



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

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

							if torchify:
														A    =       [torch.from_numpy(lowerCamelCase__      ) for image in image_inputs]

							return image_inputs

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=10					,   lowerCamelCase__=10					,   lowerCamelCase__=False					,   lowerCamelCase__=False      )			->				List[str]:
							A    =       []
							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
														A    =       [Image.fromarray(np.moveaxis(lowerCamelCase__					,   0					,   -1      )      ) for frame in video]

							if torchify:
														A    =       [torch.from_numpy(lowerCamelCase__      ) for frame in video]

							return video

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=False					,   lowerCamelCase__=False					,   lowerCamelCase__=False      )			->				str:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

							return video_inputs






class   UpperCAmelCase__		:
		lowerCAmelCase_							:						int							=  None



		def        A_		(			self	:     Tuple					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.image_processing_class(**self.image_processor_dict					)
									A    =       json.loads(image_processor.to_json_string()					)
									for key, value in self.image_processor_dict.items():
																self.assertEqual(obj[key]   ,				snake_case					)



		def        A_		(			self	:     Tuple					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.image_processing_class(**self.image_processor_dict					)

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

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



		def        A_		(			self	:     int					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       self.image_processing_class(**self.image_processor_dict					)

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

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



		def        A_		(			self	:     Tuple					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self.image_processing_class()
									self.assertIsNotNone(snake_case					)



		@require_torch
		@require_vision
		def        A_		(			self	:     Optional[int]					)      ->    int:






									'''simple docstring'''

									if self.test_cast_dtype is not None:
																# Initialize image_processor
																A    =       self.image_processing_class(**self.image_processor_dict					)

																# create random PyTorch tensors
																A    =       prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=snake_case   ,				torchify=snake_case					)

																A    =       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					)

																A    =       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					)

																A    =       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					):
																							A    =       image_processor(snake_case   ,				return_tensors='pt'					).to(torch.bfloataa   ,				'cpu'					)

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







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[str]:
							A    =       [701, 301, 132, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(lowerCamelCase__					,   len(lowerCamelCase__      )      ):
																					A    =       collection[i]
																					A    =       i
																					while j >= gap and collection[j - gap] > insert_value:
																												A    =       collection[j - gap]
																												j -= gap
																					if j != i:
																												A    =       insert_value
							return collection


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							A     =      input('Enter numbers separated by a comma:\n').strip()
							A     =      [int(item) for item in user_input.split(',')]
							print(shell_sort(unsorted))



