUNfB1q1LrUEjwWp3jfwKlpnmYsUBhjCa/pFwYixF+nq64pnvtXVcNFsg6uwHtBsNucXYBWveW1nkpQH7t/j4ERaD6tpMmvx5Pz1dD+oxjs3CTfudOJzibUufVHFRW6E44aFwFjXlgrIu7jzqbx6k4G3eFS1OvM/JKxbHF6sQvM/klLFAYo6GpxcXlUiv5lN6t4elm5GBA4vI5IFs2m4q4SrKFt2eMMGyrdcIxR1EcnkMXcrBF7r6IaOmF63rqiqk1A0UGS3J10VAS5Yymunmud92lnW4eOkk7LRdU+xtv6skChWlceqe/PObbWBwlvWOiYZtiXe6qTjhOtXn3R6ZpfKLXufPAiE6iJV1zcHpHm0SNBTykYuVOlzbCHeXasL0/9BNQXXWFiGoIDf7lM1rkVEY+DiZLnh7kZNm9jYkV+0mOJlCMK5QlyIV3RGepo4kcQ6mjh3Hc/7O/j0gpwWA/D9HGr0mUs2GCBYq2ZvPETBZL57J83Ox/XKz3nctGgTwJu9mh+pf4H3S3e9xs9EtZoDAmR0+m9Y8wraOgqkpXf+Lua/jlba8EtAW8g3QRlAaK1PQnHLP1veOi7xp779UdTEujNhdkPnhhn7Sm6d0BGhVRjtzNI3Ob915xXV5ZjR3Wajcmawma7k60HQi06mj0S1mgMEYNK/vtgPSl6e7ibNB5YhT0x1kk/3OQmZOLG+weEnCKEKNR9JfP1Pv0sV1D1SFfqxPSUCLX+jCOw/nsIvwRlyrWAV5uuH1gJDTJ2a1Atfz3ydET7dFxpFS4DKBb/nZ4ocS6dSj60ZNet5n0UhYoTIP8czxDHfU+rlsoWho7GNBR609qq0NpIM3j5e6Ca7pLXgX5pTThWDcKgnEMaGKx4tB855D28PVosk2VZeD6E23j4gbETpGWlcUY7xpvvXbjijIg4Wd5vzyAbrrojjGwQGEa5Kd9F9T1raYMBnSU+TtNNGxTYOt7xyUzvwQ/ymlUb3cX3DVUw1OBFYFCdgHth1p7b5ja0CtEnekrpeMuXC5WTTabjcR1QFGWtI65FvCSLBWMhQUKUy/puSXYdEI6m2/TwgtDo+S6ClMU9Nnt0to3FAiOgcOgX2vTQCcPMaRTkCiMVGzOc4rkqc+M3fPltjMilUrMHBSJFt4mRimbC7IKIIt7ot0gyXuD0R6RQ6XjLYA+ZXvhhyLrTDfWt7bvdbvJL2eBwtTLrwcuiHkOxFRTBwMqs2jKC3XDAbVm121JfIKAFu10hWJkblUP9NnqTzhenVC3wRtjP9Awt6U7z6k1XvcM6wDNogwHJDi9o12cXYDYG8TStaoM45z3NX8dCs1eO7lGWpNYkv9e/j5s/OR2FiiMUYMBSVfc0piWRwrxKdjz/J2G0jzlRcBFqeWvPti0zfFYsvMsCsukotNb+kcgxF+aRKtJ9OfvsECxmTTPzR57xPXupKzmK8BP+AWokicp97gFcHEVEfn5vyUYvQkWKEydkB/H2SwpNDi0UxDatjJhMCBBBVlHV8g3nICocXA4TEzzdGvtj6gQX3Uey4XL0ufP2CeFpRX4avsZNYJ2/3AND4usLJcHflKhTBAQ1tPae8TUBzl2k90BgIHV8WiBfJRWVGFXklwTYmni9dI7vaX0zhdbk0TLs7GwQGGM8j6hMzuToYhB1ilpTbN3/DQ48ExDnTy6Cce6YtkV8VLbKWOf/LAnGTlF0lkmpfdMmg7e3JD3SZk806XTKMCZvz40jbMz0O1GsXRBJa5x2dt8dSiZx3WDUknIhnZDdmEZvt8tzQcyFv4LY2olr6Rc+HEQ/p6umNDNxMGAhBo9ARB7PRyS8F5S9EipxzGCKXo+M8sPpGhjEiljdkorKkVrscKDIzUcPSG4vdi2Z/O47BTXypyn5o6eLN5+Rp3lZiwsUJhaWRmfipLyKtXltFGOlsdW6tZdr4ND4uGn61zKOAxkHmvwJZRK6x8pGS2dyizAUZ5wbJf8uj8FmfmlYj0+NhSdQ/1gMwKl4yhr7gljygmS7Nw6yPkognFZtBony6l7i1BVKc3eIZxdRf1Jfkk5Fu+Qur+UuWPGwAKFadD7ZFpjvE+oHTEjQVco6sh22MrsCRqW9ff8Bm3va1rfK+6ijP1QUVmFT/Vs7R8aFQVNQx0ZSg1VSCzgL5kKMhrHyUmNojijGte6SMWym09ZMIqStBHIlzt1oseLbsbvdicjr0TqYpzc0/i/HRYozBWcSM9H/PkctWize5sA0zfC6R0dg+cCAe10/7wn/27wJZN6hKtnGn/EpagOo4x98GdCGpKzpbPYYVFB6N3WNAOrZofcoJVptJzesdk0z2Q5zWPR6cZx+tb200XX0P+2nlH10t1XG99GzwKFaWAwYCOiJ8QxPYHS1cEFipsXMP4V3e01/wdUSKH9uqBxAiO7hIh1Rl6paA9k7IOqqmp8slE/eqLx2hOC609sl5CuUtQLwFXOJ9Eal7Dj9CVRA2V2SnKB46ukNc0D6jxBWFVcKpCOd9d2D0fHIKlL0RhYoDAGkJulYrPu7tqIwYBE7gVdQWhodyDQBg7AloZMk8jdUUl/7f7UJE8Utr63HzYcz8SJDKkbpk+7FhjcMRCahlKSiv8JzVOJHGLtPWKaMOF4kssuFJVVYv/ZyzA7R34HKqThhOh+M8qd3PDp5qRGi3EWKIwB649liHYw4ppuYQjwbsTk4WOygiYcPXqiQLHNaxboOno2vw0U1D8QcEzXEHVg3N+H03nCsR1AHVkfb0xUb/9rVJRoLdc0WYlArhxVJXFCEUHGtuimEyjXKWkeS3Tz6E8u7j0dK+JSkZJTLG6O6hKMbq1NKxdggcJYOL3joN07dVXU971DWpOfxAa9tE8tUOfUxO5h6oTjDcd4wrGts/N0llrfFRPmh9ExUhpP03B6x/YJ7ASE9xbLns5nEOmUbn6BQpHhZEn8IKgLqsL64JNNOjE+txGF4CxQGJW03GK1R54GAw7p1IjQM0UFzu2Q1oFRUv6T0TH634CHv7Q+uBRIjTO6m4et720f/ejJQ7YQPSFYoNhfsazzLhxPzxfHfLMR/6Nu3Xs61hzNwOmL0hy2AR1aoX/7ViZvkgUKo/LLPt1gQHKOdTZ1MCAhCqSqdekdWzgANye+wcCIp+Ub1cDfz9bbdjyoYyBC/ZUJx5m4LKffGNvjQPJl7DgtFTu3D/QWnVqah6aRn9kqrX1ChCMoY6N0k1xl9dM8ZjNtq6rSpXecnFHdYxoWNjF6QrBAYQRkpLNsr95gwEand/TM2Ry9vbguBtwPtJKLxSgkeuQ3oyYc0wwLak9lbBP9zp0HRnQyfTK4NTi/WzeNnKInfMJhu7RoC7QdJJYxzufR2em8+dI8dBzLkSZyo+NIbMlwx+EUyWCyext/DI8OatRmWaAwwmPj0WVxajHT8OhgkeIxmeLLumFi5Psh5zyZGri6ywWzMuteBMqKjDRt4zSPLZJwIVcUoBNh/p64sW8juuOsnd6JGmPNPWHMgV43D3mibD11SZgGmtXavtd0LNRLZc4d2fhUJgsUBm/8dQz/HJcKMAO83PDS9Y0M4574C6iq0BXH8tlW3ZDDYif5gE8dEjs+qvOpseH+6BwqeQfsPXsZ52WDL8Y2KC6rxOM/6WqN7h3eER6ujRgdYXV7+5HW3BPGXHYHTtLX/nXOO0XkPE4u2m40dHJ15A9p7e6H/d5DxSR2olOwT+PmuMmwQHFwftybjC9klz9yLl00oy86BPk0bmPsHmt627GT/EW17T3JP6bWpzoZDBDkCceS2Rl1nI1/bzPu+GoPcuWJwFrktdVHkZhZINZdw/0xc5DsKqx1Ci8BafHSOqwH4GsDHUdM/dBE+fbDxLKDcwa6OZ1t+nRjqjtUplx3m4KPt+qivA+OjGpcLaMMCxQHhtxJn//9sHr75SndMCSqcblClObrzrZ8w4CIAWbaSzsmuAsw4D5pXVEMrH+pzqdO6S3VoSimbY484fhwSi5u/nQHnv7lEE5mFIhCvyd/jheiRWusO5qBpbukEfOebs74aHpv24meJG3SFbxz945ddvNc57Kz6XUocbr0zpmIKdgoCx4qE9A/bjUGFigOCk2zfGDpflF4Sdw5pD1mDIxs/AZPrQUqZfv2rpMBZ/7TMoqRzwBecvtdws9A8u5anxbR0hsD5DY9Ohs/kup4E44pSvLv3w/j+o+34UCyYVia6js+26JzrNQCGXklePoXOQIB4PlJsYgK0fjEYn0U91iCBYr90PV6acqwqEPZhcMpl1UrepPJTZGFLBXhRuLd47pW4gdGdISbS9O+B/hbxAGhvOOcJXtxWQ6LD+8cjOcnNdGvRD+9w+ZsxkPzKkY/p7v99zNSy15DnigOZH0v0jl7z2PUu5vw7a5zait8x2AfzJvQRS11envNcTFjRCv7/ORP8er/2LjYUMwYaCOpHdXefoO0dvVSuz8YO8C7FdBxlFhGOF1CH6dEbG3sdOND5H0i/UNejp6KPw9LheBBvh6N7wTVgwWKA3bsPPzDQZySc+JUxPTR9D5wbYrSLS8GTq2T1hQNiJRynIyR9L0TCJELk1MPGtpF63FtjzC4uTipdSiOMOGY0jlTKZ3z6yF1BIO3uwuenRiDvx8dLvwVHh4dLe6nj+ORHw6KyIW1+XLbGWxLlMRSiJ8H3pza0zZM2RQuHgfy5Zb29kMBN09r7xFjwTRPo+pQSMTqHas+yx2gWjrdc3UH4YTdVFigOBivrz6m/jG28HbDl7OvEp07TYJqTxSvhJhrARcpfMgYCX1e+m3HG16Wanpq0MLbHaPkCceZ+aXCNt1eySkqw/O/J+C6j7fhoF46Z1LPcGx4coTwEaFhlsSjY6JxteyzcKmgDP/6/gDKzdE62QRR9daa4+rt/07rjVY+7rApDNxjub3Y7oi5FtU0+FEMD9yNbSfSTT/hSTkAXDoplqVtBuF/h6XX+3u6mi1ayALFgfhhT7I4s9N17PRD+8Z27NSZ3pnS9O05Ih1HADGTpXVBBrD1XYeccEypEeosG/3uZlFcqpyRUaTvu3sGYuHtfREeYOjRQ4ZnH9zWB60DPNVW7Lf+1gmE5m4pfnTZQbW2677hHTGskSZVVoXt7e0bzwA4RY8TyxCnHHQuTRDCurHeJ3+7jEJFla6e0c+ziSe9Mnyq6yDQ2TYVGCq8ekN3DG7MrJ3arLDJ/4SgGTP0Rcs0jvH/kYuNy4CdC6XBgq06GjxlVEwI/DxdkV9SgTVH0vGfsu7wcreRrhAjzMz+/cdhA18GSudQhOSuoR3UiEltUIRi4Yy+mPbZTiEOqHW+b7uWmNjMdvKv/nlUnT/SrbU/nhrfxfJvevkcUHRJ+l+kQnVxrb+u477KcqCitMbjZdJ9iuGiX2up24yxzzTP8VWqJ8rmkzeiV9sWxr2W/kYSfhHLalcvvJwkpVm93Fxw59AOZttFFigOwLmsQjz43X5V4d49tAOmDzBTwR4dyEpl5d15AuAqhQ2ZRtCqAzB4ruSJQl8Ua/8N3PadwVMor3tt93D8uO88CkorRPfKdbIVvi2nc95ecwLf79FFTIjJPcPx3KSuV0RM6qJPu5aiU+bFFUfE7Xm/HEKXMD90DJZM7iwNCcbvd+taiimqU5+oajJVlcCKh4E4w78Rs8L29vZL5wmocvWCc0UxJrrswf0n0vDIGEloNMjJv4ES6UTiWMBwZBdI0cvbB7YzazqTUzx2Tl5JOe5evBc5cjfBiM7B+L9rY8z3BsdkB0GlfY1pGlc/CfiGSms6u1Fa+PSY0qe1XVjfUzqH0o6j3tmE73brxElUiC++v2cgPq4lndMQdwyOVAUbCbiHvjsg0i6WJj23BM/8eki9/eJ13cTPYTHow1r5qGXFCfkZDX7IcttnrIu7D5ypZhBAS6cC+KRsFScLRhGnK459/1J/cU0F/PdebRjxbSocQbFjaMbCv74/qIac6YD50e1N7NipeQZ3/E9p7eYNRI01z3YdGQ8/YOxLwO8PSrf/ng/cv9Wg8HhQh0CEB3giLbdEFDxTd4utFWEeupCDf/9xBPF66RwfSueMjcadQ+pP59QHdcq8cVMPHEvLE34xNFL+ud8T8O4tvSzWRSNain+OU08CJnQLxW1XNb3Fsl5xsub/gIPfSrfJ06LPLPGFIyKYVPzo4iav3XXX6rqhx+Vrai9mPyP7T/Mc/lUsJzvvFJ1nk3s2EJEtuAgkSl2bBe4hWJ8nWVTc3C8CYXIdmLlggWJhqDK6uLwSvh7N/1G/tvqYOk67pejY6Q9/MxUvCc7tAIrkThISJ+7e5tu2I9PzNmDPF0DqASDzKHBgMXDVPerDzvKEYzImo7TdqkOpuGNwe9gClwvL8PbaEyJyop/OoZ/n/67tapYDnI+HKz6d2RfXf7wdRWWV+O1ACvpHthLhZ0vwxdYkbE/MUgcBvnGThVuKN70B7PpEWtNclZu+MBgCxzBGEzUWFW5+cC3PxzjnfVhw7ELDAoUMJeWZaz+XD0EVnEFu9vcPlye0mxGWxxaktKJSuF72eGkN5n5/ACczrmwdtRTf7T6Hr7efVUNvn87sh8hAM3Ts6HNMv3uH0ztmg85aJ76pu/3Pa0CRNHxLQX8a7jc7z2nS5r2mUKf6DDJbo2tFnERTOufegfhweh+znn2RYyt5jyi8tOKIiNqYG+p8eGftCbEmTfLfab3Q0pLRrB0fA5vf0N2+7gMWJ0zjoUgZOX9Te7BTMSpOrmt4jIZe987SkqHimtKqZukIrQELFAuy5eQlYUlOv+8/D6VhwvtbhFA5kW5ZoUJumi/+IRUKEv+5oTsGdjRDx44+5HZ6bKW0ppAwFcgy5qPtAKDHNGldnA1sftPQxiDMHwM76KzvNzfWCbIZIPF0/7f78H/LE9Q0CKVzyL149aNXY0gny7Th0kGTWh6JssoqPLj0gPE5diMoKqsQxnD6LcWNnmVlDPuXAGv1XIcnLJA6vRimCbj2uFldDy/bItKidZJ+GEhPEMujTlE4XS2dKD040vzRE4IFigVZeyTd4LaBUPnOMkLlzKVCcSBWOnbuGdYBt15lgdB2yn6d0yTZJnv6m/89HB2qRaHaHoJSPpmG3h736BWkfSlPpNYiG09kYv2xTPU2DRD756mRYv+bOqujISht1Ked1DqZklOMx3+MM1u06dVVR5F0Sarv6tEmAE+Os2A7LtUJUFGswsj5XMDKmIeOI1DiJv2PjHE+iG1HpU60WtFzjv2hTHIMH9s1VJwwWQIWKBYsUKUWUMXLYf7EGAT56kK/fyaYX6jkFkszduiaGNUlGPOvbeKMHWO6d2I5vWMRAtoAwx6X1tWVUmGkXvh1TEwI2gdKAoaK26gwVIss2nRaXVMKhNpvQ/2bxzqdim3J3E0pIqZJq59sSmzydv8+nIYf9pxXvR8+uK235VqKT64BfqOp1/LvfvC/gBHPWOa9GMfDxQ3lnaX5ad5OpSg6LEfGa1JZARz6SSzL4YqVlYPF+qFRlomeEE36j3rjjTdEMdhjjz2m3ldSUoK5c+ciMDAQvr6+mDp1KjIypC9qheTkZEyaNAne3t4ICQnBvHnzUFEhFd3YC/vPXVYHhY3sEoz7R3TC1qdHi7C2JYSK1LFzAElyx07nUF+R1yeXTbNDX5KKe6yTC9BFalVjLMCQh4EAuSOEhreRkZteseycYTpTJMUlWEvsO5uNfecuq/UmN/TW1c40F61beAkBodStvrvuJLadavxQwbTcYjzzqxTmJl66PtZyXitntgI/3aEWJYqUDhn6sTcJY0b8+t+qrrtmrRct+rW6CxdKkdD1lX2QAz8M6RQoDBE1J1D27t2Lzz77DD176grRiMcffxwrV67Ezz//jM2bNyM1NRU33aQr4qqsrBTipKysDDt27MCSJUuwePFivPDCC7An1hzRibLxsWHimhw/Kaxdn1B56Lv9OJ5u+pnwf/48hq3yQZfOFv93x1Vmsxu+gvRDQM45ad1+mDQdk7EMbl7AuFd0t6ntmFw/Zab2i1BnKZEnSqYGBuXp8+lmXfSEajRIVFmDq6OD8fjYzqq+fmTZQSE0GlPs+8SP8WqUcmL3MEwzw9TWWrmwH/jhNqBC/p12uwmY/D6LE8b8RA5BnqtUpzjCKQ57jp2ptzj2t8qrxTUN67QkjRIoBQUFmDFjBr744gu0bKlTT7m5ufjyyy/x3//+F6NHj0a/fv3w9ddfCyGya9cu8Zy1a9fi6NGjWLp0KXr37o2JEyfi1VdfxcKFC4VosQeoCnrt0XR15o0y4E3hSqGic19dnZCOa97fapJQoRH0i3cYduy0k0P/FkEpjiU4vWN5ut0ItBsirbNPA3s+Ux/ydndV22epWJP+FrQCda0ptSfk2zLFCtETff41KkpEMwnyjqGoZVmFaUMFP9+ShJ1JWerPtOCmHpZpKc44Aiy9CSiTpo4jegJw0+eAs32MNWA0hrMLcjpI3TweThXI2r/c8PHiy8Dx1WKZVe2HTVW9hS0+RVAsuluNeRGlcCgKMnasoTHX/v37UV5ebnB/TEwM2rVrh507d4rbdN2jRw+EhobqitEnTEBeXh6OHNF1nuhTWloqHte/aJljafm4cFk6O6N5NwHetUcydEJlVKOFCoWqqYVS4bUbe2CA3N1hMdThgE5AjJS7ZCwIfQFOpNZS+Ytw81uSWZLM7MHthRAmlu461yzOqaZGTygVZVHbdyOg6M1703qjTQvJnfZAcg4W/HXM6NdTm/K7Bi3FvcWEabOTdRr45gbVShztrwamLZHM1RjGQoQMnq6uI1JWG7YbH1kuzWwCsKJyiKhBmTuyk2X9fhojUJYtW4YDBw5gwQK98fAy6enpcHd3R4sWhgOHSIzQY8pz9MWJ8rjyWG3QewUEBKiXtm0t6NJoBpToCTE+1vBnbUio/HtybK1C5cGl+68ogky6WCAEjDImm0LoFgs3K1w8AVySDtJoNwjwa/jnY8xAeC+g7yxpXZoH/POq+hD5hyj27lT39NvBC7A21DGzIi5VrCkFZbbZT02EPEoWzewLd7l7iLyCVsZL+1kfhaUVeHRZnNod9+CITuYZtlmT3AvAN1PUXD/a9AOm/yCl+hjGgnh2GISLLtLx/KrKeJxJlorAa1rb/1I5XNQ4UveOpTFJoJw/fx6PPvoovvvuO3h6Nk8VPjF//nyRPlIutB9aZq1e/clYIwSKvlChM83ahMpfh9Mx8QOdUMktKsc9S/Yhr6RC7eh45hozzthpMHpC1VQcPWlWRv9bmhhNHPgGSItXH6pZLGtt4zZqe1a+zGcPjhTurlqhZ0QLvHBdrHr72V8PCS+Z+nhl5VHRwk/0igjA4+OkehazQlExEie58vEtJBaY8Ys0/oBhLI2TE1LbTBRLV6cqpO78Ubr/UiJwYY9YHq9qiyPV7fHQyKhmqSczSaBQCiczMxN9+/aFq6uruFAh7IcffijWFAmhOpKcHEPHRuriCQuTCkXpumZXj3JbeU5NPDw84O/vb3DRKuezi3BUjnTQgczUYWe1CZVgvyuFyjUfbFE9GLqE+uEDS3Xs1OseywKlWfENAUY8Ld+olgpm5TBs9zYBGNRRSu1RJ9emkzrfEWvY2S/bq5vqO1s2S9MSMwa2w419pJqYwrJKIfwpSlIbqxPSxPRoxTKA2qTN7t9COf5vbwSy5BboVh2BWb9zATrTrLQYcJu6bnVm1RXeJ79WXo12rXzEpPHmwKT/sjFjxiAhIQFxcXHqpX///qJgVlm7ublhw4YN6mtOnDgh2ooHD5Z6pumatkFCR2HdunVCdMTG6s5qbJV1R/W6d7rVLrgaI1ReqCFUaFAcEUgdO7P7N8+sn+wzUgcP0boP0EIbYXuHYsD9QCvZd+DcduDo7+pD9wzTGbf9z4rGbWS9TzNwCEo5BupFArUC5c5fu7G7CFUTpzILhNNtTZvv1JxiEWFReOn6bua39C4tAL6bBmTIrcv+bYA7/uD0KdPstIsdiHNOUro4piQexVnngUNSJKWy2gm/Vw7FAyM6mW/gbAOY9C5+fn7o3r27wcXHx0d4ntCa6kPmzJmDJ554Ahs3bhQRl7vuukuIkkGDBoltjB8/XgiRWbNmIT4+HmvWrMHzzz8vCm8pUmLrmFp/Ygyebi64uxahQnn0T2f1Q9tWzTSkT797h2fvWAdXd2DC67rba18AyqWC7NExIeggf3nuOJ2FI6m5zb57VKC7ZKfUUUYRPXOPXzcn1AG1aGY/Vdz/EZcqiowVqLaLnGeVNOqkHuG4pV+EeXeivARYdrsaQod3kCROWPwzVsDJ2RmJwePE2tmpGgW/PaamHLdU9YSTXxim9mu+bjyzy6D33nsPkydPFgZtw4cPF2mb3377TX3cxcUFq1atEtckXGbOnIk77rgDr7yi5/Vgo1Dr4p4z0lA3+qKICjGveZO+UPl8Vj8xx+Sq9s0YAubhgNqA5h51GiOtc5OBfV+JJeWE6e/DmsZtP+07L/4PCAoDN5t4biSdgn3x1s06L6dXVh3FweTLahfSbvn/uXWAJ16/0cwtxZXlwC93A2c2S7c9A4A7fgeCos33HgxjIu69b1HXwSnr1fWvlcNFI4aHa/O1ujtVNzi6UHtQmzFFa6hgVkv1KD/vO495v0jh4PuHd7Sczbw1yE0B3ovVFe89JLWNM1aCfDIWDdG1od65Sh1gN+SNf8RQPvLE2fbM6GazlS+vrMLItzeJDh5i9SNXI7a1dv4/G5qrowg6EiOv3dQD9y7ZJwp9SZMsu3eQeQdu0rDN5fcDCZJ1ONx8JHFCQyIZxorklZQj9fU+iHHWNaPkVXtjrPMX2PjsNU0ueDfl+5tn8ZiRtWasP9Ecx//UrTl6Yn1CuwEt5WjJ+d1AWaGatqACUMW47Rs53dIc0CBMRZyM6BxsM+KEeHZiDPpHSqaTqbkluOvrvWoX0tyRUeYVJ3ROuPpJnTihaeDTv2dxwmgCf083HPSXI7QyqyoHYcbQLs3ejccCxYy5963yyHtqD+7T1tALxubRT++we6w26DhSuq4sA5J1Ea07BrcX0RPiu93JIqpiaSgQq2/MRoV0tgR15Xx8e1+D8RNE77Yt8OjYaPOKk/Uvqmk5McvqlsW63yXDaICK2BsMbv/pPBKzh0Q2+36wQDETW05dREm5ZJs9LjbUajNHLELhJaljhKAOEkrxMNan4wjdOmmTuqSUjmLcRqmeXw+kWHxXNp28iOPysEv6Uldanm0JMrz78LY+UP51fURLcW/zthRvfRfY/oF8wwm48VMgZpL5ts8wZqBPr77YWSkd549WRaL7wHGWcU1uABYoFjBnG9/NztoDj68Cqqt03ic8rEwbtB+us7/XEyg1jdu+agbjtk83GUZPLG2BbSmGRAXh/dv64OroIHw2qz8iA83YUrz7MwMHYEz+L9Bzmvm2zzBmIjbcHy96PIV55ffhvqpnMMdK3XgsUMxARWUVNhzPUM+6LD1Aqdnh4YDaxCcQCJc7UNITpEiXTLfWAerfITmg/nPccsZtB5Ivq90uHYN9zNZeby2u79Ua384ZiGHRQebb6LmdwF+KyR6FWV8B+t9tvu0zjBmhDMCzNw/DmYgb8cwtoxDSTIX2V+yHVd7Vzth79rIIpRMjY0KatQ3L4hTnAElyG2RAW6B1X2vvEaOPfu2C0q4qc8/VuijK/7YlNUv0hLrX7Cq9aS52fKhbD3sCGPqoNfeGYRpkdEwofnlwiJoutgYsUDRqzqYZTv4NVEnii9M7GhcoNdI8IzuHiIgGsSspG4dTzG/cRjNs1h2Tooeh/h64QbaPZ/TITgJO/CWt/VoDo/7P2nvEMDYBCxQzdC8o9SfUOTEqJgR2BQ8H1DbtBgMuHrUKFIpk1BwiaG4+33JaGQck3qvZoocVZVJaK+EX6VrL7PlCmp1EDLgHcHGz9h4xjE2gnRGjNsqR1DzV+2FwpyDRQ2430IyQ0/JcJZ8QoO1Aa+8RUxM3L6DdQODMFiAnWZqX1EonSm7qE4F31pzA5aJyrIxPFROvqVvFHKTlFmP5QalDyM/TFdMHWMiePT8DyDgsX44A6YeBSyeAqgqdj8i9/wBhPaA5SvOBg0ultasn0PdOa+8Rw9gMLFDMac5mb+mdxHVAhTSUEF0nA852VFtjb2keEihKFEVPoNDAyZmDIvHRP4nCeIzm5JBIMQfUHURmcMQdgyPh11RxTlEREh4kQPQFSaHkL1Qn5AOz9b/ALV9Dc8T9AJRK083R4xapsJlhGKNggdJE1h7R1Z+Q/4n9pne4e0fTAmXDKzqB0v8ug4dnDYoUJmokJr7bdQ7/GhXVZEfI3KJyfL87WazdXZ1x5xCdKDJLVKQ+nF2BoC6Smy5F+IqypKnOWc8DgRoyiCM7+92f6m4PetCae8MwNgcLlCaQnFVkYE7VXDNPmgWasnpqrbT2agm0H2btPWLqIry3NGiuJFfq5KEvRmddeRm1CF7fqw1+PXBBTOala3KbbQrf7jqLwrJKsaYJv8qE7TohARL/g/FREQWa7hvWHQiVL7QO6gy4euiMz0ickU/Pjo+A696HZiDxlH1aNy+JBBXDMEbDAsVc3Tv2Zs6WtBEoK5DWXa7lwj4tQ6k3+gIkQ73iy0D6IaB1b4OnUAErCRMlNTNzYGSj24FLyivx9XZpxg9tgiacNliHsXgSUJJjXFRECJJuQGgPwDek/s6x/nOAre8BZflA3HfAyGcBP43Mwdq1SLfm6AnDmAwLFLPVn2jkoGguOL1je2keEihKmqeGQKHBfUOjArE9MQtns4qw4Xhmo1OSP++/gKzCMrG+tkd4w26rp9YZipOGoiKm4NUCuOpuyT6ealFIFIx7GVbn4kldgXmLSKDzNdbeI4axObjNuJFkFZRi31mde2ZUiC/shspy4MRqae3uB3QaZe09Yhqio97vqEa7scI9w3SRjv9tTWq0a/IXW5JMGwqo70Q8/UdgXiJwxx/AhNeA3tOl7pvGiBOFQQ/pWq1pCB+luqzNns906wH3cYE5wzQCFiiNZMOxTCjjTewuekIdIcoZb+cJTfvyYJoHKg71j5DWNNmYaohqMKJzMDrJxm1kTZ9wwfQv8tWH05GcXSTWNK+me5sA02qZosaa3+yPUjq9b5fW1DGz90tY3X2ZuncINx+gz0zr7g/D2CgsUMxQfzLB3upPjrE5m81BX/qKqyy1hp/ffcVTJOM2XRTlSxPt78mUsOZQwAahaI5ayzQJcLFQVnnIw4CTfDjb9QlQLnkTWYWD3wLlhdKaIkSUhmIYxmRYoDSCwtIKbDklDWYL8fNArwg7OgBVVQLH/5TWrl5A9Dhr7xFjBtt7hZv6tkFLb6ngedWhNGG2ZixbT13C0TTJ06NnhG4YodHpHfLSsWQEKfYGaU0dQlQwa63/nz2f624PfMA6+8EwdgALlEaw9dRFlFVUiTUVGtrVcLTkXboW0KgxgLsZx80zlqXjiDoHByp4urkIXxRCGLftOGf05slLRT964tRQqqayQlfLRKkO/ToZSzDsMd16+4fS+zc3NHOHHH0JSmcFRTf/PjCMncACpREos3eI8d3C7De9EzvFmnvCmAq15IbIXhupB6WW41qYOTgS7i7Sv/73u8+JiGBDxJ/PwY7TWWLdIcgHE4z5u0/eARRLheToPB5ws7BPUHgvoNMYaZ1zTjJva270jdk4esIwTYIFiomUV1aJFk3Cz8MVgzvakXU1GXwpIXlnNyB6vLX3iGlsFIWMy85uq/UpIX6emNJbGqFOxm2/7Jf8UYyNntx7dUe4GBM1NEjvNFMt07DHdett71HhDJoNMqM7u1VaB0bpxBLDMI2CBYqJ7D2TjdzicrEeGRMibL7tBjrrzkvR1TNwcZ9d1qEQc67WWdN/tf0MKpWWtFpIuliAv+WRDuQYS3UsxondVbphfs0ldsnxuE1/aU2utYnrYbXoiZ6bL8MwpsP/QSZi18MBj/2hW8eyOZtNEjlEcmVtQKDEhPmLNmHiXFYR1h/T/V3X5IutSWog4u6hHUQdS4OkHgDyU6V1p9GAhx+aBaqLqRlFaQ4Ks4CEn6W1hz/Q67bmeV+GsWNYoJjYZqkMB6Qc/sguwbAbyLvh8G/S2slFagllbA8SAhFXSeusRCDnfJ1PJft7hS+3nqn1OZl5Jfh1f4qa0pwxqJ32W9VpNAM50xLntgPJV7Zcm539X+smf/eZ1XyCjGHsGBYoJnA4JQ+pudJBaEhUYNPHy2sFKqb8ZgqQK3+ZdRjOY+HtJc1TRzePYtwWLTsg7zmbLQpha/Ll9jMoq5Q61m4f1A7+xvzNU7hFqT8hb5LOE9GsUGpl6GPNF0Uh52XVHM4JGHCvZd+PYRwEFiiNHQ5oL+6xRdmSOEmL081JmfimtfeKaYY6FGoTNoiibDOMouSVlOP7XclqxHDOUN1z6yXzKJAtm8BFDrWO2O1xC+Av18qc/AvIOGq596JokZLO6jIRaGXk58QwTL2wQGlEezGlucfGhsA+xMn1QFq8dNsnGLhzFRDcxdp7xjSFNv0Ad1+dQKmnk+WGPm0Q6OMu1n8mpCE1R2fc9t2uZOTLLchT+7VBiL+RbcJKcaw1B026ugOD/6W7TcMELcUubi1mGEvAAsVIzl4qxImMfLHu07aFaNW0aaiob8n1QHqCdNsnBLjzTyCkq7X3jGkqLm5SNwtBpnsU0agDKnidKRu3USfPkh1nxbqkvFKNqJAgp9Zio9FvL46xYi1Tv9nS/B+CClgVAzVzkrIfuLBHWofESulRhmHMAgsUI1l31I7M2QovAUuuAzJkceIbKokTjpzYaZqn7joUggSK0i7//Z5kFJRW4LcDKbhUUCrum9g9DB2DjZzWTakd5e+K2n0DjGhJthTkgqxENKorgR0fm/89dutNLab3MvcgRIZxYFigNKr+xIbbiwsuSuIk84h02zdMFidy1wNjH3QYYVQdiuJtcoNs3JZfUoFle5Lx+RYThwLWmt7RwKDJAfcBbt7S+sA3kjg3F/npus43itRQ3QvDMGaDBYoR0JnkvnOSbXhUiK/xZ5NK7vvHmcB5OQysCXEih/z9wiVxwvNC7A9K1VHajiBHWeo0qQf9KcdvrzmBs1lFYk0DAXuaMgzTGu6x9eHdCuh3p7SuKDaMeDSVfV8DVfLnSu/hLgshhmHMAgsUI9hwLEOtMzQpenJhP7DuBemg/eV44K9ngVJ59HxzU5AJLJkMXDwm3aYOByFOoqyzP4xloVSDkuYpLwQu7Kv36V3C/FTjtlJ5EKbJ0ZO8NL16jG7ShGEtMHiuzryOJg2XSrVkTaKiFNj3pc436Kp7mr5NhmEMYIFiBGv0hgMaNSRNwWDkezWwexGwaDBweiOaFQpFL54EXDwu3faPkLp1tPIFwli13VjhnhqFsN1a69xmjeK4fnpnMjRDQATQ81ZpXZID7F/S9G1SakeZ+k2RInoPhmHMCguUBqCCwW2JUt46zN8TPdoEGH+GdfhXae3iAbjKXT/USfDtDcAfcyX31mYRJ5OBSyel2wFtJXHSyoSuDMa2BwcaKVCGRwepxm1K9IS8Umw2vaPP0Ed1650fS/+fjYXCqfpzdwY92LR9YxjG/gRKUVnDY+KbypaTF1Emh7zHxYbC2ZgprsSJv6SzNSJ2CvDgDiBSbv0kDi4FFg40LCo0NxRyp8hJ1inpdkA7WZywkZRDQGf1gXJ90YW9QElevU8nMfKfG7oj1N8Dk3qGi+4dkzx1lOnJLdsDod2hKahDLUaO6uSnAYd+bPy2zu/WGRuG9wbaDjTPPjIMYz8C5e7F+5BdWGbR91Bm7xDju5lQfxL/g27de7qUTpm9Epj8HuAuz+koSAd+nAH8NFuqETEneamyOEmUbreQxQl9eTCOl+ahNttzOxp8+sCOgdj9f2Ox8Pa+cHUx4fBw8m/pPZToiRbbbfXt76l4vUreX1PZtUi35tZihrEYNi1QDqfk4uZFO3A+W+o4MDfllVXYcFwSDn6erhjYwUjLbhIbp9ZJa7/WupZPmhHS/25g7m4geoLu+Ud/BxYOAOJ/rNf102hyLwBfXwtky62iLSKlgtiWkiEX40CYWIfSaAzSOxqdhN32KqD91dKahLt+zYwp/1vKz0pdUt1vMu8+MgxjHwKFSLpUiJsW7cCR1Fyzb3t3UrbwhSBGx4SoZlYNQq6VytkkjV13rjGensyrbv8RuOl/gFcr3cC+5fcB391S7wTaBqHXUuTksjxXhSImJE4ogsI4HuQoSwP7LClQqDMtcYPOV4cM2rTKsBpDBE09Idj7P93/Np1suHqYd/8YhrEPgdIhSPIduJhfils/24UdcjGr1YcDxumnd26v/TkUFu55CzB3D9B9qu7+xHXAJ4OkA2GVrt3TKKgAV4gTya4cLTsAd64GWrQ1bTuM/eDVAmjdV1pTi3m+riPNbNDfbGWprnuHIoVapdMYIKyntE49WO+05ysoKwL2L5bWzm6SQGEYxmJo+EjSMN/cPRB927VQu21mf70HK+PlqaJNpLq6Wh0OSJGTEV2CjXshzbbRt/puyATNNxi4+Svgth8k4zSirAD480nJtyRL5+hZL5fPSeIk55x0u1Un4K7V1rUaZ7TXzWPKF3KjZu9oqL24rhODYY8bRlGMhSKjFOkkKLXjZ8OO0gxjA9i0QGnp447v7hmEsV0lx8zyymo8/MNBfFVjbHxjSEjJRXpeiVgPiwqCr4ds9GRS9GS68W8Ycy3w0C6g72zdfee2A4uGANveByrr6ViiiAm1EivD0AKjpLSOv2Rfzjg4lqxDKS8BTq6R1p4tdEMKtQx11VF0Ufk8Ug6Y3lrMU4sZxuLYtEAhvNxd8OnMfrjtKl0a45VVR7Hgr2Ooqmp8weka/e4dY91jyU484Sdp7eIOdLvJ9HD89R8Cd6yQCluJihJg/YvA/8boJg/rk31GEie5ijiJlsWJHI1hmIgBgKuX7gvZHIXY+hEZivgRXa6VJilrHaoJ0/dF2f5+w685s0U3IoLaitvIaTOGYbQhUBYtWoSePXvC399fXAYPHoy//vpLfXzkyJHCS0H/8sADhmcaycnJmDRpEry9vRESEoJ58+ahoqJpfibUDrngph54ZIwunfLZ5iQ89XO86MRpDEp6hyLCY7oaKVCoUFBxl+wyUZoD0tiQ/EM7gUFzaQ+k+8h34fORwD//0ZlM0eRYIU7kotqgzpI48bPxacuMeXHzBCIHS+u8FF3ruTk4tkK75mz10Wu6NMWbOLoCuCR7BRk1tfh+y+4bwzCmC5SIiAi88cYb2L9/P/bt24fRo0djypQpOHJEnowL4N5770VaWpp6eeutt9THKisrhTgpKyvDjh07sGTJEixevBgvvPACmgqJoSfGdRZGU4otwW8HUzBnyT4UlpomgJIuFuBUpnRW2K9dSzHt1Sjiv9ete9VRHGvKqPhrXgfmrAOCY6T7qiqALW8Dn14tWW1/PQnIuyA9Rs8R4oTz4kwzpXko7UiGhISbD9BpFGxKtA16SL5RLfmi1AVFKU+s1tkGaLWNmmEcWaBcd911uPbaaxEdHY3OnTvjtddeg6+vL3bt2qU+hyIjYWFh6oUiLQpr167F0aNHsXTpUvTu3RsTJ07Eq6++ioULFwrRYg5mDorEohl91ZZgcoKd/sUuMZHYWNYdzTDdnI2cNJWDtU8wEDUGZvNuuH8LMPxp3cCzSyeAX+4C8uWC4OCuwOxVgK88vZZhmkOgJO8EirKkdfQ4wE1OI9kK1IXjIY+uiF8mmRvWxp4vJBFDDLjHNtJYDOPINSgUDVm2bBkKCwtFqkfhu+++Q1BQELp374758+ejqEhnorZz50706NEDoaG6L/0JEyYgLy/PIArTVK7pHo6lcwbC31P6Qj90QTJ0S5ZHyDfEWj2BMs7Y9uIjvwGVssjqMc28BzHyWhj9HHDfZslaWx+aGksOsdQNxDB1EdpD57lzZmv9Rdf2MHvHGDz9gavmSOuqcmDnwto9Xg5+K61pnlbfO5t3HxnGgTFZoCQkJIioiYeHh6gvWb58OWJjY8Vjt99+u4iObNy4UYiTb7/9FjNnzlRfm56ebiBOCOU2PVYXpaWlQsToX9QajDoY0KEVfn5giBjwR5zNKsJNi7YL99n6yMwvwYFkqZWwc6gvOgT5wKLdO6YQ1h24ZwMw7hXAOwjoMByYvQLwMWHiLOOYkDeJ0m5cmqubJdNYyKNHEShUEB49HjYJDfqjYZ4EeZxQJLTmyIpS+XjT4xbAx0g3aYZhml+gdOnSBXFxcdi9ezcefPBBzJ49W6RtiPvuu09ERChKMmPGDHzzzTdCwJw+baSXRx0sWLAAAQEB6qVtW7ljZ9sH9e9rmB9+e2iIOqH1UkEZbv1sJ7aekgtZa2HDsUy1ycFoczYqsEvZpztTDesBi+HiKnUgzEuUZvuwOGEalebZ2LRtkcmZkmKk7VI0whahtGgf+SSKupH2fmkowri1mGFsR6C4u7sjKioK/fr1E8KhV69e+OCD2oXCwIHSlM/ERKlrgGpSMjIMnSyV2/RYXVA0Jjc3V72cPy93rRxZDlw8We/+tm7hhZ8fGIyr2rcUtwvLKnHX13vx+8GUBocDTuhmpECJ+97y0ZOa8IAyxlSUmVBE0mbH7N6pjSEP68YB7F4kOcYSp//RdTzRDB+KYDIMYzs+KFVVVSIFUxsUaSHCwyVPDqpVoRRRZqZucu+6detEIa2SJqoNSicprc3KRX53YNOCBvexhbc7vp0zUPUzqaiqxmM/xuGLLYYpInKj3Z4oFf2FB3iiexsjzgppIqoyut3JRQoDM4wWadVB569zfrfui9hUKMSoCBT6Yif/E1v/XBTPIir6PbhUJ1YUOHrCMNoWKBTJ2LJlC86ePSuEBt3etGmTSOdQGoc6cqgFmR5fsWIF7rjjDgwfPlx4pxDjx48XQmTWrFmIj4/HmjVr8Pzzz2Pu3LlChDQKiqJkNFxg6+nmgkUz++H2gbqhea+tPob/rDqqGrptOpGJMtk3hcQMtS4bZeBE3hJKJwN30jC2kOahgm7qwmkMmcd09V+RQ+0jzag/RHDHR0DGUSBxvXSbBm2SrxHDMNoVKBT5INFBdShjxozB3r17hcgYN26cSP2sX79eiJCYmBg8+eSTmDp1Klau1FX6u7i4YNWqVeKaoilUQEvbe+WVV5rwI1QbFUUR7+/shNdu6I7Hx3ZW7/vftjMimlJWUaWasxHjjU3vUBGdvvkTw9h7u/HxVfaT3lGgurGocdKaXJl/nKF7bMB9V04kZxjG4hg5YEbiyy/1CshqQIWrmzc3nNeOjIzE6tWy6VFT8QkBKi5K3QRp8UB4rwZfQlGRR8dGI8TfA88tTwAFT1bEpyK7sAzx53PEc6g9mbqAGqQ0X9fJQHNI+CyLsak6lE1Nrz+JmQS74eonpMnMhBIhIgO6PrOsulsM46g423xxm8LG10166fQB7fDZrP7wkA3dtiVeQr7sOEvW9m4uRnw0R/8AyuU8fvepkl8Jw2gZapMNk1KuSD8EFMpGa8ZCrqrKTKg2/YCACNgN7QZLc3b0oaJ3mpHFMEyzY9sChVIq/m2k9cm/gQtyq6+RjIsNxff3DkSAl6GpmtHDAQ28T5pobc8w1kjz0LC/xqZ3YibDrqCas2GPG943gOfuMIy1sG2BQvM0hj+lu73xNZM30S+yFX59cDBaB0iGbiRWhnc2wpX18lng3DbdBGE6m2QYe69DMXCPtcOZNNETgLaDpHX3m4FgXb0awzAarkHRJL1nAtveA3KSJd+Cczt1k1uNJCrED3/8axiWH7yAwR2D4ONhxMcS/6PePkxnXxLGtlIZ5P5KnTymCJT8dKk9WZn/FBQFu3TcnfmrlMbikw6GsSq2HUEhXN2BEc80KYpC0MTi+4Z3Qo8IeXhYQz4QaveOE9Dztka9J8NYBXdvXa1FzjmprsRRu3dqw8NXOsmhYwvDMFbD9gUKQQKhVSdpfXZr010yGyJ5F3BZPqjTfJMAuQ6GYWwFZS6PKXUotj4ckGEYm8I+BArNpxn5rGEURRmoYwni9azte3FxLGODdBylWxuT5qEhemflmityo7XkvCmGYRi7EShKm29wjLSmPHniBsu8T3kxcOR3ae3uC3S1s04GxjEI7w14yOlMijjSYLz6OLkGqKrQRU+45ophGAtjPwKFnB6bI4py/E/d+PXYGwB3H/O/B8M0R9Sxw9XSujgbyJC9TRy1e4dhGM1hPwKF6DoFCJUnjqYekLxRzI01JhczjDXbjUsLgNNyRNI3FIi4yvL7xjCMw+Nsdy2Co/7PMIrSUOjaFPLSgKSNugFi7YaYb9sMo1WBQkPzKkp01vb0f8YwDGNh7O9IQ6PfKb9OkJfBcb3QdFM59CNQXaVzseUDNWPLBEbpnJjJP6hcFiE14e4dhmGsgP19w1Lx3qjndLc3LgCqKpu+XQPvExIo7H3C2MH/ihJFqSgGLuy58jkVpVKBLOEZALSX61YYhmEsjP0JFCJ6nC5PfvEYcGR507eZehC4eFznxNmqY9O3yTBan25MHT5l+bropIvh3CqGYRhLYZ8CpWYUZdMCoFJukWwsBtETLo5l7NCwrTaBop8i5fQOwzDNiH0KFIJC15FDpXVWIpDwU+O3VVEGJPwirV09gW43mGcfGcba+IVJc3WUKGFxju4xSo1SWz3h5g10Gm2dfWQYxiGxX4FSM4qy+U2gsrxx2zq1RvKKUEbMUy6eYewFpQ6FCsAVt1gieSdQlCWto8YCbl7W2T+GYRwS+xUoRPuhuoPv5bNA3HeN206cXnqHvU8YR2k3ZnM2hmGsiH0LFGLU87r1lnekrgRTKLwkRVAIv3DDGSYMYy9C3snFUKBQ15oiUJzdgM7jrbd/DMM4JPYvUNpeBUTLB9fc88CBb0x7PdWeKDNIek6TLPUZxp7w8NN1vWWdAnIvSE7MeSm6CAunNRmGaWbsX6AQ+u6yW9+VBv4ZC08uZhwuzbOZzdkYhrE6jiFQWveRiluJ/DRg39fGvS7jKJAWr9tGiDwtmWHsWqBs1AkUJ2fJ/4RhGKaZcQyBQoycr1tv+y9QVtjwazh6wjgKEf0Bd19pfWyV1JpP0Lwp32Cr7hrDMI6J4wiUsO5ArOxfUngR2PNF/c8nY7dDP+mKBHvcbPl9ZBhrQQ6xkUN0tvcKnN5hGMZKOI5AUaMoTtJ6+wdAqWzhXRsU5i7IkNadJwDerZpnHxlGC2keBZpezDAMYwUcS6BQDUmPW6Q1Ga/t+rTu58bppXd6c3qHcUCBQnVXLdpaa28YhnFwHEugECOf1Xk+7PzI0Npbge5TLL69A4Gocc27jwxjDUJiAR+9ehNO7zAMY0UcT6AEdtIN+yvJBXYuvPI5NP24UjZ0o4iLq3vz7iPDWGs8hCrGndg9lmEYq+J4AoUYMQ9wdpXWuxYBRfKcHQWeXMw4KqOfB/rMBKZ8DARFW3tvGIZxYBxToLRsD/SZJa3L8oEdH+oeyzoNnN+tC3mH97LOPjKMNQhoA0xZKIkUhmEYK+KYAoUY/hTgIqdudn8GFFysPXpCYW+GYRiGYZoVxxUoARFAv7ukdXkRsP19oKoKiF+mc9Ck2TsMwzAMwzQ7jitQiKufAFw9pfXe/wGHf5UGChKdxgB+YVbdPYZhGIZxVBxboJAAueoeaV1RAvwxV/dYby6OZRiGYRhr4dgChRj6GODmI62V1mKPAKALO2gyDMMwjLVggUKD0AbeZ3hf9xsBNzn1wzAMwzBMs8MChRjyCODup7vNk4sZhmEYxqqwQCFoEOA1C6TOnZjJQNsB1t4jhmEYhnFoZDtVBn1nSb4nLvyRMAzDMIy14QiKPixOGIZhGEYTsEBhGIZhGEZzsEBhGIZhGEZzsEBhGIZhGMa2BcqiRYvQs2dP+Pv7i8vgwYPx119/qY+XlJRg7ty5CAwMhK+vL6ZOnYqMjAyDbSQnJ2PSpEnw9vZGSEgI5s2bh4qKCvP9RAzDMAzDOJZAiYiIwBtvvIH9+/dj3759GD16NKZMmYIjR46Ixx9//HGsXLkSP//8MzZv3ozU1FTcdNNN6usrKyuFOCkrK8OOHTuwZMkSLF68GC+88IL5fzKGYRiGYWwWp+rq6uqmbKBVq1Z4++23cfPNNyM4OBjff/+9WBPHjx9H165dsXPnTgwaNEhEWyZPniyES2hoqHjOp59+imeeeQYXL16Eu7u7Ue+Zl5eHgIAA5ObmikgOwzAMwzDax5Tv70bXoFA0ZNmyZSgsLBSpHoqqlJeXY+zYsepzYmJi0K5dOyFQCLru0aOHKk6ICRMmiB1WojC1UVpaKp6jf2EYhmEYxn4xWaAkJCSI+hIPDw888MADWL58OWJjY5Geni4iIC1atDB4PokReoyga31xojyuPFYXCxYsEIpLubRt29bU3WYYhmEYxp4FSpcuXRAXF4fdu3fjwQcfxOzZs3H06FFYkvnz54twkHI5f/68Rd+PYRiGYRjrYrJ1KkVJoqKixLpfv37Yu3cvPvjgA9x6662i+DUnJ8cgikJdPGFhYWJN13v27DHYntLlozynNihaQxeGYRiGYRyDJvugVFVViRoREitubm7YsGGD+tiJEydEWzHVqBB0TSmizMxM9Tnr1q0ThTKUJmIYhmEYhjE5gkKplokTJ4rC1/z8fNGxs2nTJqxZs0bUhsyZMwdPPPGE6Owh0fHwww8LUUIdPMT48eOFEJk1axbeeustUXfy/PPPC+8UjpAwDMMwDNMogUKRjzvuuANpaWlCkJBpG4mTcePGicffe+89ODs7C4M2iqpQh84nn3yivt7FxQWrVq0StSskXHx8fEQNyyuvvGLKbjAMwzAMY+c02QfFGlChLNW5ULEs+6AwDMMwjG1ANiHUiUv1qhToMGuRrBbIysoS19xuzDAMwzC2B5WJ2KVAoRoXggpwG/oBTeWqq64SnUla36altsv7alvbdfR9Vc7GzB1NtaXP1VLb5X3lfbXEdilpQ001rVu3bvC5NilQqM6FIHFi7hQP1cnYwjYttV3eV9vaLu+rhDLA1BE/V0ttl/eV99VS2yW7EuV73KJtxvYGdRTZwjYttV3eV9vaLu+rZbClz9VS2+V95X219mdgk0WyPCyQYRiCjwUMY7/YZASFPFNefPFF9k5hGAeHjwUMY7/YZASFYRiGYRj7xiYjKAxTF05OTvj999+tvRsMw1gZPhbYPixQNMrOnTtF9fSkSZPgyNx555244YYb4IhQ6+zdd98t2vGo6j0yMhKPPvqo6gPUEDSGgg7SZIjE2C58LJDgY8HdDncsYIGiUb788ksxy2jLli1ITU1t0rYqKyvFUEfGdkhKSkL//v1x6tQp/PDDD0hMTMSnn34qhnHSmIjs7Gxr7yLTTPCxwLFJcuBjAQsUDVJQUIAff/xRzCyis6bFixdfoYT//PNPMQvJ09NTDGM8fPiw+hx6Po0CWLFihRjOSAWEZGpn67Rv3x7vv/++wX29e/fGSy+9BHuD2vDoTGnt2rUYMWKEGNBJgzrXr1+PlJQUPPfcc+J5NPPqmWeeEWZl9HuOiooSX2hnz57FqFGjxHNatmwp/mboDJSxLfhYUDt8LJjoEMcCTQoURw7lET/99BNiYmLQpUsXzJw5E1999ZVw39Nn3rx5ePfdd4XDX3BwMK677jqUl5erjxcVFeHNN9/E//73Pxw5cgQhISFW+EmYxkBnRDSE86GHHoKXl5fBY2FhYZgxY4b40qK/CRreSWdVH374IY4dO4bPPvsMvr6+4iD166+/itecOHFCDPj84IMPYGvwsYCPBY5MtoMfC2zSSdbeIdVLByPimmuuER4PmzdvxsiRI9XnUGulMkV6yZIliIiIwPLlyzFt2jRxHx2gaJJ0r169rPRTMI2FQrl0wOnatWutj9P9ly9fFl9I9AW2bt06jB07VjzWsWPHK0ZC0BcSnUUztgcfCxybUw5+LNBkBEWfv//+G8OGDRMfamBgICZPnozTp0+rj1P4ikJWv/32mwhjeXt7i39EKiyzRUjh7tmzB9OnTxe3XV1dceutt4oDlT6Ue9T/46MzLFLNChQSpLAvY7s05ABAf/tUPElhX0eAjwV8LHBUqh30WKB5gVJYWIgnnngC+/btE0VB5N9/4403XlHoRXm4p556CnFxcejcubP4p66oqICtQQcf2m+q1qYDEl0WLVokQnR09mQsFA6kg7U9Qb/7mv+o+qFse4Fyx/S70/+S0Yfup1xyzZCvvcPHAj4WKPCxwDGOBZoXKFOnTsVNN90kflFUBEU52ISEBBw9etTgeXRAoiIyOiC9/PLLOHfunKh2tiXoYPTNN9+IfDIdXJVLfHy8OEhRflFh165d6ppCfCdPnqwzDGgvUH6d8qf6NudnzpyBvUHRAQrZU1i+uLjY4LH09HR899134ky6R48e4suZQv61QWfOSueGPcDHAj4WKPCxAA5xLHC2hRwcnQFRPo1mbVD1NlGzEl0/hBkeHi6uMzMzYUusWrVKHGDmzJmD7t27G1zo4Kwf2n3llVfEWSRV7FMhYVBQkN0XE44ePRrffvsttm7dKr6YZs+eLcKa9sjHH38sqvInTJgg2kvJB4FSHHSwatOmDV577TXxv0CfAfkjkCEVHaCps4Ny0QR5JdDZF/1dXbx4UXSE2DJ8LOBjgQIfC/52iGOB5gUKVaRTJfMXX3yB3bt3iwtRVlZm8Dw3Nzd1rYQzba3fnw46VOBEw89qQgclCm0fOnRI3H7jjTeEUU+/fv2Ekl65cqWqku0J+h1SaJuYP3++yLFS7QGdIdNBuFOnTrBHoqOjxe+bvoyp2JF+zvvuu0/UVlBNhVL0RiH/m2++WVT5U7fHvffeK1IhBB28KILw7LPPIjQ0FP/6179gy/CxQIKPBXwsuM9RjgXVGmT27NnVU6ZMqb506RIlGau3bNmiPrZ161Zx3/Lly8XtM2fOiNsHDx5Un3P58mVx38aNG6vtDfqZ6Gejn9ERmDBhQvXcuXOtvRuMleBjQd3wsYCxdzTdZkzFP5SD+/zzz0WolkK5pAAZ+4fC29u3bxdhygceeMDau8NYGT4WOC58LHBcXLUcyqNK7WXLluGRRx4RuVdqnyMTGn0PAMY+oVwq9fY/+eSTmDJlirV3h7ESfCxg+FjguDhRGAUagwyJqFKfioMYhnFc+FjAMI6Ls9ZCeVRlTKE8xQ2PYRjHg48FDMNoKsXDoTyGYQg+FjAMo8kUD8MwDMMwjo2mUjwMwzAMwzAECxSGYRiGYTSH1QQKWfaSMyTNlSC3R7Ln1ScjI0PYNtPjNJWUqvnJ6lofajGk1+pfavbJkwX0kCFD4Ofnh7CwMDzzzDM2OTiMYewVcxwLCHLVJAt0Hx8fYYU/fPhwg/kl5EI7Y8YM8RhNRCYbeVux/GYYR8RqAoUseGkU+sKFC694jMpiyLo4KSkJf/zxBw4ePChmCVA1v2Ldq0B2vjQ0Srm89dZb6mM0WOvaa68VBzTaxo8//ogVK1awwRPDaAhzHAtInND/+fjx47Fnzx5RYEt23uSfokDi5MiRI1i3bp3oECJhRJbhDMNolGoNoG9XTZw4cULcd/jwYfW+ysrK6uDg4OovvvhCvW/EiBHVjz76aJ3bnT9/fnX//v0N7luxYkW1p6dndV5entl/DoZhrHMsGDhwYPXzzz9f53aPHj0qtrN37171vr/++qvaycmpOiUlxSI/C8MwTUOTNSg0uZHw9PRU76MzIQ8PD2zbts3guTRumqZ3krskDZAqKioy2I7+NggvLy+UlJRg//79Fv85GIax/LGAJhXT4MCQkBCRzqVhaDRITv9YQREWSuv0799fvY+iMLQtZeggwzDaQpMChSYxtmvXTggOMmyiaaVvvvkmLly4INI4CrfffjuWLl2KjRs3iufS+O2ZM2eqj9N46h07duCHH35AZWUlUlJSxGhyQn87DMNoE2OOBZT+IV566SWR8qVR9H379sWYMWPUWhWa8ksCRh+y0KdJsPQYwzDaQ5MChcal//bbbzh58qQ4gFBhHImQiRMnGuSUKX9MIqRHjx4iv/zNN99g+fLlOH36tHic8tFvv/22KJylM67OnTuLmhRCfzsMw2gTY44FNK+HuP/++3HXXXehT58+eO+998S8nq+++srKPwHDMI1Fs9/S/fr1Q1xcHHJycsSZEp0VZWVloWPHjnW+ZuDAgeI6MTFRve+JJ54Q26Dpp5cuXVJdKevbDsMwtnMsoOnGRGxsrMHrunbtKv7vCergo1SQPtTNR5099BjDMNpDswJFISAgAMHBwSJUu2/fvnptr+kgpn/AUqDWRWpRpPoTSve0bdtWhIAZhrEd6joWtG/fXvx/nzhxwuD5FHWhjh9i8ODBQuDo1579888/IvqinNgwDKMtrDaLh/wH9CMdZ86cEQKDwriUc/7555/FwYjWCQkJePTRR0W7IaVtCErjfP/99yJlExgYiEOHDuHxxx8X3gc9e/ZUt0spHmo/pHAwhYrfeOMN/PTTT3BxcbHKz80wjHmPBXQCMm/ePLz44ouiXbl3795YsmQJjh8/jl9++UWNptBxgGpUPv30U5SXl4s25Ntuu02IG4ZhNEi1ldi4caNo+6t5mT17tnj8gw8+qI6IiKh2c3OrbteunWghLC0tVV+fnJxcPXz48OpWrVpVe3h4VEdFRVXPmzevOjc31+B9Ro0aVR0QECBai6kVcfXq1c3+szIMY7ljgcKCBQvE87y9vasHDx5cvXXrVoPHs7KyqqdPn17t6+tb7e/vX33XXXdV5+fnN9vPyTCMafCwQIZhGIZhNIfma1AYhmEYhnE8WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDGM3kKvs77//bu3dYBjGDLBAYRimydx5551CHNDk8JrMnTtXPEbPMRcvvfSSsLRnGMZ+YYHCMIxZoCGcy5YtQ3FxsXpfSUmJmJlFc3QYhmFMgQUKwzBmgSaEk0ihoZwKtCZx0qdPH/W+0tJSPPLIIwgJCYGnpyeGDRuGvXv3qo9v2rRJRFw2bNiA/v37w9vbG0OGDFGnFS9evBgvv/wy4uPjxfPoQvcpXLp0CTfeeKN4XXR0NFasWNFsnwHDMOaDBQrDMGbj7rvvxtdff63e/uqrr3DXXXcZPOfpp5/Gr7/+KiYOHzhwAFFRUZgwYQKys7MNnvfcc8/h3Xffxb59++Dq6iq2Tdx666148skn0a1bN6SlpYkL3adA4mXatGliwjlNO58xY8YV22YYRvuwQGEYxmzMnDkT27Ztw7lz58Rl+/bt4j6FwsJCLFq0CG+//TYmTpyI2NhYfPHFF/Dy8sKXX35psK3XXnsNI0aMEM959tlnsWPHDpEyouf6+voK0RIWFiYudJ8C1bpMnz5dCJ/XX38dBQUF2LNnT7N+DgzDNB1XM2yDYRhGEBwcjEmTJomUCw1Kp3VQUJD6+OnTp1FeXo6hQ4eq97m5uWHAgAE4duyYwbZ69uyprsPDw8V1ZmZmg/Us+q/z8fGBv7+/eB3DMLYFCxSGYcwKpWL+9a9/ifXChQsbvR0SLgpUZ0JUVVWZ9Drltca8jmEYbcEpHoZhzMo111yDsrIyESmh2hJ9OnXqBHd3d5H6UaDnUZEspXKMhbZRWVlp1v1mGEZbcASFYRiz4uLioqZraK0PpVwefPBBzJs3D61atRLpmrfeegtFRUWYM2eO0e/Rvn17nDlzBnFxcYiIiICfnx88PDzM/rMwDGM9WKAwDGN2qO6jLt544w2Rcpk1axby8/NFK/GaNWvQsmVLo7c/depU0cI8atQo5OTkiM4hcxrBMQxjfZyqqZKNYRiGYRhGQ3ANCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMw0Br/D25AhTA9anQAAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loaded_model = TiDEModel.load_from_checkpoint(\n", + " model_name=\"my_model\", best=False, map_location=\"cpu\"\n", + ")\n", + "\n", + "prediction = model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "val_passengers.plot(label=\"Ground truth\")\n", + "prediction.plot(label=\"Forecast\", title=\"Trained TiDE model\")" + ] + }, { "cell_type": "markdown", "id": "b9251561", @@ -97,14 +237,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ea8456ae", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce4f4174dac540ff94f2a9f23ddf2d60", + "model_id": "978df6b652114b3dac5ac6851d2ccb9f", "version_major": 2, "version_minor": 0 }, @@ -127,7 +267,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -137,8 +277,6 @@ } ], "source": [ - "from darts.utils.likelihood_models.torch import QuantileRegression\n", - "\n", "model = Chronos2Model(\n", " input_chunk_length=24,\n", " output_chunk_length=12,\n", @@ -178,7 +316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a7240f650574baea33dffef644c445b", + "model_id": "42721d10e66843c0baf65c54cb052183", "version_major": 2, "version_minor": 0 }, @@ -224,21 +362,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96c4a779951049e3ad618b32acfcddad", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" ] @@ -271,19 +395,13 @@ } ], "source": [ - "pred_full_finetuned = full_finetuned_model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - ")\n", "pred_full_finetuned_loaded = full_finetuned_loaded_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "pred_full_finetuned.plot(label=\"Forecast of the full finetuned model\", linestyle=\"-.\")\n", "pred_full_finetuned_loaded.plot(\n", " label=\"Forecast of the loaded full finetuned model\",\n", - " linestyle=\"--\",\n", " title=\"Full finetuning\",\n", ")" ] @@ -296,27 +414,6 @@ "We can also verify numericaly that the prediction of the trained model is identical to the prediction of the loaded model" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "599402d3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(pred_full_finetuned.values(), pred_full_finetuned_loaded.values())" - ] - }, { "cell_type": "markdown", "id": "3cabab8a", @@ -335,14 +432,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "id": "33fa7fc4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3ae6034d5c34f78a9db426821509270", + "model_id": "c45c382dfda14a5d8b852aa8a0dc6ba6", "version_major": 2, "version_minor": 0 }, @@ -359,10 +456,11 @@ " input_chunk_length=24,\n", " output_chunk_length=12,\n", " # likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", - " enable_finetuning={\"unfreeze\": [\"output_patch_embedding\"]},\n", + " # enable_finetuning={\"unfreeze\": [\"output_patch_embedding\"]},\n", + " enable_finetuning={\"freeze\": [\"*encoder*\"]},\n", " n_epochs=100,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", - " # random_state=42,\n", + " random_state=42,\n", ")\n", "partial_finetuned_model.fit(train_passengers, verbose=True)\n", "partial_finetuned_model.save(\"partial_finetuned.pt\")\n", @@ -373,28 +471,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "50830283", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2dce1e400f7e481c84f87eb5eca9377f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" ] }, - "execution_count": 18, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -427,23 +511,14 @@ } ], "source": [ - "pred_partial_finetuned = partial_finetuned_model.predict(\n", - " n=len(val_passengers),\n", - " series=train_passengers,\n", - " random_state=42,\n", - ")\n", "pred_partial_finetuned_loaded = partial_finetuned_loaded_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", " random_state=42,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "pred_partial_finetuned.plot(\n", - " label=\"Forecast of the partial finetuned model\", linestyle=\"-.