





import json
import os
import tempfile

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


if is_torch_available():
					import numpy as np
					import torch

if is_vision_available():
					from PIL import Image





def     lowerCamelCase__  (  UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Tuple=False							,     UpperCamelCase__      :							List[str]=False							,     UpperCamelCase__      :							int=False    )      ->			Optional[Any]:



					'''simple docstring'''




					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					_snake_case						=					[]
					for i in range(image_processor_tester.batch_size    ):
										if equal_resolution:
															_snake_case						=					_snake_case						=					image_processor_tester.max_resolution
										else:
															# To avoid getting image width/height 0
															_snake_case						=					image_processor_tester.min_resolution
															if getattr(UpperCamelCase__							,     'size_divisor'							,     UpperCamelCase__    ):
																				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																				_snake_case						=					max(image_processor_tester.size_divisor							,     UpperCamelCase__    )
															_snake_case				,  _snake_case						=					np.random.choice(np.arange(UpperCamelCase__							,     image_processor_tester.max_resolution    )							,     2    )
										image_inputs.append(
										    np.random.randint(255							,     size=(image_processor_tester.num_channels, width, height)							,     dtype=np.uinta    )    )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										_snake_case						=					[Image.fromarray(np.moveaxis(UpperCamelCase__							,     0							,     -1    )    ) for image in image_inputs]

					if torchify:
										_snake_case						=					[torch.from_numpy(UpperCamelCase__    ) for image in image_inputs]

					return image_inputs





def     lowerCamelCase__  (  UpperCamelCase__      :							str							,     UpperCamelCase__      :							Optional[int]=10							,     UpperCamelCase__      :							Optional[int]=10							,     UpperCamelCase__      :							Optional[int]=False							,     UpperCamelCase__      :							Any=False    )      ->			int:



					'''simple docstring'''




					_snake_case						=					[]
					for i in range(image_processor_tester.num_frames    ):
										video.append(np.random.randint(255							,     size=(image_processor_tester.num_channels, width, height)							,     dtype=np.uinta    )    )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										_snake_case						=					[Image.fromarray(np.moveaxis(UpperCamelCase__							,     0							,     -1    )    ) for frame in video]

					if torchify:
										_snake_case						=					[torch.from_numpy(UpperCamelCase__    ) for frame in video]

					return video





def     lowerCamelCase__  (  UpperCamelCase__      :							Tuple							,     UpperCamelCase__      :							int=False							,     UpperCamelCase__      :							Optional[Any]=False							,     UpperCamelCase__      :							Dict=False    )      ->			Optional[Any]:



					'''simple docstring'''




					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					_snake_case						=					[]
					for i in range(image_processor_tester.batch_size    ):
										if equal_resolution:
															_snake_case						=					_snake_case						=					image_processor_tester.max_resolution
										else:
															_snake_case				,  _snake_case						=					np.random.choice(
															    np.arange(image_processor_tester.min_resolution							,     image_processor_tester.max_resolution    )							,     2    )
															_snake_case						=					prepare_video(
															    image_processor_tester=UpperCamelCase__							,     width=UpperCamelCase__							,     height=UpperCamelCase__							,     numpify=UpperCamelCase__							,     torchify=UpperCamelCase__							,     )
										video_inputs.append(UpperCamelCase__    )

					return video_inputs




class       UpperCamelCase_		:
					lowerCAmelCase_				=						None




					def 						lowerCAmelCase      (		self	)		->				List[str]:
										_snake_case						=					self.image_processing_class(**self.image_processor_dict	)
										_snake_case						=					json.loads(image_processor.to_json_string()	)
										for key, value in self.image_processor_dict.items():
															self.assertEqual(obj[key]					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					self.image_processing_class(**self.image_processor_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
															_snake_case						=					os.path.join(lowerCAmelCase_					,    'image_processor.json'	)
															image_processor_first.to_json_file(lowerCAmelCase_	)
															_snake_case						=					self.image_processing_class.from_json_file(lowerCAmelCase_	)

										self.assertEqual(image_processor_second.to_dict()					,    image_processor_first.to_dict()	)




					def 						lowerCAmelCase      (		self	)		->				Dict:
										_snake_case						=					self.image_processing_class(**self.image_processor_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
															_snake_case						=					image_processor_first.save_pretrained(lowerCAmelCase_	)[0]
															check_json_file_has_correct_format(lowerCAmelCase_	)
															_snake_case						=					self.image_processing_class.from_pretrained(lowerCAmelCase_	)

										self.assertEqual(image_processor_second.to_dict()					,    image_processor_first.to_dict()	)




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					self.image_processing_class()
										self.assertIsNotNone(lowerCAmelCase_	)






					@require_torch
					@require_vision
					def 						lowerCAmelCase      (		self	)		->				List[str]:
										if self.test_cast_dtype is not None:
															# Initialize image_processor
															_snake_case						=					self.image_processing_class(**self.image_processor_dict	)

															# create random PyTorch tensors
															_snake_case						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=lowerCAmelCase_					,    torchify=lowerCAmelCase_	)

															_snake_case						=					image_processor(lowerCAmelCase_					,    return_tensors='pt'	)
															# for layoutLM compatiblity
															self.assertEqual(encoding.pixel_values.device					,    torch.device('cpu'	)	)
															self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa	)

															_snake_case						=					image_processor(lowerCAmelCase_					,    return_tensors='pt'	).to(torch.floataa	)
															self.assertEqual(encoding.pixel_values.device					,    torch.device('cpu'	)	)
															self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa	)

															_snake_case						=					image_processor(lowerCAmelCase_					,    return_tensors='pt'	).to('cpu'					,    torch.bfloataa	)
															self.assertEqual(encoding.pixel_values.device					,    torch.device('cpu'	)	)
															self.assertEqual(encoding.pixel_values.dtype					,    torch.bfloataa	)

															with self.assertRaises(lowerCAmelCase_	):
																				_snake_case						=					image_processor(lowerCAmelCase_					,    return_tensors='pt'	).to(torch.bfloataa					,    'cpu'	)

															# Try with text + image feature
															_snake_case						=					image_processor(lowerCAmelCase_					,    return_tensors='pt'	)
															encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]	)}	)
															_snake_case						=					encoding.to(torch.floataa	)

															self.assertEqual(encoding.pixel_values.device					,    torch.device('cpu'	)	)
															self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa	)
															self.assertEqual(encoding.input_ids.dtype					,    torch.long	)










import math
from timeit import timeit





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			int:



					'''simple docstring'''




					_snake_case						=					0
					_snake_case						=					abs(UpperCamelCase__    )
					while True:
										_snake_case						=					n // 10
										digits += 1
										if n == 0:
															break
					return digits





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			int:



					'''simple docstring'''




					return 1 if n == 0 else math.floor(math.log(abs(UpperCamelCase__    )							,     10    ) + 1    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			int:



					'''simple docstring'''




					return len(str(abs(UpperCamelCase__    )    )    )





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					from collections.abc import Callable

					def benchmark_a_function(UpperCamelCase__      :							Callable							,     UpperCamelCase__      :							int    ) -> None:
										_snake_case						=					F'''{func.__name__}({value})'''
										_snake_case						=					timeit(F'''__main__.{call}'''							,     setup='import __main__'    )
										print(F'''{call}: {func(UpperCamelCase__    )} -- {timing} seconds'''    )

					for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
										for func in (num_digits, num_digits_fast, num_digits_faster):
															benchmark_a_function(UpperCamelCase__							,     UpperCamelCase__    )
										print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()



