





def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						while a != 0:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				str   =					b % a, a
						return b







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if gcd(_lowerCamelCase			,   _lowerCamelCase     ) != 1:
												SCREAMING_SNAKE_CASE__     :				List[str]   =					f"""mod inverse of {a!r} and {m!r} does not exist"""
												raise ValueError(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					1, 0, a
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					0, 1, m
						while va != 0:
												SCREAMING_SNAKE_CASE__     :				int   =					ua // va
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
						return ua % m






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							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     Any							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     int = 32							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,	a				:     Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,	a				:     bool = True							,	a				:     Any=7							,	a				:     str=30							,	a				:     Dict=4_00							,	a				:     Optional[int]=3							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_88}
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size_divisor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Tuple   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_pad
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max_resolution

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

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((13_33 / 8_00) * size    )
																			if max(a							,	a    ) > max_size:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max_size / max(a							,	a    )
																									SCREAMING_SNAKE_CASE__     :				int   =					newh * scale
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					neww * scale

																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					int(newh + 0.5    ), int(neww + 0.5    )
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					(
																			    newh // self.size_divisor * self.size_divisor,
																			    neww // self.size_divisor * self.size_divisor,
																			)

													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in image_inputs:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[0]    )[0]
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[1]    )[1]

													return expected_height, expected_width







@require_torch
@require_vision
class    _a     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						BridgeTowerImageProcessor if is_vision_available() else None

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BridgeTowerImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Tuple    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													SCREAMING_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
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     Optional[int]    )	->Any:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				str   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_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
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					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
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, 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      A_  (      self				:     str    )	->Optional[int]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					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
													SCREAMING_SNAKE_CASE__     :				Any   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_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,
													    )							,	)