\"\n", - ")\n", "pred_partial_finetuned_loaded.plot(\n", " label=\"Forecast of the loaded partial finetuned model\",\n", - " linestyle=\"--\",\n", " title=\"Partial finetuning\",\n", ")" ] @@ -456,27 +531,6 @@ "Again, we verify that the prediction of the fine-tuned model is the same as the loaded model to make sure that saving/load works correctly" ] }, - { - "cell_type": "code", - "execution_count": 19, - "id": "01717b70", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(pred_partial_finetuned.values(), pred_partial_finetuned_loaded.values())" - ] - }, { "cell_type": "markdown", "id": "022127ca", @@ -489,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "id": "3b81f2e2", "metadata": {}, "outputs": [ @@ -523,20 +577,20 @@ " \n", " 0\n", " Base Model\n", - " 15.206931\n", + " 15.206929\n", " 70.817078\n", " \n", " \n", " 1\n", " Full Fine-tuning\n", - " 3.124407\n", - " 13.061282\n", + " 2.871620\n", + " 12.922942\n", " \n", " \n", " 2\n", " Partial Fine-tuning\n", - " 14.173130\n", - " 63.913483\n", + " 6.979350\n", + " 33.685387\n", " \n", " \n", "\n", @@ -544,12 +598,12 @@ ], "text/plain": [ " Model MAPE (%) MAE\n", - "0 Base Model 15.206931 70.817078\n", - "1 Full Fine-tuning 3.124407 13.061282\n", - "2 Partial Fine-tuning 14.173130 63.913483" + "0 Base Model 15.206929 70.817078\n", + "1 Full Fine-tuning 2.871620 12.922942\n", + "2 Partial Fine-tuning 6.979350 33.685387" ] }, - "execution_count": 20, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -562,8 +616,8 @@ "results = []\n", "all_predictions = {\n", " \"Base Model\": prediction,\n", - " \"Full Fine-tuning\": pred_full_finetuned,\n", - " \"Partial Fine-tuning\": pred_partial_finetuned,\n", + " \"Full Fine-tuning\": pred_full_finetuned_loaded,\n", + " \"Partial Fine-tuning\": pred_partial_finetuned_loaded,\n", "}\n", "\n", "for name, pred in all_predictions.items():\n", @@ -621,7 +675,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.13.7" } }, "nbformat": 4, From 66fa1b2a9954abedb03582380db9ebdad4b30391 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Tue, 24 Feb 2026 14:02:22 +0100 Subject: [PATCH 14/26] doc: update example notebook to demonstrate finetuning for pytorch model and foundation model --- ...oundation-Model-Fine-Tuning-examples.ipynb | 265 ++++++++++++------ 1 file changed, 187 insertions(+), 78 deletions(-) diff --git a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb index 09f5a4ac18..3c906878f1 100644 --- a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb +++ b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -5,14 +5,24 @@ "id": "da55dd6c", "metadata": {}, "source": [ - "# Full Model Fine-Tuning and partial fine-tuning\n", - "This example notebook presents how fine-tuning can be applied to the a pytorch model using built-in Darts features.\n", + "# Fine-Tuning PyTorch Models and Foundation Models\n", "\n", - "The following fine-tuning methods will be shown:\n", - "1) **Full fine-tuning**: All model weights are retrained. This is natively supported by setting `enable_finetuning=True`.\n", - "2) **Partial fine-tuning**: Specific layers are frozen via name patterns. This is natively supported using the `enable_finetuning` parameter with a dictionary configuration.\n", + "This example notebook demonstrates how to train, save, load, and fine-tune PyTorch-based models in Darts. We cover two scenarios:\n", "\n", - "To be useful, a fine-tuned model should be easily saved and loaded. We will also demonstrate how to persist the model weights." + "1. **Regular PyTorch Models** (e.g., TiDE, N-BEATS, DLinear):\n", + " * Training a model from scratch.\n", + " * Saving and loading the model.\n", + " * Fine-tuning the loaded model (updating weights on new data).\n", + "\n", + "2. **Foundation Models** (e.g., Chronos):\n", + " * **Zero-shot inference**: Using the model without any training (`enable_finetuning=False`).\n", + " * **Full fine-tuning**: Retraining all model weights (`enable_finetuning=True`).\n", + " * **Partial fine-tuning**: Retraining only specific layers while freezing others (`enable_finetuning={\"freeze\": ...}` or `{\"unfreeze\": ...}`).\n", + "\n", + "The `enable_finetuning` parameter controls the behavior:\n", + "* `enable_finetuning=True`: Enables global fine-tuning (all weights are trainable).\n", + "* `enable_finetuning=False`: Disables fine-tuning (all weights are frozen).\n", + "* `enable_finetuning={...}`: Enables partial fine-tuning via \"freeze\" or \"unfreeze\" patterns.\n" ] }, { @@ -89,19 +99,23 @@ "id": "477c039b", "metadata": {}, "source": [ - "## Regular PyTorch Model" + "## 1. Regular PyTorch Model: Training, Saving, Loading and Fine-Tuning\n", + "\n", + "We start with a regular PyTorch model, `TiDEModel` in this case.\n", + "\n", + "First, we train a model from scratch. By default, `enable_finetuning=None`, which implies standard training for regular models (all weights are trainable).\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "7ddac1ed", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95bc1c1b4f204ace85ac6675f3486185", + "model_id": "1fa1e08ee7864ec9b50b6918730e851b", "version_major": 2, "version_minor": 0 }, @@ -115,7 +129,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "820c5f8d822340f1a92000014aa40225", + "model_id": "d74a6f4ed9854c8c90f6f19d8888e7d2", "version_major": 2, "version_minor": 0 }, @@ -132,7 +146,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -148,36 +162,42 @@ } ], "source": [ - "model = TiDEModel(\n", + "# Regular training (default enable_finetuning=None)\n", + "tide_model = TiDEModel(\n", " input_chunk_length=24,\n", " output_chunk_length=12,\n", " use_reversible_instance_norm=True,\n", " likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", " random_state=42,\n", " n_epochs=100,\n", - " model_name=\"my_model\",\n", - " save_checkpoints=True,\n", + " model_name=\"my_tide_model\",\n", + " save_checkpoints=True, # saves the best model\n", " force_reset=True,\n", + " # enable_finetuning=None, # default: full training for regular models\n", ")\n", "\n", - "model.fit(train_passengers, verbose=True)\n", - "prediction = model.predict(\n", + "tide_model.fit(train_passengers, verbose=True)\n", + "\n", + "# Save the model manually (optional, as save_checkpoints=True already saves checkpoints)\n", + "tide_model.save(\"tide_model_trained.pt\")\n", + "\n", + "tide_prediction = tide_model.predict(\n", " n=len(val_passengers), series=train_passengers, random_state=42\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "prediction.plot(label=\"Forecast\", title=\"Trained TiDE model\")" + "tide_prediction.plot(label=\"Forecast\", title=\"Trained TiDE model\")" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "5e414418", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc3bf9eb0f15480a9c00635a1f3f8a11", + "model_id": "0a9b69b427284c3496bfce99fb540543", "version_major": 2, "version_minor": 0 }, @@ -190,17 +210,45 @@ }, { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "aef4a384bcd6404d99732fab29977795", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "Training: | | 0/? [00:00" + ] + }, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -210,17 +258,34 @@ } ], "source": [ - "loaded_model = TiDEModel.load_from_checkpoint(\n", - " model_name=\"my_model\", best=False, map_location=\"cpu\"\n", - ")\n", + "# Load the model (we can use the manually saved one)\n", + "loaded_model = TiDEModel.load(\"tide_model_trained.pt\")\n", "\n", - "prediction = model.predict(\n", + "# Initial prediction before fine-tuning\n", + "prediction_before = loaded_model.predict(\n", " n=len(val_passengers),\n", " series=train_passengers,\n", " random_state=42,\n", ")\n", "val_passengers.plot(label=\"Ground truth\")\n", - "prediction.plot(label=\"Forecast\", title=\"Trained TiDE model\")" + "prediction_before.plot(\n", + " label=\"Loaded model (before fine-tuning)\", title=\"Loaded TiDE model\"\n", + ")\n", + "\n", + "# Fine-tune the loaded model\n", + "# Since enable_finetuning=None (default) was used, calling fit() continues training on all weights.\n", + "# We train for a few more epochs (fine-tuning)\n", + "loaded_model.fit(train_passengers, epochs=10, verbose=True)\n", + "\n", + "# Predict after fine-tuning\n", + "prediction_after = loaded_model.predict(\n", + " n=len(val_passengers),\n", + " series=train_passengers,\n", + " random_state=42,\n", + ")\n", + "prediction_after.plot(\n", + " label=\"Loaded model (after fine-tuning)\", title=\"Fine-tuned TiDE model\"\n", + ")" ] }, { @@ -228,23 +293,36 @@ "id": "b9251561", "metadata": {}, "source": [ - "# Model prediction out-of-the-box\n", - "Let's see how the model behaves on the validation data without any fine-tuning. For that we:\n", + "# 2. Foundation Model: Zero-shot, Full Fine-Tuning and Partial Fine-Tuning\n", + "\n", + "Now we move to foundation models (e.g. `Chronos`). These models are pre-trained on large datasets and can often be used \"zero-shot\" (without training).\n", + "\n", + "However, fine-tuning them on your specific data often improves performance.\n", + "\n", + "The `enable_finetuning` parameter controls how the model is updated during `fit()`:\n", + "* `enable_finetuning=False` (Default): **Zero-shot inference**. The model weights are frozen. `fit()` is used only to setup the model (scaling, etc.) but no training is performed.\n", + "* `enable_finetuning=True`: **Full fine-tuning**. All weights are trainable.\n", + "* `enable_finetuning={\"freeze\": [...]}`: **Partial fine-tuning (Freeze)**. specified layers are frozen.\n", + "* `enable_finetuning={\"unfreeze\": [...]}`: **Partial fine-tuning (Unfreeze)**. All layers are frozen *except* the specified ones.\n", + "\n", + "### 2.1 Zero-Shot Inference\n", + "Let's see how the model behaves on the validation data without any fine-tuning (`enable_finetuning=False`).\n", + "For that we:\n", "- Create the model\n", - "- Call fit to load the model internally (no training is done)\n", - "- Predict on the validation set" + "- Call `fit()` to load the model internally (no training is done)\n", + "- Predict on the validation set\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ea8456ae", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "978df6b652114b3dac5ac6851d2ccb9f", + "model_id": "bfd07773360b4bf5b4d0953bca11c399", "version_major": 2, "version_minor": 0 }, @@ -261,7 +339,7 @@ "" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -300,23 +378,23 @@ "id": "1313019f", "metadata": {}, "source": [ - "# 1. Full fine-tuning\n", + "### 2.2 Full fine-tuning (`enable_finetuning=True`)\n", "\n", - "In this method, all the model weights are retrained. This is simply enabled by passing `enable_finetuning=True` to the model constructor. \n", + "In this method, all the model weights are retrained. This is enabled by passing `enable_finetuning=True` to the model constructor.\n", "\n", "When fine-tuning is enabled, Darts will treat the foundation model like a standard trainable model during `fit()`. Saving and loading follows the standard Darts API via the `save()` and `load()` methods.\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "72832dff", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "42721d10e66843c0baf65c54cb052183", + "model_id": "50a527daeefe4cb4ba36ea0bdfc0869e", "version_major": 2, "version_minor": 0 }, @@ -332,7 +410,7 @@ "full_finetuned_model = Chronos2Model(\n", " input_chunk_length=24,\n", " output_chunk_length=12,\n", - " # likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", + " likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", " enable_finetuning=True,\n", " n_epochs=100,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", @@ -355,14 +433,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "id": "9bbd219e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d55727c8b2140718b8e64a16901acaf", + "model_id": "1c8bc723957340fd9d986ad56e2c968c", "version_major": 2, "version_minor": 0 }, @@ -379,13 +457,13 @@ "" ] }, - "execution_count": 7, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -419,27 +497,29 @@ "id": "3cabab8a", "metadata": {}, "source": [ - "# 2. Partial fine-tuning with layer freezing\n", + "### 2.3 Partial fine-tuning with layer freezing/unfreezing\n", "\n", - "Partial fine-tuning allows you to update only a subset of the model's parameters, which is useful for preserving general knowledge while adapting to specific patterns. \n", + "Partial fine-tuning allows you to update only a subset of the model's parameters, which is useful for preserving general knowledge while adapting to specific patterns.\n", "\n", - "Darts foundation models natively support this via the `enable_finetuning` parameter. You can pass a dictionary with:\n", + "Darts foundation models (as well as regular PyTorch models) support this via the `enable_finetuning` parameter. You can pass a dictionary with:\n", "- `{'freeze': ['pattern1', 'pattern2']}`: To freeze specific layers (keeping others trainable).\n", "- `{'unfreeze': ['pattern1', 'pattern2']}`: To freeze everything *except* the matching layers.\n", "\n", - "In this example, we will unfreeze only the output head of the model." + "In the example below, we show two approaches (comment/uncomment to try):\n", + "1. **Freeze encoders**: We freeze the encoder layers, training only the rest (e.g. decoder/head).\n", + "2. **Unfreeze head**: We freeze everything except the output head.\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "33fa7fc4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c45c382dfda14a5d8b852aa8a0dc6ba6", + "model_id": "832e77ab6439462db9397b7484541557", "version_major": 2, "version_minor": 0 }, @@ -452,16 +532,24 @@ } ], "source": [ + "# Example 2.3: Partial fine-tuning by UNFREEZING layers\n", + "# Here we want to fine-tune ONLY specific layers (e.g. `output_patch_embedding`), keeping the backbone frozen.\n", + "# Note: Layer names depend on the model architecture.\n", + "\n", "partial_finetuned_model = Chronos2Model(\n", " input_chunk_length=24,\n", " output_chunk_length=12,\n", - " # likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", - " # enable_finetuning={\"unfreeze\": [\"output_patch_embedding\"]},\n", - " enable_finetuning={\"freeze\": [\"*encoder*\"]},\n", + " likelihood=QuantileRegression([0.1, 0.5, 0.9]),\n", + " # OPTION 1: Unfreeze specific layers (freeze everything else)\n", + " # This trains ONLY the layers matching \"output_patch_embedding\"\n", + " enable_finetuning={\"unfreeze\": [\"output_patch_embedding.*\"]},\n", + " # OPTION 2: Freeze specific layers (train everything else)\n", + " # enable_finetuning={\"freeze\": [\"*encoder*\"]},\n", " n_epochs=100,\n", " pl_trainer_kwargs={\"accelerator\": \"gpu\"},\n", " random_state=42,\n", ")\n", + "\n", "partial_finetuned_model.fit(train_passengers, verbose=True)\n", "partial_finetuned_model.save(\"partial_finetuned.pt\")\n", "\n", @@ -471,14 +559,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "id": "50830283", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf5dfa7b05fc42d894c35ac5abf52788", + "model_id": "79eb08d785554210bb70452fa54055d5", "version_major": 2, "version_minor": 0 }, @@ -495,13 +583,13 @@ "" ] }, - "execution_count": 9, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -543,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "id": "3b81f2e2", "metadata": {}, "outputs": [ @@ -576,34 +664,48 @@ " \n", " \n", " 0\n", - " Base Model\n", - " 15.206929\n", - " 70.817078\n", + " Trained TiDE Model\n", + " 7.183538\n", + " 31.838135\n", " \n", " \n", " 1\n", - " Full Fine-tuning\n", - " 2.871620\n", - " 12.922942\n", + " Loaded TiDE Model (Fine-Tuned)\n", + " 7.088335\n", + " 31.208529\n", " \n", " \n", " 2\n", - " Partial Fine-tuning\n", - " 6.979350\n", - " 33.685387\n", + " Chronos Zero-Shot (Base)\n", + " 15.206929\n", + " 70.817078\n", + " \n", + " \n", + " 3\n", + " Chronos Full Fine-tuning\n", + " 8.920057\n", + " 40.941208\n", + " \n", + " \n", + " 4\n", + " Chronos Partial Fine-tuning\n", + " 8.660080\n", + " 39.234516\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model MAPE (%) MAE\n", - "0 Base Model 15.206929 70.817078\n", - "1 Full Fine-tuning 2.871620 12.922942\n", - "2 Partial Fine-tuning 6.979350 33.685387" + " Model MAPE (%) MAE\n", + "0 Trained TiDE Model 7.183538 31.838135\n", + "1 Loaded TiDE Model (Fine-Tuned) 7.088335 31.208529\n", + "2 Chronos Zero-Shot (Base) 15.206929 70.817078\n", + "3 Chronos Full Fine-tuning 8.920057 40.941208\n", + "4 Chronos Partial Fine-tuning 8.660080 39.234516" ] }, - "execution_count": 10, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -615,9 +717,11 @@ "\n", "results = []\n", "all_predictions = {\n", - " \"Base Model\": prediction,\n", - " \"Full Fine-tuning\": pred_full_finetuned_loaded,\n", - " \"Partial Fine-tuning\": pred_partial_finetuned_loaded,\n", + " \"Trained TiDE Model\": tide_prediction,\n", + " \"Loaded TiDE Model (Fine-Tuned)\": prediction_after,\n", + " \"Chronos Zero-Shot (Base)\": prediction,\n", + " \"Chronos Full Fine-tuning\": pred_full_finetuned_loaded,\n", + " \"Chronos Partial Fine-tuning\": pred_partial_finetuned_loaded,\n", "}\n", "\n", "for name, pred in all_predictions.items():\n", @@ -641,13 +745,18 @@ "While the results on this small \"toy\" dataset (Air Passengers) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility of the fine-tuning API.\n", "\n", "In real-world scenarios with larger datasets:\n", - "- **Full Fine-tuning** offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", - "- **Partial Fine-tuning** provides a good middle ground by updating only the most relevant layers (like the output head).\n", + "- **Regular Models**: Fine-tuning allows updating a pre-trained model on new data, potentially saving training time compared to retraining from scratch.\n", + "- **Foundation Models**:\n", + " - **Zero-Shot**: Quick and easy, no training required.\n", + " - **Full Fine-tuning**: Offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", + " - **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n", "\n", "### Summary\n", "In this notebook, we have seen:\n", - "1. How to enable **native full fine-tuning** in Darts foundation models.\n", - "2. How to use **layer freezing patterns** to perform partial fine-tuning without manual weight manipulation." + "1. How to train, save, load, and naturally fine-tune **regular PyTorch models**.\n", + "2. How to apply **zero-shot inference** with foundation models.\n", + "3. How to perform **full fine-tuning** on foundation models.\n", + "4. How to use **layer freezing/unfreezing** for **partial fine-tuning**.\n" ] }, { From 7b2c657eedeca9523f08f0a64c7894789d198846 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Tue, 24 Feb 2026 14:54:12 +0100 Subject: [PATCH 15/26] rename _setup_fine_tuning to _setup_finetuning and update notebook to avoid autoregression --- .../forecasting/torch_forecasting_model.py | 4 +- ...oundation-Model-Fine-Tuning-examples.ipynb | 82 +++++++++---------- 2 files changed, 43 insertions(+), 43 deletions(-) diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index 81f2d241aa..f40400d87e 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -511,10 +511,10 @@ def _init_model(self, trainer: pl.Trainer | None = None) -> PLForecastingModule: _get_runs_folder(self.work_dir, self.model_name), INIT_MODEL_NAME ) ) - self._setup_fine_tuning(model) + self._setup_finetuning(model) return model - def _setup_fine_tuning(self, model: PLForecastingModule): + def _setup_finetuning(self, model: PLForecastingModule): """ Sets up the model for fine-tuning based on `self.enable_finetuning`. """ diff --git a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb index 3c906878f1..4e0b15ead9 100644 --- a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb +++ b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -90,8 +90,8 @@ "# convert to float32 as Chronos-2 works with float32 input\n", "data = AirPassengersDataset().load().astype(np.float32)\n", "train_passengers, val_passengers = data.split_before(\n", - " len(data) - 2 * 12\n", - ") # last 2 years for validation" + " len(data) - 12\n", + ") # last year for validation" ] }, { @@ -115,7 +115,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fa1e08ee7864ec9b50b6918730e851b", + "model_id": "6c3039b2947540efa536eb2883e8422a", "version_major": 2, "version_minor": 0 }, @@ -129,7 +129,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d74a6f4ed9854c8c90f6f19d8888e7d2", + "model_id": "089d5fe30e944c5a9c03aad582d353c2", "version_major": 2, "version_minor": 0 }, @@ -152,7 +152,7 @@ }, { "data": { - "image/png": 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9S3FNQmTFv4bhvm/2CYFCQuXuJXvx5LjOeGhkFJyd2TnaluAiWY1AhaYVFRUidfPjjz+KVApFVJYuXYrjx483OMhu3rx5WLx4sejkOXXqFP773//it99+w1NPPaVGYQYNGiQ6cGjbmzdvxvPPP2/yflIh7ldffYWvv/4aJ0+eFPUeR44cqfc1kZGRIpKxatUqXLx4EQUFBXU+15j9NGV7DMPYJ/vk9A7RP1ISKASldJbPHYqJ3cPEbQrGvrP2JB78bj8KSiussq9M42CBohGoPfjgwYMYO3Ys5s+fj169egmx8tFHHwmR8eqrr9b7+htuuEHUm1BRbLdu3fDZZ58JEUGtvAokLEgEUd0HtfZSgaup3Hrrrfj3v/8tWnlpO+fOncODDz5Y72vIw+Xll18WBbfUnkytxfXR0H6auj2GYexboPTTEygEFcd+MqMv5k3oIupTiDVHMnDDwu1IusgnNLaCU7UN9mPl5eUhICAAubm58Pf3N3iMuknOnDmDDh06iK4ThqkP/nthGNuDUju9X16L/NIKBPl6YO9zY+oc/EneKI/+cFC0IRN+Hq54/7beGNM1tJn3mmno+7smHEFhGIZhbIqTGflCnCjpnfqmko/qItWldA71Fbfpdfd8sw8fbjiFKrmGhdEmLFAYhmEY260/kQtk66M91aU8NBTX9tDVpfx33Uk8sHQ/8kuM7xJkmhcWKAzDMIxNsf9sdp31J3Xh4+GKhbf3xdPX6OpS1h6V6lJOc12KJmGBwjAMw9hkBMXD1RndWgcY/TpKBVG78dd3XgV/T8ll4/TFQtzw8XasP5phsf1lGgcLFIZhGMZmyMgrwYXLxWLdq20LuLua/jU2skuI8MbqEupnUJfy/vqTXJeiIVigMAzDMDY5f0ff/8RUIgN98NtDQzCppzTOg3h//Snc9y3XpWgFFigMwzCMzbDvXLZJBbIN1aV8PL0Pnp0YA8Vkdv2xDExZuB2JmVyXYm1YoDAMwzA2wwG9Dp6+7ZomUJS6lAdGdMLiuwYgwEua5ZVEdSkLuS7F2rBAYRiGYWyC4rJKHEmVBgRGh/iihbc0l8scDO8cjJX/GoaYMKkuhWzx535/AGm5Ur0L0/ywQGEYhmFsgrjzOaiQi1ibmt6pjXaB3qIuZYw8/bi0ogo7T2eZ/X0Y42CBoiHuvPNOEW6seUlMTIQtQsMLW7RoYe3dYBjGTtivV3/SL7KVRd7D290VD3fJxXr3p/Cp23tIOK97T0bjAiUlJQUzZ85EYGCgmDzbo0cP7Nu3T32cRvu88MILCA8PF4/T8DuarqtPdnY2ZsyYIXz46Qtszpw5PJFW5pprrkFaWprBhebEmEpZWZlF9o9hGEZrE4zNSl4aem57EFHOqbjGZS+ckzZa5n0Y8wqUy5cvY+jQoXBzc8Nff/2Fo0eP4t1330XLlro/lLfeegsffvghPv30U+zevRs+Pj6YMGGCGMqmQOLkyJEjWLduHVatWoUtW7bgvvvuM2VX7BYPDw+EhYUZXFxcXLB582YMGDBAPE7ijyb50sRfBZpaTFN9afpvUFCQ+MyJw4cPY+LEifD19RWTf2fNmoVLly6pr6uqqhK/s6ioKLHtdu3a4bXXXlMff+aZZ9C5c2d4e3ujY8eOYpJxebmuBS8+Ph6jRo2Cn5+fEJw0gZgE66ZNm3DXXXeJgVBKJOill15qts+RYRj7gvxJlALZIF93RAZ6m/9NKsqAn2fDuTBTvSvw8kFUVFaZ/72YBpGs9IzkzTffRNu2bfH111+r9+mf3VP05P3338fzzz+PKVOmiPu++eYb8cX4+++/47bbbsOxY8fw999/Y+/evejfv794zkcffYRrr70W77zzDlq3bm3KLjkEFLWiz4dSQPR5Hj9+HPfee6+Yvqv/pb9kyRI8+OCD2L59u7idk5OD0aNH45577sF7772H4uJiITimTZuGf/75Rzxn/vz5+OKLL8Tjw4YNExEb2r4CCQ9K1dDvJSEhQbwv3ff000+rYrNPnz5YtGiREFJxcXFCwA4ZMkT8LVA07cSJE+K5JJIYhmEaw6nMAnUicb8GBgQ2mnX/Bs7vNrirT/UJnMwoQGzr+ifvMlYWKCtWrBBn5rfccos4o2/Tpg0eeugh8aVF0Nj69PR0kdZRoLHKAwcOxM6dO4VAoWtK6yjihKDnOzs7i4jLjTfeeMX7lpaWiov+uGaT+WwEUKBTxc2Cbwhw/2aTXkIRJf0vcop+UASDhOHHH38s/iljYmKQmpoqxAYJAPrsiOjoaBENUfjPf/4jxMPrr7+u3vfVV1+JbZ08eVJEYj744AOx3dmzZ4vHO3XqJISKAolNhfbt2+Opp57CsmXLVIGSnJyMefPmiX1S9kH/d0/7S1EghmEYc/mfGDt/xyQO/Qzs/lRau7ijxMkTnhV56O2ciJXnL7JA0bpASUpKEmfKTzzxBP7v//5PREEeeeQRuLu7iy84EicERUz0odvKY3QdEhJiuBOurmjVqpX6nJosWLAAL7/8MpoEiZP8VGgdSpfQZ6xAKbK5c+di8ODBBmcMlGqjup0LFy6ItAxB6RV9KP2ycePGWiMXp0+fFhEWEn5jxoypc39+/PFHkbKj59P7UVqJUjkK9LdAEZpvv/1WCE0SryRyGIZhzMl+PQdZsxfIph8GVjysuz3xLRQc2QjPM3/Ay6kMWYn7gIF8XNO0QKF6BYp8KGfkdHZONQ5Ub6KcgVsCSkPQF6F+BIWiACZHM5qbRrwnCRKqB2kM9Fp9SFBcd911IjVXE4qekOCsD4p2UQqHxCFFzigiQtETqjtSoBTT7bffjj///FPUJb344oviObVFwhiGYZpaIEuzd7q3MWM0ozgH+GkWUCH7nfSZCfS7E75Ua3fmD3GXW8oeALea7z0Z8wsU+lKLjY01uK9r16749ddfxVoJ5WdkZIjnKtDt3r17q8/JzDRMtdBZOXX21JUKoOJNujQJE1MtWkL5jKnGR4miUJ0J1YJERETU+bq+ffuK11FqhqJUNaF0DHVabdiwQURBarJjxw5ERkbiueeeU+87d+7cFc+jFBRdHn/8cUyfPl3UKJFAochaZWVlE35yhmEYIDO/BMnZRWLdKyIAHq4u5tlwVRWw/AEgWz5ZC+8FXPsO2cvCs+NQ9WltCw6htKLSfO/LmL+Lh9IKSsGjAtUy0JeYUjBLIoO+8PSjHVRbQikKgq4ptbB//371OVSwSdEZqlVhroTqfM6fP4+HH35YFLD+8ccfIlJBUSWl/qQ2KDVEwo9EA6XjKE2zZs0a0V1DwoGKbKmOhepJqPiWHt+1axe+/PJLVcBQjQlFROgxSvUsX75c3T4V3VLnEHXskHAh0UTvQ4KKIGFEURz6e6DOoaIi6QDDMAyjifTOtneBk39Ja6+WwLRvATcv6XZwVxQ7S+nxvk4ncCKtEbWPTPMJFDpDpi8wSvGQedj333+Pzz//XHwREnR2T22uVJxJBbXU9XHHHXeIDpAbbrhBPIe+vMjrgwpr9+zZI77U6EuOCmi5g6d2qBh59erV4vPq1asXHnjgAeEdo1/AWhv0edLnS2Jk/PjxwrOGfj9UpKwIG2obfvLJJ0WxLf1ubr31VjXCdf3114vfOf1+KAJGERV6vgJ17WRlZYnfMUVQqDuIinqVeiHq5KF9pW0GBwcbFPAyDMNY1f8kcQPwj2Kp4ARM/R/QUjrZFjg7I6tlL7EMdspF0snD5nlfxmicqilvYGKXCdWEkPkaRUzoLF7p4iFoc3R2T8KFIiXUEfLJJ5+ILzAFOqunL72VK1eKL8qpU6eKs3Nj21ApKkP1EOSxoV+wSZDfCnUT0b5RhIBh6oP/XhhG+9B04fjzOWJ98N/j0NKniTN4Lp8DPh8BFMvCZ9TzwIh5VzwtZcWraHPgHbH+sc183Hrvs017Xwb1fX83qQaFmDx5srjUBUVRXnnlFXGpC+rYoegLwzAMwzQ4IDAlV6w7Bfs0XZyUl0hFsYo46TwRuPrJWp8aFDsCkAWK/0VdWQLTPPAsHoZhGEazxF/QGxBojvqT1U8BafHSumUH4MZPRTqnNjza9UcFpMLYqNIjQiwxzQcLFIZhGEaz7NerP+nX1AnG+5cAB7+V1q5ewK1LAa96Bpq6eyPVq4tYRjul4MSZs017f8YkWKAwDMMwmmXf2WzzFMim7JeiJwrXfwiEdW/wZYWhOtfzS8e2Nv79GZOxW4FiYu0v46Dw3wnDaHtAoBJBCfRxR4cgQzNKoynMAn68A6iUp7wPuB/oOc2ol3pH6UZ/OJ3f1bj3ZxqF3QkUGlRHsOcGYwzK34nyd8MwjHZIvKgbENi3sQMCqyqBX+8G8i5It9sOBMb/x+iXh3cfoa5DcuJMf3+m0ZjcxaN1yJuDfD4ULw9vb2/LTL1kbD5yQuKE/k7o74X+bhiG0W79SaPTO//8B0jaJK19QoBblgCuxncCubcIQ4pza7SpSkXnilPIL8iHn69f4/aFcWyBQiiW+TUt9RmmJiROeNoyw2iTfXoOsv0bUyB7bBWw7b/S2skFuGUx4K8bw2IsGS16o012KjycKnAqYQe6D55g+r4wJmOXAoUiJjQLiKYml9PAJ4apBUrrcOSEYbTL/nPZegMCA0x78aVE4PcHdbfHvwq0183XMYXKNgOB7NViXZi4DWCB0izYpUBRoC8f/gJiGIaxPS7ml+JsllQj1rONiQMCSwuAH2cCpfL8nG43AYMeavS+BMYOBxKktXf6vkZvh3HwIlmGYRjGzvxPTKk/oc68FQ8DF49Jt4NjgOs/EhOKG0u76F64XC3VnbQrTJCmIDMWhwUKwzAMo9n0jskCZfenwJHfpLW7n2TG5mHcnLe6cHV1wSnPbmIdgHzkpRxt0vYY42CBwjAMw2h6grHRAuXcDmCt3pT3GxcBQdFm2Z/coH7qOuPwRrNsk6kfFigMwzCMpigpr8RheUBgxyAfBPp6NPyi/HTg5zuBKsk3BcMeB7peZ7Z9cu84WF1XnGXDtuaABQrDMAyjKQ5dyEV5ZbXx0ZPKcuCn2UBBhnS7wwhglF4kxQy0iR2C0mrJ0DEw64BZt83UDgsUhmEYRlPs06s/Mcr/hNI6ig29fwRw81eAi3mbVDuGtsJhdBLrkIpUIF8WQ4zFYIHCMAzDaIr9egZt/SJb1f/kQz9LhbGEizsw7RvAJ8js++Ts7IQLfj3V27kneXCgpWGBwjAMw2hrQGCyJFBaeruhU7BP/X4nqx7X3Z74FhChK2Y1N6XhA9Q1CxTLwwKFYRiG0QxJlwqQU1Su1p/UO0stZR9Qli+tu14P9LvTovvm31nnROuessei78WwQGEYhmE0On+nwfROyn7dOmZSk8zYjCG2Y3ucrGoj1kEFJ4CyQou+n6PDAoVhGIbRpP9JgwWyKXrdNG0sl9pRaNvKC4ecY8TaFZWovsC295aEBQrDMAyjOYt7dxdn9GhoQKASQfEIAFpJHTaWhNJNl1r1VW8XJG63+Hs6MixQGIZhGE1wqaAUZy5JaZPubfzh6VbPgMDcFCA/TVq36UNtNs2yjy6Rg9R16WkWKJaEBQrDMAyjuQGB/dubUH/SDOkdhbYduyGzuoVY+106CFRVNtt7OxosUBiGYRjbm2BsJYHSs20L7KvqLNYelYVAJg8OtBQsUBiGYRhNYAsCJTzAE8fcpMnGRHUyz+WxFCxQGIZhGE0MCEy4IA0I7BDkg6D6BgRSWiU1Tmdt7xfWTHspFcoWhuoEUREXyloMFigMwzCM1aHpxWWVVWLdt10D0ZNLp3QGbW10XTXNRUD7fiiqlgSUkzIDiDE7LFAYhmEYG/M/sU56R6FHZCDiqqS2Zu/iNCD3QrPvgyPAAoVhGIbRlINs/wbrT/ZZVaB0bxOAvdVddHdwHYpFYIHCMAzDWJXq6mockAcEBnjRgEBfIyMoTkDr3mhuQvw8ccaru3qbC2UtAwsUhmEYxqokXSpEdmGZ2r3j7FzPTJ3yYiDjiLQOjgE8/GANqiOuQmW1tJ9lZ3ZYZR/sHRYoDMMwjFXZbzAgsIH0TnoCUFUhrSOaP72j0LldG5yobifW7peOASV5VtsXe4UFCsMwDGNV9p3LNqH+xLoFsgo0J2ivbNjmhCrgwl6r7Yu9wgKFYRiG0UQHj5uLE3q1lWzktS5QekYEYH8VF8paEhYoDMMwjNWg2pOki9KAwG6tA+ofEKgvUFw9gZBYWIsW3u5I9e+l3q5igWJ2WKAwDMMw2hgQ2FB6pygbyE6S1uG9ABc3WJPQdlFIqQ6UblCKp7Lcqvtjb7BAYRg9J8sHl+7Hvd/sQ24xH2gYptnrTxo0aDugifSOQs82Adgnp3mcK4qlAl7GbLBAYRyeC5eL8Niyg5j80Tb8dTgd645m4Me9ydbeLYZxwA6eVjZRf6LQM0I32VjAaR6zwgKFcVhyi8rx+upjGP3OZvwel2rw2MHkHKvtF8M4CqUVlTiUIg0IjAz0RrBfPQMCNShQurfxVyMoAp7LY1Zczbs5hrGNg+K3O8/ho38SDVI5LbzdUFxWidKKKhySp6oyDGPhAYEVVcb5n1RX6wSKVyugZXtYGz9PN5QHdkFevhf8nYqFo6wT7adTPUZzjNFwBIVxGKqqqrEiPhVj/7sZ//nzmCpO3F2dcf+Ijtg8bxR6yy2OKTnFuJhfauU9ZhhHmr/TQHonJxkouqSLnmhEBPRoG4iDVdFi7VSQAVw+a+1dshs4gsI4BLuSsrBg9THE60VG6Ph2Y582eHJ8F7Rp4SXuIw+G3Wekor1DF3Iwpmuo1faZYewdW5pgXJ9h275DnTHC5ZCuDqVVB2vvll3AAoWxa05l5OPNv49j/bFMg/uHRQXh2YkxYippTfMlhfjzLFAYxqIDAmWB4u/piiijBwRqS6DQMePd6hp1KL2nW3OXHDPF89JLL8HJycngEhMToz4+cuTIKx5/4IEHDLaRnJyMSZMmwdvbGyEhIZg3bx4qKuS5CgxjJjLzSjD/t0OY8P4WA3ESE+aHJXcPwLdzBlwhToheEToXS/1oC8Mw5uXMpUJkyQMC+zY0IPAKgdIXWoHM5Q5Vd0JFtfx1yp081ougdOvWDevXr9dtwNVwE/feey9eeeUV9TYJEYXKykohTsLCwrBjxw6kpaXhjjvugJubG15//fXG/xQMI1NYWoHPtyThi61JKCqrVO8P8/fEE+M7Y2rfCLjUcyCMaOmFVj7uwt2SUjx0lkdCm2EYKxq0VVYAqXHSukUk4BMEreDl7oK2ocE4nN0evZ2SgIvHJUM57wZqahjzCxQSJCQw6oIESV2Pr127FkePHhUCJzQ0FL1798arr76KZ555RkRn3N3dTd0dhhFUVFbhx33n8d66U7hUoCtu9fVwxYMjO+HuoR3EgaQhSIz0igjAxhMXcbmoHOezi9EuUCeyGYYxv0Bp0P/k4jGAjNA0lt7Rr0PZf6kLejvLLrfn9wBdrrH2bjleF8+pU6fQunVrdOzYETNmzBApG32+++47BAUFoXv37pg/fz6KiorUx3bu3IkePXoIcaIwYcIE5OXl4ciRI3W+Z2lpqXiO/oVhCIpwkLEapXKeW35YFSeuzk6YPTgSm+eNxNxRUUaJE33zJYX4C+yHwjCWLJCl/1Wle86o9E5Ef2iNnm1bYC/7oVg3gjJw4EAsXrwYXbp0EemZl19+GVdffTUOHz4MPz8/3H777YiMjBQC5tChQyIycuLECfz222/i9enp6QbihFBu02N1sWDBAvFeDKMPFbG+tvoY9shdNwoTu4fh6Wti0CHIp1Hb1T9Y0ntc16t1k/eVYRgdOUVlSMwsEOturf0bPoHQaIGsvuX9h+woa12BMnHiRHXds2dPIVhIkPz000+YM2cO7rvvPvVxipSEh4djzJgxOH36NDp16tTonaRIzBNPPKHepghK27ZtG709xvb5evsZvLzyqMF9ZPT0f9d2bdjwqQH0O3nYsI1hrJze0Z/B4+QChPWE1ogJ90OOS0ucrQpFe+cMaX8rSgHXBpxxGcsZtbVo0QKdO3dGYmJirY+TgCGUx6k2JSMjw+A5yu366lo8PDzg7+9vcGEc2wn2rb9PqLcpUvLpzL745YHBTRYnRKCvh+qLkpCSK+pbGIaxkv9JWSGQKZ+MhMYC7tqrCfNwdUFMmD/2V8tRlMpSXVEvYx2BUlBQIKIjFCmpjbg46RekPD548GAkJCQgM1PX9rlu3TohOGJjY5uyK4wDQXNyisulDp2xXUOw9vHhuKZ7uFm7bZQ0D71P4kUpFM0wjPkHBDbYwUNf9NVVmk3vKPSICOA6FGsKlKeeegqbN2/G2bNnRZvwjTfeCBcXF0yfPl0IFerI2b9/v3h8xYoVooV4+PDhIh1EjB8/XgiRWbNmIT4+HmvWrMHzzz+PuXPniigJwxjDjsRLevUm4XBzMf/EBoM0z3lO8zCMuaDZO0rxedtWXgjx97Tp+hP9OhSebGxeTDqyX7hwQYgRKpKdNm0aAgMDsWvXLgQHB4sWYWofJhFC5m1PPvkkpk6dipUrV6qvJzGzatUqcU3RlJkzZwoRo++bwjANsU1PoAyNsowfAlneK8RxJw/DmI3DqbliIKdR83dsSaBEtMDp6ta4XO2rEyg0OJBpniLZZcuW1fkYFa1SdKUhqKh29erVprwtw6jkl5SrDq+dgn0QFtDA2VcjIZdZyhjR8YUM2xiGMX96x6iaMaVA1s0HCNY5l2uN6FBfuLu6Yn9VNMa6HASKs4FLp4BgvagKYxI8zZixKailuLKq2qLRE8XgTZkNcjwtHyVyzQvDME1j37ls4wtkCzKBXNlrq3UfwNl4P6PmhlLNsa39sY/rUMwGCxTGpmiO9E7NNE9FVTWOprE5IMOYw1hRaTH283RF5xA/46InGpu/Uxc0y4vrUMwHCxTGptiRmCWuaZzOoI6BFn0vsrxXOHSe0zwM01TOZRXhUoE8ILCdqQMCtVt/om95n1DdEaXVcvUEC5QmwQKFsRky80twIiNfrHtEtECAl5tF30+/UJYnGzOMmf1PjKo/2WdTAoW6/0rhLkSKIPu0lKZiGgULFMZm2Hlaip4QQztZNnpCkPGSu9zCzDN5GKbp7NerP+nXUP0JVagrERSfECAgAlqnY7AvvN1datSh7LbmLtk0LFAYm2HbKV39yTAL158Q7q7O6Bou5ciTLhYit7jc4u/JMPbMPrmDx8WYAYHZSUBJri56YkYjRktBPxd1AHIdinlggcLYTHHddrlA1sPVGX3NYGlvaprncAqneRimKQMCT8kDAmPD/eHt7mrCBGPtp3f0Dduo1ViFBUqjYYHC2ARns4qQmlsi1le1bwVPN5dmM19SiONCWYZpNAeSTfU/sa0CWX3L+8vwR2KVPAU9LR4oK7L2btkkLFAYm0CJnhBDoixff6LQu63+ZGMWKAzT1PSOUf4nNQUKeaDYCNRqTKhzearKgVS9dmnGaFigMDYnUJqj/kShY5CvMG0jDnEnD8OYxcOoQYv7ijIg7ZC0DowCvJonpWsOIgO9hceLOtmY4DRPo2CBwmgeco7dmSR18Ph7uqJba11Uw9KQTwN5GxBpuSXIzJPSTAzDGE9KTrEq8Kn+pMERFRmHgcpSm0vvEDRVndqNuVC26bBAYTTP0dQ85BRJHTRDOgWJSvnmpKdemof9UBjGdNYeSVfX13QPs9v6E/3atbPVYbhY7S/dcX4PUCUNSGSMhwUKo3m2n9a3t2+++pOaOWUingtlGcZk1ugJlAndjBEoB2xboIioqxP2K3UopbnAxWPW3i2bgwUKY2MFss1Xf1K7oywLFIYxhayCUjHkk2gf6I3OodIQTqMiKM5uQFgP2BrUyUPs5TRPk2CBwmia0opK7D0rHdzCAzzRMcin2fehdYAngnzdxZry6OTJwjCMcWw4lgl5ADkmdA8TNRr1QuZsl05KaxInrh6wNdq08EKgj7sugkKwQDEZFiiMpjlwLgcl5VVq/UmDBzcLQO+ppHnITZYGnjEMYxx/m5reSY0ja0abTe8oxwyKohypbo/iaunkBudZoJgKCxTGdtqLo5u//qQ2wzZO8zCMcRSUVqgjKkL9PdBb7//IXgtk9etQyuGK+OpO0h05yUBeqrV3y6ZggcLYTIEsRVCshUEnz3nu5GEYY9h0IhNllVIEdHxsmGjbdxiBUtOwjeA0j0mwQGE0S15Judo1Ex3ii1D/BrwTzMHls8DFE/V28rCjLMMYx9+HTUzv6AsUD3/JpM1GUQpl93OhbKNhgcJolt1J2Wpx3VBLd+9Q4euOj4APegMLBwDndho83MrHHW1beYn14dRcVMhnhQzD1E5JeSU2Hs8U6wAvNwzs2IB7LEEpkPw0nb29s+1+RdEJFaW1DlRFo6pajhxxHYpJ2O5vn3Go+hOLChSy1f7jX8Da53XFeUf/qDOKQkW7JzOkqawMw9TOjtOXUFhWKdZjuobAzcXZYdI7+mmePPjgRHWEdEd6AlCab+3dshlYoDCaFyiUtjbq7KsxFF4CvpkCxC01vL+W4V6c5mEY41lzOENdX2NqeoeI6A9bRzJsA/YpdSjVVcCFfdbdKRuCBQqjSTLySnAqs0A1SvP3dLPAmxwFvhgFJO+Qbrt6Ap5yMSwNKquU7PUVaL6GAnfyMEz987PWHZMEipebC4Z3DjbuhXYWQVHqUHguT+NggcJoNjysMNQS3Tsn/ga+HCe1/hG+YcBdq4FOY6TbFcVApqE1dfc2ASKaQ3AnD8PUDZkrZheWifWIzsHwdHNp+EU0qybloLT2bwP4GRl1sYFOHjWCQnAditGwQGE0ybZT0vRis9efUDHs9g+BH24DyuQ6kvDewH0bpTM2/bO2GmkeHw9XRIf4ifWJjHwUy/l1hmHqnr1j1HBAgtxjy+T6jDZ9YQ9QcX1ESy+kIAjp1XKa+vxeoLLC2rtmE7BAYTQHWckrERRPN2f0jTTC3MkYKkqBP+YC6/6tK4aNvQG46y/Av/WVB0b9gWUyvWQ/FAphH03jKArD1Pb/u/aIlN5xdXbCqJgQh0zvGKaGnXRzecoLgYzD1t4tm4AFCqM5ki4VIi23RKyvat8KHq5GhIcbouCiXAz7ne6+kfOBWxYD7t66+8J7AU7OdQoUfUfZOE7zMMwVHE7JQ0pOsTrck1qMHVmg9GhTS5qH61CMggUKozl2mLu9OOMI8MVoIHmnrhj25q+Bkc/S0AzD57r7AMEx0jrzKFBmOHent95kY+7kYZj60zsTuoUa/0JVoDhJaVc7oZdaKKsvUAx9lpjaYYHCaI5t+vN3mipQTvwFfDkeyJWLYf3CpZRO95vqfo2S5qmulHwL9OgS5gd3V2d1sjHDMLUPByTtPy7WSIFSXqJLe9AJgqc/7IVucqvx8eq2KHSSo7VnNgNVXMPWECxQGE1BtR07T0sFsi283RAb7t/4Ytht7wM/TNcVw5Iz5b3/NFyA17pv7WFnQJhNKft05lIhcosMW5EZxpE5fbEAibI9QL92LRHiZ+R4CjoRqKqwu/QOQSmuDkE+qIQLtlT2kO4svgxc2GvtXdM8LFAYTXE4JRd5JdKBakinQOOGi9VWDPv7Q8D6F3XFsN1uBO5crSuGrQ99AVOLYZtBmieF0zwMU3t6x4Q2YYP6E/vo4KnNQ2l9RR/dnSfXWG+HbAQWKIx9TS+mYtgl1wPx3+vuG/l/Us2JfjFsfYR0A1w86imU1Z9szAKFYRTWNGY4IJGi565qZxEUooec5tlU1QvVVGNDsEBpEBYojKbYkZjV+PqT9MNSMaxihOTqJXXpjHzmymLY+nB1B8LkUGz2aSkcqwc52yrEcx0KwwhSc4rV/4eu4f5oF2jkCYF+BIVODEK7wd5Quv+yEIDz3l2lOzOPADnnrbtjGocFCqOp6afkQEm0aeGFSFMOcMdXX1kMe/dfUmqnMRikeWR3S5kOgT7w83AVa46gMIzE2sZ27xRlA9lJujZ/FwuMtbAy3Vr7qy7Um6r1ji2nOIpSHyxQGM1w4NxllFZUqfUnTsZEPUQx7HvAstslAySlyPXejVJRbGPRDzPXSPNQXYwyYyMzvxTpsmcLwzgya2RzNpPcY2vWedlhekdxoY4K8RXrX/JidQ9wmqdeWKAw2mwvjg4yshj2QWD9S7pi2O5TpZk6/uFN25nWdUdQrkzzcBSFcWxo7s4eOfpJkc8uodJICKPQPwGwgwnGDRm2HaqMRJm3HGE6s+UKryVGBwsURjNsl9uLicGdAo0ohr0OiP9Bd9+o54CpXwJuXk3fmcAowMO/1lZjffMlgtM8jKOz/liGsAggrukWZlz000E6eGqOySAjurOthknLihJJpDC1wgKF0QS5xeVIkCMRdPbVoH/Cb/cA53frimGnfQOMeNq0Ytj6cHYGWstulvlpQF5anREUNmxjHB39+pPxpnTvUIpWESheLYGWHWCvKJ08xDYnvVTWyb+ts0M2AAsURhPsSsqCfAKGIVFGRE+SNklrnxDg7r+B2Cnm3ymDNI9hHUqYvyeC/TzUFE+VsvMM42AUlFZgyykpPRvi54E+euK9QXKSgcKLuvoTc51gaBDqbHJzkX6+r1LboVqxMji1VhJqzBWwQGE0wXZT7O3JJlqh93RdpMPcGEw2NkzzUAi7l9w6mF9SgbNZcoEuwzgYm09cRJlc3D6+W6hp5op2OiCwNjzdXDA+VoouXSh0RkbgVdIDeSk83bgOWKAwmhIoLs5OGNChVf1PVqInRMeRltupejp5atahcJqHcVQa7R7rYAKFmD2kvbr+o0j2WiI4zVMrLFAYq0NtuqcvFqo28n6e9fggUCg0SY6gUIi03WDL7Zh/GymFpKR4aoRhe+qFsuO4UJZxQEorKvHP8Uyx9vd0xaCODaRna6Iv/B1AoFzVvqVI9RDfZslT04mTa623UxqGBQqjqfTO0Ia6dy6f0ZmxtRtono6duqB8uJLmKcnVmUnVGkFhgcI4HjtOZ4kaFGJs11AxTNNoKiuAtDhp3SIS8Gni5HIbgFLDdw6JFOsL1cFI85CLgmlwYKHuOMg0QqC89NJL4gPWv8TE6FRgSUkJ5s6di8DAQPj6+mLq1KnIyNCZ9xDJycmYNGkSvL29ERISgnnz5qGiQp5iyTgkBgKlofoT/fROhxGwOAaTjQ3TPC283VW32yOpeSivlPLwDOOIs3dM6t4hLh4HyoscJnqiMKV3GzGpnVihpnmqgVPrrLpfdhFB6datG9LS0tTLtm3b1Mcef/xxrFy5Ej///DM2b96M1NRU3HTTTerjlZWVQpyUlZVhx44dWLJkCRYvXowXXnjBfD8RY1NUV1erAwK93FzQp13L+l+gpHeIjqMsvHc1Dpy1TDZWCmXJAfdEer7l94dhNAL5nqw7Kp2Aero5Y0TnYNM24GD1J/rFsrde1Vas11XoFfiz7X3TBYqrqyvCwsLUS1CQdMabm5uLL7/8Ev/9738xevRo9OvXD19//bUQIrt2ScPb1q5di6NHj2Lp0qXo3bs3Jk6ciFdffRULFy4UooVxPE5fLEBGXqlYU3Gsu2s9f5JVVboOHo8Ay3Xv6KNvl1+LYZvBZGNO8zAOxP5zl5FVKB23SZx4ubuYtgE7n2BcH7MGRYrZPAero5ELyQIfiRuAynJr75ptC5RTp06hdevW6NixI2bMmCFSNsT+/ftRXl6OsWPHqs+l9E+7du2wc+dOcZuue/TogdBQ3SCpCRMmIC8vD0eOHKnzPUtLS8Vz9C+MfbBdb3rx0Ib8T9IP6SYLd7gacDbxgNgYfAKl/DiRdkjKm9dl2HaeO3kYx+Hvw03o3tFPmTq5AOE94UhEtPTGuNhQVMIFGyvln700D0iWviuZRgiUgQMHipTM33//jUWLFuHMmTO4+uqrkZ+fj/T0dLi7u6NFC0OTHhIj9BhB1/riRHlceawuFixYgICAAPXStq0UHmMcuP7Eku3FNVHO7iqKgYvHrphSSq3RBEdQGEdKzSrtxa7OThgTY8L0YqKsEMg8Kq1DYgF3HzgaSsvxP5V6UVoeHth4gUIpmVtuuQU9e/YUkY/Vq1cjJycHP/30EyzJ/PnzRQpJuZw/f96i78c0DxWVVdiZJEVQWvm4o2uYPPvGGIO2ZhUodRfKeru7IlqeUnoyIx9FZVzwzdg/VBSeklOszs0KkIs+jSYtHqiusvv5O/UxuGOgGOuxuaoXKqtlczsWKOZrM6ZoSefOnZGYmCjqUaiOhASLPtTFQ48RdF2zq0e5rTynNjw8PODv729wYWyfw6l5woVVOcjV60BZXgKck8Offq2lYX7NhUEnz5V1KOTdQpDbPR24GcbeaZI5mwMXyOpDXbB3DIkUNSj7qrtId2adArJOW3vX7EOgFBQU4PTp0wgPDxdFsW5ubtiwYYP6+IkTJ0SNyuDBkpkWXSckJCAzUzL2IdatWycER2xsbFN2hbF5/5MG0jsX9kgpFiV60pwzO8J7AU7OdXby9JQ7eQiebMw4kkChf8PxsSamd2oKlIj+cFRu7NNGGNxtrNTv5mHTtkYJlKeeekq0D589e1Z059x4441wcXHB9OnTRW3InDlz8MQTT2Djxo2iaPauu+4SomTQoEHi9ePHjxdCZNasWYiPj8eaNWvw/PPPC+8UipIwjoVJ83eSrJTeITx8gWDZ7yfjKFAuC6VaO3m4UJaxb5IuFuBkRoFY02DAEP8GJo/XJ1DcfHT/Ww4IpYin9W+LDVV6UVq2vW+cQLlw4YIQI126dMG0adOEIRu1EAcHS/3v7733HiZPniwM2oYPHy7SNr/99pv6ehIzq1atEtckXGbOnIk77rgDr7zyiim7wdgBJeWV2HdO6siJaOmFdrLhmXEFss1g0FZXmqe6Uurm0aNLmB885PZodpRl7J01R3Rp+mu6NyK9Q9PIaYoxQVYBzdGNp2HuGNweiWiD81XS92j12e1AKXsqEa6mfJDLli2r93FPT0/haUKXuoiMjBTFtYxjs+/sZXUCaoPRk+IcXWqFzrb8GnFQbCpUyBe3VFrTvpDNvgzZe1M3z4HkHJzLKsLlwjK09HFv/n1kGJurP3HMAll96ORsdJdQ/JPYG7Od18Gpqhw4vRGIvR6ODs/iYazCNr30zpCGBMq57bqK/+ZO7xjRyXOFH0oKp3kY+x3sqQzGjAnzQ2RgI9qDuUC21pbjfwzSPNzNQ7BAYazCDtnenhjS0IBAa/mf6BPSDXBxr7OTR7G8J7hQlrFX1h5tYvSEYIFyBVdHByGjVX8UVUu1mBUn1kjO2Q4OCxSm2ckpKkOCHGWgs7AgXw/jBAo5TkYOhVVwdQfC5MFe2aeltFNdERSuQ2HslCa7x1ZX6wSKTzAQwKabSsvx7UM7Y3tVd3HbtfgikHYQjg4LFKbZ2ZWUJY5TRrnH5qUCl07qzrY8reiBYzA40PDg0T7QW7QLEnHnc4XTJsPYE1RbtftMtli3a+WNruF+pm8kOwkokQV8m/7NaxegcW7qG4FtzrpjTMmRv+DosEBhrFp/oun2YhMM2+gMSPFDuVRQirTckubeO4axKBuOZ4oJxsSEbqHib95k9Ou3OL1jgK+HK/x7TFJv5yesgqPDAoVpdnbIAwJphgdNMNZ0e3FdhbI1IihEr7Y6PxRO8zD2nN5pVHvxFROMuYOnJjeO6I/DVdKMnuD8Y6jMTYMjwwKFaVZSc4qRdKlQrPu0awEfj3o63SlNoggUN28g4ipYlcBowN2vzk4efUdZSvMwjL1QWFqBracuinWwnwf6tG3ZuA3pRx5b6w3JYwQdg32R2EJXZ3d86y9wZFigMFZzjx3SkL091Z4UyGdtkUMAVyu7DTs7S8ZSRH4qkJdW60wegiMojD2x+eRFlMq+ReNiQ+ufm1UXFWU6k8NWnQDvBqKnDkrbgTeo66LDju0ZxgKFaVZ2nJbSO8Sw6CDttxfXm+YxjKKE+nsi1F8SUQkXclEl5+sZxp7M2a5pbHtx5hGgslRac/1JnfQZNAaXIaWLY4v3IzFVd1LnaLBAYZoN6mxRCmS93V0MvEMaFCgdrFx/oqB/YK0nzZNfWqGmshjGliHH53+OSQNe/TxdMahjA75FdcH+J0bh7OKCi+HS8c7HqRRb1/8BR4UFCtNsJGYW4GK+dAY1sEMruMvza2qlsgI4u01aewcCoZI/gNXR7+SpZbKxfpqHDdsYezFVJMFNjO0aWv//bX1c4AnGxhKhl+ZxTVyL3OJyOCIsUBirtBc36H9CXTKlebroCdV/aIGACMlgSomg1PA70Z9szHUojL0NB6T24kahX/Du6qmdEw6N4h0zDpWQhiiOwH78vFceruhgaOSozzgC2+X2YqMEipbai/Uh7wclPE2GU2Q8pUfPNnoRlAvcycPYNuR7sk62t6eJ3cM7y+LcVC6ekArLCXKDdvM0417aIZ7+KG0zWCzbOV/Elp3bHbKmjQUK0yxUVFZhd5IkUAJ93NEl1M/2CmRrTfMY+qEEeLuhQ5A0QO1oap46sZlhbJEDyZdxqaBMrEd0Doa3ez22APVx+h/dOmqMmfbOvvHufq26jsnbgU0npTogR4IFCtMs0IRfJY9N04vrbVMsKwQu7JHWLdtLFy1hMNn4ysGBSpqnrLIKJ9Lzm3PPGMasrGnq7J3aBEqn0U3cKweh8zXqcrRLHBbvOAdHgwUK0yxsP6VXf9LQ9OLknUBlmTajJ1dY3l9ZKKvfnRTHdSiMDXfd/S23F5Pr85iuIY3bUEWpruDdLxwIjjHjXtoxgZ1QHRgllv2dTiDu5BmcvlgAR4IFCtMsbD9tQoGsFtuL9fEJBFpESuu0eKnjqC7Le+7kYWyUo2l5uHC5WKyptbiFt3vjNpS8C6go1kVPeECg0ThFTxDXrk5VGOF8CN/udKwoCgsUxuIUl1XiwLkcdQpq21bexg8I1KJA0U/z0IH34jGDh7q1DoCLnMI6xIWyjF2kdxrZvUNweqfxdJYECjHKJQ4/7zuP/BLHaTlmgcJYnL1ns0U9hlHRk8IsIF22ww7rKUUrtEg9aR5PNxe1CPhUZr6YY8IwttxePL5J9Scb5IWTNlO2WqbdYMDDXyxHOcehuKwcv+6/AEeBBQrTrPN3hkY1IDjObNZme3FN9J0wazFs6yUbtlFn4OEUjqIwtsWZS4U4kZGvDvWkMQ6NoiATSE+Q1uG9AJ8GTlAYQ1zdgU6jxLKlUwH6OJ3CNzvPOUzLMQsUplnrTxocEKjl9mJ96GDr5FxnJ08vPcO2eC6UZRxx9k7N/2dO75ihm+egGKGxVe+kz55hgcJYlMuFZTiSKjnCxob7o5WPu3ERFBd3KbypVTx8gaAu0jrjKFAuFwHWmMlDsGEbY8sChduLrUzUOCk9RgLFWfJdWrLjLBwBFiiMxacXK27wDU4vzj4DXJb/8SIGAO6S4Znm0zzVlbowtkznUF94ukn/XjyTh7GlgvaP/zmFg8nS3yzVUrWXjQdNhv7xFYHi5gO0HWDGPXUgfIPVY01X5/NojUvYeCIT57LsfxgpCxTGoqyIT1HXwxoqkDWoP9FwekehTR/dukaax9XFGd1bS2keatXMKpDHzDOMRi3tf9p7HiPf2Yh31p5U77++d+vGbzTzKFAgF9q2Hwa4ephhTx2UzoZpHtJ+VIti77BAYSwGTS7eII9pD/HzwJCGDNqSbEygNGTYpjfZmJx0GUaLZmx0Nn7tB1vx9K+HkJEnCWlqk581KBL3D+/Y+I1zesd8dB6vLse6xInrn/adt/sOwUYOVmCYhvn9YAoq5Grzqf0iRFShTqqqdBEUaqtrrRed0Co0kZVqZcj1tpZOHv3JxpTmGdWlkU6cDGMBqLtswV/HDIZ4EmO7huLZiV0QFdLAvKyGSFTai1mgNJmwnpILb34ahrocgSdKkV8CLD+YgpmDZNNIO4QjKIzFzsx+3HdevT2tf9v6X5BxGCjK0oWDXVxtowUwrIe0zkoEinPqtLxnwzZGK1y4XITHf4zD5I+2GYgT6jxbdt8g/G92/6aLEyoaP7dDWge0BYKim7jXDo6Tk2ra5lZdhiHOR9RiWTrW2issUBiLcCA5B4mZ0tyIAe1bqRN+7ab+xIjJxpGB3gjwclMjKPZ8IGG0T25xORasPobR724WZ94KbVt54aPpffD73KHC0t4skDiplOuuyMeD7e2bTrTOVfa2gKPi+lRmgWhEsFdYoDAWgSyZFaZd1UD0xJb8T0wwbHNyclLTPFmFZUjJMWxFZpjmoLSiEv/bmoQRb2/EZ1uSUFYhuTq38HbDvyfHYv0TI3Bdr9bi79VscP2J+ek4AnCRCo2vBh1rpBOexXbccmwDcXTG1qDCrZXxqWLt6+GKa3uENTztVAkH+4YBQZ1hMygzeeoolO3dtgW2ypOcKc0T0bKBOUQMYyYoYrfyUBreXnMc57N14tjd1Rl3DW2Ph0ZGqRE+s3N6o7xw0u48LVvD3QfoMBxIXAev4nQM803HtoJwbDiWgfPZRQ3POLNBOILCmJ3VCWkoLKsU6+t6hcPbvQEdfGEvUF6ki57YUjg4MBpw96tToBgYtrEfCtNM7E7Kwg0Lt+ORHw6q4oT+rW7q0wb/PDkC8yd2tZw4yU8HMo/oBLx3K8u8j4MPD3yoTaK4pj6Epbvss+WYIyiM2aH2N6OLY22xvVgfZ2egdW/g7FYgP1U6OPvpIkZsec80J4mZ+Xjjr+NYL7f368/AIlHSvY3u79FicHrHckTr2o2vKtsLd5cRYhDrsr3n8djYzvByd4E9wREUxqycvliAvWcvi3V0iK9IcZhWf2KD4eB60jwh/p4ID5AGrSVcyBX1AAxjbjLzSzD/twSMf2+LgTiJCfPD4ruuwtI5A5tHnBAsUCxHy0ggJFYs3dL2Y1qsl1oA/UecrvDZXmCBwpiVn/fpRoHfelXbhgvvSvJ0LqxUe+LfBOdKTRi2XTk4UOmMoLTX5hMXm3PPGDunpLwS7607iZFvb8IPe5JFuJ8I9ffAWzf3xJ+PXI2RXULMWwBbH+RnpNSfUOoz4qrmeV+HjKJU477wJPVuKpa1t05BFiiM2aiorMKvBySB4urshBv7tGn4Ree2S7NsbDG9U1sEpRbDNn278N/t8CyHsR5P/RyPDzacQpFc80VF6fMmdMGmp0aJ9Co5wjYrGQlAkTxplwo6XSxU5+LIdNbZ3rfL2qJGqY+n52PfOSl6bS+wQGHMxsYTF4W9veJGGejrYb/txfqQEZVPsC7FU+Ms5uqoIATKU5wp/J5XUm6NvWTsjCOpuVh1KE09IZg9OBKb543E3FFR1qtFMEjvjLLOPtg7EVcBnnLqPHEDZg3QnQCtPybPPrITWKAwFimOpfSOUSgCxckZiBwKm4TC50qapyQHyNaFXQmy+CefCYI8KP5O0I2yZ5jGsmjTaXX9/KSueHlKd+NOCiwJ159YHhdXIHqctC7Nw1ifM2rjo72lkFmgMGYr0vvnuFScF+bvieGd5YhCfVDHy8Xj0pq+4L2MKKi1iTSPoaMscYNeukvfxZNhGkPSxQL8mSBFT4J83XHbgHbW3iWgrBBI3iWtW0QCrZowaJAxOs0TcH4DesoF0JTmSc8tgb3AAoUxC8sPpIiR7cTUfm2My33bcntxfY6ytU02jghQ7f53nclCWi67yjKN59PNp9VM4t3DOsDTTQPtpWe3S4MzleiJLfkZ2Rqd6POVv75PrsEIvRPCLSftJ4rCAoUx+2DAW/qZmN6x1fbiOmfyXClQqItiilwsS18sK+Ikp12GMRUamfDbASkK5+fpillamWarn96JGmPNPbF/vFsBbQdJ66xTGB9eqD60mQUKw+jYf+4yki5K/yADO7RC+4YGAyrf0opAcfUCIgbApvEJBFrIYfbUOKCy4oqn3NBbl+b5nQUK00i+2JKECjlaeeeQ9vDzdNOWQHFyAdpfbe29cShX2a75u1Rn4K2nLoqOSnuABQpjneLYrETJeZWIHAy4SWZmdpHmqSjW1dboQcJNaQk8lpaH4+l5zb2HjI1zqaAUy/Ymi7WXmwvuGtoBmiD3AnDphLSO6G/b9WQ2KFBcEtdgWHSQWOeVVCDOTsZqsEBhmkRBaYXa6ujn4YqJ3cONe6E9tBebmOYh9L1hfj/IURTGNL7efgYl5dLZ8fQB7dBKbl+3OupwQO7eaTaCY3RR27PbMaajt92leZokUN544w2RW3/sscfU+0aOHCnu07888MADBq9LTk7GpEmT4O3tjZCQEMybNw8VFVeGxBnts/pQmmoSdV3v1sb7L+gLFHuZdtqmfkdZYnLPcLWAeEVcCqoU60+GaQDyz/lmhzQUzs3FCfcO10j0hOD24ubHyUnXzVNVjtFu8oBGFijA3r178dlnn6Fnz55XPHbvvfciLS1Nvbz11lvqY5WVlUKclJWVYceOHViyZAkWL16MF154ofE/BWM19ItjbzVmMCBRVQmc2SqtvVoCYVf+Ddkk4b11lfW1dPIQ5FMxXA7FpuaWYM/Z7ObcQ8aG+XbnOeSXSidyU/tGIDxAmsNidej/OUmOoHgEGEYSGcsSrUvztLjwD7qG+4v1oQu5Ih3okAKloKAAM2bMwBdffIGWLVte8ThFRsLCwtSLv7/0oRFr167F0aNHsXTpUvTu3RsTJ07Eq6++ioULFwrRwtjW5FQqkCW6hPqhp97k3nqhItLSXF30hCYC2wMevkBQF2mdeRQor72VWN8TxR4HfDHmp7isEl9tOyPWFIC7f0QnaIa0OKBYtljvSPb2rtbeI8eh/TDATU7tnFyLEdHS3C+lWNbWadQ3w9y5c0UUZOzYsbU+/t133yEoKAjdu3fH/PnzUVRUpD62c+dO9OjRA6Ghoep9EyZMQF5eHo4c0YWo9CktLRWP618YbQ0GnGbMYEAF5WzLHtqL60rzVFUA6Qm1PmVcbCi85VTYn4fSeMIx0yBUGJtVKJ3ATerZWvXU0V56h9uLmxU3T6CjPFKgMBOTAnUu1fbgKmuyQFm2bBkOHDiABQsW1Pr47bffLqIjGzduFOLk22+/xcyZM9XH09PTDcQJodymx2qD3isgIEC9tG1rZCqBsRjleoMBKR9u1GBAhTN2ZNBWk9Z9dOs60jze7q64pluYWnG/8bjtH0gYy0HjET7fohuf8NBIDUVPriiQ5fk71uzmic3ZCB/55GfLqUs2X+NmkkA5f/48Hn30UREh8fSsvS30vvvuExERipJQGuibb77B8uXLcfq0bm6EqZDQyc3NVS+0H4x1IVv7SwVlakTA6G6CsiI9O+x2QEsNFfqZ21G2jk4eYopBNw+neZi6ob+PNNm+fExMiFpnoAlK84Hzu6V1q05Ay/bW3iPHI2Yy4Cyl1VyOLsfQTlKaJ7uwDIdT5VS6IwiU/fv3IzMzE3379oWrq6u4bN68GR9++KFYUwFsTQYOHCiuExMTxTXVpGRkGE5cVG7TY7Xh4eEh6lj0L4x1+VmvOJbGuhvN+V06O2yqP7E3O+zQ7oCLe72dPAQdRILkwW4k9nKLecIxcyU0PmLRZt3J3UOjoqApzm6T0pkEd+9YzySyoxyJzj2PqaGS7QOxycbTPCYJlDFjxiAhIQFxcXHqpX///iJSQmsXlytbTOl+Ijxc8scYPHiw2AYJHYV169YJ0REbG9v0n4ixOJl5Jdgo/+GHB3ji6mgjBgPas/+JPq7ukkhRzOiKazdMognH1ysTjiur8Jc8+I1h9PnrcBrOXJJcmgd3DES/yCubEqwKtxdrg+5T1eWQks3abTcmB/HlD1pGoPj5+YnCV/2Lj48PAgMDxZrSONSRQ5GWs2fPYsWKFbjjjjswfPhwtR15/PjxQojMmjUL8fHxWLNmDZ5//nlReEuREkb7/Ko3GPDmfhHGDQasbUCgvfif1JfmoQ6HOrihjyRQCJ5wzNQ242rhRl30ZK7WoidE4gbpmlIM1FHCWIeYSWrk1i9xFaKDpBKMg8mXkVukoegspQOPrzT66Wbt73R3d8f69euFCImJicGTTz6JqVOnYuVK3Q5RlGXVqlXimqIpVEBLIuaVV14x564wFjxo/tyYwYBEUTaQFi+tQ3sAviZEXuzMsI3o0SYAHYOlbozdZ7LFEDiG0Q/P00gEZRr20ChdC6kmuHwWyJYFFM3S8uTUu9XwDACix0vrgnTMbC1FZOk8clviJWiGuO9NenqTG9Y3bdKF7Km7hmpSGiIyMhKrV69u6lszVmDv2ctI0gs5twvU2Ss3yJktJHHss71YH32jqjo6eQhqy6YBgv9dd1LcpgnHD2qtQ4Ox2onAxxuluj2l9sToNn5rdO9EcXrH6nS/CTi+SizHVW3Di7hOrDefzMSknkaOILEk5At1ZLlJL7EThyxG04MBHaG9WJ+gaMDdT1qnHqz3qfoTjtm0jVHYcyZbNUGMDvHFuK6G1gyagOtPtEXna1TTtvCUNfB1q1LrUEjwWp3jfwKlpnmYsUBhjCa/pFwYixF+nq64pnvtXVcNFsg6uwHtBsNucXYBWveW1nkpQH7t/j4ERaD6tpMmvx5Pz1dD+oxjs3CTfudOJzibUufVHFRW6E44aFwFjXlgrIu7jzqbx6k4G3eFS1OvM/JKxbHF6sQvM/klLFAYo6GpxcXlUiv5lN6t4elm5GBA4vI5IFs2m4q4SrKFt2eMMGyrdcIxR1EcnkMXcrBF7r6IaOmF63rqiqk1A0UGS3J10VAS5Yymunmud92lnW4eOkk7LRdU+xtv6skChWlceqe/PObbWBwlvWOiYZtiXe6qTjhOtXn3R6ZpfKLXufPAiE6iJV1zcHpHm0SNBTykYuVOlzbCHeXasL0/9BNQXXWFiGoIDf7lM1rkVEY+DiZLnh7kZNm9jYkV+0mOJlCMK5QlyIV3RGepo4kcQ6mjh3Hc/7O/j0gpwWA/D9HGr0mUs2GCBYq2ZvPETBZL57J83Ox/XKz3nctGgTwJu9mh+pf4H3S3e9xs9EtZoDAmR0+m9Y8wraOgqkpXf+Lua/jlba8EtAW8g3QRlAaK1PQnHLP1veOi7xp779UdTEujNhdkPnhhn7Sm6d0BGhVRjtzNI3Ob915xXV5ZjR3Wajcmawma7k60HQi06mj0S1mgMEYNK/vtgPSl6e7ibNB5YhT0x1kk/3OQmZOLG+weEnCKEKNR9JfP1Pv0sV1D1SFfqxPSUCLX+jCOw/nsIvwRlyrWAV5uuH1gJDTJ2a1Atfz3ydET7dFxpFS4DKBb/nZ4ocS6dSj60ZNet5n0UhYoTIP8czxDHfU+rlsoWho7GNBR609qq0NpIM3j5e6Ca7pLXgX5pTThWDcKgnEMaGKx4tB855D28PVosk2VZeD6E23j4gbETpGWlcUY7xpvvXbjijIg4Wd5vzyAbrrojjGwQGEa5Kd9F9T1raYMBnSU+TtNNGxTYOt7xyUzvwQ/ymlUb3cX3DVUw1OBFYFCdgHth1p7b5ja0CtEnekrpeMuXC5WTTabjcR1QFGWtI65FvCSLBWMhQUKUy/puSXYdEI6m2/TwgtDo+S6ClMU9Nnt0to3FAiOgcOgX2vTQCcPMaRTkCiMVGzOc4rkqc+M3fPltjMilUrMHBSJFt4mRimbC7IKIIt7ot0gyXuD0R6RQ6XjLYA+ZXvhhyLrTDfWt7bvdbvJL2eBwtTLrwcuiHkOxFRTBwMqs2jKC3XDAbVm121JfIKAFu10hWJkblUP9NnqTzhenVC3wRtjP9Awt6U7z6k1XvcM6wDNogwHJDi9o12cXYDYG8TStaoM45z3NX8dCs1eO7lGWpNYkv9e/j5s/OR2FiiMUYMBSVfc0piWRwrxKdjz/J2G0jzlRcBFqeWvPti0zfFYsvMsCsukotNb+kcgxF+aRKtJ9OfvsECxmTTPzR57xPXupKzmK8BP+AWokicp97gFcHEVEfn5vyUYvQkWKEydkB/H2SwpNDi0UxDatjJhMCBBBVlHV8g3nICocXA4TEzzdGvtj6gQX3Uey4XL0ufP2CeFpRX4avsZNYJ2/3AND4usLJcHflKhTBAQ1tPae8TUBzl2k90BgIHV8WiBfJRWVGFXklwTYmni9dI7vaX0zhdbk0TLs7GwQGGM8j6hMzuToYhB1ilpTbN3/DQ48ExDnTy6Cce6YtkV8VLbKWOf/LAnGTlF0lkmpfdMmg7e3JD3SZk806XTKMCZvz40jbMz0O1GsXRBJa5x2dt8dSiZx3WDUknIhnZDdmEZvt8tzQcyFv4LY2olr6Rc+HEQ/p6umNDNxMGAhBo9ARB7PRyS8F5S9EipxzGCKXo+M8sPpGhjEiljdkorKkVrscKDIzUcPSG4vdi2Z/O47BTXypyn5o6eLN5+Rp3lZiwsUJhaWRmfipLyKtXltFGOlsdW6tZdr4ND4uGn61zKOAxkHmvwJZRK6x8pGS2dyizAUZ5wbJf8uj8FmfmlYj0+NhSdQ/1gMwKl4yhr7gljygmS7Nw6yPkognFZtBony6l7i1BVKc3eIZxdRf1Jfkk5Fu+Qur+UuWPGwAKFadD7ZFpjvE+oHTEjQVco6sh22MrsCRqW9ff8Bm3va1rfK+6ijP1QUVmFT/Vs7R8aFQVNQx0ZSg1VSCzgL5kKMhrHyUmNojijGte6SMWym09ZMIqStBHIlzt1oseLbsbvdicjr0TqYpzc0/i/HRYozBWcSM9H/PkctWize5sA0zfC6R0dg+cCAe10/7wn/27wJZN6hKtnGn/EpagOo4x98GdCGpKzpbPYYVFB6N3WNAOrZofcoJVptJzesdk0z2Q5zWPR6cZx+tb200XX0P+2nlH10t1XG99GzwKFaWAwYCOiJ8QxPYHS1cEFipsXMP4V3e01/wdUSKH9uqBxAiO7hIh1Rl6paA9k7IOqqmp8slE/eqLx2hOC609sl5CuUtQLwFXOJ9Eal7Dj9CVRA2V2SnKB46ukNc0D6jxBWFVcKpCOd9d2D0fHIKlL0RhYoDAGkJulYrPu7tqIwYBE7gVdQWhodyDQBg7AloZMk8jdUUl/7f7UJE8Utr63HzYcz8SJDKkbpk+7FhjcMRCahlKSiv8JzVOJHGLtPWKaMOF4kssuFJVVYv/ZyzA7R34HKqThhOh+M8qd3PDp5qRGi3EWKIwB649liHYw4ppuYQjwbsTk4WOygiYcPXqiQLHNaxboOno2vw0U1D8QcEzXEHVg3N+H03nCsR1AHVkfb0xUb/9rVJRoLdc0WYlArhxVJXFCEUHGtuimEyjXKWkeS3Tz6E8u7j0dK+JSkZJTLG6O6hKMbq1NKxdggcJYOL3joN07dVXU971DWpOfxAa9tE8tUOfUxO5h6oTjDcd4wrGts/N0llrfFRPmh9ExUhpP03B6x/YJ7ASE9xbLns5nEOmUbn6BQpHhZEn8IKgLqsL64JNNOjE+txGF4CxQGJW03GK1R54GAw7p1IjQM0UFzu2Q1oFRUv6T0TH634CHv7Q+uBRIjTO6m4et720f/ejJQ7YQPSFYoNhfsazzLhxPzxfHfLMR/6Nu3Xs61hzNwOmL0hy2AR1aoX/7ViZvkgUKo/LLPt1gQHKOdTZ1MCAhCqSqdekdWzgANye+wcCIp+Ub1cDfz9bbdjyoYyBC/ZUJx5m4LKffGNvjQPJl7DgtFTu3D/QWnVqah6aRn9kqrX1ChCMoY6N0k1xl9dM8ZjNtq6rSpXecnFHdYxoWNjF6QrBAYQRkpLNsr95gwEand/TM2Ry9vbguBtwPtJKLxSgkeuQ3oyYc0wwLak9lbBP9zp0HRnQyfTK4NTi/WzeNnKInfMJhu7RoC7QdJJYxzufR2em8+dI8dBzLkSZyo+NIbMlwx+EUyWCyext/DI8OatRmWaAwwmPj0WVxajHT8OhgkeIxmeLLumFi5Psh5zyZGri6ywWzMuteBMqKjDRt4zSPLZJwIVcUoBNh/p64sW8juuOsnd6JGmPNPWHMgV43D3mibD11SZgGmtXavtd0LNRLZc4d2fhUJgsUBm/8dQz/HJcKMAO83PDS9Y0M4574C6iq0BXH8tlW3ZDDYif5gE8dEjs+qvOpseH+6BwqeQfsPXsZ52WDL8Y2KC6rxOM/6WqN7h3eER6ujRgdYXV7+5HW3BPGXHYHTtLX/nXOO0XkPE4u2m40dHJ15A9p7e6H/d5DxSR2olOwT+PmuMmwQHFwftybjC9klz9yLl00oy86BPk0bmPsHmt627GT/EW17T3JP6bWpzoZDBDkCceS2Rl1nI1/bzPu+GoPcuWJwFrktdVHkZhZINZdw/0xc5DsKqx1Ci8BafHSOqwH4GsDHUdM/dBE+fbDxLKDcwa6OZ1t+nRjqjtUplx3m4KPt+qivA+OjGpcLaMMCxQHhtxJn//9sHr75SndMCSqcblClObrzrZ8w4CIAWbaSzsmuAsw4D5pXVEMrH+pzqdO6S3VoSimbY484fhwSi5u/nQHnv7lEE5mFIhCvyd/jheiRWusO5qBpbukEfOebs74aHpv24meJG3SFbxz945ddvNc57Kz6XUocbr0zpmIKdgoCx4qE9A/bjUGFigOCk2zfGDpflF4Sdw5pD1mDIxs/AZPrQUqZfv2rpMBZ/7TMoqRzwBecvtdws9A8u5anxbR0hsD5DY9Ohs/kup4E44pSvLv3w/j+o+34UCyYVia6js+26JzrNQCGXklePoXOQIB4PlJsYgK0fjEYn0U91iCBYr90PV6acqwqEPZhcMpl1UrepPJTZGFLBXhRuLd47pW4gdGdISbS9O+B/hbxAGhvOOcJXtxWQ6LD+8cjOcnNdGvRD+9w+ZsxkPzKkY/p7v99zNSy15DnigOZH0v0jl7z2PUu5vw7a5zait8x2AfzJvQRS11envNcTFjRCv7/ORP8er/2LjYUMwYaCOpHdXefoO0dvVSuz8YO8C7FdBxlFhGOF1CH6dEbG3sdOND5H0i/UNejp6KPw9LheBBvh6N7wTVgwWKA3bsPPzDQZySc+JUxPTR9D5wbYrSLS8GTq2T1hQNiJRynIyR9L0TCJELk1MPGtpF63FtjzC4uTipdSiOMOGY0jlTKZ3z6yF1BIO3uwuenRiDvx8dLvwVHh4dLe6nj+ORHw6KyIW1+XLbGWxLlMRSiJ8H3pza0zZM2RQuHgfy5Zb29kMBN09r7xFjwTRPo+pQSMTqHas+yx2gWjrdc3UH4YTdVFigOBivrz6m/jG28HbDl7OvEp07TYJqTxSvhJhrARcpfMgYCX1e+m3HG16Wanpq0MLbHaPkCceZ+aXCNt1eySkqw/O/J+C6j7fhoF46Z1LPcGx4coTwEaFhlsSjY6JxteyzcKmgDP/6/gDKzdE62QRR9daa4+rt/07rjVY+7rApDNxjub3Y7oi5FtU0+FEMD9yNbSfSTT/hSTkAXDoplqVtBuF/h6XX+3u6mi1ayALFgfhhT7I4s9N17PRD+8Z27NSZ3pnS9O05Ih1HADGTpXVBBrD1XYeccEypEeosG/3uZlFcqpyRUaTvu3sGYuHtfREeYOjRQ4ZnH9zWB60DPNVW7Lf+1gmE5m4pfnTZQbW2677hHTGskSZVVoXt7e0bzwA4RY8TyxCnHHQuTRDCurHeJ3+7jEJFla6e0c+ziSe9Mnyq6yDQ2TYVGCq8ekN3DG7MrJ3arLDJ/4SgGTP0Rcs0jvH/kYuNy4CdC6XBgq06GjxlVEwI/DxdkV9SgTVH0vGfsu7wcreRrhAjzMz+/cdhA18GSudQhOSuoR3UiEltUIRi4Yy+mPbZTiEOqHW+b7uWmNjMdvKv/nlUnT/SrbU/nhrfxfJvevkcUHRJ+l+kQnVxrb+u477KcqCitMbjZdJ9iuGiX2up24yxzzTP8VWqJ8rmkzeiV9sWxr2W/kYSfhHLalcvvJwkpVm93Fxw59AOZttFFigOwLmsQjz43X5V4d49tAOmDzBTwR4dyEpl5d15AuAqhQ2ZRtCqAzB4ruSJQl8Ua/8N3PadwVMor3tt93D8uO88CkorRPfKdbIVvi2nc95ecwLf79FFTIjJPcPx3KSuV0RM6qJPu5aiU+bFFUfE7Xm/HEKXMD90DJZM7iwNCcbvd+taiimqU5+oajJVlcCKh4E4w78Rs8L29vZL5wmocvWCc0UxJrrswf0n0vDIGEloNMjJv4ES6UTiWMBwZBdI0cvbB7YzazqTUzx2Tl5JOe5evBc5cjfBiM7B+L9rY8z3BsdkB0GlfY1pGlc/CfiGSms6u1Fa+PSY0qe1XVjfUzqH0o6j3tmE73brxElUiC++v2cgPq4lndMQdwyOVAUbCbiHvjsg0i6WJj23BM/8eki9/eJ13cTPYTHow1r5qGXFCfkZDX7IcttnrIu7D5ypZhBAS6cC+KRsFScLRhGnK459/1J/cU0F/PdebRjxbSocQbFjaMbCv74/qIac6YD50e1N7NipeQZ3/E9p7eYNRI01z3YdGQ8/YOxLwO8PSrf/ng/cv9Wg8HhQh0CEB3giLbdEFDxTd4utFWEeupCDf/9xBPF66RwfSueMjcadQ+pP59QHdcq8cVMPHEvLE34xNFL+ud8T8O4tvSzWRSNain+OU08CJnQLxW1XNb3Fsl5xsub/gIPfSrfJ06LPLPGFIyKYVPzo4iav3XXX6rqhx+Vrai9mPyP7T/Mc/lUsJzvvFJ1nk3s2EJEtuAgkSl2bBe4hWJ8nWVTc3C8CYXIdmLlggWJhqDK6uLwSvh7N/1G/tvqYOk67pejY6Q9/MxUvCc7tAIrkThISJ+7e5tu2I9PzNmDPF0DqASDzKHBgMXDVPerDzvKEYzImo7TdqkOpuGNwe9gClwvL8PbaEyJyop/OoZ/n/67tapYDnI+HKz6d2RfXf7wdRWWV+O1ACvpHthLhZ0vwxdYkbE/MUgcBvnGThVuKN70B7PpEWtNclZu+MBgCxzBGEzUWFW5+cC3PxzjnfVhw7ELDAoUMJeWZaz+XD0EVnEFu9vcPlye0mxGWxxaktKJSuF72eGkN5n5/ACczrmwdtRTf7T6Hr7efVUNvn87sh8hAM3Ts6HNMv3uH0ztmg85aJ76pu/3Pa0CRNHxLQX8a7jc7z2nS5r2mUKf6DDJbo2tFnERTOufegfhweh+znn2RYyt5jyi8tOKIiNqYG+p8eGftCbEmTfLfab3Q0pLRrB0fA5vf0N2+7gMWJ0zjoUgZOX9Te7BTMSpOrmt4jIZe987SkqHimtKqZukIrQELFAuy5eQlYUlOv+8/D6VhwvtbhFA5kW5ZoUJumi/+IRUKEv+5oTsGdjRDx44+5HZ6bKW0ppAwFcgy5qPtAKDHNGldnA1sftPQxiDMHwM76KzvNzfWCbIZIPF0/7f78H/LE9Q0CKVzyL149aNXY0gny7Th0kGTWh6JssoqPLj0gPE5diMoKqsQxnD6LcWNnmVlDPuXAGv1XIcnLJA6vRimCbj2uFldDy/bItKidZJ+GEhPEMujTlE4XS2dKD040vzRE4IFigVZeyTd4LaBUPnOMkLlzKVCcSBWOnbuGdYBt15lgdB2yn6d0yTZJnv6m/89HB2qRaHaHoJSPpmG3h736BWkfSlPpNYiG09kYv2xTPU2DRD756mRYv+bOqujISht1Ked1DqZklOMx3+MM1u06dVVR5F0Sarv6tEmAE+Os2A7LtUJUFGswsj5XMDKmIeOI1DiJv2PjHE+iG1HpU60WtFzjv2hTHIMH9s1VJwwWQIWKBYsUKUWUMXLYf7EGAT56kK/fyaYX6jkFkszduiaGNUlGPOvbeKMHWO6d2I5vWMRAtoAwx6X1tWVUmGkXvh1TEwI2gdKAoaK26gwVIss2nRaXVMKhNpvQ/2bxzqdim3J3E0pIqZJq59sSmzydv8+nIYf9pxXvR8+uK235VqKT64BfqOp1/LvfvC/gBHPWOa9GMfDxQ3lnaX5ad5OpSg6LEfGa1JZARz6SSzL4YqVlYPF+qFRlomeEE36j3rjjTdEMdhjjz2m3ldSUoK5c+ciMDAQvr6+mDp1KjIypC9qheTkZEyaNAne3t4ICQnBvHnzUFEhFd3YC/vPXVYHhY3sEoz7R3TC1qdHi7C2JYSK1LFzAElyx07nUF+R1yeXTbNDX5KKe6yTC9BFalVjLMCQh4EAuSOEhreRkZteseycYTpTJMUlWEvsO5uNfecuq/UmN/TW1c40F61beAkBodStvrvuJLadavxQwbTcYjzzqxTmJl66PtZyXitntgI/3aEWJYqUDhn6sTcJY0b8+t+qrrtmrRct+rW6CxdKkdD1lX2QAz8M6RQoDBE1J1D27t2Lzz77DD176grRiMcffxwrV67Ezz//jM2bNyM1NRU33aQr4qqsrBTipKysDDt27MCSJUuwePFivPDCC7An1hzRibLxsWHimhw/Kaxdn1B56Lv9OJ5u+pnwf/48hq3yQZfOFv93x1Vmsxu+gvRDQM45ad1+mDQdk7EMbl7AuFd0t6ntmFw/Zab2i1BnKZEnSqYGBuXp8+lmXfSEajRIVFmDq6OD8fjYzqq+fmTZQSE0GlPs+8SP8WqUcmL3MEwzw9TWWrmwH/jhNqBC/p12uwmY/D6LE8b8RA5BnqtUpzjCKQ57jp2ptzj2t8qrxTUN67QkjRIoBQUFmDFjBr744gu0bKlTT7m5ufjyyy/x3//+F6NHj0a/fv3w9ddfCyGya9cu8Zy1a9fi6NGjWLp0KXr37o2JEyfi1VdfxcKFC4VosQeoCnrt0XR15o0y4E3hSqGic19dnZCOa97fapJQoRH0i3cYduy0k0P/FkEpjiU4vWN5ut0ItBsirbNPA3s+Ux/ydndV22epWJP+FrQCda0ptSfk2zLFCtETff41KkpEMwnyjqGoZVmFaUMFP9+ShJ1JWerPtOCmHpZpKc44Aiy9CSiTpo4jegJw0+eAs32MNWA0hrMLcjpI3TweThXI2r/c8PHiy8Dx1WKZVe2HTVW9hS0+RVAsuluNeRGlcCgKMnasoTHX/v37UV5ebnB/TEwM2rVrh507d4rbdN2jRw+EhobqitEnTEBeXh6OHNF1nuhTWloqHte/aJljafm4cFk6O6N5NwHetUcydEJlVKOFCoWqqYVS4bUbe2CA3N1hMdThgE5AjJS7ZCwIfQFOpNZS+Ytw81uSWZLM7MHthRAmlu461yzOqaZGTygVZVHbdyOg6M1703qjTQvJnfZAcg4W/HXM6NdTm/K7Bi3FvcWEabOTdRr45gbVShztrwamLZHM1RjGQoQMnq6uI1JWG7YbH1kuzWwCsKJyiKhBmTuyk2X9fhojUJYtW4YDBw5gwQK98fAy6enpcHd3R4sWhgOHSIzQY8pz9MWJ8rjyWG3QewUEBKiXtm0t6NJoBpToCTE+1vBnbUio/HtybK1C5cGl+68ogky6WCAEjDImm0LoFgs3K1w8AVySDtJoNwjwa/jnY8xAeC+g7yxpXZoH/POq+hD5hyj27lT39NvBC7A21DGzIi5VrCkFZbbZT02EPEoWzewLd7l7iLyCVsZL+1kfhaUVeHRZnNod9+CITuYZtlmT3AvAN1PUXD/a9AOm/yCl+hjGgnh2GISLLtLx/KrKeJxJlorAa1rb/1I5XNQ4UveOpTFJoJw/fx6PPvoovvvuO3h6Nk8VPjF//nyRPlIutB9aZq1e/clYIwSKvlChM83ahMpfh9Mx8QOdUMktKsc9S/Yhr6RC7eh45hozzthpMHpC1VQcPWlWRv9bmhhNHPgGSItXH6pZLGtt4zZqe1a+zGcPjhTurlqhZ0QLvHBdrHr72V8PCS+Z+nhl5VHRwk/0igjA4+OkehazQlExEie58vEtJBaY8Ys0/oBhLI2TE1LbTBRLV6cqpO78Ubr/UiJwYY9YHq9qiyPV7fHQyKhmqSczSaBQCiczMxN9+/aFq6uruFAh7IcffijWFAmhOpKcHEPHRuriCQuTCkXpumZXj3JbeU5NPDw84O/vb3DRKuezi3BUjnTQgczUYWe1CZVgvyuFyjUfbFE9GLqE+uEDS3Xs1OseywKlWfENAUY8Ld+olgpm5TBs9zYBGNRRSu1RJ9emkzrfEWvY2S/bq5vqO1s2S9MSMwa2w419pJqYwrJKIfwpSlIbqxPSxPRoxTKA2qTN7t9COf5vbwSy5BboVh2BWb9zATrTrLQYcJu6bnVm1RXeJ79WXo12rXzEpPHmwKT/sjFjxiAhIQFxcXHqpX///qJgVlm7ublhw4YN6mtOnDgh2ooHD5Z6pumatkFCR2HdunVCdMTG6s5qbJV1R/W6d7rVLrgaI1ReqCFUaFAcEUgdO7P7N8+sn+wzUgcP0boP0EIbYXuHYsD9QCvZd+DcduDo7+pD9wzTGbf9z4rGbWS9TzNwCEo5BupFArUC5c5fu7G7CFUTpzILhNNtTZvv1JxiEWFReOn6bua39C4tAL6bBmTIrcv+bYA7/uD0KdPstIsdiHNOUro4piQexVnngUNSJKWy2gm/Vw7FAyM6mW/gbAOY9C5+fn7o3r27wcXHx0d4ntCa6kPmzJmDJ554Ahs3bhQRl7vuukuIkkGDBoltjB8/XgiRWbNmIT4+HmvWrMHzzz8vCm8pUmLrmFp/Ygyebi64uxahQnn0T2f1Q9tWzTSkT797h2fvWAdXd2DC67rba18AyqWC7NExIeggf3nuOJ2FI6m5zb57VKC7ZKfUUUYRPXOPXzcn1AG1aGY/Vdz/EZcqiowVqLaLnGeVNOqkHuG4pV+EeXeivARYdrsaQod3kCROWPwzVsDJ2RmJwePE2tmpGgW/PaamHLdU9YSTXxim9mu+bjyzy6D33nsPkydPFgZtw4cPF2mb3377TX3cxcUFq1atEtckXGbOnIk77rgDr7yi5/Vgo1Dr4p4z0lA3+qKICjGveZO+UPl8Vj8xx+Sq9s0YAubhgNqA5h51GiOtc5OBfV+JJeWE6e/DmsZtP+07L/4PCAoDN5t4biSdgn3x1s06L6dXVh3FweTLahfSbvn/uXWAJ16/0cwtxZXlwC93A2c2S7c9A4A7fgeCos33HgxjIu69b1HXwSnr1fWvlcNFI4aHa/O1ujtVNzi6UHtQmzFFa6hgVkv1KD/vO495v0jh4PuHd7Sczbw1yE0B3ovVFe89JLWNM1aCfDIWDdG1od65Sh1gN+SNf8RQPvLE2fbM6GazlS+vrMLItzeJDh5i9SNXI7a1dv4/G5qrowg6EiOv3dQD9y7ZJwp9SZMsu3eQeQdu0rDN5fcDCZJ1ONx8JHFCQyIZxorklZQj9fU+iHHWNaPkVXtjrPMX2PjsNU0ueDfl+5tn8ZiRtWasP9Ecx//UrTl6Yn1CuwEt5WjJ+d1AWaGatqACUMW47Rs53dIc0CBMRZyM6BxsM+KEeHZiDPpHSqaTqbkluOvrvWoX0tyRUeYVJ3ROuPpJnTihaeDTv2dxwmgCf083HPSXI7QyqyoHYcbQLs3ejccCxYy5963yyHtqD+7T1tALxubRT++we6w26DhSuq4sA5J1Ea07BrcX0RPiu93JIqpiaSgQq2/MRoV0tgR15Xx8e1+D8RNE77Yt8OjYaPOKk/Uvqmk5McvqlsW63yXDaICK2BsMbv/pPBKzh0Q2+36wQDETW05dREm5ZJs9LjbUajNHLELhJaljhKAOEkrxMNan4wjdOmmTuqSUjmLcRqmeXw+kWHxXNp28iOPysEv6Uldanm0JMrz78LY+UP51fURLcW/zthRvfRfY/oF8wwm48VMgZpL5ts8wZqBPr77YWSkd549WRaL7wHGWcU1uABYoFjBnG9/NztoDj68Cqqt03ic8rEwbtB+us7/XEyg1jdu+agbjtk83GUZPLG2BbSmGRAXh/dv64OroIHw2qz8iA83YUrz7MwMHYEz+L9Bzmvm2zzBmIjbcHy96PIV55ffhvqpnMMdK3XgsUMxARWUVNhzPUM+6LD1Aqdnh4YDaxCcQCJc7UNITpEiXTLfWAerfITmg/nPccsZtB5Ivq90uHYN9zNZeby2u79Ua384ZiGHRQebb6LmdwF+KyR6FWV8B+t9tvu0zjBmhDMCzNw/DmYgb8cwtoxDSTIX2V+yHVd7Vzth79rIIpRMjY0KatQ3L4hTnAElyG2RAW6B1X2vvEaOPfu2C0q4qc8/VuijK/7YlNUv0hLrX7Cq9aS52fKhbD3sCGPqoNfeGYRpkdEwofnlwiJoutgYsUDRqzqYZTv4NVEnii9M7GhcoNdI8IzuHiIgGsSspG4dTzG/cRjNs1h2Tooeh/h64QbaPZ/TITgJO/CWt/VoDo/7P2nvEMDYBCxQzdC8o9SfUOTEqJgR2BQ8H1DbtBgMuHrUKFIpk1BwiaG4+33JaGQck3qvZoocVZVJaK+EX6VrL7PlCmp1EDLgHcHGz9h4xjE2gnRGjNsqR1DzV+2FwpyDRQ2430IyQ0/JcJZ8QoO1Aa+8RUxM3L6DdQODMFiAnWZqX1EonSm7qE4F31pzA5aJyrIxPFROvqVvFHKTlFmP5QalDyM/TFdMHWMiePT8DyDgsX44A6YeBSyeAqgqdj8i9/wBhPaA5SvOBg0ultasn0PdOa+8Rw9gMLFDMac5mb+mdxHVAhTSUEF0nA852VFtjb2keEihKFEVPoNDAyZmDIvHRP4nCeIzm5JBIMQfUHURmcMQdgyPh11RxTlEREh4kQPQFSaHkL1Qn5AOz9b/ALV9Dc8T9AJRK083R4xapsJlhGKNggdJE1h7R1Z+Q/4n9pne4e0fTAmXDKzqB0v8ug4dnDYoUJmokJr7bdQ7/GhXVZEfI3KJyfL87WazdXZ1x5xCdKDJLVKQ+nF2BoC6Smy5F+IqypKnOWc8DgRoyiCM7+92f6m4PetCae8MwNgcLlCaQnFVkYE7VXDNPmgWasnpqrbT2agm0H2btPWLqIry3NGiuJFfq5KEvRmddeRm1CF7fqw1+PXBBTOala3KbbQrf7jqLwrJKsaYJv8qE7TohARL/g/FREQWa7hvWHQiVL7QO6gy4euiMz0ickU/Pjo+A696HZiDxlH1aNy+JBBXDMEbDAsVc3Tv2Zs6WtBEoK5DWXa7lwj4tQ6k3+gIkQ73iy0D6IaB1b4OnUAErCRMlNTNzYGSj24FLyivx9XZpxg9tgiacNliHsXgSUJJjXFRECJJuQGgPwDek/s6x/nOAre8BZflA3HfAyGcBP43Mwdq1SLfm6AnDmAwLFLPVn2jkoGguOL1je2keEihKmqeGQKHBfUOjArE9MQtns4qw4Xhmo1OSP++/gKzCMrG+tkd4w26rp9YZipOGoiKm4NUCuOpuyT6ealFIFIx7GVbn4kldgXmLSKDzNdbeI4axObjNuJFkFZRi31mde2ZUiC/shspy4MRqae3uB3QaZe09Yhqio97vqEa7scI9w3SRjv9tTWq0a/IXW5JMGwqo70Q8/UdgXiJwxx/AhNeA3tOl7pvGiBOFQQ/pWq1pCB+luqzNns906wH3cYE5wzQCFiiNZMOxTCjjTewuekIdIcoZb+cJTfvyYJoHKg71j5DWNNmYaohqMKJzMDrJxm1kTZ9wwfQv8tWH05GcXSTWNK+me5sA02qZosaa3+yPUjq9b5fW1DGz90tY3X2ZuncINx+gz0zr7g/D2CgsUMxQfzLB3upPjrE5m81BX/qKqyy1hp/ffcVTJOM2XRTlSxPt78mUsOZQwAahaI5ayzQJcLFQVnnIw4CTfDjb9QlQLnkTWYWD3wLlhdKaIkSUhmIYxmRYoDSCwtIKbDklDWYL8fNArwg7OgBVVQLH/5TWrl5A9Dhr7xFjBtt7hZv6tkFLb6ngedWhNGG2ZixbT13C0TTJ06NnhG4YodHpHfLSsWQEKfYGaU0dQlQwa63/nz2f624PfMA6+8EwdgALlEaw9dRFlFVUiTUVGtrVcLTkXboW0KgxgLsZx80zlqXjiDoHByp4urkIXxRCGLftOGf05slLRT964tRQqqayQlfLRKkO/ToZSzDsMd16+4fS+zc3NHOHHH0JSmcFRTf/PjCMncACpREos3eI8d3C7De9EzvFmnvCmAq15IbIXhupB6WW41qYOTgS7i7Sv/73u8+JiGBDxJ/PwY7TWWLdIcgHE4z5u0/eARRLheToPB5ws7BPUHgvoNMYaZ1zTjJva270jdk4esIwTYIFiomUV1aJFk3Cz8MVgzvakXU1GXwpIXlnNyB6vLX3iGlsFIWMy85uq/UpIX6emNJbGqFOxm2/7Jf8UYyNntx7dUe4GBM1NEjvNFMt07DHdett71HhDJoNMqM7u1VaB0bpxBLDMI2CBYqJ7D2TjdzicrEeGRMibL7tBjrrzkvR1TNwcZ9d1qEQc67WWdN/tf0MKpWWtFpIuliAv+WRDuQYS3UsxondVbphfs0ldsnxuE1/aU2utYnrYbXoiZ6bL8MwpsP/QSZi18MBj/2hW8eyOZtNEjlEcmVtQKDEhPmLNmHiXFYR1h/T/V3X5IutSWog4u6hHUQdS4OkHgDyU6V1p9GAhx+aBaqLqRlFaQ4Ks4CEn6W1hz/Q67bmeV+GsWNYoJjYZqkMB6Qc/sguwbAbyLvh8G/S2slFagllbA8SAhFXSeusRCDnfJ1PJft7hS+3nqn1OZl5Jfh1f4qa0pwxqJ32W9VpNAM50xLntgPJV7Zcm539X+smf/eZ1XyCjGHsGBYoJnA4JQ+pudJBaEhUYNPHy2sFKqb8ZgqQK3+ZdRjOY+HtJc1TRzePYtwWLTsg7zmbLQpha/Ll9jMoq5Q61m4f1A7+xvzNU7hFqT8hb5LOE9GsUGpl6GPNF0Uh52XVHM4JGHCvZd+PYRwEFiiNHQ5oL+6xRdmSOEmL081JmfimtfeKaYY6FGoTNoiibDOMouSVlOP7XclqxHDOUN1z6yXzKJAtm8BFDrWO2O1xC+Av18qc/AvIOGq596JokZLO6jIRaGXk58QwTL2wQGlEezGlucfGhsA+xMn1QFq8dNsnGLhzFRDcxdp7xjSFNv0Ad1+dQKmnk+WGPm0Q6OMu1n8mpCE1R2fc9t2uZOTLLchT+7VBiL+RbcJKcaw1B026ugOD/6W7TcMELcUubi1mGEvAAsVIzl4qxImMfLHu07aFaNW0aaiob8n1QHqCdNsnBLjzTyCkq7X3jGkqLm5SNwtBpnsU0agDKnidKRu3USfPkh1nxbqkvFKNqJAgp9Zio9FvL46xYi1Tv9nS/B+CClgVAzVzkrIfuLBHWofESulRhmHMAgsUI1l31I7M2QovAUuuAzJkceIbKokTjpzYaZqn7joUggSK0i7//Z5kFJRW4LcDKbhUUCrum9g9DB2DjZzWTakd5e+K2n0DjGhJthTkgqxENKorgR0fm/89dutNLab3MvcgRIZxYFigNKr+xIbbiwsuSuIk84h02zdMFidy1wNjH3QYYVQdiuJtcoNs3JZfUoFle5Lx+RYThwLWmt7RwKDJAfcBbt7S+sA3kjg3F/npus43itRQ3QvDMGaDBYoR0JnkvnOSbXhUiK/xZ5NK7vvHmcB5OQysCXEih/z9wiVxwvNC7A9K1VHajiBHWeo0qQf9KcdvrzmBs1lFYk0DAXuaMgzTGu6x9eHdCuh3p7SuKDaMeDSVfV8DVfLnSu/hLgshhmHMAgsUI9hwLEOtMzQpenJhP7DuBemg/eV44K9ngVJ59HxzU5AJLJkMXDwm3aYOByFOoqyzP4xloVSDkuYpLwQu7Kv36V3C/FTjtlJ5EKbJ0ZO8NL16jG7ShGEtMHiuzryOJg2XSrVkTaKiFNj3pc436Kp7mr5NhmEMYIFiBGv0hgMaNSRNwWDkezWwexGwaDBweiOaFQpFL54EXDwu3faPkLp1tPIFwli13VjhnhqFsN1a69xmjeK4fnpnMjRDQATQ81ZpXZID7F/S9G1SakeZ+k2RInoPhmHMCguUBqCCwW2JUt46zN8TPdoEGH+GdfhXae3iAbjKXT/USfDtDcAfcyX31mYRJ5OBSyel2wFtJXHSyoSuDMa2BwcaKVCGRwepxm1K9IS8Umw2vaPP0Ed1650fS/+fjYXCqfpzdwY92LR9YxjG/gRKUVnDY+KbypaTF1Emh7zHxYbC2ZgprsSJv6SzNSJ2CvDgDiBSbv0kDi4FFg40LCo0NxRyp8hJ1inpdkA7WZywkZRDQGf1gXJ90YW9QElevU8nMfKfG7oj1N8Dk3qGi+4dkzx1lOnJLdsDod2hKahDLUaO6uSnAYd+bPy2zu/WGRuG9wbaDjTPPjIMYz8C5e7F+5BdWGbR91Bm7xDju5lQfxL/g27de7qUTpm9Epj8HuAuz+koSAd+nAH8NFuqETEneamyOEmUbreQxQl9eTCOl+ahNttzOxp8+sCOgdj9f2Ox8Pa+cHUx4fBw8m/pPZToiRbbbfXt76l4vUreX1PZtUi35tZihrEYNi1QDqfk4uZFO3A+W+o4MDfllVXYcFwSDn6erhjYwUjLbhIbp9ZJa7/WupZPmhHS/25g7m4geoLu+Ud/BxYOAOJ/rNf102hyLwBfXwtky62iLSKlgtiWkiEX40CYWIfSaAzSOxqdhN32KqD91dKahLt+zYwp/1vKz0pdUt1vMu8+MgxjHwKFSLpUiJsW7cCR1Fyzb3t3UrbwhSBGx4SoZlYNQq6VytkkjV13rjGensyrbv8RuOl/gFcr3cC+5fcB391S7wTaBqHXUuTksjxXhSImJE4ogsI4HuQoSwP7LClQqDMtcYPOV4cM2rTKsBpDBE09Idj7P93/Np1suHqYd/8YhrEPgdIhSPIduJhfils/24UdcjGr1YcDxumnd26v/TkUFu55CzB3D9B9qu7+xHXAJ4OkA2GVrt3TKKgAV4gTya4cLTsAd64GWrQ1bTuM/eDVAmjdV1pTi3m+riPNbNDfbGWprnuHIoVapdMYIKyntE49WO+05ysoKwL2L5bWzm6SQGEYxmJo+EjSMN/cPRB927VQu21mf70HK+PlqaJNpLq6Wh0OSJGTEV2CjXshzbbRt/puyATNNxi4+Svgth8k4zSirAD480nJtyRL5+hZL5fPSeIk55x0u1Un4K7V1rUaZ7TXzWPKF3KjZu9oqL24rhODYY8bRlGMhSKjFOkkKLXjZ8OO0gxjA9i0QGnp447v7hmEsV0lx8zyymo8/MNBfFVjbHxjSEjJRXpeiVgPiwqCr4ds9GRS9GS68W8Ycy3w0C6g72zdfee2A4uGANveByrr6ViiiAm1EivD0AKjpLSOv2Rfzjg4lqxDKS8BTq6R1p4tdEMKtQx11VF0Ufk8Ug6Y3lrMU4sZxuLYtEAhvNxd8OnMfrjtKl0a45VVR7Hgr2Ooqmp8weka/e4dY91jyU484Sdp7eIOdLvJ9HD89R8Cd6yQCluJihJg/YvA/8boJg/rk31GEie5ijiJlsWJHI1hmIgBgKuX7gvZHIXY+hEZivgRXa6VJilrHaoJ0/dF2f5+w685s0U3IoLaitvIaTOGYbQhUBYtWoSePXvC399fXAYPHoy//vpLfXzkyJHCS0H/8sADhmcaycnJmDRpEry9vRESEoJ58+ahoqJpfibUDrngph54ZIwunfLZ5iQ89XO86MRpDEp6hyLCY7oaKVCoUFBxl+wyUZoD0tiQ/EM7gUFzaQ+k+8h34fORwD//0ZlM0eRYIU7kotqgzpI48bPxacuMeXHzBCIHS+u8FF3ruTk4tkK75mz10Wu6NMWbOLoCuCR7BRk1tfh+y+4bwzCmC5SIiAi88cYb2L9/P/bt24fRo0djypQpOHJEnowL4N5770VaWpp6eeutt9THKisrhTgpKyvDjh07sGTJEixevBgvvPACmgqJoSfGdRZGU4otwW8HUzBnyT4UlpomgJIuFuBUpnRW2K9dSzHt1Sjiv9ete9VRHGvKqPhrXgfmrAOCY6T7qiqALW8Dn14tWW1/PQnIuyA9Rs8R4oTz4kwzpXko7UiGhISbD9BpFGxKtA16SL5RLfmi1AVFKU+s1tkGaLWNmmEcWaBcd911uPbaaxEdHY3OnTvjtddeg6+vL3bt2qU+hyIjYWFh6oUiLQpr167F0aNHsXTpUvTu3RsTJ07Eq6++ioULFwrRYg5mDorEohl91ZZgcoKd/sUuMZHYWNYdzTDdnI2cNJWDtU8wEDUGZvNuuH8LMPxp3cCzSyeAX+4C8uWC4OCuwOxVgK88vZZhmkOgJO8EirKkdfQ4wE1OI9kK1IXjIY+uiF8mmRvWxp4vJBFDDLjHNtJYDOPINSgUDVm2bBkKCwtFqkfhu+++Q1BQELp374758+ejqEhnorZz50706NEDoaG6L/0JEyYgLy/PIArTVK7pHo6lcwbC31P6Qj90QTJ0S5ZHyDfEWj2BMs7Y9uIjvwGVssjqMc28BzHyWhj9HHDfZslaWx+aGksOsdQNxDB1EdpD57lzZmv9Rdf2MHvHGDz9gavmSOuqcmDnwto9Xg5+K61pnlbfO5t3HxnGgTFZoCQkJIioiYeHh6gvWb58OWJjY8Vjt99+u4iObNy4UYiTb7/9FjNnzlRfm56ebiBOCOU2PVYXpaWlQsToX9QajDoY0KEVfn5giBjwR5zNKsJNi7YL99n6yMwvwYFkqZWwc6gvOgT5wKLdO6YQ1h24ZwMw7hXAOwjoMByYvQLwMWHiLOOYkDeJ0m5cmqubJdNYyKNHEShUEB49HjYJDfqjYZ4EeZxQJLTmyIpS+XjT4xbAx0g3aYZhml+gdOnSBXFxcdi9ezcefPBBzJ49W6RtiPvuu09ERChKMmPGDHzzzTdCwJw+baSXRx0sWLAAAQEB6qVtW7ljZ9sH9e9rmB9+e2iIOqH1UkEZbv1sJ7aekgtZa2HDsUy1ycFoczYqsEvZpztTDesBi+HiKnUgzEuUZvuwOGEalebZ2LRtkcmZkmKk7VI0whahtGgf+SSKupH2fmkowri1mGFsR6C4u7sjKioK/fr1E8KhV69e+OCD2oXCwIHSlM/ERKlrgGpSMjIMnSyV2/RYXVA0Jjc3V72cPy93rRxZDlw8We/+tm7hhZ8fGIyr2rcUtwvLKnHX13vx+8GUBocDTuhmpECJ+97y0ZOa8IAyxlSUmVBE0mbH7N6pjSEP68YB7F4kOcYSp//RdTzRDB+KYDIMYzs+KFVVVSIFUxsUaSHCwyVPDqpVoRRRZqZucu+6detEIa2SJqoNSicprc3KRX53YNOCBvexhbc7vp0zUPUzqaiqxmM/xuGLLYYpInKj3Z4oFf2FB3iiexsjzgppIqoyut3JRQoDM4wWadVB569zfrfui9hUKMSoCBT6Yif/E1v/XBTPIir6PbhUJ1YUOHrCMNoWKBTJ2LJlC86ePSuEBt3etGmTSOdQGoc6cqgFmR5fsWIF7rjjDgwfPlx4pxDjx48XQmTWrFmIj4/HmjVr8Pzzz2Pu3LlChDQKiqJkNFxg6+nmgkUz++H2gbqhea+tPob/rDqqGrptOpGJMtk3hcQMtS4bZeBE3hJKJwN30jC2kOahgm7qwmkMmcd09V+RQ+0jzag/RHDHR0DGUSBxvXSbBm2SrxHDMNoVKBT5INFBdShjxozB3r17hcgYN26cSP2sX79eiJCYmBg8+eSTmDp1Klau1FX6u7i4YNWqVeKaoilUQEvbe+WVV5rwI1QbFUUR7+/shNdu6I7Hx3ZW7/vftjMimlJWUaWasxHjjU3vUBGdvvkTw9h7u/HxVfaT3lGgurGocdKaXJl/nKF7bMB9V04kZxjG4hg5YEbiyy/1CshqQIWrmzc3nNeOjIzE6tWy6VFT8QkBKi5K3QRp8UB4rwZfQlGRR8dGI8TfA88tTwAFT1bEpyK7sAzx53PEc6g9mbqAGqQ0X9fJQHNI+CyLsak6lE1Nrz+JmQS74eonpMnMhBIhIgO6PrOsulsM46g423xxm8LG10166fQB7fDZrP7wkA3dtiVeQr7sOEvW9m4uRnw0R/8AyuU8fvepkl8Jw2gZapMNk1KuSD8EFMpGa8ZCrqrKTKg2/YCACNgN7QZLc3b0oaJ3mpHFMEyzY9sChVIq/m2k9cm/gQtyq6+RjIsNxff3DkSAl6GpmtHDAQ28T5pobc8w1kjz0LC/xqZ3YibDrqCas2GPG943gOfuMIy1sG2BQvM0hj+lu73xNZM30S+yFX59cDBaB0iGbiRWhnc2wpX18lng3DbdBGE6m2QYe69DMXCPtcOZNNETgLaDpHX3m4FgXb0awzAarkHRJL1nAtveA3KSJd+Cczt1k1uNJCrED3/8axiWH7yAwR2D4ONhxMcS/6PePkxnXxLGtlIZ5P5KnTymCJT8dKk9WZn/FBQFu3TcnfmrlMbikw6GsSq2HUEhXN2BEc80KYpC0MTi+4Z3Qo8IeXhYQz4QaveOE9Dztka9J8NYBXdvXa1FzjmprsRRu3dqw8NXOsmhYwvDMFbD9gUKQQKhVSdpfXZr010yGyJ5F3BZPqjTfJMAuQ6GYWwFZS6PKXUotj4ckGEYm8I+BArNpxn5rGEURRmoYwni9azte3FxLGODdBylWxuT5qEhemflmityo7XkvCmGYRi7EShKm29wjLSmPHniBsu8T3kxcOR3ae3uC3S1s04GxjEI7w14yOlMijjSYLz6OLkGqKrQRU+45ophGAtjPwKFnB6bI4py/E/d+PXYGwB3H/O/B8M0R9Sxw9XSujgbyJC9TRy1e4dhGM1hPwKF6DoFCJUnjqYekLxRzI01JhczjDXbjUsLgNNyRNI3FIi4yvL7xjCMw+Nsdy2Co/7PMIrSUOjaFPLSgKSNugFi7YaYb9sMo1WBQkPzKkp01vb0f8YwDGNh7O9IQ6PfKb9OkJfBcb3QdFM59CNQXaVzseUDNWPLBEbpnJjJP6hcFiE14e4dhmGsgP19w1Lx3qjndLc3LgCqKpu+XQPvExIo7H3C2MH/ihJFqSgGLuy58jkVpVKBLOEZALSX61YYhmEsjP0JFCJ6nC5PfvEYcGR507eZehC4eFznxNmqY9O3yTBan25MHT5l+bropIvh3CqGYRhLYZ8CpWYUZdMCoFJukWwsBtETLo5l7NCwrTaBop8i5fQOwzDNiH0KFIJC15FDpXVWIpDwU+O3VVEGJPwirV09gW43mGcfGcba+IVJc3WUKGFxju4xSo1SWz3h5g10Gm2dfWQYxiGxX4FSM4qy+U2gsrxx2zq1RvKKUEbMUy6eYewFpQ6FCsAVt1gieSdQlCWto8YCbl7W2T+GYRwS+xUoRPuhuoPv5bNA3HeN206cXnqHvU8YR2k3ZnM2hmGsiH0LFGLU87r1lnekrgRTKLwkRVAIv3DDGSYMYy9C3snFUKBQ15oiUJzdgM7jrbd/DMM4JPYvUNpeBUTLB9fc88CBb0x7PdWeKDNIek6TLPUZxp7w8NN1vWWdAnIvSE7MeSm6CAunNRmGaWbsX6AQ+u6yW9+VBv4ZC08uZhwuzbOZzdkYhrE6jiFQWveRiluJ/DRg39fGvS7jKJAWr9tGiDwtmWHsWqBs1AkUJ2fJ/4RhGKaZcQyBQoycr1tv+y9QVtjwazh6wjgKEf0Bd19pfWyV1JpP0Lwp32Cr7hrDMI6J4wiUsO5ArOxfUngR2PNF/c8nY7dDP+mKBHvcbPl9ZBhrQQ6xkUN0tvcKnN5hGMZKOI5AUaMoTtJ6+wdAqWzhXRsU5i7IkNadJwDerZpnHxlGC2keBZpezDAMYwUcS6BQDUmPW6Q1Ga/t+rTu58bppXd6c3qHcUCBQnVXLdpaa28YhnFwHEugECOf1Xk+7PzI0Npbge5TLL69A4Gocc27jwxjDUJiAR+9ehNO7zAMY0UcT6AEdtIN+yvJBXYuvPI5NP24UjZ0o4iLq3vz7iPDWGs8hCrGndg9lmEYq+J4AoUYMQ9wdpXWuxYBRfKcHQWeXMw4KqOfB/rMBKZ8DARFW3tvGIZxYBxToLRsD/SZJa3L8oEdH+oeyzoNnN+tC3mH97LOPjKMNQhoA0xZKIkUhmEYK+KYAoUY/hTgIqdudn8GFFysPXpCYW+GYRiGYZoVxxUoARFAv7ukdXkRsP19oKoKiF+mc9Ck2TsMwzAMwzQ7jitQiKufAFw9pfXe/wGHf5UGChKdxgB+YVbdPYZhGIZxVBxboJAAueoeaV1RAvwxV/dYby6OZRiGYRhr4dgChRj6GODmI62V1mKPAKALO2gyDMMwjLVggUKD0AbeZ3hf9xsBNzn1wzAMwzBMs8MChRjyCODup7vNk4sZhmEYxqqwQCFoEOA1C6TOnZjJQNsB1t4jhmEYhnFoZDtVBn1nSb4nLvyRMAzDMIy14QiKPixOGIZhGEYTsEBhGIZhGEZzsEBhGIZhGEZzsEBhGIZhGMa2BcqiRYvQs2dP+Pv7i8vgwYPx119/qY+XlJRg7ty5CAwMhK+vL6ZOnYqMjAyDbSQnJ2PSpEnw9vZGSEgI5s2bh4qKCvP9RAzDMAzDOJZAiYiIwBtvvIH9+/dj3759GD16NKZMmYIjR46Ixx9//HGsXLkSP//8MzZv3ozU1FTcdNNN6usrKyuFOCkrK8OOHTuwZMkSLF68GC+88IL5fzKGYRiGYWwWp+rq6uqmbKBVq1Z4++23cfPNNyM4OBjff/+9WBPHjx9H165dsXPnTgwaNEhEWyZPniyES2hoqHjOp59+imeeeQYXL16Eu7u7Ue+Zl5eHgIAA5ObmikgOwzAMwzDax5Tv70bXoFA0ZNmyZSgsLBSpHoqqlJeXY+zYsepzYmJi0K5dOyFQCLru0aOHKk6ICRMmiB1WojC1UVpaKp6jf2EYhmEYxn4xWaAkJCSI+hIPDw888MADWL58OWJjY5Geni4iIC1atDB4PokReoyga31xojyuPFYXCxYsEIpLubRt29bU3WYYhmEYxp4FSpcuXRAXF4fdu3fjwQcfxOzZs3H06FFYkvnz54twkHI5f/68Rd+PYRiGYRjrYrJ1KkVJoqKixLpfv37Yu3cvPvjgA9x6662i+DUnJ8cgikJdPGFhYWJN13v27DHYntLlozynNihaQxeGYRiGYRyDJvugVFVViRoREitubm7YsGGD+tiJEydEWzHVqBB0TSmizMxM