



'''simple docstring'''

import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						Dict      ,						a_   :						List[Any]      ,						a_   :						bool = True      ,						a_   :						Dict[str, int] = None      ,						a_   :						int = 3_2      ,						a_   :						bool = True      ,						a_   :						Union[int, float] = 1 / 2_5_5      ,						a_   :						bool = True      ,						a_   :						bool = True      ,						a_   :						Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]      ,						a_   :						Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]      ,						a_   :						bool = True      ,						a_   :						Optional[Any]=7      ,						a_   :						Any=3_0      ,						a_   :						Optional[int]=4_0_0      ,						a_   :						Optional[Any]=3      ,						)			->					Union[str, Any]:
										snake_case:    int      							=parent
										snake_case:    Union[str, Any]      							=do_resize
										snake_case:    Union[str, Any]      							=size if size is not None else {'shortest_edge': 2_8_8}
										snake_case:    Union[str, Any]      							=size_divisor
										snake_case:    Dict      							=do_rescale
										snake_case:    List[Any]      							=rescale_factor
										snake_case:    Tuple      							=do_normalize
										snake_case:    List[Any]      							=do_center_crop
										snake_case:    Union[str, Any]      							=image_mean
										snake_case:    Optional[Any]      							=image_std
										snake_case:    Union[str, Any]      							=do_pad
										snake_case:    int      							=batch_size
										snake_case:    Optional[Any]      							=num_channels
										snake_case:    str      							=min_resolution
										snake_case:    List[str]      							=max_resolution


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Optional[int]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "size_divisor": self.size_divisor,
										}




							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						str      ,						a_   :						Optional[Any]=False			)			->					str:
										if not batched:
													snake_case:    str      							=self.size['shortest_edge']
													snake_case:    Union[str, Any]      							=image_inputs[0]
													if isinstance(a_      ,						Image.Image			):
																snake_case							,  snake_case:    Tuple      							=image.size
													else:
																snake_case							,  snake_case:    Dict      							=image.shape[1], image.shape[2]
													snake_case:    Tuple      							=size / min(a_      ,						a_			)
													if h < w:
																snake_case							,  snake_case:    List[Any]      							=size, scale * w
													else:
																snake_case							,  snake_case:    List[str]      							=scale * h, size

													snake_case:    List[Any]      							=int((1_3_3_3 / 8_0_0) * size			)
													if max(a_      ,						a_			) > max_size:
																snake_case:    List[Any]      							=max_size / max(a_      ,						a_			)
																snake_case:    List[str]      							=newh * scale
																snake_case:    Optional[int]      							=neww * scale

													snake_case							,  snake_case:    Dict      							=int(newh + 0.5			), int(neww + 0.5			)
													snake_case							,  snake_case:    List[str]      							=(
													    newh // self.size_divisor * self.size_divisor,
													    neww // self.size_divisor * self.size_divisor,
													)

										else:
													snake_case:    Any      							=[]
													for image in image_inputs:
																snake_case							,  snake_case:    Union[str, Any]      							=self.get_expected_values([image]			)
																expected_values.append((expected_height, expected_width)			)
													snake_case:    int      							=max(a_      ,						key=lambda a_			: item[0]			)[0]
													snake_case:    Any      							=max(a_      ,						key=lambda a_			: item[1]			)[1]

										return expected_height, expected_width






@require_torch
@require_vision
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Any        =				BridgeTowerImageProcessor if is_vision_available() else None


							def    UpperCamelCase     (				self   :						str			)			->					Dict:
										snake_case:    Tuple      							=BridgeTowerImageProcessingTester(self			)


							@property
							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Tuple:
										return self.image_processor_tester.prepare_image_processor_dict()


							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(a_      ,						'image_mean'			)			)
										self.assertTrue(hasattr(a_      ,						'image_std'			)			)
										self.assertTrue(hasattr(a_      ,						'do_normalize'			)			)
										self.assertTrue(hasattr(a_      ,						'do_resize'			)			)
										self.assertTrue(hasattr(a_      ,						'size'			)			)
										self.assertTrue(hasattr(a_      ,						'size_divisor'			)			)


							def    UpperCamelCase     (				self   :						Any			)			->					List[Any]:
										pass


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Any:
										# Initialize image processor
										snake_case:    Dict      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										snake_case:    Union[str, Any]      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						Image.Image			)

										# Test not batched input
										snake_case:    List[str]      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Optional[int]      							=self.image_processor_tester.get_expected_values(a_			)
										self.assertEqual(
										    encoded_images.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

										# Test batched
										snake_case:    str      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Optional[Any]      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)
										self.assertEqual(
										    encoded_images.shape      ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,						)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Dict:
										# Initialize image processor
										snake_case:    Optional[Any]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										snake_case:    str      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_      ,						numpify=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						np.ndarray			)

										# Test not batched input
										snake_case:    Optional[Any]      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Optional[int]      							=self.image_processor_tester.get_expected_values(a_			)
										self.assertEqual(
										    encoded_images.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

										# Test batched
										snake_case:    int      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    str      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)
										self.assertEqual(
										    encoded_images.shape      ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,						)




							def    UpperCamelCase     (				self   :						Any			)			->					Any:
										# Initialize image processor
										snake_case:    int      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										snake_case:    Dict      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_      ,						torchify=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						torch.Tensor			)

										# Test not batched input
										snake_case:    int      							=image_processing(image_inputs[0]      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    List[str]      							=self.image_processor_tester.get_expected_values(a_			)
										self.assertEqual(
										    encoded_images.shape      ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,						)

										# Test batched
										snake_case:    Union[str, Any]      							=image_processing(a_      ,						return_tensors='pt'			).pixel_values

										snake_case							,  snake_case:    Optional[int]      							=self.image_processor_tester.get_expected_values(a_      ,						batched=a_			)
										self.assertEqual(
										    encoded_images.shape      ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,						)









'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




