"""simple docstring"""





import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)
class  _snake_case     (					unittest.TestCase		):






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     ["a", "b", "c"]

													# Defaults to last layer if both are None
													__lowerCamelCase					:		Any		   =     get_aligned_output_features_output_indices(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["c"]    )
													self.assertEqual(UpperCAmelCase							,					[2]    )

													# Out indices set to match out features
													__lowerCamelCase					:		Optional[int]		   =     get_aligned_output_features_output_indices(["a", "c"]							,					UpperCAmelCase							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["a", "c"]    )
													self.assertEqual(UpperCAmelCase							,					[0, 2]    )

													# Out features set to match out indices
													__lowerCamelCase					:		Optional[Any]		   =     get_aligned_output_features_output_indices(UpperCAmelCase							,					[0, 2]							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["a", "c"]    )
													self.assertEqual(UpperCAmelCase							,					[0, 2]    )

													# Out features selected from negative indices
													__lowerCamelCase					:		str		   =     get_aligned_output_features_output_indices(UpperCAmelCase							,					[-3, -1]							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["a", "c"]    )
													self.assertEqual(UpperCAmelCase							,					[-3, -1]    )






						def       lowerCamelCase__     (       self      :			Any    ):
													# Stage names must be set
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0, 1)							,					UpperCAmelCase    )

													# Out features must be a list
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(("a", "b")							,					(0, 1)							,					["a", "b"]    )

													# Out features must be a subset of stage names
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0, 1)							,					["a"]    )

													# Out indices must be a list or tuple
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(UpperCAmelCase							,					0							,					["a", "b"]    )

													# Out indices must be a subset of stage names
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(UpperCAmelCase							,					(0, 1)							,					["a"]    )

													# Out features and out indices must be the same length
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0,)							,					["a", "b", "c"]    )

													# Out features should match out indices
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0, 2)							,					["a", "b", "c"]    )

													# Out features and out indices should be in order
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["b", "a"]							,					(0, 1)							,					["a", "b"]    )

													# Check passes with valid inputs
													verify_out_features_out_indices(["a", "b", "d"]							,					(0, 1, -1)							,					["a", "b", "c", "d"]    )







						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Tuple		   =     BackboneMixin()

													__lowerCamelCase					:		Any		   =     ["a", "b", "c"]
													__lowerCamelCase					:		Dict		   =     ["a", "c"]
													__lowerCamelCase					:		Optional[Any]		   =     [0, 2]

													# Check that the output features and indices are set correctly
													self.assertEqual(backbone.out_features							,					["a", "c"]    )
													self.assertEqual(backbone.out_indices							,					[0, 2]    )

													# Check out features and indices are updated correctly
													__lowerCamelCase					:		int		   =     ["a", "b"]
													self.assertEqual(backbone.out_features							,					["a", "b"]    )
													self.assertEqual(backbone.out_indices							,					[0, 1]    )

													__lowerCamelCase					:		List[Any]		   =     [-3, -1]
													self.assertEqual(backbone.out_features							,					["a", "c"]    )
													self.assertEqual(backbone.out_indices							,					[-3, -1]    )
"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)