






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__		(			_a				:	List[Any]     ,					_a				:	Union[str, Any]=False     ,					_a				:	Any=False     ,					_a				:	Union[str, Any]=False			):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

		if torchify:
				snake_case_ :							int         =				[torch.from_numpy(_a			) for image in image_inputs]

		return image_inputs

def    lowerCAmelCase__		(			_a				:	Any     ,					_a				:	Optional[Any]=10     ,					_a				:	int=10     ,					_a				:	int=False     ,					_a				:	List[str]=False			):
		snake_case_ :							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
				snake_case_ :							Dict         =				[Image.fromarray(np.moveaxis(_a     ,					0     ,					-1			)			) for frame in video]

		if torchify:
				snake_case_ :							Any         =				[torch.from_numpy(_a			) for frame in video]

		return video

def    lowerCAmelCase__		(			_a				:	Optional[int]     ,					_a				:	int=False     ,					_a				:	Any=False     ,					_a				:	Optional[Any]=False			):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

		return video_inputs




class 						UpperCAmelCase_  :


						'''simple docstring'''




						A   :      Optional[int]						 =    None






						def 					_lowerCAmelCase				(		self )       -> int:
								snake_case_ :							Optional[int]         =				self.image_processing_class(**self.image_processor_dict )
								snake_case_ :							Dict         =				json.loads(image_processor.to_json_string() )
								for key, value in self.image_processor_dict.items():
										self.assertEqual(obj[key]						, _SCREAMING_SNAKE_CASE )






						def 					_lowerCAmelCase				(		self )       -> str:
								snake_case_ :							Any         =				self.image_processing_class(**self.image_processor_dict )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case_ :							int         =				os.path.join(_SCREAMING_SNAKE_CASE						, "image_processor.json" )
										image_processor_first.to_json_file(_SCREAMING_SNAKE_CASE )
										snake_case_ :							List[Any]         =				self.image_processing_class.from_json_file(_SCREAMING_SNAKE_CASE )

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






						def 					_lowerCAmelCase				(		self )       -> List[str]:
								snake_case_ :							Union[str, Any]         =				self.image_processing_class(**self.image_processor_dict )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case_ :							Dict         =				image_processor_first.save_pretrained(_SCREAMING_SNAKE_CASE )[0]
										check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE )
										snake_case_ :							List[str]         =				self.image_processing_class.from_pretrained(_SCREAMING_SNAKE_CASE )

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






						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							Union[str, Any]         =				self.image_processing_class()
								self.assertIsNotNone(_SCREAMING_SNAKE_CASE )






						@require_torch
						@require_vision
						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								if self.test_cast_dtype is not None:
										# Initialize image_processor
										snake_case_ :							Optional[int]         =				self.image_processing_class(**self.image_processor_dict )

										# create random PyTorch tensors
										snake_case_ :							Any         =				prepare_image_inputs(self.image_processor_tester						, equal_resolution=_SCREAMING_SNAKE_CASE						, torchify=_SCREAMING_SNAKE_CASE )

										snake_case_ :							Any         =				image_processor(_SCREAMING_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 )

										snake_case_ :							List[str]         =				image_processor(_SCREAMING_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 )

										snake_case_ :							Union[str, Any]         =				image_processor(_SCREAMING_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(_SCREAMING_SNAKE_CASE ):
												snake_case_ :							Optional[int]         =				image_processor(_SCREAMING_SNAKE_CASE						, return_tensors="pt" ).to(torch.bfloataa						, "cpu" )

										# Try with text + image feature
										snake_case_ :							List[Any]         =				image_processor(_SCREAMING_SNAKE_CASE						, return_tensors="pt" )
										encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
										snake_case_ :							Dict         =				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 )








from __future__ import annotations

from collections.abc import Generator

def    lowerCAmelCase__		(			_a				:	int			):
		if not isinstance(_a     ,					_a			) or n < 1:
				raise Exception("Sequence only defined for positive integers"			)

		yield n
		while n != 1:
				if n % 2 == 0:
						n //= 2
				else:
						snake_case_ :							List[str]         =				3 * n + 1
				yield n

def    lowerCAmelCase__		(			):
		snake_case_ :							Optional[int]         =				43
		snake_case_ :							Union[str, Any]         =				tuple(collatz_sequence(_a			)			)
		print(_a			)
		print(F'''Collatz sequence from {n} took {len(_a			)} steps.'''			)


if __name__ == "__main__":
							main()
