

'''simple docstring'''
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       a__    (  _SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    int=False     ,							_SCREAMING_SNAKE_CASE					:    Dict=False     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=False							)			-> str:


	"""simple docstring"""





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

	UpperCAmelCase_			:	List[Any]      =   []
	for i in range(image_processor_tester.batch_size							):
		if equal_resolution:
			UpperCAmelCase_			:	Tuple      =   image_processor_tester.max_resolution
		else:
			# To avoid getting image width/height 0
			UpperCAmelCase_			:	Tuple      =   image_processor_tester.min_resolution
			if getattr(_SCREAMING_SNAKE_CASE     ,							"size_divisor"     ,							_SCREAMING_SNAKE_CASE							):
				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
				UpperCAmelCase_			:	Tuple      =   max(image_processor_tester.size_divisor     ,							_SCREAMING_SNAKE_CASE							)
			UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   np.random.choice(np.arange(_SCREAMING_SNAKE_CASE     ,							image_processor_tester.max_resolution							)     ,							2							)
		image_inputs.append(
		    np.random.randint(2_55     ,							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
		UpperCAmelCase_			:	Any      =   [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE     ,							0     ,							-1							)							) for image in image_inputs]

	if torchify:
		UpperCAmelCase_			:	List[Any]      =   [torch.from_numpy(_SCREAMING_SNAKE_CASE							) for image in image_inputs]

	return image_inputs


def       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[Any]     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=10     ,							_SCREAMING_SNAKE_CASE					:    Tuple=10     ,							_SCREAMING_SNAKE_CASE					:    List[str]=False     ,							_SCREAMING_SNAKE_CASE					:    List[str]=False							)			-> Optional[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   []
	for i in range(image_processor_tester.num_frames							):
		video.append(np.random.randint(2_55     ,							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
		UpperCAmelCase_			:	List[Any]      =   [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE     ,							0     ,							-1							)							) for frame in video]

	if torchify:
		UpperCAmelCase_			:	Optional[int]      =   [torch.from_numpy(_SCREAMING_SNAKE_CASE							) for frame in video]

	return video


def       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[int]     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]=False     ,							_SCREAMING_SNAKE_CASE					:    str=False     ,							_SCREAMING_SNAKE_CASE					:    Optional[int]=False							)			-> Dict:


	"""simple docstring"""





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

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

	return video_inputs





class  _snake_case						:
			__A   :     str          =None






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	int      =   self.image_processing_class(**self.image_processor_dict			)
				UpperCAmelCase_			:	Tuple      =   json.loads(image_processor.to_json_string()			)
				for key, value in self.image_processor_dict.items():
					self.assertEqual(obj[key]       ,_snake_case			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[int]      =   self.image_processing_class(**self.image_processor_dict			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					UpperCAmelCase_			:	int      =   os.path.join(_snake_case       ,"image_processor.json"			)
					image_processor_first.to_json_file(_snake_case			)
					UpperCAmelCase_			:	Tuple      =   self.image_processing_class.from_json_file(_snake_case			)

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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	List[Any]      =   self.image_processing_class(**self.image_processor_dict			)

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

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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	List[Any]      =   self.image_processing_class()
				self.assertIsNotNone(_snake_case			)






			@require_torch
			@require_vision
			def 						UpperCamelCase__				(   self			):
				if self.test_cast_dtype is not None:
					# Initialize image_processor
					UpperCAmelCase_			:	Dict      =   self.image_processing_class(**self.image_processor_dict			)

					# create random PyTorch tensors
					UpperCAmelCase_			:	Union[str, Any]      =   prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_snake_case       ,torchify=_snake_case			)

					UpperCAmelCase_			:	int      =   image_processor(_snake_case       ,return_tensors="pt"			)
					# for layoutLM compatiblity
					self.assertEqual(encoding.pixel_values.device       ,torch.device("cpu"			)			)
					self.assertEqual(encoding.pixel_values.dtype       ,torch.floataa			)

					UpperCAmelCase_			:	List[Any]      =   image_processor(_snake_case       ,return_tensors="pt"			).to(torch.floataa			)
					self.assertEqual(encoding.pixel_values.device       ,torch.device("cpu"			)			)
					self.assertEqual(encoding.pixel_values.dtype       ,torch.floataa			)

					UpperCAmelCase_			:	Any      =   image_processor(_snake_case       ,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(_snake_case			):
						UpperCAmelCase_			:	List[str]      =   image_processor(_snake_case       ,return_tensors="pt"			).to(torch.bfloataa       ,"cpu"			)

					# Try with text + image feature
					UpperCAmelCase_			:	Tuple      =   image_processor(_snake_case       ,return_tensors="pt"			)
					encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]			)}			)
					UpperCAmelCase_			:	Union[str, Any]      =   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			)






'''simple docstring'''

from itertools import permutations







def __A  (    ):
     _UpperCAmelCase					:  Union[str, Any]         =    list(map("""""".join ,       permutations("""0123456789"""       )       )       )
     return result[99_9999]


if __name__ == "__main__":
   print(solution())