9Tnr1q0ThTKUJmIYhmEYhjE5gkKplokTJ4rC1/z8fNGxs2nTJqxZs0bUhsyZMwdPPPGE6Owh0fHwww8LUUIdPMT48eOFEJk1axbeeustUXfy/PPPC+8UjpAwDMMwDNMogUKRjzvuuANpaWlCkJBpG4mTcePGicffe+89ODs7C4M2iqpQh84nn3yivt7FxQWrVq0StSskXHx8fEQNyyuvvGLKbjAMwzAMY+c02QfFGlChLNW5ULEs+6AwDMMwjG1ANiHUiUv1qhToMGuRrBbIysoS19xuzDAMwzC2B5WJ2KVAoRoXggpwG/oBTeWqq64SnUla36altsv7alvbdfR9Vc7GzB1NtaXP1VLb5X3lfbXEdilpQ001rVu3bvC5NilQqM6FIHFi7hQP1cnYwjYttV3eV9vaLu+rhDLA1BE/V0ttl/eV99VS2yW7EuV73KJtxvYGdRTZwjYttV3eV9vaLu+rZbClz9VS2+V95X219mdgk0WyPCyQYRiCjwUMY7/YZASFPFNefPFF9k5hGAeHjwUMY7/YZASFYRiGYRj7xiYjKAxTF05OTvj999+tvRsMw1gZPhbYPixQNMrOnTtF9fSkSZPgyNx555244YYb4IhQ6+zdd98t2vGo6j0yMhKPPvqo6gPUEDSGgg7SZIjE2C58LJDgY8HdDncsYIGiUb788ksxy2jLli1ITU1t0rYqKyvFUEfGdkhKSkL//v1x6tQp/PDDD0hMTMSnn34qhnHSmIjs7Gxr7yLTTPCxwLFJcuBjAQsUDVJQUIAff/xRzCyis6bFixdfoYT//PNPMQvJ09NTDGM8fPiw+hx6Po0CWLFihRjOSAWEZGpn67Rv3x7vv/++wX29e/fGSy+9BHuD2vDoTGnt2rUYMWKEGNBJgzrXr1+PlJQUPPfcc+J5NPPqmWeeEWZl9HuOiooSX2hnz57FqFGjxHNatmwp/mboDJSxLfhYUDt8LJjoEMcCTQoURw7lET/99BNiYmLQpUsXzJw5E1999ZVw39Nn3rx5ePfdd4XDX3BwMK677jqUl5erjxcVFeHNN9/E//73Pxw5cgQhISFW+EmYxkBnRDSE86GHHoKXl5fBY2FhYZgxY4b40qK/CRreSWdVH374IY4dO4bPPvsMvr6+4iD166+/itecOHFCDPj84IMPYGvwsYCPBY5MtoMfC2zSSdbeIdVLByPimmuuER4PmzdvxsiRI9XnUGulMkV6yZIliIiIwPLlyzFt2jRxHx2gaJJ0r169rPRTMI2FQrl0wOnatWutj9P9ly9fFl9I9AW2bt06jB07VjzWsWPHK0ZC0BcSnUUztgcfCxybUw5+LNBkBEWfv//+G8OGDRMfamBgICZPnozTp0+rj1P4ikJWv/32mwhjeXt7i39EKiyzRUjh7tmzB9OnTxe3XV1dceutt4oDlT6Ue9T/46MzLFLNChQSpLAvY7s05ABAf/tUPElhX0eAjwV8LHBUqh30WKB5gVJYWIgnnngC+/btE0VB5N9/4403XlHoRXm4p556CnFxcejcubP4p66oqICtQQcf2m+q1qYDEl0WLVokQnR09mQsFA6kg7U9Qb/7mv+o+qFse4Fyx/S70/+S0Yfup1xyzZCvvcPHAj4WKPCxwDGOBZoXKFOnTsVNN90kflFUBEU52ISEBBw9etTgeXRAoiIyOiC9/PLLOHfunKh2tiXoYPTNN9+IfDIdXJVLfHy8OEhRflFh165d6ppCfCdPnqwzDGgvUH6d8qf6NudnzpyBvUHRAQrZU1i+uLjY4LH09HR899134ky6R48e4suZQv61QWfOSueGPcDHAj4WKPCxAA5xLHC2hRwcnQFRPo1mbVD1NlGzEl0/hBkeHi6uMzMzYUusWrVKHGDmzJmD7t27G1zo4Kwf2n3llVfEWSRV7FMhYVBQkN0XE44ePRrffvsttm7dKr6YZs+eLcKa9sjHH38sqvInTJgg2kvJB4FSHHSwatOmDV577TXxv0CfAfkjkCEVHaCps4Ny0QR5JdDZF/1dXbx4UXSE2DJ8LOBjgQIfC/52iGOB5gUKVaRTJfMXX3yB3bt3iwtRVlZm8Dw3Nzd1rYQzba3fnw46VOBEw89qQgclCm0fOnRI3H7jjTeEUU+/fv2Ekl65cqWqku0J+h1SaJuYP3++yLFS7QGdIdNBuFOnTrBHoqOjxe+bvoyp2JF+zvvuu0/UVlBNhVL0RiH/m2++WVT5U7fHvffeK1IhBB28KILw7LPPIjQ0FP/6179gy/CxQIKPBXwsuM9RjgXVGmT27NnVU6ZMqb506RIlGau3bNmiPrZ161Zx3/Lly8XtM2fOiNsHDx5Un3P58mVx38aNG6vtDfqZ6Gejn9ERmDBhQvXcuXOtvRuMleBjQd3wsYCxdzTdZkzFP5SD+/zzz0WolkK5pAAZ+4fC29u3bxdhygceeMDau8NYGT4WOC58LHBcXLUcyqNK7WXLluGRRx4RuVdqnyMTGn0PAMY+oVwq9fY/+eSTmDJlirV3h7ESfCxg+FjguDhRGAUagwyJqFKfioMYhnFc+FjAMI6Ls9ZCeVRlTKE8xQ2PYRjHg48FDMNoKsXDoTyGYQg+FjAMo8kUD8MwDMMwjo2mUjwMwzAMwzAECxSGYRiGYTSH1QQKWfaSMyTNlSC3R7Ln1ScjI0PYNtPjNJWUqvnJ6lofajGk1+pfavbJkwX0kCFD4Ofnh7CwMDzzzDM2OTiMYewVcxwLCHLVJAt0Hx8fYYU/fPhwg/kl5EI7Y8YM8RhNRCYbeVux/GYYR8RqAoUseGkU+sKFC694jMpiyLo4KSkJf/zxBw4ePChmCVA1v2Ldq0B2vjQ0Srm89dZb6mM0WOvaa68VBzTaxo8//ogVK1awwRPDaAhzHAtInND/+fjx47Fnzx5RYEt23uSfokDi5MiRI1i3bp3oECJhRJbhDMNolGoNoG9XTZw4cULcd/jwYfW+ysrK6uDg4OovvvhCvW/EiBHVjz76aJ3bnT9/fnX//v0N7luxYkW1p6dndV5entl/DoZhrHMsGDhwYPXzzz9f53aPHj0qtrN37171vr/++qvaycmpOiUlxSI/C8MwTUOTNSg0uZHw9PRU76MzIQ8PD2zbts3guTRumqZ3krskDZAqKioy2I7+NggvLy+UlJRg//79Fv85GIax/LGAJhXT4MCQkBCRzqVhaDRITv9YQREWSuv0799fvY+iMLQtZeggwzDaQpMChSYxtmvXTggOMmyiaaVvvvkmLly4INI4CrfffjuWLl2KjRs3iufS+O2ZM2eqj9N46h07duCHH35AZWUlUlJSxGhyQn87DMNoE2OOBZT+IV566SWR8qVR9H379sWYMWPUWhWa8ksCRh+y0KdJsPQYwzDaQ5MChcal//bbbzh58qQ4gFBhHImQiRMnGuSUKX9MIqRHjx4iv/zNN99g+fLlOH36tHic8tFvv/22KJylM67OnTuLmhRCfzsMw2gTY44FNK+HuP/++3HXXXehT58+eO+998S8nq+++srKPwHDMI1Fs9/S/fr1Q1xcHHJycsSZEp0VZWVloWPHjnW+ZuDAgeI6MTFRve+JJ54Q26Dpp5cuXVJdKevbDsMwtnMsoOnGRGxsrMHrunbtKv7vCergo1SQPtTNR5099BjDMNpDswJFISAgAMHBwSJUu2/fvnptr+kgpn/AUqDWRWpRpPoTSve0bdtWhIAZhrEd6joWtG/fXvx/nzhxwuD5FHWhjh9i8ODBQuDo1579888/IvqinNgwDKMtrDaLh/wH9CMdZ86cEQKDwriUc/7555/FwYjWCQkJePTRR0W7IaVtCErjfP/99yJlExgYiEOHDuHxxx8X3gc9e/ZUt0spHmo/pHAwhYrfeOMN/PTTT3BxcbHKz80wjHmPBXQCMm/ePLz44ouiXbl3795YsmQJjh8/jl9++UWNptBxgGpUPv30U5SXl4s25Ntuu02IG4ZhNEi1ldi4caNo+6t5mT17tnj8gw8+qI6IiKh2c3OrbteunWghLC0tVV+fnJxcPXz48OpWrVpVe3h4VEdFRVXPmzevOjc31+B9Ro0aVR0QECBai6kVcfXq1c3+szIMY7ljgcKCBQvE87y9vasHDx5cvXXrVoPHs7KyqqdPn17t6+tb7e/vX33XXXdV5+fnN9vPyTCMafCwQIZhGIZhNIfma1AYhmEYhnE8WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDMMwDKM5WKAwDGM3kKvs77//bu3dYBjGDLBAYRimydx5551CHNDk8JrMnTtXPEbPMRcvvfSSsLRnGMZ+YYHCMIxZoCGcy5YtQ3FxsXpfSUmJmJlFc3QYhmFMgQUKwzBmgSaEk0ihoZwKtCZx0qdPH/W+0tJSPPLIIwgJCYGnpyeGDRuGvXv3qo9v2rRJRFw2bNiA/v37w9vbG0OGDFGnFS9evBgvv/wy4uPjxfPoQvcpXLp0CTfeeKN4XXR0NFasWNFsnwHDMOaDBQrDMGbj7rvvxtdff63e/uqrr3DXXXcZPOfpp5/Gr7/+KiYOHzhwAFFRUZgwYQKys7MNnvfcc8/h3Xffxb59++Dq6iq2Tdx666148skn0a1bN6SlpYkL3adA4mXatGliwjlNO58xY8YV22YYRvuwQGEYxmzMnDkT27Ztw7lz58Rl+/bt4j6FwsJCLFq0CG+//TYmTpyI2NhYfPHFF/Dy8sKXX35psK3XXnsNI0aMEM959tlnsWPHDpEyouf6+voK0RIWFiYudJ8C1bpMnz5dCJ/XX38dBQUF2LNnT7N+DgzDNB1XM2yDYRhGEBwcjEmTJomUCw1Kp3VQUJD6+OnTp1FeXo6hQ4eq97m5uWHAgAE4duyYwbZ69uyprsPDw8V1ZmZmg/Us+q/z8fGBv7+/eB3DMLYFCxSGYcwKpWL+9a9/ifXChQsbvR0SLgpUZ0JUVVWZ9Drltca8jmEYbcEpHoZhzMo111yDsrIyESmh2hJ9OnXqBHd3d5H6UaDnUZEspXKMhbZRWVlp1v1mGEZbcASFYRiz4uLioqZraK0PpVwefPBBzJs3D61atRLpmrfeegtFRUWYM2eO0e/Rvn17nDlzBnFxcYiIiICfnx88PDzM/rMwDGM9WKAwDGN2qO6jLt544w2Rcpk1axby8/NFK/GaNWvQsmVLo7c/depU0cI8atQo5OTkiM4hcxrBMQxjfZyqqZKNYRiGYRhGQ3ANCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMwmoMFCsMwDMMw0Br/D25AhTA9anQAAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -197,7 +197,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0a9b69b427284c3496bfce99fb540543", + "model_id": "9a073bf8b15c4d48ac77d917aeced8ad", "version_major": 2, "version_minor": 0 }, @@ -211,7 +211,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aef4a384bcd6404d99732fab29977795", + "model_id": "89e6fb80160a4996a1c4b7c09e664a4c", "version_major": 2, "version_minor": 0 }, @@ -225,7 +225,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7e3af717b7c4edb805ed595c5f605b1", + "model_id": "914e1eb4534046dd812f301168af5f4c", "version_major": 2, "version_minor": 0 }, @@ -248,7 +248,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -322,7 +322,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bfd07773360b4bf5b4d0953bca11c399", + "model_id": "75bd97b22f464f84a64852cda538462b", "version_major": 2, "version_minor": 0 }, @@ -345,7 +345,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -387,14 +387,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "id": "72832dff", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50a527daeefe4cb4ba36ea0bdfc0869e", + "model_id": "ece6e8f582954cda9ed2accf577c06c6", "version_major": 2, "version_minor": 0 }, @@ -433,14 +433,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "id": "9bbd219e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c8bc723957340fd9d986ad56e2c968c", + "model_id": "f2f8b82cd71b4119b236f60380d070a5", "version_major": 2, "version_minor": 0 }, @@ -457,13 +457,13 @@ "" ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -512,14 +512,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "id": "33fa7fc4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "832e77ab6439462db9397b7484541557", + "model_id": "01a5ce370aa44ed8818854e4cbf7c217", "version_major": 2, "version_minor": 0 }, @@ -559,14 +559,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "id": "50830283", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79eb08d785554210bb70452fa54055d5", + "model_id": "b6d58d057e704143b3b3f4327833ff9d", "version_major": 2, "version_minor": 0 }, @@ -583,13 +583,13 @@ "" ] }, - "execution_count": 17, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -631,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "id": "3b81f2e2", "metadata": {}, "outputs": [ @@ -665,47 +665,47 @@ " \n", " 0\n", " Trained TiDE Model\n", - " 7.183538\n", - " 31.838135\n", + " 6.838281\n", + " 31.289665\n", " \n", " \n", " 1\n", " Loaded TiDE Model (Fine-Tuned)\n", - " 7.088335\n", - " 31.208529\n", + " 5.420352\n", + " 24.888033\n", " \n", " \n", " 2\n", " Chronos Zero-Shot (Base)\n", - " 15.206929\n", - " 70.817078\n", + " 9.357551\n", + " 43.654480\n", " \n", " \n", " 3\n", " Chronos Full Fine-tuning\n", - " 8.920057\n", - " 40.941208\n", + " 9.446595\n", + " 46.352612\n", " \n", " \n", " 4\n", " Chronos Partial Fine-tuning\n", - " 8.660080\n", - " 39.234516\n", + " 6.191853\n", + " 28.622393\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Model MAPE (%) MAE\n", - "0 Trained TiDE Model 7.183538 31.838135\n", - "1 Loaded TiDE Model (Fine-Tuned) 7.088335 31.208529\n", - "2 Chronos Zero-Shot (Base) 15.206929 70.817078\n", - "3 Chronos Full Fine-tuning 8.920057 40.941208\n", - "4 Chronos Partial Fine-tuning 8.660080 39.234516" + " Model MAPE (%) MAE\n", + "0 Trained TiDE Model 6.838281 31.289665\n", + "1 Loaded TiDE Model (Fine-Tuned) 5.420352 24.888033\n", + "2 Chronos Zero-Shot (Base) 9.357551 43.654480\n", + "3 Chronos Full Fine-tuning 9.446595 46.352612\n", + "4 Chronos Partial Fine-tuning 6.191853 28.622393" ] }, - "execution_count": 18, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } From b5a6c1957c1eb32096fde9c8a77b155824d52054 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Tue, 24 Feb 2026 15:31:22 +0100 Subject: [PATCH 16/26] update unit test for foundation model with the new fine tuning logic --- .../models/forecasting/test_foundation.py | 243 ++++++++++-------- ...oundation-Model-Fine-Tuning-examples.ipynb | 4 +- 2 files changed, 144 insertions(+), 103 deletions(-) diff --git a/darts/tests/models/forecasting/test_foundation.py b/darts/tests/models/forecasting/test_foundation.py index c3f4a43c22..f3826dab97 100644 --- a/darts/tests/models/forecasting/test_foundation.py +++ b/darts/tests/models/forecasting/test_foundation.py @@ -6,7 +6,6 @@ import numpy as np import pytest -import torch from darts import TimeSeries, concatenate from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs @@ -19,7 +18,6 @@ ) from darts.models import Chronos2Model -from darts.utils.callbacks.fine_tuning import LayerFreezeCallback, PeftCallback def generate_series(n_variables: int, length: int, prefix: str): @@ -184,47 +182,78 @@ def test_local_dir(self, mock_method, caplog): config_path.rmdir() test_local_dir.rmdir() + @patch( + "darts.models.components.huggingface_connector.hf_hub_download", + side_effect=mock_download, + ) + def test_default_no_finetuning(self, mock_method): + # Default behavior: enable_finetuning=False (no training) + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + **tfm_kwargs, + ) + # Check that the given parameters remain unchanged, but that enable_finetuning is False + # (because if not specified, it is None, but we want it to be False by default for foundation models) + assert model.input_chunk_length == 12 + assert model.output_chunk_length == 6 + assert model.model_params["enable_finetuning"] is False + mock_method.assert_called() + + # calling `fit()` should NOT use `trainer.fit()` when finetuning is disabled + with patch("pytorch_lightning.Trainer.fit") as mock_fit: + model.fit( + series=self.series, + future_covariates=self.future_cov, + ) + mock_fit.assert_not_called() + + # foundation model should be deterministic by default + assert model.model_created + + # predictions should allow n > output_chunk_length (autoregressive) + pred = model.predict(n=10) + assert isinstance(pred, TimeSeries) + assert len(pred) == 10 + assert pred.n_components == self.series.n_components + @patch( "darts.models.components.huggingface_connector.hf_hub_download", side_effect=mock_download, ) def test_full_finetuning(self, mock_method, tmpdir): - # 1. Training activation + # 1. Enable Full Fine-tuning model = Chronos2Model( input_chunk_length=12, output_chunk_length=6, enable_finetuning=True, - n_epochs=5, + n_epochs=1, **tfm_kwargs, ) - assert model._requires_training is True + assert model.model_params["enable_finetuning"] is True - # Capture initial weights + # Initialize model (this will train for 1 epoch, but that's fine for verification) model.fit(self.series) - initial_params = { - n: p.clone() for n, p in model.internal_model.named_parameters() - } - - # 2. Weight update - # We need to actually train for 1 epoch. tfm_kwargs usually has "accelerator": "cpu" - model.fit(self.series, epochs=1) - - # Check if at least some weights changed - any_changed = False - for n, p in model.internal_model.named_parameters(): - if not torch.equal(initial_params[n], p): - any_changed = True - break - assert any_changed, "The weights should be updated after fine-tuning" + + # Verify all parameters require grad + for n, p in model.model.named_parameters(): + assert p.requires_grad is True, ( + f"Parameter {n} should require grad for full fine-tuning" + ) # 3. Persistence (Save/Load) - save_path = os.path.join(tmpdir, "model.pt") + save_path = os.path.join(tmpdir, "model_full_ft.pt") model.save(save_path) + + # Load back loaded_model = Chronos2Model.load(save_path) + assert loaded_model.model_params["enable_finetuning"] is True + # Check predictions match pred_orig = model.predict(n=6, series=self.series) pred_loaded = loaded_model.predict(n=6, series=self.series) - assert np.allclose(pred_orig.values(), pred_loaded.values()), ( + # Relax tolerance slightly for floating point differences across save/load on CPU + assert np.allclose(pred_orig.values(), pred_loaded.values(), atol=1e-6), ( "Prediction of the fine-tuned model and the saved/loaded fine-tuned model should be the same" ) @@ -232,104 +261,116 @@ def test_full_finetuning(self, mock_method, tmpdir): "darts.models.components.huggingface_connector.hf_hub_download", side_effect=mock_download, ) - def test_partial_finetuning(self, mock_method): - # 1. Callback injection + def test_partial_finetuning_block_freeze(self, mock_method): + # Test freezing specific layers (partial fine-tuning) + # We freeze the encoder, so only other parts (like head/decoder) should be trainable + + # For this test, let's freeze 'encoder' model = Chronos2Model( input_chunk_length=12, output_chunk_length=6, - enable_finetuning=True, - freeze_patterns=["encoder.block.0"], - unfreeze_patterns=["encoder.block.0.layer.0"], # Example unfreeze + enable_finetuning={"freeze": ["encoder.*"]}, + n_epochs=1, **tfm_kwargs, ) - assert any( - isinstance(c, LayerFreezeCallback) - for c in model.trainer_params["callbacks"] - ) - # 2. Freezing logic - # We call fit to initialize the model and trigger the callback setup automatically - model.fit(self.series, epochs=5) - - # Check requires_grad status. - found_any = False - for name, param in model.internal_model.named_parameters(): - if name.startswith("encoder.block.0"): - found_any = True - if name.startswith("encoder.block.0.layer.0"): - assert param.requires_grad is True, ( - f"Parameter {name} should be trainable" - ) - else: - assert param.requires_grad is False, ( - f"Parameter {name} should be frozen" - ) - assert found_any, "No parameters matched the freeze patterns, test is invalid" + # Initialize model + model.fit(self.series) - @patch( - "darts.models.components.huggingface_connector.hf_hub_download", - side_effect=mock_download, - ) - def test_finetuning_misconfiguration(self, mock_method): - # Warning if freeze_patterns assigned but enable_finetuning is False - with patch( - "darts.models.forecasting.foundation_model.logger.warning" - ) as mock_warning: - _ = Chronos2Model( - input_chunk_length=12, - output_chunk_length=6, - enable_finetuning=False, - freeze_patterns=["some_pattern"], - **tfm_kwargs, - ) - mock_warning.assert_called_once() - assert "enable_finetuning` is False" in mock_warning.call_args[0][0] + # Check requires_grad status + frozen_found = False + trainable_found = False + + for name, param in model.model.named_parameters(): + if "encoder" in name: + assert param.requires_grad is False, ( + f"Parameter {name} should be frozen" + ) + frozen_found = True + elif param.requires_grad: + trainable_found = True + + assert frozen_found, "Should have found frozen parameters matching 'encoder'" + assert trainable_found, "Should have found some trainable parameters" @patch( "darts.models.components.huggingface_connector.hf_hub_download", side_effect=mock_download, ) - def test_lora_callback(self, mock_method, tmpdir): - pytest.importorskip("peft") - from peft import LoraConfig, PeftModel - - lora_config = LoraConfig(target_modules=["q", "v"]) - callback = PeftCallback(peft_config=lora_config) - - # Avoid duplicate pl_trainer_kwargs - kwargs = {k: v for k, v in tfm_kwargs.items() if k != "pl_trainer_kwargs"} - pl_trainer_kwargs = tfm_kwargs.get("pl_trainer_kwargs", {}).copy() - pl_trainer_kwargs["callbacks"] = [callback] + def test_partial_finetuning_unfreeze(self, mock_method): + # Test unfreezing specific layers (partial fine-tuning) + # Everything is frozen EXCEPT the specified patterns + # Let's unfreeze only the 'encoder' (or part of it) model = Chronos2Model( input_chunk_length=12, output_chunk_length=6, - enable_finetuning=True, - pl_trainer_kwargs=pl_trainer_kwargs, - **kwargs, + enable_finetuning={"unfreeze": ["encoder.*"]}, + n_epochs=1, + **tfm_kwargs, ) - # 1. Initialize and fit - model.fit(self.series, epochs=5) + # Initialize model + model.fit(self.series) - # Verify transformation happened - assert isinstance(model.internal_model, PeftModel), ( - "Internal model should be a PeftModel after fit" + # Check requires_grad status + unfrozen_found = False + frozen_found = False + + for name, param in model.model.named_parameters(): + if "encoder" in name: + assert param.requires_grad is True, ( + f"Parameter {name} should be unfrozen" + ) + unfrozen_found = True + else: + assert param.requires_grad is False, ( + f"Parameter {name} should be frozen" + ) + frozen_found = True + + assert unfrozen_found, ( + "Should have found unfrozen parameters matching 'encoder'" ) + assert frozen_found, "Should have found frozen parameters (non-matching)" - # 2. Checkpoint merging test (via save/load) - save_path = os.path.join(tmpdir, "lora_model.pt") - model.save(save_path) + @patch( + "darts.models.components.huggingface_connector.hf_hub_download", + side_effect=mock_download, + ) + def test_finetuning_misconfiguration(self, mock_method): + # 1. Invalid dict key + with pytest.raises( + ValueError, + match="If `enable_finetuning` is a dict, it must contain exactly one key: 'freeze' or 'unfreeze'.", + ): + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning={"invalid_key": ["pattern"]}, + **tfm_kwargs, + ) - # Loading back should yield a standard model (weights merged) - loaded_model = Chronos2Model.load(save_path) - assert not isinstance(loaded_model.internal_model, PeftModel), ( - "Loaded model should have merged weights and not be a PeftModel" - ) + model.fit(self.series) - # Verify predictions match - pred_orig = model.predict(n=6, series=self.series) - pred_loaded = loaded_model.predict(n=6, series=self.series) - assert np.allclose(pred_orig.values(), pred_loaded.values()), ( - "Prediction of the fine-tuned model and the saved/loaded fine-tuned model should be the same" - ) + # 2. Invalid dict value type + with pytest.raises(ValueError, match="must be a list of strings"): + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning={"freeze": "not_a_list"}, + **tfm_kwargs, + ) + + model.fit(self.series) + + # 3. Both keys (impossible due to dict construction, but multiple keys) + with pytest.raises(ValueError, match="must contain exactly one key"): + model = Chronos2Model( + input_chunk_length=12, + output_chunk_length=6, + enable_finetuning={"freeze": ["p1"], "unfreeze": ["p2"]}, + **tfm_kwargs, + ) + + model.fit(self.series) diff --git a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb index 4e0b15ead9..a953ccdb46 100644 --- a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb +++ b/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -512,14 +512,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "33fa7fc4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01a5ce370aa44ed8818854e4cbf7c217", + "model_id": "7e3007e5db914b64baba8b3ea45f3770", "version_major": 2, "version_minor": 0 }, From 13fc4abb8142430bc16044b813c3e7b46bb98c90 Mon Sep 17 00:00:00 2001 From: Alain Gysi Date: Tue, 24 Feb 2026 16:07:18 +0100 Subject: [PATCH 17/26] Modify Chronos to use quantiles by default when fine-tuning --- darts/models/forecasting/chronos2_model.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index 2415ec178d..379e0261f1 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -631,6 +631,9 @@ def __init__( [0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99]. Default: ``None``, which will make Chronos-2 deterministic (median quantile only). + If fine-tuning is enable (``enable_finetuning`` is ``True`` or a dict), and ``likelihood`` is not specified, + it will default to all quantiles used during pre-training to avoid catastrophic forgetting and distribution + shift. hub_model_name The model ID on HuggingFace Hub. Default: ``"amazon/chronos-2"``. Other available variants include ``"autogluon/chronos-2-small"`` and ``"autogluon/chronos-2-synth"``. @@ -854,6 +857,14 @@ def encode_year(idx): ) quantiles = chronos_config["quantiles"] + + # handle default likelihood for fine-tuning + # if fine-tuning is enabled and likelihood is not specified, we use the quantiles + # used during pre-training to avoid catastrophic forgetting/distribution shift + if kwargs.get("enable_finetuning") and likelihood is None: + likelihood = QuantileRegression(quantiles) + self.model_params["likelihood"] = likelihood + # by default (`likelihood=None`), model is deterministic # otherwise, only QuantileRegression likelihood is supported and quantiles must be # a subset of Chronos-2 quantiles @@ -876,6 +887,16 @@ def encode_year(idx): logger, ) + if kwargs.get("enable_finetuning") and set(user_quantiles) != set( + quantiles + ): + logger.warning( + f"You are fine-tuning on a subset of the model's original quantiles ({user_quantiles}). " + f"This effectively changes the model's output definition (loss function) and might degrade " + f"its pre-trained knowledge. Consider fine-tuning on all pre-trained quantiles: {quantiles}, " + f"implied by default when `enable_finetuning=True` and `likelihood=None`." + ) + self.hf_connector = hf_connector super().__init__(**kwargs) From 9021e20326abcab864b2e24ca9563ae1a6980375 Mon Sep 17 00:00:00 2001 From: dennisbader Date: Fri, 27 Feb 2026 10:15:07 +0100 Subject: [PATCH 18/26] rename example notebook and add it to tests and docs --- .github/workflows/merge.yml | 2 +- darts/models/forecasting/chronos2_model.py | 4 ++-- docs/source/examples.rst | 22 ++++++++++++++----- ...undation-Model-Fine-Tuning-examples.ipynb} | 0 4 files changed, 19 insertions(+), 9 deletions(-) rename examples/{26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb => 27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb} (100%) diff --git a/.github/workflows/merge.yml b/.github/workflows/merge.yml index fe561bb8ca..0b6d754ae5 100644 --- a/.github/workflows/merge.yml +++ b/.github/workflows/merge.yml @@ -96,7 +96,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-SKLearnModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb, 24-SKLearnClassifierModel-examples.ipynb, 25-Chronos-2-examples.ipynb, 26-NeuralForecast-examples.ipynb] + example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-SKLearnModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb, 24-SKLearnClassifierModel-examples.ipynb, 25-Chronos-2-examples.ipynb, 26-NeuralForecast-examples.ipynb, 27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb] steps: - name: "Clone repository" uses: actions/checkout@v4 diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index 6987eeba16..573077e5c1 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -6,8 +6,8 @@ * `Chronos-2 Foundation Model Examples `__ -* `Tine Tuning Examples - `__ +* `Fine Tuning Examples + `__ """ import math diff --git a/docs/source/examples.rst b/docs/source/examples.rst index e12d4deb00..c76f6be89c 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -187,7 +187,7 @@ TFT model example notebook: examples/13-TFT-examples.ipynb TimeSeries Dense Encoder (TiDE) Model -======================================= +===================================== TiDE model example notebook: @@ -197,7 +197,7 @@ TiDE model example notebook: examples/18-TiDE-examples.ipynb TimeSeries Mixer (TSMixer) Model -======================================= +================================ TSMixer model example notebook: @@ -217,7 +217,7 @@ NeuralForecastModel example notebook: examples/26-NeuralForecast-examples.ipynb Chronos-2 Model -======================================= +=============== Chronos-2 Foundation model example notebook: @@ -226,8 +226,18 @@ Chronos-2 Foundation model example notebook: examples/25-Chronos-2-examples.ipynb +Torch and Foundation Model Fine-Tuning +====================================== + +Torch and Foundation model fine-tuning example notebook: + +.. toctree:: + :maxdepth: 1 + + examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb + Ensemble Models -============================= +=============== Ensemble models example notebook: @@ -257,7 +267,7 @@ Gaussian process filter model example notebook: examples/11-GP-filter-examples.ipynb Anomaly Detection -======================================= +================= Anomaly detection example notebook: @@ -267,7 +277,7 @@ Anomaly detection example notebook: examples/22-anomaly-detection-examples.ipynb Dynamic Time Warping (DTW) -============================= +========================== Dynamic time warping example notebook: diff --git a/examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb similarity index 100% rename from examples/26-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb rename to examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb From 9736f21d0e03a2cff426e5f241824ac437b983ca Mon Sep 17 00:00:00 2001 From: dennisbader Date: Fri, 27 Feb 2026 13:37:36 +0100 Subject: [PATCH 19/26] some refactoring --- darts/models/forecasting/chronos2_model.py | 2 +- darts/models/forecasting/foundation_model.py | 6 +- .../forecasting/torch_forecasting_model.py | 111 ++++++++---------- .../progress_bar.py => callbacks.py} | 0 darts/utils/callbacks/__init__.py | 5 - 5 files changed, 55 insertions(+), 69 deletions(-) rename darts/utils/{callbacks/progress_bar.py => callbacks.py} (100%) delete mode 100644 darts/utils/callbacks/__init__.py diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index 573077e5c1..f96ac680d3 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -6,7 +6,7 @@ * `Chronos-2 Foundation Model Examples `__ -* `Fine Tuning Examples +* `Fine-Tuning Examples `__ """ diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index 4946093d60..c1c68c5740 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -150,9 +150,9 @@ def encode_year(idx): your forecasting use case. Default: ``False``. enable_finetuning Enables model fine-tuning. Only effective, if not `None`. - If a bool, specifies whether to perform full fine tuning / training (all parameters are updated) - or keep all parameters frozen. - If a dict, specifies which parameters to fine tune. Must only contain one key-value record. Can be used to: + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine tune. Must only contain one key-value + record. Can be used to: - Unfreeze specific parameters, while keeping everything else frozen: `{"unfreeze": ["patterns.to.freeze"]}` - Freeze specific parameters, while keeping everything else unfrozen: `{"freeze": ["patterns.to.freeze"]}` diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index 7e789baa1b..a6cb4b3a4c 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -270,9 +270,9 @@ def encode_year(idx): your forecasting use case. Default: ``False``. enable_finetuning Enables model fine-tuning. Only effective, if not `None`. - If a bool, specifies whether to perform full fine tuning / training (all parameters are updated) - or keep all parameters frozen. - If a dict, specifies which parameters to fine tune. Must only contain one key-value record. Can be used to: + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine tune. Must only contain one key-value + record. Can be used to: - Unfreeze specific parameters, while keeping everything else frozen: `{"unfreeze": ["patterns.to.freeze"]}` - Freeze specific parameters, while keeping everything else unfrozen: `{"freeze": ["patterns.to.freeze"]}` @@ -372,6 +372,8 @@ def encode_year(idx): # pl_module_params must be set in __init__ method of TorchForecastingModel subclass self.pl_module_params: dict | None = None + # fine-tuning control + self._verify_enable_finetuning(enable_finetuning) self.enable_finetuning = enable_finetuning @classmethod @@ -518,64 +520,27 @@ def _setup_finetuning(self, model: PLForecastingModule): """ Sets up the model for fine-tuning based on `self.enable_finetuning`. """ - # Default behavior (None): fine-tuning (training) is enabled, all parameters are trainable - # This means we don't need to do anything, as parameters are trainable by default. + # default behavior (None): all parameters are trainable if self.enable_finetuning is None: return - # Boolean behavior if isinstance(self.enable_finetuning, bool): - requires_grad = self.enable_finetuning - for param in model.parameters(): - param.requires_grad = requires_grad - return - - # Dict behavior - if isinstance(self.enable_finetuning, dict): - keys = list(self.enable_finetuning.keys()) - if len(keys) != 1 or keys[0] not in ["freeze", "unfreeze"]: - raise_log( - ValueError( - "If `enable_finetuning` is a dict, it must contain exactly one key: 'freeze' or 'unfreeze'." - ), - logger, - ) - - mode = keys[0] + # boolean behavior; freeze all or none + patterns = [] + make_trainable = not self.enable_finetuning + else: + # dict behavior; freeze or unfreeze only the given patterns + # guaranteed to only have on key-value pair from (verified at model creation) + mode = list(self.enable_finetuning)[0] + make_trainable = mode == "unfreeze" patterns = self.enable_finetuning[mode] - if not isinstance(patterns, list) or not all( - isinstance(p, str) for p in patterns - ): - raise_log( - ValueError( - "The value of `enable_finetuning` dict must be a list of strings (patterns)." - ), - logger, - ) - - # "unfreeze" mode: freeze all, then unfreeze specific - if mode == "unfreeze": - for param in model.parameters(): - param.requires_grad = False - self._apply_freeze_patterns(model, patterns, freeze=False) - - # "freeze" mode: unfreeze all, then freeze specific - elif mode == "freeze": - for param in model.parameters(): - param.requires_grad = True - self._apply_freeze_patterns(model, patterns, freeze=True) - - @staticmethod - def _apply_freeze_patterns( - model: PLForecastingModule, patterns: list[str], freeze: bool - ): - """ - Applies freezing or unfreezing to parameters matching the given patterns. - """ + # freeze (or unfreeze) the patterns and unfreeze (or freeze) the remaining parameters for name, param in model.named_parameters(): if any(fnmatch.fnmatch(name, p) for p in patterns): - param.requires_grad = not freeze + param.requires_grad = make_trainable + else: + param.requires_grad = not make_trainable def _setup_trainer( self, @@ -804,6 +769,35 @@ def _verify_past_future_covariates(self, past_covariates, future_covariates): logger=logger, ) + @staticmethod + def _verify_enable_finetuning( + enable_finetuning: bool | dict[str, list[str]] | None, + ) -> None: + """Verify the `enable_finetuning` input.""" + if enable_finetuning is None or isinstance(enable_finetuning, bool): + return + + # dict + keys = list(enable_finetuning.keys()) + if len(keys) != 1 or keys[0] not in ["freeze", "unfreeze"]: + raise_log( + ValueError( + "If `enable_finetuning` is a dict, it must contain exactly one key: 'freeze' or 'unfreeze'." + ), + logger, + ) + + patterns = enable_finetuning[keys[0]] + if not isinstance(patterns, list) or not all( + isinstance(p, str) for p in patterns + ): + raise_log( + ValueError( + "The value of the `enable_finetuning` dict must be a list of strings (patterns)." + ), + logger, + ) + def _update_covariates_use(self): """Based on the Forecasting class and the training_sample attribute, update the uses_[past/future/static]_covariates attributes.""" @@ -2544,13 +2538,10 @@ def min_train_samples(self) -> int: @property def _requires_training(self) -> bool: """Whether the model should be trained when calling a `fit*` method.""" - # Multiple cases, enable_finetuning is None, we retrain the model - # If enable_finetuning is a dictionary, we retrain the model (not necessarily all weights) - # Otherwise, it's a boolean so we return the value - if self.enable_finetuning is None or self.enable_finetuning is dict: - return True - - return self.enable_finetuning + # no training only if fine-tuning is explicitly disabled + if self.enable_finetuning is False: + return False + return True def _check_optimizable_historical_forecasts( self, diff --git a/darts/utils/callbacks/progress_bar.py b/darts/utils/callbacks.py similarity index 100% rename from darts/utils/callbacks/progress_bar.py rename to darts/utils/callbacks.py diff --git a/darts/utils/callbacks/__init__.py b/darts/utils/callbacks/__init__.py deleted file mode 100644 index 5e0391b415..0000000000 --- a/darts/utils/callbacks/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from darts.utils.callbacks.progress_bar import TFMProgressBar - -__all__ = [ - "TFMProgressBar", -] From 0f77ac62b39db257e2ceda807ec5dae858838bfe Mon Sep 17 00:00:00 2001 From: dennisbader Date: Fri, 27 Feb 2026 15:19:56 +0100 Subject: [PATCH 20/26] training specific quantile loss for foundation models and tests --- darts/models/forecasting/chronos2_model.py | 61 +++-- darts/models/forecasting/foundation_model.py | 5 + darts/models/forecasting/timesfm2p5_model.py | 44 ++-- .../test_torch_forecasting_model.py | 216 ++++++++++++++++++ 4 files changed, 282 insertions(+), 44 deletions(-) diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index f96ac680d3..5668cda872 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -27,7 +27,10 @@ ) from darts.models.components.huggingface_connector import HuggingFaceConnector from darts.models.forecasting.foundation_model import FoundationModel -from darts.models.forecasting.pl_forecasting_module import PLForecastingModule +from darts.models.forecasting.pl_forecasting_module import ( + PLForecastingModule, + io_processor, +) from darts.utils.data.torch_datasets.utils import PLModuleInput, TorchTrainingSample from darts.utils.likelihood_models.torch import QuantileRegression @@ -95,7 +98,8 @@ def __init__( all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` base class. """ - + # for fine-tuning, model should be trained on pre-trained quantiles + enable_finetuning = kwargs.pop("enable_finetuning", False) super().