feat: implement ignore_index and resolve conflicts

Signed-off-by: Rusheel Sharma <rusheelhere@gmail.com>

Author: Rusheel86

tests/transforms/croppad/test_pad_nd_dtypes.py

@@ -1,110 +0,0 @@
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#     http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Tests for pad_nd dtype support and backend selection.
-Validates PyTorch padding preference and NumPy fallback behavior.
-"""
-
-from __future__ import annotations
-
-import unittest
-from unittest.mock import Mock, patch
-
-import torch
-from parameterized.parameterized import parameterized
-
-import monai.transforms.croppad.functional as F
-from monai.transforms.croppad.functional import pad_nd
-
-DTYPES = [torch.bool, torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8, torch.float32]
-MODES_DTYPES = [
-    ("constant", torch.bool),
-    ("constant", torch.int8),
-    ("constant", torch.float32),
-    ("reflect", torch.bool),
-    ("reflect", torch.int8),
-    ("reflect", torch.float32),
-    ("replicate", torch.bool),
-    ("replicate", torch.int8),
-    ("replicate", torch.float32),
-]
-
-
-class TestPadNdDtypes(unittest.TestCase):
-    def test_pad_uses_pt_for_bool(self):
-        """Test that pad_nd uses PyTorch backend for bool dtype in constant mode."""
-        img = torch.ones((1, 4, 4), dtype=torch.bool)
-        to_pad = [(0, 0), (1, 1), (2, 2)]
-        with (
-            patch.object(F, "_pt_pad", wraps=F._pt_pad) as mock_pt,
-            patch.object(F, "_np_pad", wraps=F._np_pad) as mock_np,
-        ):
-            out = pad_nd(img, to_pad, mode="constant", value=0)
-
-        self.assertTrue(mock_pt.called)
-        self.assertFalse(mock_np.called)
-        self.assertEqual(out.dtype, img.dtype)
-        self.assertEqual(out.shape, (1, 6, 8))
-
-    def test_pad_falls_back_to_np_if_pt_raises(self):
-        """Test that pad_nd falls back to NumPy when PyTorch raises NotImplementedError."""
-        img = torch.ones((1, 4, 4), dtype=torch.bool)
-        to_pad = [(0, 0), (1, 1), (2, 2)]
-        with (
-            patch.object(F, "_pt_pad", new=Mock(side_effect=NotImplementedError("no"))) as mock_pt,
-            patch.object(F, "_np_pad", wraps=F._np_pad) as mock_np,
-        ):
-            out = pad_nd(img, to_pad, mode="constant", value=0)
-
-        self.assertTrue(mock_pt.called)
-        self.assertTrue(mock_np.called)
-        self.assertEqual(out.dtype, img.dtype)
-        self.assertEqual(out.shape, (1, 6, 8))
-
-    @parameterized.expand(DTYPES)
-    def test_pad_dtype_no_error_and_dtype_preserved(self, dtype):
-        """Test that pad_nd handles various dtypes without error and preserves dtype.
-        Args:
-            dtype: Input dtype under test.
-        """
-        img = torch.ones((1, 4, 4), dtype=dtype)
-        to_pad = [(0, 0), (1, 1), (2, 2)]
-        out = pad_nd(img, to_pad, mode="constant", value=0)
-
-        self.assertEqual(out.shape, (1, 6, 8))
-        self.assertEqual(out.dtype, img.dtype)
-
-    @parameterized.expand(MODES_DTYPES)
-    def test_pad_multiple_modes_dtype_preserved(self, mode, dtype):
-        """Test that pad_nd preserves dtype across multiple padding modes.
-        Args:
-            mode: Padding mode under test.
-            dtype: Input dtype under test.
-        """
-        img = torch.ones((1, 4, 4), dtype=dtype)
-        to_pad = [(0, 0), (1, 1), (2, 2)]
-
-        kwargs = {"value": 0} if mode == "constant" else {}
-        out = pad_nd(img, to_pad, mode=mode, **kwargs)
-
-        self.assertEqual(out.shape, (1, 6, 8))
-        self.assertEqual(out.dtype, img.dtype)
-
-    def test_value_with_non_constant_mode_raises(self):
-        """Test that pad_nd raises ValueError when 'value' is provided with non-constant mode."""
-        img = torch.ones((1, 4, 4))
-        to_pad = [(0, 0), (1, 1), (2, 2)]
-        with self.assertRaises(ValueError):
-            pad_nd(img, to_pad, mode="reflect", value=0)
-
-
-if __name__ == "__main__":
-    unittest.main()