






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 ConvNextImageProcessor

class      UpperCamelCase_							(				unittest.TestCase       ):




						def __init__(    self		,	lowercase		,	lowercase=7		,	lowercase=3		,	lowercase=18		,	lowercase=30		,	lowercase=400		,	lowercase=True		,	lowercase=None		,	lowercase=0.875		,	lowercase=True		,	lowercase=[0.5, 0.5, 0.5]		,	lowercase=[0.5, 0.5, 0.5]		,	)			->						Optional[Any]:
													_a  :    int			=						size if size is not None else {'''shortest_edge''': 20}
													_a  :    List[str]			=						parent
													_a  :    Tuple			=						batch_size
													_a  :    Union[str, Any]			=						num_channels
													_a  :    Union[str, Any]			=						image_size
													_a  :    Any			=						min_resolution
													_a  :    Optional[Any]			=						max_resolution
													_a  :    Tuple			=						do_resize
													_a  :    Dict			=						size
													_a  :    Tuple			=						crop_pct
													_a  :    Optional[int]			=						do_normalize
													_a  :    Optional[Any]			=						image_mean
													_a  :    Optional[int]			=						image_std







						def      snake_case__(    self       )			->						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,
													    "crop_pct": self.crop_pct,
													}

@require_torch
@require_vision
class      UpperCamelCase_							(				UpperCamelCase ,       unittest.TestCase       ):
						lowercase									=  ConvNextImageProcessor if is_vision_available() else None




						def      snake_case__(    self       )			->						int:
													_a  :    Union[str, Any]			=						ConvNextImageProcessingTester(self       )




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




						def      snake_case__(    self       )			->						Optional[Any]:
													_a  :    Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict       )
													self.assertTrue(hasattr(lowercase		,	'''do_resize'''       )       )
													self.assertTrue(hasattr(lowercase		,	'''size'''       )       )
													self.assertTrue(hasattr(lowercase		,	'''crop_pct'''       )       )
													self.assertTrue(hasattr(lowercase		,	'''do_normalize'''       )       )
													self.assertTrue(hasattr(lowercase		,	'''image_mean'''       )       )
													self.assertTrue(hasattr(lowercase		,	'''image_std'''       )       )




						def      snake_case__(    self       )			->						str:
													_a  :    Tuple			=						self.image_processing_class.from_dict(self.image_processor_dict       )
													self.assertEqual(image_processor.size		,	{'''shortest_edge''': 20}       )

													_a  :    Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict		,	size=42       )
													self.assertEqual(image_processor.size		,	{'''shortest_edge''': 42}       )




						def      snake_case__(    self       )			->						str:
													pass




						def      snake_case__(    self       )			->						Any:
													# Initialize image_processing
													_a  :    Optional[Any]			=						self.image_processing_class(**self.image_processor_dict       )
													# create random PIL images
													_a  :    Tuple			=						prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowercase       )
													for image in image_inputs:
																				self.assertIsInstance(lowercase		,	Image.Image       )

													# Test not batched input
													_a  :    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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )		,	)

													# Test batched
													_a  :    Union[str, Any]			=						image_processing(lowercase		,	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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )		,	)




						def      snake_case__(    self       )			->						List[str]:
													# Initialize image_processing
													_a  :    Any			=						self.image_processing_class(**self.image_processor_dict       )
													# create random numpy tensors
													_a  :    Optional[int]			=						prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowercase		,	numpify=lowercase       )
													for image in image_inputs:
																				self.assertIsInstance(lowercase		,	np.ndarray       )

													# Test not batched input
													_a  :    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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )		,	)

													# Test batched
													_a  :    Any			=						image_processing(lowercase		,	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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )		,	)







						def      snake_case__(    self       )			->						List[Any]:
													# Initialize image_processing
													_a  :    Optional[int]			=						self.image_processing_class(**self.image_processor_dict       )
													# create random PyTorch tensors
													_a  :    Any			=						prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowercase		,	torchify=lowercase       )
													for image in image_inputs:
																				self.assertIsInstance(lowercase		,	torch.Tensor       )

													# Test not batched input
													_a  :    Union[str, 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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )		,	)

													# Test batched
													_a  :    List[str]			=						image_processing(lowercase		,	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.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )		,	)







from __future__ import annotations






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list[int]:







							"""simple docstring"""





							_a  :    Optional[Any]			=						[0] * max_one

							for i in range(0    ,	UpperCAmelCase     ):
														_a  :    int			=						i

							for i in range(2    ,	UpperCAmelCase     ):
														if totients[i] == i:
																					for j in range(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     ):
																												totients[j] -= totients[j] // i

							return totients






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		bool:







							"""simple docstring"""





							return sorted(str(UpperCAmelCase     )     ) == sorted(str(UpperCAmelCase     )     )






def    UpperCamelCase__  (   UpperCAmelCase = 10000000     )     ->		int:







							"""simple docstring"""





							_a  :    List[Any]			=						1  # i
							_a  :    Any			=						0  # φ(i)
							_a  :    Tuple			=						get_totients(max_n + 1     )

							for i in range(2    ,	max_n + 1     ):
														_a  :    List[str]			=						totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase    ,	UpperCAmelCase     ):
																					_a  :    Any			=						i
																					_a  :    Union[str, Any]			=						t

							return min_numerator


if __name__ == "__main__":
						print(F'''{solution() = }''')