__init__(**kwargs) self.d_model = d_model self.d_kv = d_kv @@ -188,7 +192,7 @@ def __init__( quantiles_tensor = torch.tensor(quantiles) self.register_buffer("quantiles", quantiles_tensor, persistent=False) - # gather indices of user-specified quantiles + # gather indices of user-specified quantiles (used at prediction time) user_quantiles: list[float] = ( self.likelihood.quantiles if isinstance(self.likelihood, QuantileRegression) @@ -196,6 +200,15 @@ def __init__( ) self.user_quantile_indices = [quantiles.index(q) for q in user_quantiles] + # during fine-tuning, train on ALL pre-trained quantiles to preserve the + # full distribution; prediction uses only user-specified quantiles + if enable_finetuning: + self._finetuning_likelihood = QuantileRegression(quantiles) + self._finetuning_quantile_indices = list(range(self.num_quantiles)) + else: + self._finetuning_likelihood = None + self._finetuning_quantile_indices = None + self.output_patch_embedding = _ResidualBlock( in_dim=self.d_model, h_dim=self.d_ff, @@ -457,6 +470,7 @@ def _forward( # 3. Chronos-2 uses normalized values for loss computation, while Darts uses denormalized values. # We need to think about how best to implement Chronos-2 `RINorm` in `io_processor()` without # breaking existing behavior, while also allowing fine-tuning with normalized loss. + @io_processor def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: """Chronos-2 model forward pass. @@ -545,17 +559,21 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: # select only target variables quantile_preds = quantile_preds[:, :, : self.n_targets, :] - # select only user-specified quantiles or median if deterministic - quantile_preds = quantile_preds[:, :, :, self.user_quantile_indices] + # during training (fine-tuning), output all pre-trained quantiles for loss; + # during prediction, output only user-specified quantiles + if self.training: + quantile_preds = quantile_preds[:, :, :, self._finetuning_quantile_indices] + else: + quantile_preds = quantile_preds[:, :, :, self.user_quantile_indices] return quantile_preds + def _compute_loss(self, output, target, criterion, sample_weight): + # compute loss on pre-trained quantiles + return self._finetuning_likelihood.compute_loss(output, target, sample_weight) -class Chronos2Model(FoundationModel): - # Fine-tuning is turned off for now pending proper fine-tuning support - # and configuration. - _allows_finetuning = False +class Chronos2Model(FoundationModel): def __init__( self, input_chunk_length: int, @@ -631,9 +649,9 @@ def __init__( [0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99]. Default: ``None``, which will make Chronos-2 deterministic (median quantile only). - If fine-tuning is enable (``enable_finetuning`` is ``True`` or a dict), and ``likelihood`` is not specified, - it will default to all quantiles used during pre-training to avoid catastrophic forgetting and distribution - shift. + When fine-tuning is enabled, the training loss is always computed on all pre-trained quantiles to + preserve the full distribution, regardless of the ``likelihood`` setting. The ``likelihood`` parameter + only affects prediction output. hub_model_name The model ID on HuggingFace Hub. Default: ``"amazon/chronos-2"``. Other available variants include ``"autogluon/chronos-2-small"`` and ``"autogluon/chronos-2-synth"``. @@ -809,8 +827,6 @@ def encode_year(idx): [[1005.6928 ]] [[1005.69617]]] - .. note:: - Fine-tuning of Chronos-2 is not supported at the moment. .. note:: Chronos-2 is licensed under the `Apache-2.0 License `_, copyright Amazon.com, Inc. or its affiliates. By using this model, you agree to the terms and conditions of @@ -855,13 +871,6 @@ def encode_year(idx): quantiles = chronos_config["quantiles"] - # handle default likelihood for fine-tuning - # if fine-tuning is enabled and likelihood is not specified, we use the quantiles - # used during pre-training to avoid catastrophic forgetting/distribution shift - if kwargs.get("enable_finetuning") and likelihood is None: - likelihood = QuantileRegression(quantiles) - self.model_params["likelihood"] = likelihood - # by default (`likelihood=None`), model is deterministic # otherwise, only QuantileRegression likelihood is supported and quantiles must be # a subset of Chronos-2 quantiles @@ -884,16 +893,6 @@ def encode_year(idx): logger, ) - if kwargs.get("enable_finetuning") and set(user_quantiles) != set( - quantiles - ): - logger.warning( - f"You are fine-tuning on a subset of the model's original quantiles ({user_quantiles}). " - f"This effectively changes the model's output definition (loss function) and might degrade " - f"its pre-trained knowledge. Consider fine-tuning on all pre-trained quantiles: {quantiles}, " - f"implied by default when `enable_finetuning=True` and `likelihood=None`." - ) - self.hf_connector = hf_connector super().__init__(**kwargs) diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index c1c68c5740..a4f85ddcef 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -167,6 +167,11 @@ def encode_year(idx): # extract pytorch lightning module kwargs self.pl_module_params = self._extract_pl_module_params(**self.model_params) + # pass fine-tuning flag to the PLModule so it can set up training-specific + # quantile handling (separate from prediction-time likelihood) + if self.enable_finetuning: + self.pl_module_params["enable_finetuning"] = True + use_reversible_instance_norm: bool | dict = self.pl_module_params.get( "use_reversible_instance_norm", False ) diff --git a/darts/models/forecasting/timesfm2p5_model.py b/darts/models/forecasting/timesfm2p5_model.py index c6be065239..b1a0f2aa33 100644 --- a/darts/models/forecasting/timesfm2p5_model.py +++ b/darts/models/forecasting/timesfm2p5_model.py @@ -106,7 +106,8 @@ def __init__( all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` base class. """ - + # for fine-tuning, model should be trained on pre-trained quantiles + enable_finetuning = kwargs.pop("enable_finetuning", False) super().__init__(**kwargs) # default model parameters (config.json is ignored) @@ -138,7 +139,7 @@ def __init__( self.output_chunk_shift + (self.output_chunk_length or 0), ) - # gather indices of user-specified quantiles + # gather indices of user-specified quantiles (used at prediction time) user_quantiles: list[float] = ( self.likelihood.quantiles if isinstance(self.likelihood, QuantileRegression) @@ -151,6 +152,18 @@ def __init__( self.config.quantiles.index(q) + 1 for q in user_quantiles ] + # during fine-tuning, train on ALL pre-trained quantiles to preserve the + # full distribution; prediction uses only user-specified quantiles + # (indices offset by +1 because index 0 is the unused mean output) + if enable_finetuning: + self._finetuning_likelihood = QuantileRegression(self.config.quantiles) + self._finetuning_quantile_indices = list( + range(1, self.num_quantiles_plus_one) + ) + else: + self._finetuning_likelihood = None + self._finetuning_quantile_indices = None + def _forward( self, inputs: torch.Tensor, @@ -197,7 +210,6 @@ def _forward( return output_ts - # TODO: fine-tuning support @io_processor def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: """TimesFM 2.5 model forward pass. @@ -296,18 +308,23 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: # -> (B, T, C, W) renormed_outputs = renormed_outputs[:, self.future_slice, :, :] - # select only user-specified quantiles or median if deterministic - # -> (B, T, C, N) - renormed_outputs = renormed_outputs[:, :, :, self.user_quantile_indices] + # during training (fine-tuning), output all pre-trained quantiles for loss; + # during prediction, output only user-specified quantiles + if self.training: + renormed_outputs = renormed_outputs[ + :, :, :, self._finetuning_quantile_indices + ] + else: + renormed_outputs = renormed_outputs[:, :, :, self.user_quantile_indices] return renormed_outputs + def _compute_loss(self, output, target, criterion, sample_weight): + # compute loss on pre-trained quantiles + return self._finetuning_likelihood.compute_loss(output, target, sample_weight) -class TimesFM2p5Model(FoundationModel): - # Fine-tuning is turned off for now pending proper fine-tuning support - # and configuration. - _allows_finetuning = False +class TimesFM2p5Model(FoundationModel): def __init__( self, input_chunk_length: int, @@ -368,6 +385,9 @@ def __init__( the quantiles must be a subset of those used during TimesFM 2.5 pre-training: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]. Default: ``None``, which will make TimesFM 2.5 deterministic (median quantile only). + When fine-tuning is enabled, the training loss is always computed on all pre-trained quantiles to + preserve the full distribution, regardless of the ``likelihood`` setting. The ``likelihood`` parameter + only affects prediction output. hub_model_name The model ID on HuggingFace Hub. Default: ``"google/timesfm-2.5-200m-pytorch"``. hub_model_revision @@ -543,8 +563,6 @@ def encode_year(idx): regression between covariates and the target series (or forecast residuals) as a pre/post-processing step. You can implement a similar approach externally in Darts. See `Issue #2976 `_ for details. - .. note:: - Fine-tuning of TimesFM 2.5 is not supported at the moment. .. note:: TimesFM 2.5 is licensed under the `Apache-2.0 License `_, Copyright 2025 Google LLC. By using this model, you agree to the terms and conditions of the license. @@ -612,7 +630,7 @@ def encode_year(idx): ) self.hf_connector = hf_connector - super().__init__(enable_finetuning=False, **kwargs) + super().__init__(**kwargs) def _create_model(self, train_sample: TorchTrainingSample) -> PLForecastingModule: pl_module_params = self.pl_module_params or {} diff --git a/darts/tests/models/forecasting/test_torch_forecasting_model.py b/darts/tests/models/forecasting/test_torch_forecasting_model.py index 9ca630dc73..a929a68afe 100644 --- a/darts/tests/models/forecasting/test_torch_forecasting_model.py +++ b/darts/tests/models/forecasting/test_torch_forecasting_model.py @@ -2854,3 +2854,219 @@ def helper_create_torch_model(self, model_cls, icl, ocl, shift, **kwargs): params.update(tfm_kwargs) params.update(kwargs) return model_cls(**params) + + +class TestTorchForecastingModelFineTuning: + """Tests for enable_finetuning on TorchForecastingModel using DLinearModel.""" + + series = tg.sine_timeseries(length=40, value_frequency=0.05).astype(np.float32) + + base_kwargs = { + "input_chunk_length": 10, + "output_chunk_length": 2, + "random_state": 42, + **tfm_kwargs, + } + + def test_full_finetuning(self): + model = DLinearModel( + enable_finetuning=True, + n_epochs=1, + **self.base_kwargs, + ) + assert model.enable_finetuning is True + assert model._requires_training is True + + with patch("pytorch_lightning.Trainer.fit") as mock_fit: + model.fit(self.series) + mock_fit.assert_called_once() + + for name, p in model.model.named_parameters(): + assert p.requires_grad is True + + preds = model.predict(n=10, series=self.series) + assert len(preds) == 10 + + def test_no_training(self): + model = DLinearModel( + enable_finetuning=False, + n_epochs=1, + **self.base_kwargs, + ) + assert model.enable_finetuning is False + assert model._requires_training is False + + with patch("pytorch_lightning.Trainer.fit") as mock_fit: + model.fit(self.series) + mock_fit.assert_not_called() + + for name, p in model.model.named_parameters(): + assert p.requires_grad is False + + preds = model.predict(n=10, series=self.series) + assert len(preds) == 10 + + def test_partial_freeze(self): + model = DLinearModel( + enable_finetuning={"freeze": ["linear_seasonal.*"]}, + n_epochs=1, + **self.base_kwargs, + ) + + with patch("pytorch_lightning.Trainer.fit") as mock_fit: + model.fit(self.series) + mock_fit.assert_called_once() + + frozen_found = False + trainable_found = False + for name, p in model.model.named_parameters(): + if "linear_seasonal" in name: + assert p.requires_grad is False + frozen_found = True + else: + assert p.requires_grad is True + trainable_found = True + + assert frozen_found + assert trainable_found + + preds = model.predict(n=10, series=self.series) + assert len(preds) == 10 + + def test_partial_unfreeze(self): + model = DLinearModel( + enable_finetuning={"unfreeze": ["linear_seasonal.*"]}, + n_epochs=1, + **self.base_kwargs, + ) + + with patch("pytorch_lightning.Trainer.fit") as mock_fit: + model.fit(self.series) + mock_fit.assert_called_once() + + unfrozen_found = False + frozen_found = False + for name, p in model.model.named_parameters(): + if "linear_seasonal" in name: + assert p.requires_grad is True + unfrozen_found = True + else: + assert p.requires_grad is False + frozen_found = True + + assert unfrozen_found + assert frozen_found + + preds = model.predict(n=10, series=self.series) + assert len(preds) == 10 + + def test_finetuning_misconfiguration(self): + with pytest.raises( + ValueError, + match="must contain exactly one key: 'freeze' or 'unfreeze'", + ): + DLinearModel( + enable_finetuning={"invalid_key": ["pattern"]}, + **self.base_kwargs, + ) + + with pytest.raises(ValueError, match="must be a list of strings"): + DLinearModel( + enable_finetuning={"freeze": "not_a_list"}, + **self.base_kwargs, + ) + + with pytest.raises(ValueError, match="must contain exactly one key"): + DLinearModel( + enable_finetuning={"freeze": ["p1"], "unfreeze": ["p2"]}, + **self.base_kwargs, + ) + + def test_save_load_roundtrip(self, tmpdir): + model = DLinearModel( + enable_finetuning=True, + n_epochs=1, + **self.base_kwargs, + ) + model.fit(self.series) + + pred_before = model.predict(n=2, series=self.series) + + save_path = os.path.join(str(tmpdir), "finetuned_model.pt") + model.save(save_path) + + loaded_model = DLinearModel.load(save_path) + assert loaded_model.enable_finetuning is True + + pred_after = loaded_model.predict(n=2, series=self.series) + np.testing.assert_allclose( + pred_before.values(), + pred_after.values(), + atol=1e-6, + ) + + def test_enable_finetuning_with_load_weights(self, tmpdir): + # 1. Train and save a base model (no fine-tuning flags) + base_model = DLinearModel( + n_epochs=1, + **self.base_kwargs, + ) + base_model.fit(self.series) + save_path = os.path.join(str(tmpdir), "base_model.pt") + base_model.save(save_path) + + base_state = {n: p.clone() for n, p in base_model.model.named_parameters()} + + # 2. Create a NEW model with enable_finetuning and load_weights + ft_model = DLinearModel( + enable_finetuning={"freeze": ["linear_seasonal.*"]}, + n_epochs=1, + **self.base_kwargs, + ) + ft_model.load_weights(save_path) + + # 3. Verify requires_grad is correctly set after load_weights + frozen_found = False + for name, p in ft_model.model.named_parameters(): + if "linear_seasonal" in name: + assert p.requires_grad is False + frozen_found = True + else: + assert p.requires_grad is True + + assert frozen_found + + # 4. Verify loaded weights match saved weights + for name, p in ft_model.model.named_parameters(): + np.testing.assert_allclose( + p.detach().cpu().numpy(), + base_state[name].detach().cpu().numpy(), + atol=1e-6, + ) + + # 5. Fine-tune (fit) the model — only unfrozen params should change + frozen_before = {n: p.clone() for n, p in ft_model.model.named_parameters()} + ft_model.fit(self.series) + for name, p in ft_model.model.named_parameters(): + if "linear_seasonal" in name: + np.testing.assert_allclose( + p.detach().cpu().numpy(), + frozen_before[name].detach().cpu().numpy(), + atol=1e-6, + ) + else: + with pytest.raises(AssertionError): + np.testing.assert_allclose( + p.detach().cpu().numpy(), + frozen_before[name].detach().cpu().numpy(), + atol=1e-6, + ) + + def test_enable_finetuning_none_is_default(self): + model = DLinearModel(n_epochs=1, **self.base_kwargs) + assert model.enable_finetuning is None + assert model._requires_training is True + + model.fit(self.series) + for _, p in model.model.named_parameters(): + assert p.requires_grad is True From 9987a02d0f7cec751868cf4b5e43e954102c9cb1 Mon Sep 17 00:00:00 2001 From: dennisbader Date: Fri, 27 Feb 2026 15:25:53 +0100 Subject: [PATCH 21/26] update docs --- darts/models/forecasting/block_rnn_model.py | 12 ++++++++ darts/models/forecasting/chronos2_model.py | 18 ++++++++++- darts/models/forecasting/dlinear.py | 12 ++++++++ darts/models/forecasting/foundation_model.py | 23 +++++++++----- darts/models/forecasting/nbeats.py | 12 ++++++++ darts/models/forecasting/nf_model.py | 12 ++++++++ darts/models/forecasting/nhits.py | 12 ++++++++ darts/models/forecasting/nlinear.py | 12 ++++++++ darts/models/forecasting/rnn_model.py | 12 ++++++++ darts/models/forecasting/tcn_model.py | 12 ++++++++ darts/models/forecasting/tft_model.py | 12 ++++++++ darts/models/forecasting/tide_model.py | 12 ++++++++ darts/models/forecasting/timesfm2p5_model.py | 19 ++++++++++++ .../forecasting/torch_forecasting_model.py | 14 +++++---- darts/models/forecasting/transformer_model.py | 12 ++++++++ darts/models/forecasting/tsmixer_model.py | 12 ++++++++ .../models/forecasting/test_foundation.py | 30 ++++++------------- .../test_torch_forecasting_model.py | 5 ++-- docs/userguide/torch_forecasting_models.md | 23 +++++++++++++- 19 files changed, 238 insertions(+), 38 deletions(-) diff --git a/darts/models/forecasting/block_rnn_model.py b/darts/models/forecasting/block_rnn_model.py index f1e3e2839c..498ee698a2 100644 --- a/darts/models/forecasting/block_rnn_model.py +++ b/darts/models/forecasting/block_rnn_model.py @@ -454,6 +454,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index 5668cda872..a6e8cf80ee 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -621,6 +621,11 @@ def __init__( below for details. It is recommended to call :func:`predict()` with ``predict_likelihood_parameters=True`` or ``num_samples >> 1`` to get meaningful results. + .. tip:: + You can perform full or partial fine-tuning of the model by setting the ``enable_finetuning`` parameter. + Read more in the parameter description below and in the `Fine-Tuning Examples + `__. + Parameters ---------- input_chunk_length @@ -787,6 +792,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- @@ -870,7 +887,6 @@ def encode_year(idx): ) quantiles = chronos_config["quantiles"] - # by default (`likelihood=None`), model is deterministic # otherwise, only QuantileRegression likelihood is supported and quantiles must be # a subset of Chronos-2 quantiles diff --git a/darts/models/forecasting/dlinear.py b/darts/models/forecasting/dlinear.py index 6a29257f6f..ca5e107f0f 100644 --- a/darts/models/forecasting/dlinear.py +++ b/darts/models/forecasting/dlinear.py @@ -411,6 +411,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/foundation_model.py b/darts/models/forecasting/foundation_model.py index a4f85ddcef..8addc84542 100644 --- a/darts/models/forecasting/foundation_model.py +++ b/darts/models/forecasting/foundation_model.py @@ -17,10 +17,7 @@ class FoundationModel(MixedCovariatesTorchModel, ABC): - def __init__( - self, - **kwargs, - ): + def __init__(self, **kwargs): """Foundation Forecasting Model with PyTorch Lightning backend. This class is meant to be inherited to create a new foundation forecasting model. @@ -41,6 +38,12 @@ def __init__( instantiate a :class:`HuggingFaceConnector` and use its methods to load the model configuration inside :func:`__init__()` and to load the model weights inside :func:`_create_model()`. + + .. tip:: + You can perform full or partial fine-tuning of the model by setting the ``enable_finetuning`` parameter. + Read more in the parameter description below and in the `Fine-Tuning Examples + `__. + Parameters ---------- batch_size @@ -149,13 +152,17 @@ def encode_year(idx): whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. enable_finetuning - Enables model fine-tuning. Only effective, if not `None`. + Enables model fine-tuning. Only effective if not ``None``. If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep - all parameters frozen. If a dict, specifies which parameters to fine tune. Must only contain one key-value + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value record. Can be used to: - - Unfreeze specific parameters, while keeping everything else frozen: `{"unfreeze": ["patterns.to.freeze"]}` - - Freeze specific parameters, while keeping everything else unfrozen: `{"freeze": ["patterns.to.freeze"]}` + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. """ # Set default fine-tuning to False for foundation models if "enable_finetuning" not in self.model_params: diff --git a/darts/models/forecasting/nbeats.py b/darts/models/forecasting/nbeats.py index 89ef73dd32..8c97daa342 100644 --- a/darts/models/forecasting/nbeats.py +++ b/darts/models/forecasting/nbeats.py @@ -745,6 +745,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/nf_model.py b/darts/models/forecasting/nf_model.py index 0b4f7f36f3..07743a8fac 100644 --- a/darts/models/forecasting/nf_model.py +++ b/darts/models/forecasting/nf_model.py @@ -571,6 +571,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/nhits.py b/darts/models/forecasting/nhits.py index 0394998dc8..98f7e43e19 100644 --- a/darts/models/forecasting/nhits.py +++ b/darts/models/forecasting/nhits.py @@ -679,6 +679,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/nlinear.py b/darts/models/forecasting/nlinear.py index 1d3b5ca333..024bcd86ea 100644 --- a/darts/models/forecasting/nlinear.py +++ b/darts/models/forecasting/nlinear.py @@ -379,6 +379,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/rnn_model.py b/darts/models/forecasting/rnn_model.py index fd42087af1..8c84163b82 100644 --- a/darts/models/forecasting/rnn_model.py +++ b/darts/models/forecasting/rnn_model.py @@ -466,6 +466,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. Examples -------- diff --git a/darts/models/forecasting/tcn_model.py b/darts/models/forecasting/tcn_model.py index 6cbbccffdf..b2383be44d 100644 --- a/darts/models/forecasting/tcn_model.py +++ b/darts/models/forecasting/tcn_model.py @@ -445,6 +445,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/tft_model.py b/darts/models/forecasting/tft_model.py index fcce2fc563..40714c41a5 100644 --- a/darts/models/forecasting/tft_model.py +++ b/darts/models/forecasting/tft_model.py @@ -890,6 +890,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/tide_model.py b/darts/models/forecasting/tide_model.py index ebe229ed92..3903d611ed 100644 --- a/darts/models/forecasting/tide_model.py +++ b/darts/models/forecasting/tide_model.py @@ -584,6 +584,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/timesfm2p5_model.py b/darts/models/forecasting/timesfm2p5_model.py index b1a0f2aa33..c9bb96afba 100644 --- a/darts/models/forecasting/timesfm2p5_model.py +++ b/darts/models/forecasting/timesfm2p5_model.py @@ -9,6 +9,8 @@ * `Chronos-2 Foundation Model Examples `__ +* `Fine-Tuning Examples + `__ """ import os @@ -357,6 +359,11 @@ def __init__( below for details. It is recommended to call :func:`predict()` with ``predict_likelihood_parameters=True`` or ``num_samples >> 1`` to get meaningful results. + .. tip:: + You can perform full or partial fine-tuning of the model by setting the ``enable_finetuning`` parameter. + Read more in the parameter description below and in the `Fine-Tuning Examples + `__. + Parameters ---------- input_chunk_length @@ -522,6 +529,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index a6cb4b3a4c..6d8507ea30 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -269,13 +269,17 @@ def encode_year(idx): whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. enable_finetuning - Enables model fine-tuning. Only effective, if not `None`. + Enables model fine-tuning. Only effective if not ``None``. If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep - all parameters frozen. If a dict, specifies which parameters to fine tune. Must only contain one key-value + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value record. Can be used to: - - Unfreeze specific parameters, while keeping everything else frozen: `{"unfreeze": ["patterns.to.freeze"]}` - - Freeze specific parameters, while keeping everything else unfrozen: `{"freeze": ["patterns.to.freeze"]}` + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. """ super().__init__(add_encoders=add_encoders) suppress_lightning_warnings(suppress_all=not show_warnings) @@ -2538,7 +2542,7 @@ def min_train_samples(self) -> int: @property def _requires_training(self) -> bool: """Whether the model should be trained when calling a `fit*` method.""" - # no training only if fine-tuning is explicitly disabled + # no training if fine-tuning is explicitly disabled if self.enable_finetuning is False: return False return True diff --git a/darts/models/forecasting/transformer_model.py b/darts/models/forecasting/transformer_model.py index d1ec421037..7494ce7605 100644 --- a/darts/models/forecasting/transformer_model.py +++ b/darts/models/forecasting/transformer_model.py @@ -532,6 +532,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/models/forecasting/tsmixer_model.py b/darts/models/forecasting/tsmixer_model.py index 1bf6804e69..c340769813 100644 --- a/darts/models/forecasting/tsmixer_model.py +++ b/darts/models/forecasting/tsmixer_model.py @@ -721,6 +721,18 @@ def encode_year(idx): show_warnings whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of your forecasting use case. Default: ``False``. + enable_finetuning + Enables model fine-tuning. Only effective if not ``None``. + If a bool, specifies whether to perform full fine-tuning / training (all parameters are updated) or keep + all parameters frozen. If a dict, specifies which parameters to fine-tune. Must only contain one key-value + record. Can be used to: + + - Unfreeze specific parameters, while keeping everything else frozen: + ``{"unfreeze": ["param.name.patterns.*"]}`` + - Freeze specific parameters, while keeping everything else unfrozen: + ``{"freeze": ["param.name.patterns.*"]}`` + + Default: ``None``. References ---------- diff --git a/darts/tests/models/forecasting/test_foundation.py b/darts/tests/models/forecasting/test_foundation.py index e50ababff1..602bb92963 100644 --- a/darts/tests/models/forecasting/test_foundation.py +++ b/darts/tests/models/forecasting/test_foundation.py @@ -283,9 +283,7 @@ def test_full_finetuning(self, mock_method, tmpdir): # Verify all parameters require grad for n, p in model.model.named_parameters(): - assert p.requires_grad is True, ( - f"Parameter {n} should require grad for full fine-tuning" - ) + assert p.requires_grad is True # 3. Persistence (Save/Load) save_path = os.path.join(tmpdir, "model_full_ft.pt") @@ -299,9 +297,7 @@ def test_full_finetuning(self, mock_method, tmpdir): pred_orig = model.predict(n=6, series=self.series) pred_loaded = loaded_model.predict(n=6, series=self.series) # Relax tolerance slightly for floating point differences across save/load on CPU - assert np.allclose(pred_orig.values(), pred_loaded.values(), atol=1e-6), ( - "Prediction of the fine-tuned model and the saved/loaded fine-tuned model should be the same" - ) + assert np.allclose(pred_orig.values(), pred_loaded.values(), atol=1e-6) @patch( "darts.models.components.huggingface_connector.hf_hub_download", @@ -329,15 +325,13 @@ def test_partial_finetuning_block_freeze(self, mock_method): for name, param in model.model.named_parameters(): if "encoder" in name: - assert param.requires_grad is False, ( - f"Parameter {name} should be frozen" - ) + assert param.requires_grad is False frozen_found = True elif param.requires_grad: trainable_found = True - assert frozen_found, "Should have found frozen parameters matching 'encoder'" - assert trainable_found, "Should have found some trainable parameters" + assert frozen_found + assert trainable_found @patch( "darts.models.components.huggingface_connector.hf_hub_download", @@ -365,20 +359,14 @@ def test_partial_finetuning_unfreeze(self, mock_method): for name, param in model.model.named_parameters(): if "encoder" in name: - assert param.requires_grad is True, ( - f"Parameter {name} should be unfrozen" - ) + assert param.requires_grad is True unfrozen_found = True else: - assert param.requires_grad is False, ( - f"Parameter {name} should be frozen" - ) + assert param.requires_grad is False frozen_found = True - assert unfrozen_found, ( - "Should have found unfrozen parameters matching 'encoder'" - ) - assert frozen_found, "Should have found frozen parameters (non-matching)" + assert unfrozen_found + assert frozen_found @patch( "darts.models.components.huggingface_connector.hf_hub_download", diff --git a/darts/tests/models/forecasting/test_torch_forecasting_model.py b/darts/tests/models/forecasting/test_torch_forecasting_model.py index a929a68afe..7180b191bf 100644 --- a/darts/tests/models/forecasting/test_torch_forecasting_model.py +++ b/darts/tests/models/forecasting/test_torch_forecasting_model.py @@ -2906,9 +2906,10 @@ def test_no_training(self): preds = model.predict(n=10, series=self.series) assert len(preds) == 10 - def test_partial_freeze(self): + @pytest.mark.parametrize("freeze", [["linear_seasonal.*"], ["*linear_seasonal*"]]) + def test_partial_freeze(self, freeze): model = DLinearModel( - enable_finetuning={"freeze": ["linear_seasonal.*"]}, + enable_finetuning={"freeze": freeze}, n_epochs=1, **self.base_kwargs, ) diff --git a/docs/userguide/torch_forecasting_models.md b/docs/userguide/torch_forecasting_models.md index c1e08d62e7..31eadf1b72 100644 --- a/docs/userguide/torch_forecasting_models.md +++ b/docs/userguide/torch_forecasting_models.md @@ -21,7 +21,7 @@ We assume that you already know about covariates in Darts. If you're new to the - [Checkpoint saving and loading](#automatic-checkpointing) - [Manual model saving and loading](#manual-saving-and-loading) - [GPU training and CPU loading](#training-and-saving-on-gpu-loading-on-cpu) - - [Load pre-trained model for fine-tuning](#re-training-or-fine-tuning-a-pre-trained-model) + - [Re-training or fine-tuning a pre-trained model](#re-training-or-fine-tuning-a-pre-trained-model) - [ONNX export for inference](#exporting-model-to-onnx-format-for-inference) - [Using callbacks](#callbacks) - [Early Stopping](#example-with-early-stopping) @@ -351,6 +351,27 @@ model_finetune = SomeTorchForecastingModel(..., # use identical parameters & va model_finetune.load_weights("/your/path/to/save/model.pt") ``` +Additionally, all `TorchForecastingModel` (including `FoundationModel`) support fine-tuning through the `enable_finetuning` parameter. This allows you to freeze or unfreeze specific model parameters for transfer learning: + +```python +# full fine-tuning: all parameters are trainable +model = SomeTorchForecastingModel(..., enable_finetuning=True) + +# partial fine-tuning: freeze specific layers +model = SomeTorchForecastingModel(..., enable_finetuning={"freeze": ["layer.pattern.*"]}) + +# partial fine-tuning: only unfreeze specific layers +model = SomeTorchForecastingModel(..., enable_finetuning={"unfreeze": ["layer.pattern.*"]}) + +# optionally, load pre-trained weights (not required for foundation models) +model.load_weights("/your/path/to/save/model.pt") + +# fine-tune +model.fit(...) +``` + +For a comprehensive walkthrough of fine-tuning Torch Forecasting Models and Foundation Models, check out the [Fine-Tuning example notebook](https://unit8co.github.io/darts/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.html). + #### Exporting model to ONNX format for inference It is also possible to export the model weights to the ONNX format to run inference in a lightweight environment. The example below works for any `TorchForecastingModel` except `RNNModel` and for optional usage of past, future and / or static covariates. Note that all series and covariates must extend far enough into the past (`input_chunk_length)` and future (`output_chunk_length`) relative to the end of the target `series`. It will not be possible to forecast a horizon `n > output_chunk_length` without implementing the auto-regression logic. From f93a2177d6ca835b709fb5dd023643dbd0868a3c Mon Sep 17 00:00:00 2001 From: dennisbader Date: Fri, 27 Feb 2026 17:59:13 +0100 Subject: [PATCH 22/26] update changelog entry --- CHANGELOG.md | 3 ++- darts/models/forecasting/chronos2_model.py | 9 +++++++-- darts/models/forecasting/timesfm2p5_model.py | 9 +++++++-- 3 files changed, 16 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 74db96b7a0..f551a678ca 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,7 +13,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co - 🚀🚀 Added new forecasting model `NeuralForecastModel` to convert any of the 30+ NeuralForecast base model into a Darts `TorchForecastingModel`. This includes models such as NBEATSx, PatchTST, TimeXer, KAN, and many more. Like all Darts torch models, it supports univariate, multivariate, probabilistic forecasting, optimized backtesting and more. Depending on the base model, it also supports past, future, and static covariates. [#3002](https://github.com/unit8co/darts/pull/3002) by [Zhihao Dai](https://github.com/daidahao) - Check out our new [NeuralForecastModel Notebook](https://unit8co.github.io/darts/examples/26-NeuralForecast-examples.html) for detailed examples. [#3026](https://github.com/unit8co/darts/pull/3026) by [Dennis Bader](https://github.com/dennisbader). -- 🚀🚀 Added support for full and partial fine-tuning of foundation and torch models with integrated layer freezing and `PeftCallback` for LoRA integration. [#2964](https://github.com/unit8co/darts/issues/2964) by [Alain Gysi](https://github.com/Kurokabe) +- 🚀🚀 Added support for fine-tuning to all `TorchForecastingModel` and `FoundationModel` (such as `Chronos2Model` and `TimesFM2p5Model`) via the new `enable_finetuning` parameter. Supports full training, and partial fine-tuning by selectively freezing or unfreezing layers by name pattern. [#2964](https://github.com/unit8co/darts/issues/2964) by [Alain Gysi](https://github.com/Kurokabe). + - Check out our new [Fine-Tuning Notebook](https://unit8co.github.io/darts/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.html) for detailed examples. - Created `darts.typing` to collect typical type annotation in one place. Introduced `TimeIndex` & `TimeSeriesLike` type aliases for improved readability & maintainability of the code. Commmon type annotations can be added to this file in the future. [#3021](https://github.com/unit8co/darts/pull/3021) by [Michel Zeller](https://github.com/mizeller) - More fine-grained control over Reversible Instance Normalization for all torch models. Apart from the boolean trigger, parameter `use_reversible_instance_norm` now also supports setting the `RINorm` hyperparameters as a dictionary. [#3029](https://github.com/unit8co/darts/pull/3029) by [Zhihao Dai](https://github.com/daidahao). diff --git a/darts/models/forecasting/chronos2_model.py b/darts/models/forecasting/chronos2_model.py index a6e8cf80ee..de48d82107 100644 --- a/darts/models/forecasting/chronos2_model.py +++ b/darts/models/forecasting/chronos2_model.py @@ -569,8 +569,13 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: return quantile_preds def _compute_loss(self, output, target, criterion, sample_weight): - # compute loss on pre-trained quantiles - return self._finetuning_likelihood.compute_loss(output, target, sample_weight) + if self.training: + # compute loss on pre-trained quantiles + return self._finetuning_likelihood.compute_loss( + output, target, sample_weight + ) + else: + return super()._compute_loss(output, target, criterion, sample_weight) class Chronos2Model(FoundationModel): diff --git a/darts/models/forecasting/timesfm2p5_model.py b/darts/models/forecasting/timesfm2p5_model.py index c9bb96afba..8092c6b8ba 100644 --- a/darts/models/forecasting/timesfm2p5_model.py +++ b/darts/models/forecasting/timesfm2p5_model.py @@ -322,8 +322,13 @@ def forward(self, x_in: PLModuleInput, *args, **kwargs) -> Any: return renormed_outputs def _compute_loss(self, output, target, criterion, sample_weight): - # compute loss on pre-trained quantiles - return self._finetuning_likelihood.compute_loss(output, target, sample_weight) + if self.training: + # compute loss on pre-trained quantiles + return self._finetuning_likelihood.compute_loss( + output, target, sample_weight + ) + else: + return super()._compute_loss(output, target, criterion, sample_weight) class TimesFM2p5Model(FoundationModel): From 70169efba158efa4d2036d061a4a9052588c2355 Mon Sep 17 00:00:00 2001 From: dennisbader Date: Sat, 28 Feb 2026 13:02:14 +0100 Subject: [PATCH 23/26] update notebook --- darts/models/forecasting/tft_model.py | 2 +- ...oundation-Model-Fine-Tuning-examples.ipynb | 8495 ++++++++++++++++- 2 files changed, 7994 insertions(+), 503 deletions(-) diff --git a/darts/models/forecasting/tft_model.py b/darts/models/forecasting/tft_model.py index 40714c41a5..863fee8dc8 100644 --- a/darts/models/forecasting/tft_model.py +++ b/darts/models/forecasting/tft_model.py @@ -783,7 +783,7 @@ def __init__( lr_scheduler_kwargs Optionally, some keyword arguments for the PyTorch learning rate scheduler. Default: ``None``. use_reversible_instance_norm - Whether to use reversible instance normalization `RINorm` against distribution shift as shown in [2]_. + Whether to use reversible instance normalization `RINorm` against distribution shift as shown in [3]_. It is only applied to the features of the target series and not the covariates. If ``True``, applies ``RINorm`` with default hyperparameters. If a dictionary, defines the hyperparameters to construct the ``RINorm``. Supported parameters are ``{"affine": bool, "eps": float}``. Default: ``False``. diff --git a/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb index a953ccdb46..2cc2ba5f21 100644 --- a/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb +++ b/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -9,20 +9,14 @@ "\n", "This example notebook demonstrates how to train, save, load, and fine-tune PyTorch-based models in Darts. We cover two scenarios:\n", "\n", - "1. **Regular PyTorch Models** (e.g., TiDE, N-BEATS, DLinear):\n", - " * Training a model from scratch.\n", - " * Saving and loading the model.\n", - " * Fine-tuning the loaded model (updating weights on new data).\n", - "\n", - "2. **Foundation Models** (e.g., Chronos):\n", - " * **Zero-shot inference**: Using the model without any training (`enable_finetuning=False`).\n", - " * **Full fine-tuning**: Retraining all model weights (`enable_finetuning=True`).\n", - " * **Partial fine-tuning**: Retraining only specific layers while freezing others (`enable_finetuning={\"freeze\": ...}` or `{\"unfreeze\": ...}`).\n", - "\n", - "The `enable_finetuning` parameter controls the behavior:\n", - "* `enable_finetuning=True`: Enables global fine-tuning (all weights are trainable).\n", - "* `enable_finetuning=False`: Disables fine-tuning (all weights are frozen).\n", - "* `enable_finetuning={...}`: Enables partial fine-tuning via \"freeze\" or \"unfreeze\" patterns.\n" + "- [**Regular PyTorch Models**](#1.-Regular-TorchForecastingModel:-Training-and-Fine-Tuning) (e.g., TiDE, N-BEATS, DLinear):\n", + " - **(Pre-)Training** a model from scratch.\n", + " - **Saving and loading** the model.\n", + " - **Fine-tuning** the loaded model (updating weights on new data).\n", + "\n", + "- [**Foundation Models**](#2.-Foundation-Model:-Zero-Shot-Forecasting-and-Fine-Tuning) (e.g., Chronos 2, TimesFM 2.5):\n", + " - **Zero-shot inference** for forecasting without any training.\n", + " - **Fine-tuning** the foundation model." ] }, { @@ -57,18 +51,19 @@ "metadata": {}, "outputs": [], "source": [ + "import logging\n", "import warnings\n", "\n", - "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "\n", - "from darts.datasets import AirPassengersDataset\n", + "from darts import set_option\n", + "from darts.datasets import AirPassengersDataset, AusBeerDataset\n", + "from darts.metrics import mae\n", "from darts.models import Chronos2Model, TiDEModel\n", - "from darts.utils.likelihood_models.torch import QuantileRegression\n", "\n", "warnings.filterwarnings(\"ignore\")\n", - "import logging\n", - "\n", - "logging.disable(logging.CRITICAL)" + "logging.disable(logging.CRITICAL)\n", + "set_option(\"plotting.use_darts_style\", True)" ] }, { @@ -77,7 +72,14 @@ "metadata": {}, "source": [ "## Data Preparation\n", - "Here we just load an example dataset with 144 samples as a fast demo. The data is split between train and validation, with the 2 last years (24 samples) for validation" + "\n", + "For demonstration, we'll just load two short time series: The Air Passengers and Australian Beer Production datasets.\n", + "We will pre-train a model only on the Air Passengers series and then fine-tune it on the Beer Production datast. \n", + "We also create a train and test split for illustration.\n", + "\n", + "
\n", + " **Note**: Our two datasets have different frequencies (monthly and quarterly), but our torch models can handle this out of the box!\n", + "
" ] }, { @@ -85,13 +87,41 @@ "execution_count": 4, "id": "2f87bcc5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# convert to float32 as Chronos-2 works with float32 input\n", - "data = AirPassengersDataset().load().astype(np.float32)\n", - "train_passengers, val_passengers = data.split_before(\n", - " len(data) - 12\n", - ") # last year for validation" + "input_chunk_length = 24 # model input window\n", + "output_chunk_length = 12 # model output_window\n", + "\n", + "# load air passengers series\n", + "series_air = AirPassengersDataset().load().astype(\"float32\")\n", + "series_beer = AusBeerDataset().load().astype(\"float32\")\n", + "\n", + "# plot everything\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(9.6, 4.8))\n", + "series_air.plot(ax=ax1, title=\"AirPassengers\")\n", + "series_beer.plot(ax=ax2, title=\"AusBeer\")\n", + "\n", + "# extract training and validation sets\n", + "train_air = series_air[: -2 * output_chunk_length]\n", + "val_air = series_air[\n", + " -(2 * output_chunk_length + input_chunk_length) : -output_chunk_length\n", + "]\n", + "\n", + "train_beer = series_beer[: -2 * output_chunk_length]\n", + "val_beer = series_beer[\n", + " -(2 * output_chunk_length + input_chunk_length) : -output_chunk_length\n", + "]" ] }, { @@ -99,11 +129,14 @@ "id": "477c039b", "metadata": {}, "source": [ - "## 1. Regular PyTorch Model: Training, Saving, Loading and Fine-Tuning\n", + "## 1. Regular TorchForecastingModel: Training and Fine Tuning\n", + "\n", + "We start with a regular `TorchForecastingModel` (non-foundation model); `TiDEModel` in this case.\n", "\n", - "We start with a regular PyTorch model, `TiDEModel` in this case.\n", "\n", - "First, we train a model from scratch. By default, `enable_finetuning=None`, which implies standard training for regular models (all weights are trainable).\n" + "### 1.1. Training\n", + "\n", + "First, we train a model from scratch only on the air passengers series. By default, `enable_finetuning=None`, which implies standard training for regular models (all weights are trainable)." ] }, { @@ -115,12 +148,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c3039b2947540efa536eb2883e8422a", + "model_id": "1942da32e06e42a1b0a156825485c40f", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00" + "Validation: | | 0/? [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ModelMAPE (%)MAE
0Trained TiDE Model6.83828131.289665
1Loaded TiDE Model (Fine-Tuned)5.42035224.888033
2Chronos Zero-Shot (Base)9.35755143.654480
3Chronos Full Fine-tuning9.44659546.352612
4Chronos Partial Fine-tuning6.19185328.622393
\n", - "" - ], - "text/plain": [ - " Model MAPE (%) MAE\n", - "0 Trained TiDE Model 6.838281 31.289665\n", - "1 Loaded TiDE Model (Fine-Tuned) 5.420352 24.888033\n", - "2 Chronos Zero-Shot (Base) 9.357551 43.654480\n", - "3 Chronos Full Fine-tuning 9.446595 46.352612\n", - "4 Chronos Partial Fine-tuning 6.191853 28.622393" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "from darts.metrics import mae, mape\n", - "\n", - "results = []\n", - "all_predictions = {\n", - " \"Trained TiDE Model\": tide_prediction,\n", - " \"Loaded TiDE Model (Fine-Tuned)\": prediction_after,\n", - " \"Chronos Zero-Shot (Base)\": prediction,\n", - " \"Chronos Full Fine-tuning\": pred_full_finetuned_loaded,\n", - " \"Chronos Partial Fine-tuning\": pred_partial_finetuned_loaded,\n", - "}\n", - "\n", - "for name, pred in all_predictions.items():\n", - " results.append({\n", - " \"Model\": name,\n", - " \"MAPE (%)\": mape(val_passengers, pred),\n", - " \"MAE\": mae(val_passengers, pred),\n", - " })\n", - "\n", - "df_results = pd.DataFrame(results)\n", - "df_results" - ] - }, - { - "cell_type": "markdown", - "id": "996456e0", - "metadata": {}, - "source": [ - "### Observations\n", - "\n", - "While the results on this small \"toy\" dataset (Air Passengers) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility of the fine-tuning API.\n", - "\n", - "In real-world scenarios with larger datasets:\n", - "- **Regular Models**: Fine-tuning allows updating a pre-trained model on new data, potentially saving training time compared to retraining from scratch.\n", - "- **Foundation Models**:\n", - " - **Zero-Shot**: Quick and easy, no training required.\n", - " - **Full Fine-tuning**: Offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", - " - **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n", - "\n", - "### Summary\n", - "In this notebook, we have seen:\n", - "1. How to train, save, load, and naturally fine-tune **regular PyTorch models**.\n", - "2. How to apply **zero-shot inference** with foundation models.\n", - "3. How to perform **full fine-tuning** on foundation models.\n", - "4. How to use **layer freezing/unfreezing** for **partial fine-tuning**.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2286828a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "darts", - "language": "python", - "name": "python3" + "application/vnd.jupyter.widget-view+json": { + "model_id": "0838d398e2264b5e8af4fd7448c216b5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# setup common torch model parameters\n", + "model_params = dict(\n", + " input_chunk_length=input_chunk_length,\n", + " output_chunk_length=output_chunk_length,\n", + " use_reversible_instance_norm={\"affine\": False},\n", + " random_state=42,\n", + " save_checkpoints=True, # store checkpoints during training to retrieve the best model\n", + " model_name=\"tide\",\n", + " force_reset=True, # overwrite previous model artifacts\n", + ")\n", + "\n", + "# regular training (default enable_finetuning=None)\n", + "model_pretrained = TiDEModel(**model_params)\n", + "\n", + "# fit\n", + "model_pretrained.fit(\n", + " series=train_air,\n", + " val_series=val_air, # use a validation set to monitor model performance\n", + " load_best=True, # load the best model checkpoint at the end of training\n", + " epochs=50, # train for 50 epochs\n", + ")\n", + "\n", + "# predict after the end of the validation series\n", + "pred_air = model_pretrained.predict(n=output_chunk_length, series=val_air)\n", + "\n", + "# save the pre-trained model\n", + "model_pretrained.save(\"darts_logs/model.pt\")\n", + "\n", + "# plot\n", + "series_air[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_air.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Pre-Trained TiDE model; MAE {mae(series_air, pred_air):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "de7f553e-d249-4ac8-913d-37ef5dfc705c", + "metadata": {}, + "source": [ + "Great, this looks like a very good forecast! Now let's see how the model performs on the beer production series." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fc342586-b7e7-4211-85e8-1c64796c02cc", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ed97ffed591649649365ac563529cb18", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# predict\n", + "pred_beer = model_pretrained.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Forecast on unobserved series; MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "da294801-ad1e-4eeb-aaf1-faea13235175", + "metadata": {}, + "source": [ + "That doesn't look very good. It seems like our model does not generalize to our new beer dataset.\n", + "\n", + "### 1.2. Fine-Tuning\n", + "\n", + "Imagine the model above was a large **pre-trained model** and **full re-training** would take hours. Instead, we can also **fine-tune** parts of the model on this new dataset, to improve the performance. For this we will:\n", + "\n", + "1. Create a new `model` instance for fine-tuning with the same architectural hyperparameters, and enable fine-tuning with the `enable_finetuning` parameter. We restrict fine-tuning to only the \"decoder\" parameters.\n", + "2. Load the weights of the pre-trained model with `model.load_weights()`.\n", + "3. Fine-tune the model with `model.fit()`.\n", + "\n", + "The `enable_finetuning` model creation parameter controls how the model is updated during `fit()`. Possible values are:\n", + "* `None` (Default): **Regular training** where all weights are trainable.\n", + "* `False`: **Zero-shot inference**. The model weights are frozen. `fit()` is used only to setup the model (scaling, etc.) but no training is performed.\n", + "* `True`: **Full fine-tuning / training**. All weights are trainable.\n", + "* `{\"unfreeze\": [...]}`: **Partial fine-tuning (Unfreeze)**. None of the parameters are trainable *except* the specific unfrozen ones\n", + "* `{\"freeze\": [...]}`: **Partial fine-tuning (Freeze)**. All parameters are trainable *except* the specified frozen ones." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5e414418", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "57a380336d0046f7a616e5aa6f3dbe24", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create a new model instance for fine-tuning with the same hyperparameters but different training setup\n", + "model_finetune = TiDEModel(\n", + " enable_finetuning={\"unfreeze\": [\"*decoder*\"]},\n", + " optimizer_kwargs={\"lr\": 1e-3},\n", + " **model_params,\n", + ")\n", + "\n", + "# load the pre-trained weights\n", + "model_finetune.load_weights(\"darts_logs/model.pt\")\n", + "\n", + "# fine-tune only for 10 epochs\n", + "model_finetune.fit(train_beer, val_series=val_beer, load_best=True, epochs=10)\n", + "\n", + "# hint: you can save the fine-tuned model with: `model_finetune.save()`\n", + "\n", + "# predict after the end of the validation series\n", + "pred_beer = model_finetune.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Fine-tuned forecasts; MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "b9251561", + "metadata": {}, + "source": [ + "That looks much better! This demonstrates that **fine-tuning can significantly improve model performance** on unobserved datasets, without having to re-train the entire model from scratch.\n", + "\n", + "## 2. Foundation Model: Zero Shot Forecasting and Fine Tuning\n", + "\n", + "Now we move to foundation models (e.g. Chronos-2, TimesFM 2.5, ...). These models are pre-trained on large datasets and can be used for **zero-shot forecasting** (without training). However, **fine-tuning** them on your specific data **often improves performance**.\n", + "\n", + "### 2.1. Zero-Shot Inference\n", + "By default, our foundation models use `enable_finetuning=False` (zero-shot inference mode). Let's see how the model behaves on Australian Beer Production dataset without any training / fine-tuning.\n", + "\n", + "
\n", + " **Note**: We only load the small version of Chronos-2 to speed up things for demonstration purposes. You can always use the large model with `hub_model_name=\"amazon/chronos-2\"`\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f46a7e12-f45e-4d1b-ad9b-ffb6913a0b61", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "24141a0ec0d74a949e8107898961a348", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_params = dict(\n", + " input_chunk_length=input_chunk_length,\n", + " output_chunk_length=output_chunk_length,\n", + " random_state=42,\n", + " hub_model_name=\"autogluon/chronos-2-small\", # small chronos version\n", + ")\n", + "model = Chronos2Model(**model_params)\n", + "\n", + "# by default for foundation models, `fit()` will only load the model but does not perform fine-tuning / traing\n", + "model.fit(series=train_beer)\n", + "\n", + "# predict\n", + "pred_beer = model.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Base model (not finetuned yet); MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "611bc4af-6298-4b3e-9702-4d1207723e1a", + "metadata": {}, + "source": [ + "For a zero-shot forecast, the results are already quite decent! \n", + "\n", + "### 2.2. Fine-Tuning\n", + "\n", + "And how does it look after fine-tuning the output layers of the model on the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e9d4a755-272b-4c98-a3e2-f551ff486438", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cd84e1855a1848f6b5fe6a00a7f44c39", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# setup fine-tuning; only tune the layers matching \"output_patch_embedding\"\n", + "model_finetune = Chronos2Model(\n", + " enable_finetuning={\"unfreeze\": [\"output_patch_embedding*\"]},\n", + " save_checkpoints=True,\n", + " model_name=\"chronos\",\n", + " force_reset=True,\n", + " optimizer_kwargs={\"lr\": 1e-3},\n", + " pl_trainer_kwargs=dict(\n", + " check_val_every_n_epoch=5,\n", + " gradient_clip_val=1,\n", + " ),\n", + " **model_params,\n", + ")\n", + "\n", + "# fine-tune for 15 epochs\n", + "model_finetune.fit(\n", + " series=train_beer,\n", + " val_series=val_beer,\n", + " load_best=True,\n", + " epochs=15,\n", + ")\n", + "\n", + "# predict\n", + "pred_beer = model_finetune.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# hint: you can store the fine-tuned model with `model_finetune.save()`\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast of fine-tuned model\",\n", + " title=f\"Partial finetuning; MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "996456e0", + "metadata": {}, + "source": [ + "Again, the results are much better than before fine-tuning!\n", + "\n", + "## Conclusion & Summary\n", + "\n", + "While the results on these small \"toy\" datasets (Air Passengers, Australian Beer Production) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility and effectiveness of fine-tuning.\n", + "\n", + "**Pre-trained models** and their capability to generate **Zero-Shot** forecasts can be quite powerful in real-world scenarios with:\n", + "- **Large datasets** where training from scratch would be too complex and / or costly\n", + "- **Small datasets** where not enough data is available to robustly train a model.\n", + "\n", + "**Fine-tuning** allows updating a pre-trained model on new data, potentially improving the model performace while saving training time compared to retraining from scratch.\n", + "\n", + "- **Full Fine-tuning**: Offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", + "- **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2286828a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -784,7 +1503,6779 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.12.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "00570315a5f64b46a03abb667e260c16": { + 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darts.utils.callbacks import TFMProgressBar\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "logging.disable(logging.CRITICAL)\n", @@ -148,12 +149,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1942da32e06e42a1b0a156825485c40f", + "model_id": "08fca8f217024804962e7e561a3aa76e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# setup common torch model parameters\n", + "model_params = dict(\n", + " input_chunk_length=input_chunk_length,\n", + " output_chunk_length=output_chunk_length,\n", + " use_reversible_instance_norm={\"affine\": False},\n", + " random_state=42,\n", + " save_checkpoints=True, # store checkpoints during training to retrieve the best model\n", + " model_name=\"tide\",\n", + " force_reset=True, # overwrite previous model artifacts,\n", + " pl_trainer_kwargs={\"callbacks\": [TFMProgressBar(enable_train_bar_only=True)]},\n", + ")\n", + "\n", + "# regular training (default enable_finetuning=None)\n", + "model_pretrained = TiDEModel(**model_params)\n", + "\n", + "# fit\n", + "model_pretrained.fit(\n", + " series=train_air,\n", + " val_series=val_air, # use a validation set to monitor model performance\n", + " load_best=True, # load the best model checkpoint at the end of training\n", + " epochs=50, # train for 50 epochs\n", + ")\n", + "\n", + "# predict after the end of the validation series\n", + "pred_air = model_pretrained.predict(n=output_chunk_length, series=val_air)\n", + "\n", + "# save the pre-trained model\n", + "model_pretrained.save(\"darts_logs/model.pt\")\n", + "\n", + "# plot\n", + "series_air[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_air.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Pre-Trained TiDE model; MAE {mae(series_air, pred_air):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "de7f553e-d249-4ac8-913d-37ef5dfc705c", + "metadata": {}, + "source": [ + "Great, this looks like a very good forecast! Now let's see how the model performs on the beer production series." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fc342586-b7e7-4211-85e8-1c64796c02cc", + "metadata": {}, + "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "504c326cec1a41fdb6c599c5cd9b2ea0", - "version_major": 2, - "version_minor": 0 - }, + "image/png": 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", "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# predict\n", + "pred_beer = model_pretrained.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Forecast on unobserved series; MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "da294801-ad1e-4eeb-aaf1-faea13235175", + "metadata": {}, + "source": [ + "That doesn't look very good. It seems like our model does not generalize to our new beer dataset.\n", + "\n", + "### 1.2. Fine-Tuning\n", + "\n", + "Imagine the model above was a large **pre-trained model** and **full re-training** would take hours. Instead, we can also **fine-tune** parts of the model on this new dataset, to improve the performance. For this we will:\n", + "\n", + "1. Create a new `model` instance for fine-tuning with the same architectural hyperparameters, and enable fine-tuning with the `enable_finetuning` parameter. We restrict fine-tuning to only the \"decoder\" parameters.\n", + "2. Load the weights of the pre-trained model with `model.load_weights()`.\n", + "3. Fine-tune the model with `model.fit()`.\n", + "\n", + "The `enable_finetuning` model creation parameter controls how the model is updated during `fit()`. Possible values are:\n", + "* `None` (Default): **Regular training** where all weights are trainable.\n", + "* `False`: **Zero-shot inference**. The model weights are frozen. `fit()` is used only to setup the model (scaling, etc.) but no training is performed.\n", + "* `True`: **Full fine-tuning / training**. All weights are trainable.\n", + "* `{\"unfreeze\": [...]}`: **Partial fine-tuning (Unfreeze)**. None of the parameters are trainable *except* the specific unfrozen ones\n", + "* `{\"freeze\": [...]}`: **Partial fine-tuning (Freeze)**. All parameters are trainable *except* the specified frozen ones." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5e414418", + "metadata": {}, + "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "098490d019e44c50913428091da2dc58", + "model_id": "2e7224a066a0420997bff1225c6a4dce", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "# create a new model instance for fine-tuning with the same hyperparameters but different training setup\n", + "model_finetune = TiDEModel(\n", + " enable_finetuning={\"unfreeze\": [\"*decoder*\"]},\n", + " optimizer_kwargs={\"lr\": 1e-3},\n", + " **model_params,\n", + ")\n", + "\n", + "# load the pre-trained weights\n", + "model_finetune.load_weights(\"darts_logs/model.pt\")\n", + "\n", + "# fine-tune only for 10 epochs\n", + "model_finetune.fit(train_beer, val_series=val_beer, load_best=True, epochs=10)\n", + "\n", + "# hint: you can save the fine-tuned model with: `model_finetune.save()`\n", + "\n", + "# predict after the end of the validation series\n", + "pred_beer = model_finetune.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Fine-tuned forecasts; MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "b9251561", + "metadata": {}, + "source": [ + "That looks much better! This demonstrates that **fine-tuning can significantly improve model performance** on unobserved datasets, without having to re-train the entire model from scratch.\n", + "\n", + "## 2. Foundation Model: Zero Shot Forecasting and Fine Tuning\n", + "\n", + "Now we move to foundation models (e.g. Chronos-2, TimesFM 2.5, ...). These models are pre-trained on large datasets and can be used for **zero-shot forecasting** (without training). However, **fine-tuning** them on your specific data **often improves performance**.\n", + "\n", + "### 2.1. Zero-Shot Inference\n", + "By default, our foundation models use `enable_finetuning=False` (zero-shot inference mode). Let's see how the model behaves on Australian Beer Production dataset without any training / fine-tuning.\n", + "\n", + "
\n", + " **Note**: We only load the small version of Chronos-2 to speed up things for demonstration purposes. You can always use the large model with `hub_model_name=\"amazon/chronos-2\"`\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f46a7e12-f45e-4d1b-ad9b-ffb6913a0b61", + "metadata": {}, + "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88d1830d45c64f0d969fdd85b0707149", + "model_id": "65bba570a7d64e19af1acb152c9cc2e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "model_params = dict(\n", + " input_chunk_length=input_chunk_length,\n", + " output_chunk_length=output_chunk_length,\n", + " random_state=42,\n", + " hub_model_name=\"autogluon/chronos-2-small\", # small chronos version\n", + ")\n", + "model = Chronos2Model(**model_params)\n", + "\n", + "# by default for foundation models, `fit()` will only load the model but does not perform fine-tuning / traing\n", + "model.fit(series=train_beer)\n", + "\n", + "# predict\n", + "pred_beer = model.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast\",\n", + " title=f\"Base model (not finetuned yet); MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "611bc4af-6298-4b3e-9702-4d1207723e1a", + "metadata": {}, + "source": [ + "For a zero-shot forecast, the results are already quite decent! \n", + "\n", + "### 2.2. Fine-Tuning\n", + "\n", + "And how does it look after fine-tuning the output layers of the model on the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e9d4a755-272b-4c98-a3e2-f551ff486438", + "metadata": {}, + "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "69ed635262e049cca2d2e0ce576e1764", + "model_id": "9f486b0ae401413194843c2094368a8c", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3cddb0fcf7ba44a9aca1898797897fe3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# setup common torch model parameters\n", - "model_params = dict(\n", - " input_chunk_length=input_chunk_length,\n", - " output_chunk_length=output_chunk_length,\n", - " use_reversible_instance_norm={\"affine\": False},\n", - " random_state=42,\n", - " save_checkpoints=True, # store checkpoints during training to retrieve the best model\n", - " model_name=\"tide\",\n", - " force_reset=True, # overwrite previous model artifacts\n", - ")\n", - "\n", - "# regular training (default enable_finetuning=None)\n", - "model_pretrained = TiDEModel(**model_params)\n", - "\n", - "# fit\n", - "model_pretrained.fit(\n", - " series=train_air,\n", - " val_series=val_air, # use a validation set to monitor model performance\n", - " load_best=True, # load the best model checkpoint at the end of training\n", - " epochs=50, # train for 50 epochs\n", - ")\n", - "\n", - "# predict after the end of the validation series\n", - "pred_air = model_pretrained.predict(n=output_chunk_length, series=val_air)\n", - "\n", - "# save the pre-trained model\n", - "model_pretrained.save(\"darts_logs/model.pt\")\n", - "\n", - "# plot\n", - "series_air[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", - "pred_air.plot(\n", - " label=\"Forecast\",\n", - " title=f\"Pre-Trained TiDE model; MAE {mae(series_air, pred_air):.2f}\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "de7f553e-d249-4ac8-913d-37ef5dfc705c", - "metadata": {}, - "source": [ - "Great, this looks like a very good forecast! Now let's see how the model performs on the beer production series." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fc342586-b7e7-4211-85e8-1c64796c02cc", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ed97ffed591649649365ac563529cb18", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# predict\n", - "pred_beer = model_pretrained.predict(n=output_chunk_length, series=val_beer)\n", - "\n", - "# plot\n", - "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", - "pred_beer.plot(\n", - " label=\"Forecast\",\n", - " title=f\"Forecast on unobserved series; MAE {mae(series_beer, pred_beer):.2f}\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "da294801-ad1e-4eeb-aaf1-faea13235175", - "metadata": {}, - "source": [ - "That doesn't look very good. It seems like our model does not generalize to our new beer dataset.\n", - "\n", - "### 1.2. Fine-Tuning\n", - "\n", - "Imagine the model above was a large **pre-trained model** and **full re-training** would take hours. Instead, we can also **fine-tune** parts of the model on this new dataset, to improve the performance. For this we will:\n", - "\n", - "1. Create a new `model` instance for fine-tuning with the same architectural hyperparameters, and enable fine-tuning with the `enable_finetuning` parameter. We restrict fine-tuning to only the \"decoder\" parameters.\n", - "2. Load the weights of the pre-trained model with `model.load_weights()`.\n", - "3. Fine-tune the model with `model.fit()`.\n", - "\n", - "The `enable_finetuning` model creation parameter controls how the model is updated during `fit()`. Possible values are:\n", - "* `None` (Default): **Regular training** where all weights are trainable.\n", - "* `False`: **Zero-shot inference**. The model weights are frozen. `fit()` is used only to setup the model (scaling, etc.) but no training is performed.\n", - "* `True`: **Full fine-tuning / training**. All weights are trainable.\n", - "* `{\"unfreeze\": [...]}`: **Partial fine-tuning (Unfreeze)**. None of the parameters are trainable *except* the specific unfrozen ones\n", - "* `{\"freeze\": [...]}`: **Partial fine-tuning (Freeze)**. All parameters are trainable *except* the specified frozen ones." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5e414418", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "57a380336d0046f7a616e5aa6f3dbe24", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# create a new model instance for fine-tuning with the same hyperparameters but different training setup\n", - "model_finetune = TiDEModel(\n", - " enable_finetuning={\"unfreeze\": [\"*decoder*\"]},\n", - " optimizer_kwargs={\"lr\": 1e-3},\n", - " **model_params,\n", - ")\n", - "\n", - "# load the pre-trained weights\n", - "model_finetune.load_weights(\"darts_logs/model.pt\")\n", - "\n", - "# fine-tune only for 10 epochs\n", - "model_finetune.fit(train_beer, val_series=val_beer, load_best=True, epochs=10)\n", - "\n", - "# hint: you can save the fine-tuned model with: `model_finetune.save()`\n", - "\n", - "# predict after the end of the validation series\n", - "pred_beer = model_finetune.predict(n=output_chunk_length, series=val_beer)\n", - "\n", - "# plot\n", - "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", - "pred_beer.plot(\n", - " label=\"Forecast\",\n", - " title=f\"Fine-tuned forecasts; MAE {mae(series_beer, pred_beer):.2f}\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "b9251561", - "metadata": {}, - "source": [ - "That looks much better! This demonstrates that **fine-tuning can significantly improve model performance** on unobserved datasets, without having to re-train the entire model from scratch.\n", - "\n", - "## 2. Foundation Model: Zero Shot Forecasting and Fine Tuning\n", - "\n", - "Now we move to foundation models (e.g. Chronos-2, TimesFM 2.5, ...). These models are pre-trained on large datasets and can be used for **zero-shot forecasting** (without training). However, **fine-tuning** them on your specific data **often improves performance**.\n", - "\n", - "### 2.1. Zero-Shot Inference\n", - "By default, our foundation models use `enable_finetuning=False` (zero-shot inference mode). Let's see how the model behaves on Australian Beer Production dataset without any training / fine-tuning.\n", - "\n", - "
\n", - " **Note**: We only load the small version of Chronos-2 to speed up things for demonstration purposes. You can always use the large model with `hub_model_name=\"amazon/chronos-2\"`\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f46a7e12-f45e-4d1b-ad9b-ffb6913a0b61", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "24141a0ec0d74a949e8107898961a348", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model_params = dict(\n", - " input_chunk_length=input_chunk_length,\n", - " output_chunk_length=output_chunk_length,\n", - " random_state=42,\n", - " hub_model_name=\"autogluon/chronos-2-small\", # small chronos version\n", - ")\n", - "model = Chronos2Model(**model_params)\n", - "\n", - "# by default for foundation models, `fit()` will only load the model but does not perform fine-tuning / traing\n", - "model.fit(series=train_beer)\n", - "\n", - "# predict\n", - "pred_beer = model.predict(n=output_chunk_length, series=val_beer)\n", - "\n", - "# plot\n", - "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", - "pred_beer.plot(\n", - " label=\"Forecast\",\n", - " title=f\"Base model (not finetuned yet); MAE {mae(series_beer, pred_beer):.2f}\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "611bc4af-6298-4b3e-9702-4d1207723e1a", - "metadata": {}, - "source": [ - "For a zero-shot forecast, the results are already quite decent! \n", - "\n", - "### 2.2. Fine-Tuning\n", - "\n", - "And how does it look after fine-tuning the output layers of the model on the dataset?" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e9d4a755-272b-4c98-a3e2-f551ff486438", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cd84e1855a1848f6b5fe6a00a7f44c39", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# setup fine-tuning; only tune the layers matching \"output_patch_embedding\"\n", - "model_finetune = Chronos2Model(\n", - " enable_finetuning={\"unfreeze\": [\"output_patch_embedding*\"]},\n", - " save_checkpoints=True,\n", - " model_name=\"chronos\",\n", - " force_reset=True,\n", - " optimizer_kwargs={\"lr\": 1e-3},\n", - " pl_trainer_kwargs=dict(\n", - " check_val_every_n_epoch=5,\n", - " gradient_clip_val=1,\n", - " ),\n", - " **model_params,\n", - ")\n", - "\n", - "# fine-tune for 15 epochs\n", - "model_finetune.fit(\n", - " series=train_beer,\n", - " val_series=val_beer,\n", - " load_best=True,\n", - " epochs=15,\n", - ")\n", - "\n", - "# predict\n", - "pred_beer = model_finetune.predict(n=output_chunk_length, series=val_beer)\n", - "\n", - "# hint: you can store the fine-tuned model with `model_finetune.save()`\n", - "\n", - "# plot\n", - "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", - "pred_beer.plot(\n", - " label=\"Forecast of fine-tuned model\",\n", - " title=f\"Partial finetuning; MAE {mae(series_beer, pred_beer):.2f}\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "id": "996456e0", - "metadata": {}, - "source": [ - "Again, the results are much better than before fine-tuning!\n", - "\n", - "## Conclusion & Summary\n", - "\n", - "While the results on these small \"toy\" datasets (Air Passengers, Australian Beer Production) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility and effectiveness of fine-tuning.\n", - "\n", - "**Pre-trained models** and their capability to generate **Zero-Shot** forecasts can be quite powerful in real-world scenarios with:\n", - "- **Large datasets** where training from scratch would be too complex and / or costly\n", - "- **Small datasets** where not enough data is available to robustly train a model.\n", - "\n", - "**Fine-tuning** allows updating a pre-trained model on new data, potentially improving the model performace while saving training time compared to retraining from scratch.\n", - "\n", - "- **Full Fine-tuning**: Offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", - "- **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2286828a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "00570315a5f64b46a03abb667e260c16": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - 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"value": "Validation DataLoader 0: 100%" - } - }, - "f04da9c563bd41e583bea99dd24969e5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_8bd138fb4ad94bb0a8997b4f9979493d", - "style": "IPY_MODEL_0f873312fd884da9910007a8e372bfe0", - "value": " 1/1 [00:00<00:00, 303.56it/s]" - } - }, - "f1528de15d174fdd9d254bbbf5fea8b8": { - "model_module": "@jupyter-widgets/controls", + } + ], + "source": [ + "# setup fine-tuning; only tune the layers matching \"output_patch_embedding\"\n", + "model_finetune = Chronos2Model(\n", + " enable_finetuning={\"unfreeze\": [\"output_patch_embedding*\"]},\n", + " save_checkpoints=True,\n", + " model_name=\"chronos\",\n", + " force_reset=True,\n", + " optimizer_kwargs={\"lr\": 1e-3},\n", + " pl_trainer_kwargs=dict(\n", + " check_val_every_n_epoch=5,\n", + " gradient_clip_val=1,\n", + " callbacks=[TFMProgressBar(enable_train_bar_only=True)],\n", + " ),\n", + " **model_params,\n", + ")\n", + "\n", + "# fine-tune for 15 epochs\n", + "model_finetune.fit(\n", + " series=train_beer,\n", + " val_series=val_beer,\n", + " load_best=True,\n", + " epochs=15,\n", + ")\n", + "\n", + "# predict\n", + "pred_beer = model_finetune.predict(n=output_chunk_length, series=val_beer)\n", + "\n", + "# hint: you can store the fine-tuned model with `model_finetune.save()`\n", + "\n", + "# plot\n", + "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", + "pred_beer.plot(\n", + " label=\"Forecast of fine-tuned model\",\n", + " title=f\"Partial finetuning; MAE {mae(series_beer, pred_beer):.2f}\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "996456e0", + "metadata": {}, + "source": [ + "Again, the results are much better than before fine-tuning!\n", + "\n", + "## Conclusion & Summary\n", + "\n", + "While the results on these small \"toy\" datasets (Air Passengers, Australian Beer Production) may vary depending on the random seed and hyperparameters, they demonstrate the flexibility and effectiveness of fine-tuning.\n", + "\n", + "**Pre-trained models** and their capability to generate **Zero-Shot** forecasts can be quite powerful in real-world scenarios with:\n", + "- **Large datasets** where training from scratch would be too complex and / or costly\n", + "- **Small datasets** where not enough data is available to robustly train a model.\n", + "\n", + "**Fine-tuning** allows updating a pre-trained model on new data, potentially improving the model performace while saving training time compared to retraining from scratch.\n", + "\n", + "- **Full Fine-tuning**: Offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", + "- **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2286828a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "040dc2c5eb3141f5a0cfe980759f993e": { + "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", - 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"model_name": "HTMLModel", + "model_name": "HTMLStyleModel", "state": { - "layout": "IPY_MODEL_6470ca61aff04e26ba6295202378c436", - "style": "IPY_MODEL_3fd8b176562c4457b6e730024fa8f09d", - "value": " 1/1 [00:00<00:00, 321.85it/s]" + "description_width": "", + "font_size": null, + "text_color": null } } }, From eb433c719c0e6c8c9599c3b3c732ca3dc9cfb154 Mon Sep 17 00:00:00 2001 From: dennisbader Date: Sat, 7 Mar 2026 09:55:50 +0100 Subject: [PATCH 25/26] update notebook --- ...oundation-Model-Fine-Tuning-examples.ipynb | 440 +++++++++--------- 1 file changed, 230 insertions(+), 210 deletions(-) diff --git a/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb b/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb index 65100603a7..2610966724 100644 --- a/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb +++ b/examples/27-Torch-and-Foundation-Model-Fine-Tuning-examples.ipynb @@ -52,6 +52,7 @@ "outputs": [], "source": [ "import logging\n", + "import os\n", "import warnings\n", "\n", "import matplotlib.pyplot as plt\n", @@ -75,8 +76,8 @@ "## Data Preparation\n", "\n", "For demonstration, we'll just load two short time series: The Air Passengers and Australian Beer Production datasets.\n", - "We will pre-train a model only on the Air Passengers series and then fine-tune it on the Beer Production datast. \n", - "We also create a train and test split for illustration.\n", + "We will pre-train a model only on the Air Passengers series and then fine-tune it on the Beer Production dataset. \n", + "We also create training, evaluation and test sets for illustration.\n", "\n", "
\n", " **Note**: Our two datasets have different frequencies (monthly and quarterly), but our torch models can handle this out of the box!\n", @@ -137,7 +138,11 @@ "\n", "### 1.1. Training\n", "\n", - "First, we train a model from scratch only on the air passengers series. By default, `enable_finetuning=None`, which implies standard training for regular models (all weights are trainable)." + "First, we train the model from scratch only on the air passengers series. By default, `enable_finetuning=None`, which implies standard training for regular models (all weights are trainable).\n", + "\n", + "
\n", + " **Note**: For demonstration purposes, we only show training and predicting on single univariate `TimeSeries` (one series with one column). The process is identical for multiple (a list / sequence) uni- or multivariate series.\n", + "
" ] }, { @@ -149,7 +154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "08fca8f217024804962e7e561a3aa76e", + "model_id": "082aaee7736b442c8a8a3d8757f0d610", "version_major": 2, "version_minor": 0 }, @@ -162,7 +167,7 @@ }, { "data": { - "image/png": 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nzp3nvUdOqb2Z/35fx+DrO9Fpr5nBe+K7NfPPP/94unXr5ilWrJhKxe7cubNn8eLF5+3rzznF34X3RDovXh8bG+sZOnSoZ/ny5dmm9j744IOeiIgIz/r167P9u7P7G7P7PeF3UKNGjWz3ueyyyzwVKlTwJCUl5Zjai/GcE9ntr0Ea/4gRIzwNGzZUv2ukWyNdHSnVeM6MPibzZ2/ZskX9LurUqaN+gzjnGBc4x74qMyNFu3v37sbvCNWBUZkVfzOxPxH4J9yCiBBC7By4DQsHrHiEkLxBMUIIIXkEKeFw6cC9mN+GjIS4GYoRQgghhIQVZtMQQgghJKxQjBBCCCEkrFCMEEIIISSsUIwQQgghJKxQjBBCCCEkrNhGjKBnSG6alxESLjA+OU6J1eE4JVbENmKEEEIIIc6EYoQQQgghYYVihBBCCCFhhWKEEEIIIWGFYoQQQgghYYVihBBCCCFhhWKEEEIIIWGFYoQQQgghYYVihBBCCCFhhWKEEEIIIWGFYoQQQgghYYVihBBCCCFhhWKE+M1TTz0lF154YdjP3GWXXSajRo0K92EQQgjJJxQjYeTAgQNy3333Sd26daVIkSJSsWJF6dChg7z77rsSHx8vdmXBggUSEREhJ06csOT7EUIIsRYFw30AbmXbtm1KeJQqVUqef/55adq0qRQuXFj+++8/+eCDD6Rq1apyzTXX+Nw3KSlJChUqJHbn3LlzEhUVFe7DIISQPOPxeOSHJSKJ50SubidSpHAEz2YeoGUkTNx5551SsGBBWb58ufTv318aNWokderUkd69e8sPP/wgvXr1Ml4LqwCsJRAnRYsWleeee05tx7bY2Fh1Q2/QoIF8/vnnxj47duxQ+61atcrYBssCtsHSYLY4zJs3T1q3bi0xMTHSvn172bhxY4ZjfeGFF5TVpnjx4jJ8+HBJTEzM8u/C53bu3Fktly5dWr3/0KFDDbfK3XffrVwr5cqVk27duuV4nNm9H0hNTZWHH35YypQpI5UqVVIuJEIICRVzl4v0etQj/f7PI9X7euSJiamy55CHX4CfUIyEgaNHj8qvv/4qd911lxIXvsBN1wxusn369FGWk1tuuUVmzpypXDwPPvigrFmzRkaOHCnDhg2T+fPn+308TzzxhLz88stKGEEg4f0106ZNU58N6w2er1y5srzzzjtZvlf16tXlm2++UcsQNfv375fXX3/deP7TTz9V4unPP/+U9957L8djy8374Rz+9ddfMn78eHnmmWdkzpw5fp8DQgjJCwtXpQuPIydFnv9cpNYAiJNU+X2VR1lOiEvdNJjlIx4j1GBmjht2TmzZskUNUFgzzMBaoK0OECovvvii8dyNN96oxIbmhhtuUBYCWFjAAw88IEuXLpWXXnrJsCTkFlhaOnXqpJYfffRR6dmzpzoOxLG89tpryhqCB3j22Wdl7ty5WVpHChQooKwUoEKFCsoNZaZevXpKNGhg+ciOnN6vWbNmMnbsWOO933rrLWXpueKKK/w6B4QQkhfW70xfjoyEtVYkJUVk+gI8PNK8rsg914nceIVINF047hIjECJ79+4Vu/H3338rt8OgQYPk7Nmz5wksM+vXr5cRI0Zk2IYYFLPVILfghq6B5QMcOnRIatSooT7n9ttvz/D6du3a5ckCA1q1aiWBxHzs+vhx7IQQEkoxUjhKZPPkCPnwB4+8N0vk0HHv9n+3iNw63iMPvydya0+P3NknQmpWYlyJK8QILBRW/lxkz8ANkzk2AzEjIDo6+rx9snLnZEUkJHpacJU58NUX5mBY7R6CKAoGmf8Of47TF5kDeXH8wTp2Qggxk5Tskc17vMv1q4lUrxghT98SIY8P9sjXC0Te/MYjf6/3Pn/slMj4r0RemuqRazp45J7rIqRzy/Nd8m7FkWIkN66ScFK2bFnlRoBL4Z577vFbaAAEvCLuYsiQIcY2rDdu3Fgtly9fXv2PGIsWLVqoZXOQqD+fg3iMm2++2dgGd1B26AyZFNgqcyA3x+nP+xFCSKjYulckOe2yVK1snIiUUMuFoyJk8JUig6+MkL/XeeTNGR6Z+hvEi9eN8+0feHikSW2RUf1EbumBiZm7RQkDWMMEgkCTk5OV+2Xq1KnKHQJLyRdffCEbNmxQsRLZ8dBDD8knn3yiMmo2b94sr7zyisyYMUNGjx5tWFcuvvhilQmD9164cKE8+eSTfh8ngmQ//vhjmTRpkmzatEnFZ6xduzbbfWrWrKnU/vfffy+HDx+WuDj8SH2Tm+P05/0IISQc8SI/z3hVXcsz06ZxhHz+ZKTsnh4hzwyPkMpl059bu13ktvEeef+7EB2whaEYCRNIyV25cqV07dpVHnvsMWnevLkSJm+++aYSFOPGjct2/2uvvVbFhyBgtUmTJvL+++8rwYD0WQ1EBAQP4jSQTovgU38ZMGCAjBkzRqXP4n127twpd9xxR7b7oEbK008/rYJhkRKMdN7syOk4/X0/QggJBetM8feeuHUqqQCxf76oWCZCxgyJkJ1fR8iUsRHSrkn6c4vXMOMmwmOTvKPt27erGbKOMSDEaiBWBWKN45RYGY7TwDF4XKpM1pUEVjQXiV+jYgchSFCWIDviEz1S9Erv7bddE5HF77r73ubuv54QQgjJr5vGkyKSsMnI5kSByjNnzmS7b0yRdJfN1n38CihGCCGEED9JTfXIhl1pK4nbpErlckZGJILwBw8enGNmX2xV7/+HjoucjreFkyJoUIwQQgghfrL7EFwtaSvxG1Qn89mzZ0uJEt6Mmm+//TbHpIE63rJOim0ut45QjBBCCCH5yKSR+PWqojZKK6CFho5t/N///iefffZZlu8RWzUiQ5qwm6EYIYQQQvKRSQPLSMOGDdUiGoCijYbmtttuUzWgfBFbJX15Gy0jhBBCCPGH9TtNMR4JXsuIBuUHdAmEc+fOqSanvvpw6ZgRsHUfY0YIIYQQkk83jQZFGlEH6vLLL1frKNbYq1cvOXXqVNZiZK+7Tz/dNIQQQogfoDyXIUbO7pGSxSJVQcbMfbO+/vprqV+/vlpfs2aN6r5ubmtRrqRI8RjvstvTeylGCCGEED84fMLb+E4Rv0FZRXw1vCtdurRqY4H/wQ8//KCqWWuwj7aO7DzobbznVihGCCGEkHy4aHTwqi/q1asn33zzjRQs6O1Liz5iH3744XlBrCkpIrsOuvdroBgJI+hjAGWc+bFlyxaxI2jcV6pUqXAfBiGEhE6MJHgtI9nRuXNnefvtt411BLcuWLDgvIyarS6OG6EYCTNXXXWV7N+/P8Ojdu3afr8PIrYJIYQEn3U7PLm2jGhGjBihGoECNAa9/vrr1cSzThVTrREXx41QjISZwoULq8ZK5keBAgVk4cKF0qZNG/V85cqVVcdaDGANuvMifQyDu1y5ciq3XQdJde/eXYoVK6YCqm666SY5cuSIsR/KE48fP17q1q2r3rtGjRry3HPPGc8/8sgjKuAqJiZGlTZGx96kpCTj+X///Vep/OLFi6tKg+i0u3z5cqXyhw0bJidPnjQsPE899VTIziMhhFghkyY70GUd12dw7Ngxufrqq6ViyTjj+W0uTu+lGLEge/fulR49eshFF12kbv7vvvuufPTRR/Lss89meN2nn34qUVFRqqDOe++9JydOnJAuXbpIixYtlED4+eef5eDBg9K/f39jn8cee0xeeOEFJTLWrVsnX375ZYYocIgMuFvwHFLTJk6cKK+++qrx/KBBg6RatWqybNkyWbFihRJJiBpv3769KvQDgaItPKNHjw7RGSOEkDCIkaSjEpFyRE3ucgMmmlOmTFGVWsHGjRvl5WfvNJ53s5smwoMcJRuwffv2XLdmb31bqhw4JiGnUhmR5RMj/YoZ+eKLL6RIkSLGNqhmWCYQ8LR+/XojQvudd95RVgtYHnAOYBlBzvo///xj7Aux8scff8gvv/xibNuzZ49qZY1BDwtL+fLl5a233pJbb70110oePx6IGwCx8eabb8qQIUPOey1EDCw1EEVuhK3ZiR3gOM0fp854pGT3tNvmyUUSe+YWv+P8cD+D5dtrtS4gkR0TJdUTKc1iRf6d5E4bgTe812FAiOw9LLYALg9YPjRFixaVu+66S9q1a5chVaxDhw4SFxenxAVcKwAuEjOwosyfP1+5aDKzdetWJRLOnj1rFOLxxdSpU+WNN95Qr8fnwTWkGz+BBx54QAmZzz//XLp27Sr9+vWT2NjYfJ8HQgixAxsyuGhyDl71BeICMRFFzKBIihQtcEhOJ1dSMSOwD/hKE3Y6jhQjsFDY5XMhPnJr4vO1rxmIB1T5e/HFF897Lawi27Zty/b9lixZotwwTz/9tIpBKVmypLKKvPzyy8ZrEAeCwj3Il//pp59k7Nix6jUod0wIIe7KpMld8KovLr30UmM58txOkchKciZB5NBxkYphuoeFE0eKEX9cJVakUaNGyk1jVsiIC0E8B+I1sqJly5Zqv1q1ahk57Znz3aOjo2XevHk+3TSLFy9WrrAnnnjC2LZzp/mX5wVuJDzuv/9+ueGGG2TSpElKjCB+xVxdkBBCnJ1JA8tI3iZiSBLAJBHxdedOrhMp3daIG3GjGMnTXRuBkz179pSOHTuqWfKZM2eMmAGY7hFEieBHczjK2rVrZeDAgcrdgBQnfAHEN3feeafs3r1b7rnnHtmwYYPMmjVLWSDgIskuZgbuHURoQyAgwBSuFsSPIMsFIgGxKYg7QQVAtLXG80uXLlXBsVqs7Nq1S1k68BzcNTNnzjTePyEhQWXwIHMGIgUCCZ8D8QQggmCdgdiBLzQ+Pp5fMSHEtQXPckK7uBOOrzG2uTa91+MnU6dO9YwcOdKzf/9+T2pqqmfTpk2es2fPev744w9Pjx49PLt37/YcPnzY079/f8/MmTPVPngez2E9MTHR89Zbb3mGDx/u1+du27bNk5KS4nESQ4YM8fTu3dvncwsWLPBcdNFFnqioKE+lSpU8jzzyiCcpKcl4vlOnTp777rvvvP3wffTp08dTqlQpT3R0tKdhw4aeUaNGqe8K4Bw+++yznpo1a3oKFSrkqVGjhuf555839n/ooYc8ZcuW9RQrVswzYMAAz6uvvuopWbKk8T0OHDjQU716dXVcVapU8dx9992ehIQEY//bb79d7Y+hNXbsWI+bwLl14jglzoLjNH/UHZjikUtTPNL+lAc5IAcOHMjXPQDXSilzjfc9L03xjP3IndcPv7JpMLuGRQSlbDO7Cx5//HFVl0Kb/2fPnq0eH3zwgYpFQBzDt99+q55LTExUQZTTpk2TqlVNbQtNBbwyF/Hat2+fev/cZNMQEq4sBVi0kL3EcUqsCsdp3kk8K1K8O86hiJxeIaV2XKmswHkNOEUGJKzeEnOBSKt/1bbBV4p8+rg4itxcD/2KGTl06JASEnPnzlX1KZC1gaJaiBdAqpIuvAUQlAlTP0DgJFwAGrgLIGaw3ZcYQQwC6luYQdaGuV4GIVYFgoQQq8Nx6j8bdheS1NS0+u0JG5RrGq7tvGJkKiamJxes35YoO9E1z0Hkpqq432IEMQE4+d99950azKixjy8E8QHm7A4sI8YA4P/MmR9YzyqmADEOyOrIbBnhjJNYGc44iR3gOM07f5kTEuPXS7O2zVTQf15p29YbtCqp8RJd4KQkpJSUPUeL5Os97YpfYgTlw8Ftt92mrBuwdlx55ZUqkBGRwTqQFWAZmRsA/5uf089jH18gKwMPM6jyCVMPzd/E6nCcEjvAceo/G3d5QzwU8RukYcOL8nVPMnsMolJ3S4KUlIPHReITI6RYjLtqjfh1FqHWIArM/jG9DDOMuQodXDQ6UhixHubn4OpB8S5sJ4QQQuzYIC8vBc/MlC1b1nDVpMRtMrZvc2GyqV9iBBYOBJ4iFRQBpogTmTNnjkrXRS+VGTNmKJFx9OhRmTx5stqmK4Wi8idSVLHfxx9/rNJBfcWLEEIIIZZO601NEknckq+0Xj2Z15P2M0f/c3WPGr/tS6hTgbLiqCdy3333ye23364as11yySXSt29f1bME/1988cXSu3dvtQ9cLhMmTJCvvvpKlT9fuXKljBs3Lhh/DyGEEBJwUlI8smlP2kriZomMSA1IKwz9Hp74La4WI35XYEUVUAiLrAJP8fBFkyZNVDEtQgghxG5s3y9yVleciN+gwgx0HGV+MARNojf7FGzdB3cQY0YIIYQQkk3l1fzGi2QnRra5sAorK4gRQgghfomRDfmOFzlPjCQdkUKRia4tCU8xQgghhIQ4k0ZjjjuJjvCm0ew8IJKcnOvi6I6AYoQQQgjxxzKSsDFgYgTVyAsVKpT2vl5XTXKKyK5D7vpKKEYIIYSQbEALN0OMJO5QFVMD5aYpUKCAUS49/tga12bUUIwQQggh2bDviMhp3b0kfr2UKlVKypcvH7BzFpvmqkk+vdHY5ra4EYoRQgghxI9MGlhF8tqpN9fpvXsZM0IIIYQQn2JkQ8DiRc4TIwlmMeKu00/LCCGEEJIN63earBQJXstIUMTI2d0SGZHiyv40FCOEEEJINqzbIUFJ6z0/vTdFYgocMSwjCJx1CxQjhBBCSG7cNOcOiiQfD7hlpHZaNg0ocM6rfOISRA6fcM/XQjFCCCGEZMGxUx45dDxtJX69SsUNRIM8M9HR0UYX+7Mn17kyboRihBBCCMllJg2sGOhEH2hi0wRO4vG1rkzvpRghhBBCclV5NfDBq+en924zttEyQgghhBBZn6EnTeDTerNN793HAFZCCCHE9fgqeBZUMZJIywghhBBCTKzTYiT5lMi5fUGzjNStW9e7kBov0QVOqkXGjBBCCCEu50yCR3YeSFuJX6/+C7plRESiUner/w8e8x6DG2AAKyGEEOKDjbtMKwkbpHTp0lKuXLmgnKvSpUurB0iO22Rs3+aSjBqKEUIIISTHeJF1AW+Ql5V1JP7of8Y2t7hqKEYIIYSQnHrSBDGTJrMY8cS7r2EexQghhBCSi0yaUIkRSXRfei/FCCGEEJJdg7zURJHE7UELXs1WjNAyQgghhLiTpGSPbNFCIAEBpamhs4wkHZZCkYlqkTEjhBBCiEvZskckOSVtJX5DUBrkZSbW9P7Rsl/9j9Ti5GTnu2ropiGEEEJyiBepU6dOUBrkmalSpYoULlxYLXsStqj/IYh2H3L+10MxQgghhISpDHyGG3JkpBI96iOPuSu9l2KEEEIIyTatN/iZNJldNSlxXsuIW4JYKUYIIYSQrCwjnhQVwBoKy4ib03spRgghhBATqamedDGCLrqecyG3jIjL0nspRgghhBATuw6KJJxNW4nfoP4LvRjZJZER3nQexowQQgghLg9eLVOmTNAa5GWd3psiMZGHDcuIx+NsVw0tI4QQQkiWYsTbkyaYDfLM1KpVy/isyHPeErBxCSKHT4ijoRghhBBCssyk8XbrDRWFCxeW6tWrq+WzJ9YZ27c5PL2XYoQQQgjJyjKSEPxuvVm5as6eXOeaIFaKEUKIo0hISJCnnnpK7r77bjl9+nS4D4fYDMRmGA3yzu4RSTkdUstIxiDWbd7/XRDEWjDcB0AIIYFi79690qdPH1m2bJlxUb///vt5gkmuOXRc5Pjp8GTSnCdGEkxiZC9cR6GJWwkHFCOEEEfw999/y7XXXiv793sbjIHVq1eH9ZiI/TNp0CBPl2gPFbEutIzQTUMIsT1ffPGFdOzYMYMQAVu2pJfUJiQv8SIQBsFukJelGEk9I9EFTqpFxowQQohFSUlJkYcfflhuuukmOXvWW6UKoqRs2bJqmWKE5DeTJtQumoy1RkQKpexW/x84JnImwbm1RmgZIYTYkpMnT8o111wjEyZMMLaNGDFC5syZYwQcHjhwQOLi4sJ4lMRuhKNbb2ZKlixpCOrk0xtdkd5LMUIIsR2bN2+Wiy++WH788Ue1Dr/+22+/Le+9954yqdetW9d47bZt6X53QnItRpKOiiQdDotlxGwdiT+2RjQUIyYw82jfvr1ceuml6nHvvfeq7bNnz5a2bdsa2/HArESzdu1aGThwoHTo0EG9R2bfLiGE5AZYPtq0aSMbNngzHVCq+9dff5U777zTqFxpNnPTVUNyy8k4j+w9nG4VAeGwjGTMqDF37xXHkqdsmieffFJ69Ohx3vZWrVrJO++8c972c+fOKb/ubbfdJt27d5cPP/xQxowZo/4nhJDc1n9444035IEHHpDU1FS1rUmTJvLdd9+dl+1gtoxQjJDcsmGXaSVMab3Zd+91bnpvSNw0K1askEKFCqm0O5S6HT58uKxfv17VBCCEkJxAcOqtt94qo0aNMoQI4kWWLFniM+2SYoTkP5NmvYrbCFWDvFyJEVpGMvLKK6+oR/369VVBoXr16qnt//33n1x++eXKbDpgwADp27ev4bPVrwFFihSRatWqqe1Vq1b1aUnBw0xSUpJxESLEiujxyXEaWA4ePKiuJYsXLza2Pf744/L0009LZGSkz/NtFihbt27ld8JxmiuMyqu6QV6jBmEbO7Vr1/YuJB2WQpFnJSm1sErvteP1Bb/TgLtpECOCHzrefOrUqWp9+vTp0rJlS7VeqVIlWbdunYwePVpKly6txAnKMxctWjTD+2A9Pj7e52dMmjRJJk6cmGFbv379pH///v4eLiEhZ/dubyoeyT9r1qyRkSNHGjFmsKyOHz9eevXqleN5RkYCMm4QW7Jzp3nKSzhOffPP+vIiEuNdiV8nVaq0CdvYKVy4sLEclbpHkiRWdhzwyNZtu6RgAbEVhrAKpBi54IILjOUhQ4Yofy0sIohsN78Gwarz589XYiQ6OlrOnDmT4X2wHhOT9qVnYtiwYTJo0KAM2/bt26c6GeZGYRESDjBjwQ2S4zRwGTOwsGIyA2BFnTlzpopNyw2w3KIsPIRMxYoVlUWWcJxmx04dvJpyRuTsbmnd+k6pWbNmWIZNjRo11L0T4z8ClViLxEpySoRERteUmpXFceS7HHxW4gBR7Qg4A7CkwHqiSUxMlD179mRZYhepeZkr3iHmBJ9FMUKsDsdpYPj8888NIYLJDoQILK+5BXEjECO4DmF226hRowAdmTPgOM1I4lmPbNvvMQWvelQmTTjvOXXq1FGZqCq9t8oVatv2/RESW9V5Qax+nWV0wFy6dKmK50AMx+TJk+XUqVPKEgJ/7vHjx9XrYBaFywaVEAFmMghAmzVrltr3448/VhcGX/EihBACYHHV4FrjjxDJHMSKuBFCsmPTHliN0lYSNoQ1rTdzEGvqmc2OrzXil2UkOTlZFRbCLKNgwYLKDPr6669LsWLF5K+//pKxY8eqmUyFChXk5ptvlm7duqn9YOVAlcRx48Ypf2/jxo3VMiGEZIVucofrS61atfw+Uaw1QvxhfYbg1fXqHhfqBnluTu/1S4wgIBWmU18gqya7Vt2oBzBlyhT/j5AQ4jpghd2xw3t3gOU1L6ZypveSvPek8TbIQ3hAOIl1UXovo0EJIZbMotE0bdo0T+9BMULyXvBsfdiKnfkWI7skQlLUIsUIIYSEIV4kr2IE7mK4eABjRkhObNSZ4p5kZYkId7xIRldjisQUOKKWUGtEJ4c4CVpGCCGWFiPNmjXL03sgo09fzOHyQdA9Ib5ITfXIJi1GEreLeJIsYRmpWbOm4aKMPLdd/X86XuTISXEcFCOEEEdaRsyuGgTf79pltsMTkg6a48Unpq3Eb1L/WUGMREVFqXoj4OyJdcZ2WEecBsUIIcRSwAStxUiVKlVUe4m8wrgR4peLxkJpvRpt3Tt30ttFGFCMEEJIkEG15WPHjuXbKgJYa4Tkho1mo1nCJtUgDw8rEGsEsW4ztm3zdkdwFLSMEEIc6aIBrDVCcsOGXea03o2WsYpkGMMJmWuNOAuKEUKIY8UI3TTEf8vIRkvEi2RnGXFiei/FCCHEsWIELSd091Om95IcY0aST4gkHbKmZST1jBQp4E2jYcwIIYSESIwUKFAg383tkBapL+YQI+isTIiZ+ESP7DqoVzaq/yxpGRGRqBSvCWf/Ue9xOwlaRgghlgEpuOvWeVMY0fuqSJEiAbuYo1nn3r0OzIkk+WLzHtNKwiajfYlVKF68uJQvX14tJ532Hp8TG+ZRjBBCLMOmTZtUZ+9AuGg0jBsh/sSLIIsm3A3yshLUCcfS2yQ4zVVDMUIIcWS8iIbpvSTXNUbiN0qbNm1U9V4rEeuCIFaKEUKIa8TIli1bAvKexDlsNKf1JnjFiNWIdUF6L8UIIcTRYoS1RkiuuvV6UkUStkjbtm0td8JiDcvIVscWPqMYIYRYTowULVpUatWqFbBmYwULFlTLtIyQzK0HjJiRxB0inrNy0UUXWVeMJB2SgpFn1SJjRgghJAicPn1atm/fblhFdLfS/AIhooUN0nud2H6d5A2kyMYlpK0kbFI3/XLlylnudMaa0nujxRu5uuOASEqKc8YyLSOEEEuwdu3agLtoMl/M4+Li5NChQwF9b+KcTBorxouAihUrKmshSI33xj0lJYvsdtBQphghhFiC1atXB02MMIiV5Nytd5NlxUhERISRbpwhvddBGTUUI4QQxwavaihGSI6ZNPEbLRm8mtm6lxpvzqgRx0AxQghxlRhhjxriy01T4NwWufDCCy17cmIdnt5LMUIICTsIKtVipHLlyqoKZiBhei/xxfqdab2Kkk9L88YVJDo62rInKtZHei/dNIQQEkD2798vx44dU8vNmjUL+LmtXbu2UVWT6b0EJJ71yM6DaZVWEzZKW4vGi5wvRnZKZETy+QG4NoeWEUKIo100AA33qlevrpYpRogaB3thkdNiZJOl40UyWvdSpFjkPiMANynZGa4aihFCiKMzaTLHjRw/ftywwhD3Ype0Xk2NGjWkQIECajkycYOR3uuUIFaKEUKI4y0jmeNGGMRKzGm90bJbGjRoYOmTUqhQIVVNGMQfWWFsX7dDHAHFCCHEMmIEM79GjRoF5TOY3kvMrNxwxlhuGhsVsIq/wSQ2TVCfO7HK2EYxQgghASA5OVnWr1+vluvVq6fiO4IB03uJmX83JxrLHdtUscXJidXWvfj0asXrdjJmhBBC8s3mzZvl7NmzQXXRAFpGiDmVfNfhtDTexJ1ySbsWtjg5sUatkS1SINKblkzLCCGEBDheJBhpvRpdThswo8bdHD4hcjYl2vJl4LMUI54kKRN9VC1u2OWMhnnWd5IRQhxNKIJXQbFixaRSpUpqmWLE3azdnpo+LgrsVYX27ECsuXuvxxu5evacyDYH9KihGCGEOD6tN7Or5uDBg6qDL3Envy87YCzXrZouTKxOHZN1L/XMWkdl1FCMEEIsYRlBi/RatWoF9bMYxErA4lVeFwdo3aSEbU5KsWLFpGLFimr59IG/jO3rdortoRghhISN06dPy/bt29XyBRdcEPT0SvaoIWCDKQPlyku8tTvsQmyaq+bkvsXGtnU7GDNCCCF5Zu3atSFz0QBm1BBw8FSaNSQlXrpd1sRWJ6Wu7kCdsEkiI7wiZK1Xz9saWkYIIY4PXtXQTUNOnIyXsxFV1YkoIrulRIlitjopsUZGzTmpVMpbuG39Tvtn1FCMEEIcn9aroZuG/DBvvUhEQXUiqpQ6ZbsTEmvKqClT2BuIm3hOZOdBsTUUI4QQV2TSgNKlS0uZMmXUMtN73cm8JelNaRrX9jaes6sYiUrZ6piMGooRQkjYqmBqywjqPJQtWzYkn6tdNXv27JHExPSS4MQdLF+XntLdoUV5sbMYiTv0t7FMMUIIIXlg//79cuzYsZBZRTKLEYghnclD3MP2/YWM5c7tvLEjdqJ8+fLGGN7y73eOyaihZYT41dDs7bfflgkTJkhKSgrPHLFV8KqGGTXu5dChQxKXkt4Ur3Et+7lpQPfu3dX/qWfWSYSkZdTQTUPcQGpqqtxyyy1y9913y8MPPyzTpk0L9yERmxMuMcIgVvfy999/i8Q0UMvFCp2U4jERYmcxIqmJUqzQYSOjJjXVvtYRWkZIjsCcfdddd8nnn39ubFuxYgXPHMkXtIyQUDN/0WqRQuXUcs2K3k7RduSyyy6TIkWKqOWkE/+q/88kiOw+JO4RIyNGjJD27dvLpZdeqh733nuv8dwnn3wiXbt2lS5dusjrr7+ubmLm4kYDBw6UDh06qPeAv5hYH3yHo0ePlvfeey/D9m3btoXtmIizMmlQdbVx48Yh+1zWGnEvi5an361bNIgRuxIdHa0ECUg8tsIRQax5sow8+eST8scff6jHG2+8obYtWrRIvv76ayVIYMJfvHixzJo1Sz137tw5ZdqHGPntt9+kefPmMmbMmMD+JSQojB07Vl555RW1HBERoR6AYoTkN/5o/fr1arl+/frGLC9UAYDFixdXy0zvdZer+b9tScZ6q8b2KnaWpasm3vs7cqUY8cWPP/4offr0kWrVqkm5cuVk8ODBaps26RcqVEiuvfZaKVy4sAwfPlxdiPbu3RuojydB4IUXXpBx48YZ6x988IHRNRJixGz5IsQfNm/eLGfPng15vAiAoNZxIzt27JCkpPQbFHEuEJ4JnmrGesMa9owXOV+MrDO2rbVxRo23DJ2fYKaMB2Y0999/v9SrV0+lyHXr1i2DKXTr1q3GjQuv0WAWBNGC7VWrnp9aBUsKHmZwwYCyJaHhzTfflMcee8xYf+2111QA69SpU9X3igZnhw8fVsKTeNHjk+M0Z/791+vn1g3yQn3OIEZWrVqlssIgSMxBrU7HreN0yZIlItENjfV61Ty2DviMjY1Vj63bNxjb1m235veamwaYfosRxIhgdow3x40J69OnT5f4+HjVAlyD5YSEBLWM/83P6eexjy8mTZokEydOzLCtX79+0r9/f38Pl+QBfK9mIfLQQw/JNddcIzt37lQmbg1ccXC5kYzs3p1e4ZH45s8//zSW0RIdYyuUZB7HBQvmaV5ma9w2ThEiIDEPq+VCBVLEk7hHQjzsAk6HDh1k69bPRBK3ixSpLWu3p8qOHbslzZtuGWrXrp3ja/z+BWIWoxkyZIh89913Kio+JiZGzpzxNu0BWEaQDcD/5uf089jHF8OGDZNBgwZl2LZv3z6pXr160FuMu50vv/xSHn/8cWP9iSeekGeeecZYh/j46quv1DLEZM2a9mq/HUwwI8EFnuM0Z3bt2mUsI+A91OOoRYsWxjKsfG4ax24dp+s3bBYp4rWA1a0mUqe2/b/zfv36yWeffeaNGylSW+ISI6VgTE2pVkFsR76nA3owQ/nAJ9epUye1DlO+Nn3CkgLriQYlmFGKWccfZCYqKko9zCDmBJ/lph9PqJk5c6YMHTrUiAWBCw4xIzpoFZjN2TBv8/s4H47TnFmzZo1hIcWYCvU4gotZg2uVG8exm8Yp4pNWrT8hcmGUUezMCX97ly5dVBzm2TNrRcr0UNs27IqQGpUsZhrJBX59G5hBLF26VMVzIIZj8uTJcurUKWUt6dGjh8yYMUOJjKNHj6rnsA20atVKDQZk12Dfjz/+WBo1auQzXoSEh59//lkGDBhgVFYdOXKkvPzyyxmECDALSGbUkLyA64geO7h2hOOmwPRe98UoJRVKv3Y1qCGOICYmxpvi64CMmoJ5KQcO/y58rJhdoJ5IsWLF5JJLLpG+ffsq1w3MgMic6d27t9oPVg6UEMcse/z48aqmgDlLg4SXBQsWqEwonVVw0003yTvvvHOeEAEUIyS/oOaQJtSZNJoqVaqoQHpYaZne63z++usvkej6jsmkyZxV88ufkzNl1EQ4W4yg/ba5CqevWA88fNGkSROZMmWK/0dIggosXVdffbXRvRSCEparrGarpUqVUuPg+PHjtIwQW1VeNYPxDWG9bt065aaBRbBAAXv2KSG5LAMffYmx3qC6OEqMjHrwSdtbRuzvNCN5ZuXKlXLVVVcZwcU9e/ZU7rWcMgu0dQRBcJlTsAmxgxgxu2owhlnzyAWWkZj6jnPTAJTNqFOzgkiiNyh87XaPLWtAUYy4FMwIr7zySjl58qQRCIUg48yBw9mJEbjjzFkRhNhRjABdE4k4j2PHjqkie7rGSMUyIiWL2c+NkRVwp6sCaGnFz06eiZADR8V2UIy4EMwC0UPoyJEjah29hhBcnNuS3IwbIXkFMzYtRipVqhTWonlmMcK4EeeybNkykQIlRaIqOs5FozGLEbDOhvVTKEZcCPoJ6UaFyHRC2X4EIecWihGSVzDukG0XbqtI5jR1ihGHx4vENHCki0bTuXNnKXhus7G+ZhvdNMQGLFy40FiGRaRkyZJ+7U8xQgLhomnWrFlYTyQtI27KpDGJkerOcdGYU3xbNky3bP+58pjYDVpGXAaCVdG4EDRs2DBPtV4oRojd40VAjRo1jGBtxow41y3ozaRxZvCqmWu7pffdWb7Od6sVK0Mx4sJUXtSLAZdeemme3gNlpHUaJAufEbuKEQgR3TMDbho7ZiCQ7EGVaDT0NLtpGjpUjFzf+3KRs3vV8u5jJWw3nilGXMYff/xhLHfs2DFP74HS/JhV6hml3QY9Cb8YQZ0PVGEONzpuBBbDgwcPhvtwSIBRVhGQZhkpVFCkViVnnuZ69epJtGe7Wk6WErJ9T5zYCYoRl/H7778by3m1jJhdNWgHgAJohOQELHJIKTcunGmNNMMJ40ZcEC+C21x0PbVetyosYs6LGdEpvvWqea3e4MtvV4mdoBhxESjuBDeNdrXkp1Mp40aIv8AVgh5VVnDRaFhrxAWWkSI1RSKLODpeRNOxVXqq/M+/7xY7QTHiIv755x9JSEjIt1UEUIwQf1m9erWxbEUxwvReZ4FeW7jmZcykEUfT+wqvBQj8u/mcrVzoFCMuIhDxIr7EyPbtXj8lIXZJ69Ww1ohzWbNmjXfylSGTxpkuGo05vTcutbrhFrUDFCMuIlDxIoCWEWLnTBoNsml0d2qm9zo0eDXGPZaRMiUipHhhb68xiWksP/30k9gFihGXgD4yf/75p1ouW7ZsvjMZKEZIXsVI0aJFjZTacFO4cGEjM4xuGicGr6Zn0rghZgQ0qZ1m/YmqKLN+9F7z7QDFiEtYu3atkfUCq4ieDeaV0qVLG5VbWWuE5ERcXJwxTpo0aaJSe62CjhvB7wNN1YgzLSPlSoqULelsNw1o1ShGNEv+PSmnT58WO2CdKwIJWbxIfl00AGJGW0d27txpFFIjJCsxbDUXjYZxI84DJQdUvESBYiJRVV1jFclgGRGRlKj68ttvv4kdoBhxCYGMF9FoMZKSkiK7d9srjYyEFitm0miY3us80PJCZZKYXTQOjxfRNK5lLIrENLJN3AjFiAvAj1JbRuCvb9GiRUDel3EjxM7Bqxqm9zo5XqShazJpshMjdkjxpRhxAUi93bdvn1pu37690Rwsv1CMkNxCMULCEy/iPstI+VIRUr5U2kpMY9m1a5esX79erA7FiAsIhosGUIyQ3IBZmRYjlSpVkvLly1vqxJnHMTNqnGUZKViiibHNLTEjGawjhauIFCxlC1cNxYgLCHTwqoZihOSGAwcOyNGjRy3potGuy8qVK6tl1hqxP3v37jUswUVKN1f/o8l4nSriGhpncNXYo94IxYiLxAi67bZt2zZg74v6DDpFmOm9xI4umsxxI+jca5dUSJJDvIhESGKE1zdTp7JIVCF3xIyAxjVNf2tMY3UPQHq9laEYccGsdPPmzWr5oosuCmin1KioKNVwD1CMECeIEUDriL3RzUClcDVJTo1Siw1d5KLxFcSKJqlWT/GlGHE4wXLRZHbVoFjUiRMnAv7+xP6oZmUWFyOsNeIMJk6cKK+99pp3xdwgz9VipLH6z+quGooRhxMqMQLYMI/4Cl5dsGCBEZthVTFCy4j9O/Tec889MmLECLUMLuo42HVpvZqKZdCn5nwxYuUUX4oRl4gRxHZ06NAh4O/PIFaSHZs2bTKCCSGG4dqzIqw1Yl8QHH3VVVfJW2+9ZWwbNWqUXNRpsOvSejW43qdn1FQTKVBCVcresGGDWBWKEQdz8uRJ+ffff42W7aVK6eTzwEExQrLD7Kfu0qWLZU8W3TT2bTOAoHw9zhCk/9FHH8mrr74qm/ZEuNZNAxrXlHRiGlneVUMx4mDQpVeb5YLhogEUI8QJYgRCHd2sAWuN2IPZs2dLu3btjIDjChUqyPz58+WWW25R6xt3eV9XqhgKgYnraFwrY0YNoBghYY8X6dixY1A+g2KEZEVqaqq6Oeib/YUXXmjpk6VdNXv27JGEhIRwHw7JAkywXnjhBendu7eRho0WF8uWLTNc0WcSPLL7ULpVJL9dyu0exFqiUlujAKZVU3xpGXEwwQ5eBeXKlZNixYqpZab3kswpvbrY2WWXXSYFUHnKwpjjRhiMbU0gEgcNGiSPPfaYYfXt16+futah7pFm8x5xbbyIpkltY1FKVmmv/keKr54gWA2KEQf/aHV/BlxkUYY7GGDGoa0jO3bsUB18CbGTi0bDuBFrA4sVJlVfffWVsW3cuHEydepUlallRrtoQENzATAXUbmsSEnvPFHORqYrE6u6aihGHAqEiE5xC5ZVRKPFSHJysrpgEGJHMcKMGmsXMkPRxhUrVqh1iI8ZM2bIk08+6dMFs3F3+rJbLSMRyKhJC2I9dCpGChUpbekUX4oRhxKKeBEN40ZIZiBMFy5caAQWNm7sDaCzMhQj1uTTTz+VTp06qWrSoFatWrJkyRLp06dPlvts3OVxdSaNr7iRC9sNNCzYGzduFKtBMeJQQhEvoqEYIZnBDFYHF8IqYocAwgYNGvisGkvCA1y+o0ePlqFDh6pYBwBRgkDVnIrnactIZKRI3ariWhqbMmrqNO1laVcNxYhDZ6WLFy9Wy+hGahYLwYBihNjdRQPKlCkjjRo1MsTUmTNnwn1IrubOO++Ul19+2Vi//fbbZc6cOSpoPjvggtAxI7UqiRSOsr4QDoVlpGj51sYyxQgJCatWrTLSt+CiCfaslGKEOEGMmF2aEPRGwzUScpCFheJlAFlY77zzjrz77ruqqFlO7DsiEpfg7ngRTROTGDl0uqxUq1bNqEFltWQDWkYcSChdNKBmzZqG4GF6Lzl79qwsWrRInQikWwbbMhdIzL8X8++IhL6gmb5Z3nfffXLHHXfkel9zJo2b40VAtQoixdIata/d4e3cDuLj4y3XnZpixIGEWowUKVJEqlb1OmYpRggsComJibaKF/H1e0GBKBIekCmj6du3r1/7ZsikcVmDvOx61Ow4INK4qVeMgJUrV4qVoBhxGPCXajGCqpcXXHBBSD5Xz36PHDkip06dCslnEmtiVxeNtuTo4lkQVTpwkoQOBD7/+uuvRswbes/4gzmTpqHLLSNAixFk85ar7i1+pt35VoJixGGgKyMEAbjkkkskEuHkIcBsimf1SndjFiOdO3cWu6HjRlA4kFk1oefnn39Wrj5w7bXX+nUNW7nJI1/NS193e8xI5oyaiKJNjGWKEeIoF42GQawEIANFB37Wr1/fCJizE4wbsY6L5rrrrsv1fr+t8Einez1y6Lh3vWNzkUre3oeupokpiPXAqTIqawzQTUOCitnPTTFCQg0CV5GJYkcXjYZxI+EDFpEffvhBLZcuXVrVFckNU+d55KqHPHI63rveronIjGcjbBWvFIr03vU7vU0FwcGDB41CclaAbhqHWkaio6OlVatWIftcWkaI3eNFNA0bNjRqWSAFEt2HSWiYN2+eUSyvV69euUrlff1rjwx82iNJXg0svdqLzH01QsqWpBABNSqKxBRRi7J2u2Tonm0lV02excjq1atVmtCHH35opGIh0AizCv0wq661a9fKwIEDVYvnESNGyP79+wPzFxCDXbt2qQe4+OKLJSoqKmRnh2KEZBYj6NRrRzCb1taR48ePq2sXsZ6LBsH6j76XKqPeTA9YvfVqr0UkpgiFiCYyMkIapfWo2bZfpPEF6ZNUK7lq8iRGMFN45ZVXzus3gZk4Zub6oTvFIiL94YcfVmIEF6vmzZvLmDFjAvMXkLDHi+j+IzExMWqZ6b3uBDduHfDZrFkzKV++vNgVumpCD9x7s2bNUsu4llx55ZVZvjYp2SNDn/fIi1+mbxszROSDhyKkYEEKkaxcNTDylax8kXMsI1CvSBmtXTu9LXF2oLQyzG2IjC5cuLAMHz5c1q9fL3v37s3LxxOLxYvo2aS2jqARk9Wq+5HQjD/t0rCri0bDINbQA5eYzgTs3r27cjX7Ii7eI9c85pHPfvGuIyzknQci5JnhkYwRyYLGNdMFWoLUUvdhq4mRgv7ucOLECfnqq6/kk08+ydA3APz3339y+eWXq2jdAQMGGMVqMFOuV69ehiJZiLLHdl0sywwsKZnz+5OSkui7zaVlpGDBgsplFmpfN8TpmjVr1He3Z88eqV7dXXl1+ny7NcYA/n6zi8bO5wGWnWLFiqm2CvhdQVw7JRjSquP0m2++MZYxcfV1fIdPiPR6VGTZBu964UIiX4wRua6jR1JT0901JCONTEGsG3ZFqkaDy5cvl82bN6u6UBjrwSQ36dl+ixH0CLjhhhukePHiGba3bNlSpk6dqlwz69atU90WEQ0NcYJ8/aJFi2Z4PdZRktYXkyZNkokTJ2bY1q9fP+nfv7+/h+sajh07pqxNoEmTJmqGoWcZocLcwArpnVa72IWK3btNJSBdxC+//GJceNDmfefOnWJnkHUAIbJv3z6VJaSLoTkFK41TxH9Mnz5dLcOKjptl5vGz61BBGfJSBdl50BvUWiImRT4YdVha1TwrNh9qQadkQdzqvRP/ZevipW7dukqM4LyjwFywkx1y40Up6G9BLQiNRx555LznzBYOuHAQHzJ//nwlRmBuy9wBE+s6xiAzw4YNk0GDBmXYhgsCZtqhKuJlN8yBSDjn6BcTasxR2vh+w3EM4QTiCxd4N45TpAlu2rRJLbdu3TrHFu924IorrjCsjejjEWrXp5vG6bJly4ykBrj4Mo+flZtFBjwvcjCthkjVciI/ji8gF9TxxiWS7EG5nyJRIonnRHYeipFrLrlEpkyZYvx2rXCt9kuMIDgNarVHjx5qHSZMdFRE7MfYsWMzvBYmTagugFgCrXoB+lbAjJ9VAy1kgWTOBIFaxg/HKj8eq6EbkwHk5ofjPMXGxhrLiBtx63flxnFqjlfCzcQJf7+uxKrjGW655RZxElYapzpwFVx//fUZjmveCo/0eSK9hggyQ35+KUJqVHSG2ywUREaKNKyZKqs2i2zZJ9KkaUvjOcSNWGEc+CVGkGpljnBGzEiVKlVk6NChsnjxYmnUqJFyzcCCApcNui0CmIBQzAYDDoFJH3/8sXqtr3gRkv9MGqRPhwOm97oXJ9QXyUybNm3UpAgxUOzgGzwwadXxIpjE9u7d23huyjyP3Pxceg0RFDP7/sUIKVOCQsRfGtcUJUaQWxBTpplhMLBKEKtfcgiBp4gL0A9E5MIFg/iRv/76S8V0oB/K448/LjfffLN069ZN7Ycf9IQJE1TgK3pVwKUwbty4YP1NrgNFgrSbBvEiZcuGpwYy4gQ0TO91pxiBBTNcYjjQ4NqmW64j0M9K1SqdBGLdtIsP9w+UCcBNcsJXHrnBVMzsmg7eYmYUInmjSe10AbfjUIyRVILEE101OZz4HcBq5qmnnjKW77//fvXICtwktY+KBJYlS5YYqbRm03I4Lt6wlCG+h2LEPaDQ3pYtW9Ryu3btsowFsyOIE4GLBsA6gkB6ElhmzpyZwfp+4rRHbnnBIzPTjb1yWy+Rd+5nDZFAlYVft8OjYvwgAuG1gDcjWB3eISxzk4kWfkcRsXWxs6xcNQiKyhy0TJwJAtWd5qLRmMU9XTXBr7par0V/aXVbRiHy1LAIeX80hUggxcjaHek9akCwXDU7D3ik7g25S7mmGHEAVhQjYPv27WE9FhIanBgvomnfvr0xq6MYCTwIdNdVe2u0GSfXj6sk2/Z5nytd3BsfMnYYG94FgjqVRaIKpWcnNW8e/B41b0z3GN9nTlCM2ByY2BCvo2M2wt2ynUGs7gImWC1G4KZDsT0nUbJkSdW+Avz777+q6CMJHN9++61IZIxI/U9lV+HH5Wxarcs2jURWfhQhPdsxUDVQoEx+20be5a17ReIKXhTUHjWnznhk4ve5fz3FiM1B4RqkSoc7XkRDMeIuECuCNH1tlQtlc8ZQoa2NEF7IGiSB44sZy0UuXCpScbCx7Z7rRf54K0JqVqIQCTR3X5d+Tif9WtroHwfLiC7FESg++kGMdOzcQDFic6zkogEUI+7CyS4aDeNGgsO700/IipR3RIo2UevFokWmPhUhb9wXKVGFKESCwXUdRWqm1Yn76S+Rus16GRW89aQiECQne+T16f6JG4oRm0MxQsKJG8QIm+YFlsSzHrnj5VS5840SIgW8PVHKxxyQFR9GSP8uFCHBdtWM6pt+jk8VvzUorhoEIO9My4TvnkvPLcWIjUE6r047RG5+/fr1w31IyuyHejSA6b3OBmXFdSYNYivM0flOomLFikZNhr///lv12iJ5Y9s+j3S4yyPvpRdcFTkwSWb8336pX51CJBQMv1qkZFpfvPVHW4gUqhDwINZXpqVbRR4cmLvvlWLExqBYzcmTJ43ZmxW6iuIYtKsG2TRubZbnBtauXSuHDx82WhCgW7RT0dYRdA+HICH+M+sPj7S81SP/eOubiaQmiGy6VaonPi0d2qWXJyfBpXhMhIzwemckKaWASOU7AipGlqzxyNK13uVmsSJdcvnVUozYGKu5aDRajCCwllUrnYsbXDQaxo3knaRkjzz0Tqpc+4RHTsZ5t1UqeUpkVXuRg5NUoTMrTKTcxL3XR0jBAmkrVe4UiSwSMDfNy1PTrSIP9M99WjbFiI3RLhqrihFAV41zcZMYMf++zE0BSfYgQ+P6Jz3ykqn4dt/LRNoUukfkzGq13qdPH57GEFOtQoT075y2UqicSIWbVc2X/Kauww2nC9ZVKiMy8PLc70sxYmO0kkWMRrNmzcQqUIw4H/SyWLBggVouX768avfgZGrXrq1aHej2C1bo5WEH/lonMjstG7pQQZHX742QTx89K3N/mWGMHfSjIaHnwQEmi0XVUXCyq1o6+S1ypj3zSCMuHJV7ixfFiE2Ji4tTzbsAegpYyV9PMeIOIXzq1Cm1jOaXVmhBHkxgatauGvz2rNLp1OpMnpNusn/7/gi5t2+EzJnzq8THewtQoENvgQLaX0BCScsGEXKZjjmPaSBSpme+XDXoKYTaIiC6sMjIa0Q2btyomubmBmdfQRwevKqL1KDhkZWgGHE+bnLRaJji63+tialpw6RwlMiALuf3oqGLxlrWkVX5ENkffi8Sl5ZoNuQqkXKlIuSbb76Rzz//PFf7U4zYFLM5zWpiBCZtDWNGnInbxQjjRnJm7gqRw2khCL3ai5QoGqGykb777ju1rXjx4nL55X4EFZCA0+NikfrV0vwqpTrL4tXeat55CVJ+45t0K9j9/SPO68icExQjNsWsYHXvDKuAFvK6zDDFiPM4d+6ckcmFXkh169YVN4C4mNKlS6vlRYsWBbx8ttP4cm76+Rl0hffmhDgjHSR59dVXS+HChcN2fEQkMjJCHhiQ7ibbktBL9Tvzl+kLRHYfSheeqBmza9cu1a4kt1CMOECMWCl4NbN1ZP/+/YZ/mDgDNGbUhb9gFXFLWibiYnSw5ZEjR2TDhg3hPiTLEp/okZlpSUcosKWrcJpnynTRWIObrxIpHOmN//KU6ye//emNRcwtEOXmImcPDPDfKgIoRmxaeRUxIyA2NlZKlCghVsMcN4KUMeIc3Oii0dBVkztm/5keP9C3E2JGIlQBRH2DgkWke/fuwfuiSK6JLhwhHeut865EFJQ3pvtXqHLRapHlabq8RT2RTmlRAxQjLumUqq0NVnPRaBjE6g4xgkwaN8Eg1ry7aJYuXWoUQezWrZsUK5ZWk5yEnRG9kkVSvfEiv62LlVNncu+CzGwVgaX00KFDhis3t21KaBmxuYvGasGrGooRZwIRjDobALEiNWrUEDfRsmVLFROVuQIySefYKY/qCAuqlBPp2Pz8mTKqrhLr0LlDE5GDX6jlc6nR8nFaim5ObNnjkVmL0r9rXUgNQcq6FUhuv2uKERti5UwaDcWIc6v+IiPCjS4aEBUVJRdffLFaRoDezp07w31IlgPBjElpNeEGdhEpUCBCxRXolF7UFenVK605CrEEZcuWlUqp6WVyX/vao1Kzc+L16R7Rcdz3XBchUYXOjxehGHEwVs6k0VCMOBM3x4to6Krx30WzevVqI7PusssukzJlygT1OyL+c1GTYiLHflLLOw+KzMih68Hx0x75+EfvckwRkRHXeJfRvHXu3LlGtl3r1q1z9fm0jNhYjCDNsHr16mJFUDobs0jA9F5nihHcVNwIxUjW7D7okd/TDLcNaoi0SAsXoIvG+rRo0UJk7ysZGt5ll77+/ixkTXmXb+khUqaEV3j++OOPKv1fZ0yxUZ5DQWAQ0mW1i8aqaZVIg9TpvRAjrMlgfzDj0XUD0IKgYsWK4kbgptHtFxg3kpEpvyHVM90qoq9P5qqrKAFPrMeFcPmf+E0kzjvZ/Xu9yJ/epM3zOJfkkTdneL9ofMX39U2/D+VVeNIyYuN4Eau6aDK7alCT4uDBg+E+HJJPUHVUB6W51UUDihYtKq1atVLL69evl8OHD4f7kCzpornh8vTsP12KAEKuatWq4To8kg1G/OHeVzNYR3wxbb7IviPe5d6XiNStFmFc62EZ0XEo/jRBpBixGXbIpNEwbsRZMF7Et6sG1ViJyLodHlmVVi+rTaP0GxRdNPagVq1aUrJkSZHDUyUy2ZuCjUwZZMycV+TMJFIeSCv9DubMmSNnzpwxLGD+NHClGLEZdsik0VCMOAdcYKZOnXpeB1u3wriR8/lyzvmBq4CN8exBRESE957iSZLU3a+rbXC5IbPGzMJVIivTRGfrhiKXmAqA50d4UozY1DJSqFAhadSokVgZihHn8NJLLxmxSpjx6B4tbsVsfmbciHe2/KU3gUIiI9PrTcCNhWJnoGnTpq7pY2RXLtQT3AMfSJFC3vzsST95a8doMltFdFxQfpsgUozYiMTERKMfRuPGjY1sFatCMeIM9u3bJ+PHj1fLMLu+8MIL4naQmoogXvDPP//I6dOnxQ6gCdprr70mU6ZMCWhQ+dK1Itu9WlW6thKpVNZ7gzKPlSFDhgTs80gQM2pA8glpVW21WkTGzHuzvJs37fbI7MXe5WrlRfpeljGm7NixY2q5R48eUqRIEb8+m2LERqxdu1b1pbGDiwbobBrA9F77MmbMGKP9wO233y4NGjQI9yFZylWDoF5dldbq2VDoB/Pggw/K448/Lp9//nlQAldv7OoVItu3b5fJkyerZVjSRo4cGbDPI8HBfF8pm/iFsnKBN7/xyNlzHnnVVPodGTSFCvp2x+Wlwi7FiI2wQ7EzMzDVlS9fXi1TjNg3RmnSpElqGcFtY8eODfchWQZz3IzVXTWwbuF458+fb2x75plnjHoQ+QGVOqemlZ8pEiXSJ+20TJgwwZg83XfffexFYwMaNWqkQgDAljVzpE9anPaBYyJvzRD59GfverFokVuvTt8Pgvzbb7/NVxNEihEbYadMmsyuGlwM4WYi9gFm/NGjRxvm/CeeeELKlSsX7sOyDHYJYkXcRrt27VQVVDOwXGihmR/mrhA5fMK73KuDSImiESq+6OOPP1bb0BDvnnvuyffnkOAD1792PyIk4K7eZ43nHn7PIwlpq8N7ipQqnm4V+fvvv9U1HlxxxRVqIuovFCM2wk41RjKLEdzQ2MfDXvz0009GWWek/fGGkhHUy9CuSARpIh7DaixevFgF26KPjv4eP/30U+P5cePG5XuS4MtF88orrxjn44477mD5dxtxYdpEF9aOoqn/ycVNvNvTSgwp1829piJngXDRqPfN8xGTkIKBoS0jKAFvl94ODGK1J8nJyfLQQw8Z6whE9DcgzU3WEdx4dXVaq4DMBmQ06KBC3GQgTgYPHixdu3ZV2/bu3Svvv/9+nj8jPtEjM9N6mJQqJtK9rcjRo0fl3XffNUz2DzzwQCD+HBIizFZ33HMeHJBReMB1U6dK+jZMNHVKb36aIFKM2IQdO3YYEft2cdEAihF78tFHH8m6deuMqpn9+/cP9yFZEqvGjXzwwQeqL4i2ekCULFy4UCpXrqzW77//fuO1zz//vFGoyl9m/ykSl+BdRmZF4agIefPNN433Gz58uFSqVCn/fxAJfUaNiKxcuVKJj9reYXNekTOwZs0aVWVX/x7y6sqlGLEJdnTRAIoR+3Hq1Cn5v//7P2P95ZdftmwPJCvFjSC1MdxglvrUU0+pzBVduv+GG25QJbpLlCiRIVCxX79+Rr+rt956KyAuGkyY3njjDSMN/OGHH87nX0RCTbNmzTJYRgoUiJCPHolQjQ9HDxRp5w0pCaiLBlCM2AQ7Bq8CihH78eKLL6obFMANq3379uE+JMtSr149qVChglr+888/jeyRcLnWIEKefvppYxvSeL/44gufNYmQGYWGlvo7R+qvP6AQ1k9/eZerlhfp2FyUe+b48eNqG9xBNWvWzN8fRUIOsub0dRtBzxjTnVtGyIYvImXCnZHnTUzMVVevvfbaPH8uxYhNsFMZ+MxBfjpVjOm91mf37t0q+BDgBsYCZ9mDC7O2jsCipBvChRrUgcGsdOLEiRksWqicqwWH5tQZb5lvWEcgGAAEBIqh+cP0BSJJ3iKdMrCLyLlzicbYwXl59NFH8/+HkbC6ajCuNm9Oq/3ug61btxr3pjZt2ki1atXy/JkUIzazjCBNzlxMzOogoAkR/FqMBLLqIwk8KIal4wyQPWO2bBFrxo0cOXJExYTMnj1brUP8f/nllz4DRxFw2vV+kQc/KCuJZ0W543QzMwgJHeyaFxcNUnl1d+6+ffuyOJ6DglizIpBNEClGbABmLTotFvEimWc6Vkff0OLi4tSFk1gTZIPApA+QrYW6IsTacSMIbEfqru7/gvoOSMlGnIgvbnnBIys2iXy7uJhcfr9I0VJ15JZbbjEsO7Ck5IbdBz2qYRpoWEPkgtrJRssA8Nhjj+X/jyO2EiMImM4P9rqruRRzsSI7uWg0jBsJPLAw4SaCxmPmuhH5eT/EF5jjCdzeDM+fgD/Vel1Efvvtt5DFjeAmgWJmGzduVOvIWoEYyq5BWf/OERKTlqG9dJ1Im5EeuXbw00ZMyeuvv27EC2XHlLSKq+DGKyLkyy8nG7VM0JfEnJFB7J9R4wsUtkOqOEChtPr160t+oBixAXYrA58ZipHglWmHz3bo0KGqCRksT3ll1qxZxqweQZnoQUNy74pE1UkAN4e2UgT7+4d76MCBA2od/YLQHyenycp1nSLkjzdFKpfxBnvsPiTSd1wF6TrwbSNGIDdxQmYXTf/OqfK///0vg6uP2JsqVaoYKboQI77c67r8eyCsIoBixAbYNZNGQzESeP76Ky2NIY3PPvtMLrroojwFUKI/iTkFE+Z2q3eEthrmQk86diOYoHKqrjuEOjCLFi0yYrNy4sJ6It+O3S8XNxajK+uP22+RgnW86dzvvPOOKoaWFet2eGRVWkxj28Yiq/+aIZs2bVLrnTp1kg4dOuTzryPhBgHI+l5z+PBhQ/QGK14EUIzYAB2tjFgR3TfATsTGxvrMCiJ5Z9myZcayDkBELwlEtH/44Yd+BQq/9957RsQ84h969+7Nr8ZP0BhMpzx+//33QT1/CQkJ8vPP3o5lmL3OmzfP70JT5UulyrxXRQZfmb4tuepYkQZfyNmkCHnuueey3PfLOelj64bLJcNraRVxh6vm2LFjRtNFJFQEwmIfmZ84BszEcOHTfPLJJ6rMcJcuXZTv0XxBXLt2rQwcOFCp5hEjRih/E8ndrBXnTptio6OjbXfamjRpIqVKlVLLv/76qyQlJYX7kGwPGlNpFwECT/XFAJkwt912m0rZ1DPnnIKjzXUpkFHBAmf+g+7UsFAA/F7RhC5YQHzoCqewyMTExOTpfYoUFvnsiQj534gIMUpHVLhBpOlvMvHT733+Dbimfzk3vUdJOfnNmGC0bt3acFcRZwexfv/996qujXbRBOKakScxgsp+uGg1bpxm5xNRZsKvv/5aCZJp06apwBb4oc1mYIgRBHjhwjlmzJh8H7wbwGxXt/m2o4tGz9x1S2kUVkJxKJJ3cCPSAhWWMvyeEKeAhmQapHbi5pCTJQqzWp3OOWjQILUPyRtXX53eU/2HH34I2mnU19X8Fpky6oEMjpCZz0ZIUT3PKdFWki9YJPc9cX5g9NK1ItvT5pFdW4m889pY4zlkX1HIukOMzAhQ1dV8ixEcCC6C5noXKDcMhYSiJzAZYmaGbWDFihUq9x0/HDROQr8CtLXOzi9J7F3sLLsLdSh86k7mn3/+MUp9wy0D0MQOvv6pU6ca7bvhx2/btq1yw/hy26DuC/qI6P3Ro4RYO24EmTpogAdgEQmUJaL3pRHy59sRUq18WmvWwtVk9s7R8saXe7MMXG1ZfaORTQHr5zXXXBOQYyHWwGyJN7tpMBn65ZdfjAwuZHQFAq+z2Q9OnDghX331lbKAoMKfBia9bt26GetIOUSkv77oIUJfgwsfRAu2o0JnZmAJ0NYADUz7+gLsJsyDoGnTprY9B1deeaVyKeBiChPfhAkTxGno7ybY35E5eBWWDPPnodgUfL2oM4FJALrJwmICiySap5n7kzzyyCPG72zUqFHqN2nX8WUFYCmuUaOGSnFdsGCBsgJqYRgocPPXqbf4TWFy5+93ltU4bVpH5O/3I+XiW/bIrhPVRArEyH3vxcjxs6ny5M0iySkiU9NSeotEiSz9/tEMY8nXexL7EhERoe45cAmjEZ4ezzAy6MKIWoDm9L3npjaW32IEsy9c6DL/yJASVrRoUWMdywi0Avjf/Jx+Hvv4AimL5rLGukeGGzuHmtMEy5YtaxQ/syOtWrVSAxszdgQ/ObW6J0qqBxN0X9VAQGQeE3CLoXgZUjR1DRK4UHHu0cQMFxhYV6ZPn26MK/ym7Ty2rALSbXHuIfIwaTNP0ALB559/biwj/i4/31lW43Tm0/HSfshUOVtqgFp/apLIsrVnpGfbM3L4hLcPT8tae2XBR14LDQQY4mU4fpxHbGysEZ+GeD9MfnRhRH/GYG6qhhf0N34BbcW1CjYDk6G5DTWWtYkH/2duUY31rAKvhg0bpvzXZvbt2yfVq1e3XfXR/ADTuk6ZgznM7v7866+/3hjYcD917txZnARmB7jAB3uc6ngR/K4QMK6zaTKD8txwj916661qVoOLBkQ9rFJTpkwxXvfMM8/YMkvLiiAuTl+sMdYRrB/I64HOYMD4Qm0ZCMlAj1P0tntuyC8y+tWHRWq/IBIRKT/8XVR+WZE+oUze91mGaqvmjDniHC655BIlqgHSeytXrqysfgBJCTAQBKoMgF9iBLMpXNBQYQ+gyBJM74j9gPKBKQd55gAuGj1AMQPWszAAE8+ePXuynBnjj8v8ByLmBD8cN4kRnKOjR4/atgy8L5+6FrJw1fjqneEEgjlOUU5fZzm0bNkyxwsB3DawSA0YMEClA2PGft9992VwLeCGafexZRVQ/RSTLFh9dcxcoM4tJoK4xmoLDDJ4gjVO77zzDnnllbqyb91Gle4rBYsrNw0oHp0if//8jFEcC5NHjh9n0rJlS2MZE0gIEbQNAJjoIOQiUPj1K0HULAqdTJ48WT3wg8BMCzcVCBQEtuobKJ7XogUXQ/iuEQWOiyFmbOgY6StehDin2FlmGjZsaAhQNBRD/BHJe30RHbyaE5goINvNl/iDlSQrywrxH1ycdVApmsYh7TpQmCte5jeLJidgdVO9iY59L/LvJRIdkV70qkLkHyIeb6zR6NGjVdwKcSZNmzY1MqRwPwpGFk2exAh+aMiU0Q8MQgxaxI/AnINZGEyH+B8+RF08CbM3XPRg7oFpHkGZqCBI3JFJo8Gg1hkHyFGHD5L4h3Zz+SNG9G8QAefIxNA9ZzBZ0CnXJDiZY4EsgGYWI6EoTIesx5rw2cSvkYQlzeXyZkekVb1E2bZgmHoeLqJAuqGI9UBsJ7JqAKo767Ry3PcDHQ8FP6Qt2LZtmyclJcXjJvr27Ys8OvVYt26dxwnMmaPKN6rHTTfd5HESGJ/BHqc9e/Y0zt+WLVvy9B4HDx70zJgxwxMXFxfw4yMez759+4zvqEWLFgE5Jbt37zbes3nz5iEbpx999JHxuZ07d/aMGDHCWB83bly+joPYg4EDBxrfuX5cd911Af8cOopt4KaBCs1vR0SrANdesWLF1DJ86qHqcOoEEMCoLSNlypTJczZShQoVVE2gzBluJDAgyE8Hm8MKDNd1ftG1RULhojFz8803qzINAMGzuuI2rOF33313yI6DhA9fVvlANMbLDMWIRUEpb12nBX47BAo7AbgLtHkPsUWh6HDqFBA8jqZV2kXDapfuqcYayKqr/oB4InO7AF1P4q677jJaPBD39KjRY8I8vgMFxYhFgX9OV80MRBMiq1aqDHZTMacGr6IvFHFH3AgCvVG0DiCGI9TXA2RiocKqOXbw/vvvD+kxkPCRebwhYywYQpRixKI4LZMmXB1OnUReg1dJeFIikfYK5s6dm2WBx9zw008/GU3JYBUJtUUMVlnUotEgaBWuPuIOKlasqFyPwXTRAIoRG2TSOM0yggsZeqaANWvWyI4dO8J9SLaAlhH7AMHQs2dPo66StmzYIYvGF7gBvf/++/L444/Liy++GJZjIOFD95+BiyZYY5BixAaWkWbNmonTCFb6o1NBoK+uWYHy25itEOePcdRn0sXTkJJ96aWXSrjEFSwi6PIcyEJXxB7873//k5tuukm1I0A18GBAMWLRGw9iRgAi2QPdbMsKMG7EP9DlWrdUoIvGHsC3rguCQYz46pycE8hgQaVr/ZthgToSDpDN+dlnn6l2B8GCYsSCbN682Wgy6DQXjQYZQuiNkfmCS3xDF439QOo0BAlAywyz69WKVVcJCScUIxbEycGrZrOvNmOjRcCcOXPCfUiWhsGr9nfVzJ492699kUarU3rhGrnyyisDfnyEWAWKEQviBjECGDfiv2UEIg69nog90EGseYkbgQBFp1QAIcIidcTJUIxYECdn0pjp0qWL6nCqC0PpgkokI8jG0GMCDSadGEPkVBBsrAPQIS7QPM9OWTSEhAqKEQtbRlDyu1q1auJUYHru2rWrWsZFesWKFeE+JMuOB11ngsGr9g7W1pkx/oiRyMjIDO9BiBOhGLEYuClr0yxcNE4v+Z0fn7pbYPCq+8b4hg0bZOPGjWq5Q4cOUr58+aAdHyFWgGLEYrjFRRMIn7pbYPCqvUHpfi0mfv31V1U7xKq9aAgJFxQjFsMtwasalMzWAZnocIoUSOJbjKDJoBML4DkdlFPv0aOHWkatmIULF+a4D+NFiNugGLGwZcQNYiQYHU6dBJqkbdq0yRgPECTEfphjPnJy1ezfv9/oZn3BBRdIbGxs0I+PkHBDMWJRy0ihQoWkYcOG4gYYN5I1ugQ8YPCqfbniiivUbzo31Vi/++47Y5kuGuIWKEYsBKquInANoGW3W2bB6HCqu0Lmt8Op02DwqjMoUaKEdOrUSS2jMeS6deuyfC3jRYgboRixEGvXrjVqbbjFRaNTF80dTlEennhh8Kq7ivydOnVK5s2bp5aR1g+hTogboBixaPCqGzJpzLAaa/ZiBDNrNKsiznZH/vzzz6o9gnbROD21nxANxYiFcFsmjRkUP8tvh1Ongcyiffv2qeXWrVsrCxKxLwhERQVdsGTJEjly5Mh5r2EWDXErvLpZCLfVGDGDvhsoDw/27NmTpw6nTo4XYfCqs6wjcMfCCmIGFhGdTVayZEkjxoQQN0AxYhFwcdI3YPSzKF26tLgNumoyQjHirjGO+iOIGdGv09k3hLgBihGLsH37djl9+rQrXTQaVmPNOngVVTyJ/Wnfvr0x0YBlJCkpyaeLhim9xG1QjFgENxY7y0zNmjWladOmeepw6kRLmbaMIO25atWq4T4kEgAKFiwo3bt3V8snT56URYsWGd+3TulF7FS3bt14vomroBixCG7OpPFVqRIBrP50OHUaW7ZsUTcrbRVhVoWzXTXoWK1bIVx++eVSvHjxsB0fIeGAYsQiuDmTxgzjRrywvohzueqqq1S/GrMYoYuGuB2KEYu5aVBPolatWuJWkDVSrlw5vzqcOhEGrzoXxIxccsklahl9h/DQYgQWMHMfG0LcAsWIBTh27Jjs2rVLLaMrq5vrSZg7nMbFxeWqw6nTLSOoMUKcawF89dVXjfLw7dq1k0qVKoXxyAgJD+6961kIBq9mxDwzzKpstpNBvYmVK1eq5Xr16rkyzdtNYuT99983lplFQ9wKxYgFcHOxM19ceeWVKuvArdVY16xZY7inWOzMmTRo0EDq1q2rls3ju3fv3mE8KkLCB8WIBWDwatYdTlF/JbsOp06E9UWcD2JDzNYRgFLx7D9E3ArFSBaWihkzZkhycnLQvwCk8+my0IiXaNKkSdA/0w64OauGmTTuILMYoYuGuBmKkUygL0qHDh3k+uuvl4EDBwZVkJw4cUKl+eniXldccYVER0cH7fPshJvjRnQmDVxVbk7zdjqXXnqpsgJqKEaIm6EYyQQsImfOnFHL33zzjQwbNkxVRww0iYmJ6uKD+ABQu3ZtmTRpUsA/x84dThs2bKiWFy9eLEePHhU7WNR0VlReQQaRdkuhGi3FqXOJioqSm266yYgVY9YUcTMUI5n47rvvMqx/8cUXcscddwQ0iBLi5uabbzbSVlFX45dffmFKXzYdTn/66SexMl9++aW0bNlSBd/+8ccfeX6ff/75xxC/DF51Pq+88oosWLBAPdyc0k8IR38mt4kWCEin1FUSP/jgA3nggQcCIkjwHnivr7/+Wq3HxMSotuFI4ST2jBtB2fb777/fSMsdNGiQHDlyJE/vxeBV91lHEKxdqlSpcB8KIWGFYsQEAkl1jMiQIUOUVUT3BHnttddkzJgx+T7hL730krz++utqGWJn2rRpnAFnAWJ39EV6zpw5kpKSIlZk3LhxcujQoQxByUOHDs2Te4/Bq4QQN0IxkoWL5pprrlEBrB999JGx7bnnnpPnn38+zyd78uTJ8vDDDxvrsLj07Nkzz+/ndBDAiaZhukotXBhWY8OGDYa4LFKkiJQpU0Ytw9oFE3xeg1dhMUOqJyGEuAGKkTSSkpKMLrGYjeveEQhgfeutt4wT9sQTTygrib/MnTtXvZfmmWeekVtuuSW/35/jQQyGBr1qrARcbnDPaGva6NGjVWlvzWOPPSZLly7N9fsdPnxYduzYoZZbtWplFH4jhBCnQzGSBoIOdct29EYpVKiQcZLuuusuGT9+vLGOG9DEiRNzfZJR2rtPnz5K8ICRI0fKk08+Gajv0NEg3dmqYgTWD10jplq1asrqhXTNRx99VG2DSBkwYIAcP348V+/H5niEELdCMZKFiyYzDz30kIwdO9ZYh6BATElOoIJo9+7dVcqmLvf89ttvG7EoJHuQ8qyDe5Hie/r0aUucMpRr10GrOhaoaNGiavnpp582LGtI9YVFLDfBzwxeJYS4FYqRNHO7FiMwjaMQmS8gRmCK1/sgSBF1SbICGRXmomboyIkUUJ2lQ/xz1cDSgBRIK4A4kS1btqjljh07Sv/+/Y3nMIa++uorKVu2rFqfNWuWvPnmmzm+J4NXCSFuxW8xgiDObt26qXQ0mKB///13tX327NnStm1bZabWjwMHDhj7rV27VgWEIkNixIgRsn//frEKODZYMMBll10mJUuW9Pk6WDPgrrnzzjvVOrI78DfpWBMz8fHxqoropk2b1DoKeOEcITCR2DtuBGMXGTQAtSEgTDJbuuC2+fTTT411iNjly5dn+Z4Qt9pNg7oztWrVCtrxE0KI5fD4yfbt2z1nz55Vy2vWrPF07NjRc/z4cc93333nueOOO3zug9f36NHDM3PmTE9iYqLnrbfe8gwfPtyvz922bZsnJSXFEwyee+452NDV44033sjx9TiOoUOHGvsULlzYM2/ePOP5pKQkz9VXX208X7lyZc+OHTuCcuxu4OTJk56CBQuqc1m/fv1wH47n5ptvNr7bkSNHZhgXmcfp6NGjjdfWqVPHc+LECZ/vif3067p37x6Sv4O4E1/jlJBw47dlBDM2FOoBmA3CdI4sgOxYsWKFCghF+fPChQvL8OHDZf369aoeg9XiRcw9UbICs+EPP/zQMM0jfgBxJohpwAwXFVt1kS70nkD10Jo1awbxL3A2OIdwcQFYmnTGSTj466+/5LPPPjOyrp599tlsX49UcFgMwbZt2+TWW2/1GT9CFw0hxM3kKXfwhRdeUC4H3IThdqlbt66qt/Dff/+puhCotQAXTt++fY2LsLnCKOoxwIyN7VWrVj3v/VHFEg8zyEQJRo8YuJJwgwHNmjWTGjVq5OpzIMRwU4I7BsID/WwQqIoGe7rHDAQY+tugx0gwjt1tWTW6zDpK5992220hPwZ8h/fcc4+xjkBVjHX93Wb+HyA+CHFCSNVFhd/p06fLO++8owRrVmIEr+V4IcEcx5nHKSHBJDetDvIkRpC6iOwSWDy2bt2qbszoyzF16lTVXwWNvuAjR0l1iJOEhAQj00CDddzIfYGbeebU2X79+mUIEgwUOGYNAhF37tzp1/4TJkxQN5lFixbJqVOnMjS7Q4YFGr75+57kfCDoNAgINceRhAoICR3XUb9+fSU+fX23u3fvzrCO3wcE/O23367W0Q4AFsbGjRsbrzH3s8FviGOGBJvM45SQYGZF5kSeqyphxodGXsgaqF69upHKCC644AIV2Dl//nwlRtB5VHfC1WA9q2BOpEKiv4eZffv2qc8JdDOpP//801gePHhwntwpCGBFbRIIErMQufvuuwN2nG4HljSIW9TsWLJkiVoPZVYShObLL79srKMQHoSmGcw0cYH3NU5hyUGgNLJqYPVDWjCETfHixZWrE88BiBRYRggJFtmNU0LCRb5LPCKjZM+ePedtx2xQ+8br1KmjZpWaxMREtQ+2+wIxKTouRQOXB344gfzxwDKDyqigcuXKctFFF+Xp/XFDQQEsNHbDDPfxxx+XBx98MGDHSbxmvq5du6oGg7BEoTS8jsUIBcgi0ynaKGBnLsaWmazGKaxoiCuCRXHz5s0qKwu1ajZu3GhYCfM6Bgnxl0BfTwnJD36NRBTuQsVJXDgxm8ONHOmKLVq0UBdZXWkS8SNwf8DtATDTQ3wJzOuYFX788ceq74aveJFQguOHMNKBq/n5YSLIEjUwjh49qm5cxDkpvgia1f1nEIBttpD4A/bF7wJjBSCWBL8FBq8SQtyO33ffmTNnKpcE3C+ffPKJyiZo0KCBCgJFTAfcNbAM3HzzzaoeCYCVA7NCuHQ6d+6syqPrOg1WrrrqLxAzulEacU5peLhUdCl/xErlxv+ZFXDtIBNLA1eeuZIvXJ+EEOI2IpDfKzYARckQzxEosyL8pnDNoPU7YldQLRWxLcTaoHgc3BqIF0EnX21lCBba/QYQpwKrX+ZgbPOYQuBpbsYpXDTvvvtuhm3YB/2RihUrFsC/gJC8j1NCQoVrRyJM4xAi2vxPIWIvVw1ilRAgHUx0oKkG1XezEiL+8sorr0jz5s0zbEN2DYUIIcSNuFaMBNpFQ5wXN4I4EQSaArgfkSEWKFBrZ9q0aRnEB4JXCSHEjdhGjKDIWjDECLJ+evbsGdD3JsEDvYOQWRVsMYJieDquCWPkjTfeCHinZdQqef/994117Q4ihBC3YRsx8sQTT6haI4EAhdp0XQeUGa9QoUJA3pcEH1gS2rdvr5bRNRdVfIPBY489JqdPn1bLKOGOjLFgcOONN8rChQtVphlShgkhxI3YRowgrRhVXQNtZaGLxt6umjlz5gQlngiZYgAdnIOdqo0UeIzDQFteCCHELthGjADUaNBFyvID40XsTTDjRnz1nylfvnxAP4MQQoiNxQi46667VAG1vILCbL///rtaRoM/pIoSewGXSdmyZdXyvHnzVAG+QIGaH7oIGbJbkIJLCCEkuNhGjOh+HaiGib4veeWnn35SaaGApnF7ghojKA0PUJdDN6/LLyhsNnbsWGP9tddeM4JlCSGEBA/biJFnnnnGaIyGqq8ogpYX6KJxBsFw1Xz++eeyY8cOo9prdv1nCCGEuFCMoJeN9uWjnwyW/S0eiyJWsIwAdIDt0KFDUI6V2E+MwCpiDlQ1W0gIIYQEF9uIEX2DqFKlilGm22zlyA2IFUEreIDaIgUL5rtpMQkTKM2OmA6Avkjo5JsfJk+ebKQJo+8ShSohhIQOW4kR9CF59dVXjfV7771Xzpw5k+v96aJxFoEqDY8AWLj+NLSKEEJIaLGVGAH9+vUzfPm7du3KcBPJDrh0tBhBUKLuKEyc4ar55Zdf8vw+X375pSqEB7p06SKXXnppQI6PEEKIQ8UICkO99dZbEhUVpdaRWbNu3boc9/vvv/9Up0rQuXPnoHd7JcEHxcL0OIAYyUsDalpFCCEk/NhOjOieHo888ohxM0HtkZxuRHTROA900EUDO4AsGG3d8IcpU6YYzfDQ9wYChxBCSGixpRjRvUNq166tlhcsWKBM7bkVI7169Qr68RHrZ9Ug1oSxIoQQEn5sK0aio6OVu0bz4IMPZplRgQZ7ujDWhRdeKDVq1AjZcRLrihG0F9i4caNahkUElhFCCCGhx7ZiBPTo0cPodHrw4EEZM2aMz9d9//33xjIb4zmL5s2bG71jfvvtN1UvJLdWkXHjxhnrzKAhhJDwYWsxokt2x8TEqOV33nlH/vnnn/New3gR5xIZGWlkV50+fVrVHMkN06ZNkw0bNqhlxJ0gqJkQQkh4sL0YgctFz2rRcfWOO+5Q/2tQh0R3+kXBtJYtW4btWIk1XDW+rCLI0iKEEBIebC9GwKhRo4xqnOi4+uGHHxrPzZkzx+jyy8Z4zsTcQyY3YmT69Omyfv16tdy+fXtVcZUQQkj4cIQYQa2Jt99+21h/9NFH5fDhw2qZLhrnA4vXBRdcoJYRqHzs2LEsXwurmdkq8tRTT9EqQgghYcYRYgQgE2Lw4MFq+fjx46oOCczxOngVNSkYF+B8Vw3EBgJZs+Kbb76RtWvXquV27dpJ165dQ3aMhBBCHC5GwIQJE6RkyZJqedKkSfLyyy8bFhKUfy9SpEiYj5CEM24EQuWZZ54x1hkrQggh1sBRYqRSpUoZ2sDrKq2AKb3OBv1kChcubIgRXxV5Z86cKWvWrFHLbdu2zSBgCCGEhA9HiRFw++23n5cxg/RP1CQhzgXp3brBHXoQ6RLvGlpFCCHEujhOjBQoUEDefffdDEGJyJjQhbGIO101s2bNktWrV6vliy66SK666qqQHx8hhBCXiBHQpk0bGTlypLFOF427xQhcNowVIYQQ61JQHMqLL74ocXFxKqPm7rvvDvfhkBDQtGlTqVixomoNMH/+fDl37pxK+0Z696pVq9RrWrduTZcdIYRYDEdaRkCJEiXk888/V9180VSPuKs0PITo0qVLlVXk6aefNl7zf//3f6wrQgghFsOxYoS4k8yuGtSZWblypVpHYPPVV18dxqMjhBDiKjcNcSfmIma//PKL/Pzzz8Y664oQQog1oRghjqJy5crSrFkzlTmzfPlyY3uLFi2kV69eYT02QgghvqGbhjgOX8XMGCtCCCHWhWKEOF6MNG/eXHr37h224yGEEJI9FCPEcVxyySUZ+hDRKkIIIdaGYoQ4DqRyDx06VC136tRJrr322nAfEiGEkGygGCGO5PXXX1cBrMimQf0RQggh1oXZNMSRoPJqq1atwn0YhBBCcgGnjIQQQggJKxQjhBBCCAkrFCOEEEIICSsUI4QQQgixlxh57rnnpFu3biplcsCAAfL7778bz33yySeqN0iXLl1UNgM6pmrWrl0rAwcOlA4dOsiIESNk//79gfsrCCGEEOIeMTJo0CCZPXu2LFy4UBWTGjNmjJw4cUIWLVokX3/9tRIk06ZNk8WLF8usWbPUPufOnZOHH35YiZHffvtNVcTEfoQQQgghfqf21qpVy1iOiIiQ5ORkOXz4sPz444/Sp08fqVatmnpu8ODBSrSg4NSKFSukUKFCRvGp4cOHy+WXXy579+6VqlWrnvcZEC94mElKSpLU1FR+Y8Sy6PHJcUqsDMcpCTW5qfWUpzojL7zwghIaZ8+eVW6XunXryvbt25X7RoNtW7duVcvbtm2TevXqGc+hVDdEC7b7EiOTJk2SiRMnZtjWr18/6d+/f14Ol5CQsnv3bp5xYnk4TkmoqF27dnDEyKOPPioPPfSQsnhAcMBCEh8fL0WLFjVeg+WEhAS1jP/Nz+nnsY8vhg0bptxBZvbt2yfVq1dnNU1i6RknLvAcp8TKcJwSR1VgLVCggLRp00a++uordfGNiYmRM2fOGM9jGT1CAP43P6efxz5ZVc/EwwzcPDD1sLQ3sTocp8QOcJwSR6X2pqSkyJ49e5QZZsuWLcZ2WExiY2PVcp06dTI8l5iYqPbBdkIIIYS4G7/ESFxcnGo8BvcKAlfnzp2rmpG1aNFCevToITNmzFAi4+jRozJ58mS1DaBHCOJLkF2DwNSPP/5YGjVq5DNehBBCCCHuwm83zcyZM1UAK2qIwD3z7LPPSoMGDdSjb9++MmTIEOWTROZM79691T5wuUyYMEHGjRsn48ePl8aNG6tlQgghhJAIj7kymYVBtk7NmjUZM0IsC0T4zp07OU6JpeE4JVbENmKEEEIIIc6EvWkIIYQQElYoRgghhBASVihGCCGEEBJWKEYIIYQQElYoRgghhBASVihGCCGEEBJWKEaI0Yiwbdu2PBvE0nCcEjvAceo/FCMuoFevXrJq1apwHwYh2cJxSuwAx2lwoBghhBBCSFihGHERTz31lHz44YfG+uzZs+XOO+8M6zERkhmOU2IHOE4dKkZo+iJ2gOOU2AGOU2I3LCNGCCGEEOJOCorFWLNmjYwfP151Py1evLgMHjxYBg4cqJ57//33Zffu3ZKUlCRLliyR2rVry/PPPy9Vq1YN92ETl8FxSuwAxymxC5azjBQsWFAef/xxmT9/vhIl7777rmzYsMF4Htv79esnv/32m2rV/sEHH4T1eO1EdHS0nD171lg/evRoWI/HznCcBg+O08DBcRo8OE4dLkYaNmyoHpGRkdK4cWPp0KGD/Pvvv8bzbdq0kdatW6sf2ZVXXimbN28O6/HaiXr16smff/4pcXFxsmfPHvnuu+/CfUi2heM0eHCcBg6O0+DBcepwN83WrVvl5Zdflk2bNil3zLlz56RWrVrG82XKlDGWixQpIvHx8WE6UvvRo0cP5d7q2bOnOqfdunXLIPRI7uE4DR4cp4GD4zR4cJw6XIzANdOiRQt55ZVXlNiAy8bj8YT7sGwNBFupUqXU+ZwwYYLP11SpUkX++uuvkB+bXeE4DTwcp4GH4zTwcJy6xE2DL7pYsWJSuHBhWblypXIrkLyzfPlyJeYqV67M0xhAOE4DC8dpcOA4DSwcpy6yjNxzzz3y3HPPqcBU9Erp2LFjuA/JtuA8Ll26VFmXIO5I4OA4DRwcp8GD4zRwcJwGlwiPRXwgl19+uXz00UcZ4kMIsRocp8QOcJwSu2EJNw1NX8QOcJwSO8BxSuxI2N00NH0RO8BxSuwAxymxK5Zx0xBCCCHEnVjCTUMIIYQQ90IxQgghhBB3iZHp06fLoEGDVNouGt9p4C3COqqDXnbZZfLss8+qCqyaESNGSPv27eXSSy9Vj3vvvdd4DlVa8fquXbuqKPIxY8bImTNnQv2nEQcRjHGKvkAoQoXKtxinb7/9dsj/LuIs8jpOwaeffqqeR/mEG2+8McM185NPPlHX0y5dusjrr7/OwpPEeWKkXLly6oKNQW5m9uzZMm/ePJk0aZL88MMPqonbxIkTM7zmySeflD/++EM93njjDWP7tGnTVDM9/DDRb+XYsWPqfQix0jjFBX7Lli3y9ddfqwcq3n777bf8kkjIxymumWgNgXIKCxculKeffloKFSqknlu0aJEanxiveN3ixYtl1qxZ/JaIs8QIVHqnTp2kePHiGbbjB3DddddJhQoVpGjRojJkyBD5/vvvc/We+/btk3bt2qmS59gXn7Ft27Yg/QXEDQRjnGJfzGJLlCiheizdcMMN6qZBSCjHaUpKinz88cdKNFeqVEkiIiJU07eoqCj1/I8//ih9+vSRatWqKbEzePBgtY0Q18SMZE7sOXTokOowq0G/GpgO77zzzgzdeq+++mrV8A0WEbx+/vz5cvHFF4f02Il7yOs4zbwvlimaSajHKf5PTEyUuXPnqs7nEC0zZ840Xrd9+3YlTjR169ZVDfcIcYUYgZ/9m2++kf3798vp06cNN0tCQoL6H753uGCg7uEfxbr2cVavXl1ZReCL79y5sxQoUECuv/76sP49xJnkZ5zCejd58mQ5ceKEHDlyRKZMmWLsR0ioxqkWJbt27VJj9cUXX1TxS+gFpvvZwJqiwTLHKXGNGLnmmmvkiiuuUP7PAQMGSJs2baRgwYLKnA0uuOACiYmJUZ1nYXLE8n///aeee+GFF9RrFyxYoKwi+PEg6IoQK43TW265Rc0yESw4fPhwJZwrVqzIL4mEdJzqPlW33XabGqewgsBCopuSYsyag1mxHB0dzW+JBBXLiJHIyEgZOXKk8qHDPxkbGysNGzZUVo6sXq+BKbxXr15KhKDjLyLEly1bFsKjJ24hP+MUF/5HHnlE7YeAwJIlS0rjxo1DePTELWQ3TmvWrKmCVRErojEv165dWwVaa+Ciwf6EOEqMJCcnqxTH1NRUFUiFZfwP0/WePXuUnxOD/9VXX1XKHcDMiO6zSOFFehpM3adOnVKzUNCoUSP1g4MfFOZELGMGSoiVxunBgweVewbvuXr1amU6h4WEkFCOU1g5dCM9jFXEiMyZM0c6dOignu/Ro4fMmDFD7Y8sHIxjbCPEUeXgkfueORVy7Nix6oJ9//33y+HDh6V8+fLqIg1rBzh+/Ljyve/cuVOZGuvXry+jRo1SSh8gcBWumhUrVqj1Fi1ayKOPPqoiwQmxyjhFAzO8B24UyFRAe/dLLrmEXxAJ6TjVwvmZZ55R6eWItxs6dKgKZNVAKH/xxRdK5Fx77bVqXJutJ4QEGvamIYQQQkhYsUzMCCGEEELcCcUIIYQQQsIKxQghhBBCwgrFCCGEEELCCsUIIYQQQsIKxQghhBBCwgrFCCGEEELCCsUIIcTWtG7dWj1Q+pwQYk8oRgghOYKGa/qmf8MNN2R4DhVlUUpcP//mm28G/IxCaOj3J4Q4D4oRQohfoDHlP//8Y6x/++23qicKIYTkFYoRQkiuQc8dMHXqVPU/mrJNnz7d2G7m5MmT8uKLL6ou2m3btlVt6seMGSMHDhzI0FsF1g70TZk7d65cf/31ql8Pmrrt2LFDveapp56Sp59+2thHW0iwr5m4uDj1uk6dOkn37t3lww8/5DdLiE2gGCGE5Bo0/6tataosWLBAdSH+/ffflbhAF1gzsJTAtfP111+rTsVoW3/mzBn56aefZNiwYaqpoJlDhw7Jk08+qZqxYd+VK1eqRm4ATQXxmRo0gcOjYsWKGd7jrbfekiVLlkihQoVUg7j33ntPdVEmhFgfihFCSO4vGJGR0q9fP8Mioi0kAwYMyPC6X375RbWuB7COTJs2TbWsx/4QClg3g/cbP368ek8dk7J69WpJTEyUW2+9VT00n3zyiXqgm6yZBg0aqNgSs6Vm2bJl/HYJsQEUI4QQv+jdu7dER0crQbF8+XJp1KiRNGvWLMNr1q1bp/4vUqSIXHbZZWq5YcOGykJifl5TrFgx6dixo1quU6eOsT2zBSU7unbtqqwipUqVkjJlyqhtx44d47dLiA2gGCGE+EXx4sVVTAbcLr6sInl9T02BAgWMZY/Hk6/38Gd/Qkj4oBghhPhN//791f+lS5dWgamZady4sfofbhbEl4ANGzbIzp07MzyfW2Bh0SQkJPAbI8RhnB8CTwghOVC3bl2ZN2+eskBERUWd93y3bt1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", 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" ] @@ -181,7 +186,9 @@ " save_checkpoints=True, # store checkpoints during training to retrieve the best model\n", " model_name=\"tide\",\n", " force_reset=True, # overwrite previous model artifacts,\n", - " pl_trainer_kwargs={\"callbacks\": [TFMProgressBar(enable_train_bar_only=True)]},\n", + " pl_trainer_kwargs={\n", + " \"callbacks\": [TFMProgressBar(enable_train_bar_only=True)]\n", + " }, # reduce the verbosity\n", ")\n", "\n", "# regular training (default enable_finetuning=None)\n", @@ -199,13 +206,14 @@ "pred_air = model_pretrained.predict(n=output_chunk_length, series=val_air)\n", "\n", "# save the pre-trained model\n", - "model_pretrained.save(\"darts_logs/model.pt\")\n", + "model_path = os.path.join(\"darts_logs\", \"model.pt\")\n", + "model_pretrained.save(model_path)\n", "\n", "# plot\n", "series_air[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", "pred_air.plot(\n", " label=\"Forecast\",\n", - " title=f\"Pre-Trained TiDE model; MAE {mae(series_air, pred_air):.2f}\",\n", + " title=f\"Trained TiDE model; MAE {mae(series_air, pred_air):.2f}\",\n", ");" ] }, @@ -255,18 +263,22 @@ "\n", "### 1.2. Fine-Tuning\n", "\n", - "Imagine the model above was a large **pre-trained model** and **full re-training** would take hours. Instead, we can also **fine-tune** parts of the model on this new dataset, to improve the performance. For this we will:\n", + "Imagine the model above was a large **pre-trained model** and **full re-training** would take hours. Instead, we can also **fine-tune** parts of the model on this new dataset to, potentially, improve performance. For this we will:\n", "\n", "1. Create a new `model` instance for fine-tuning with the same architectural hyperparameters, and enable fine-tuning with the `enable_finetuning` parameter. We restrict fine-tuning to only the \"decoder\" parameters.\n", "2. Load the weights of the pre-trained model with `model.load_weights()`.\n", "3. Fine-tune the model with `model.fit()`.\n", "\n", "The `enable_finetuning` model creation parameter controls how the model is updated during `fit()`. Possible values are:\n", - "* `None` (Default): **Regular training** where all weights are trainable.\n", - "* `False`: **Zero-shot inference**. The model weights are frozen. `fit()` is used only to setup the model (scaling, etc.) but no training is performed.\n", + "* `None` (Default): **Regular training** for regular PyTorch models (TiDE, ...) and **zero-shot inference mode** for foundation models (Chronos-2, ...).\n", + "* `False`: **Zero-shot inference mode**. The model weights are frozen.\n", "* `True`: **Full fine-tuning / training**. All weights are trainable.\n", - "* `{\"unfreeze\": [...]}`: **Partial fine-tuning (Unfreeze)**. None of the parameters are trainable *except* the specific unfrozen ones\n", - "* `{\"freeze\": [...]}`: **Partial fine-tuning (Freeze)**. All parameters are trainable *except* the specified frozen ones." + "* `{\"unfreeze\": [\"*string.pattern.to.match*\", ...]}`: **Partial fine-tuning (Unfreeze)**. None of the parameters are trainable *except* the specified unfrozen ones\n", + "* `{\"freeze\": [\"*string.pattern.to.match*\", ...]}`: **Partial fine-tuning (Freeze)**. All parameters are trainable *except* the specified frozen ones.\n", + "\n", + "
\n", + " **Note**: You can inspect the model's parameters by printing the architecture with `print(model.model)` or getting the named parameters directly with `model.model.named_parameters()`\n", + "
" ] }, { @@ -278,7 +290,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e7224a066a0420997bff1225c6a4dce", + "model_id": "03ef39d0bc9e4552beefa82290cf86d1", "version_major": 2, "version_minor": 0 }, @@ -303,13 +315,15 @@ "source": [ "# create a new model instance for fine-tuning with the same hyperparameters but different training setup\n", "model_finetune = TiDEModel(\n", - " enable_finetuning={\"unfreeze\": [\"*decoder*\"]},\n", - " optimizer_kwargs={\"lr\": 1e-3},\n", + " enable_finetuning={\"unfreeze\": [\"*decoder*\"]}, # only update the decoder\n", + " optimizer_kwargs={\n", + " \"lr\": 1e-3\n", + " }, # control the optimizer, learning rate scheduler, ...\n", " **model_params,\n", ")\n", "\n", "# load the pre-trained weights\n", - "model_finetune.load_weights(\"darts_logs/model.pt\")\n", + "model_finetune.load_weights(model_path)\n", "\n", "# fine-tune only for 10 epochs\n", "model_finetune.fit(train_beer, val_series=val_beer, load_best=True, epochs=10)\n", @@ -332,7 +346,7 @@ "id": "b9251561", "metadata": {}, "source": [ - "That looks much better! This demonstrates that **fine-tuning can significantly improve model performance** on unobserved datasets, without having to re-train the entire model from scratch.\n", + "That looks much better! This demonstrates that **fine-tuning can significantly improve model performance** on new datasets, without having to re-train the entire model from scratch.\n", "\n", "## 2. Foundation Model: Zero Shot Forecasting and Fine Tuning\n", "\n", @@ -342,7 +356,11 @@ "By default, our foundation models use `enable_finetuning=False` (zero-shot inference mode). Let's see how the model behaves on Australian Beer Production dataset without any training / fine-tuning.\n", "\n", "
\n", - " **Note**: We only load the small version of Chronos-2 to speed up things for demonstration purposes. You can always use the large model with `hub_model_name=\"amazon/chronos-2\"`\n", + " **Note**:\n", + "
    \n", + "
  1. We only load the small version of Chronos-2 to speed things up for demonstration purposes. You can always use the large model with `hub_model_name=\"amazon/chronos-2\"`.\n", + "
  2. Foundation models were trained on much larger input- and output chunk lengths (windows) than in our example. Increasing the window sizes (especially the input window) can significantly improve performance.\n", + "
\n", "
" ] }, @@ -355,7 +373,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "65bba570a7d64e19af1acb152c9cc2e8", + "model_id": "1984bdf276a14e46bf451780e2c056ab", "version_major": 2, "version_minor": 0 }, @@ -368,7 +386,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -396,7 +414,7 @@ "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", "pred_beer.plot(\n", " label=\"Forecast\",\n", - " title=f\"Base model (not finetuned yet); MAE {mae(series_beer, pred_beer):.2f}\",\n", + " title=f\"Pre-trained Chronos-2; MAE {mae(series_beer, pred_beer):.2f}\",\n", ");" ] }, @@ -421,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f486b0ae401413194843c2094368a8c", + "model_id": "923b0b70c74d4f808b329aa9d501a1bf", "version_major": 2, "version_minor": 0 }, @@ -434,7 +452,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -475,8 +493,8 @@ "# plot\n", "series_beer[-3 * output_chunk_length :].plot(label=\"Ground truth\")\n", "pred_beer.plot(\n", - " label=\"Forecast of fine-tuned model\",\n", - " title=f\"Partial finetuning; MAE {mae(series_beer, pred_beer):.2f}\",\n", + " label=\"Forecast\",\n", + " title=f\"Fine-tuned Chronos-2; MAE {mae(series_beer, pred_beer):.2f}\",\n", ");" ] }, @@ -498,7 +516,9 @@ "**Fine-tuning** allows updating a pre-trained model on new data, potentially improving the model performace while saving training time compared to retraining from scratch.\n", "\n", "- **Full Fine-tuning**: Offers the most flexibility but is computationally expensive and prone to \"catastrophic forgetting\".\n", - "- **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n" + "- **Partial Fine-tuning**: Provides a good middle ground by updating only the most relevant layers (like the output head).\n", + "\n", + "For demonstration purposes, we only showed training and predicting on single univariate series (one series with one column). The process is identical for **multiple (a list / sequence) uni- or multivariate series**." ] }, { @@ -531,34 +551,38 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "040dc2c5eb3141f5a0cfe980759f993e": { - "model_module": "@jupyter-widgets/base", + "03ef39d0bc9e4552beefa82290cf86d1": { + "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "LayoutModel", + "model_name": "HBoxModel", "state": { - "flex": "2" + "children": [ + "IPY_MODEL_26a3807238e24403b2b8cf081b647f58", + "IPY_MODEL_1e3460649eb042b5b03f9bc1c3a180e8", + "IPY_MODEL_f28de96e80a34f1199a1b0188c285949" + ], + "layout": "IPY_MODEL_66f2fc789e684108ba217215fce4c6e5" } }, - "08fca8f217024804962e7e561a3aa76e": { + "0585a30408554b6491d2248c71963d07": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { - "children": [ - "IPY_MODEL_fbb4071af2cf4af7bfc62eb397668a4b", - 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