

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       snake_case_(_UpperCamelCase ,       _UpperCamelCase=False ,       _UpperCamelCase=False ,       _UpperCamelCase=False     )							->	str:



  """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						=							image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      _snake_case						=							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`
        _snake_case						=							max(image_processor_tester.size_divisor ,       _SCREAMING_SNAKE_CASE     )
      _snake_case						=							np.random.choice(np.arange(_SCREAMING_SNAKE_CASE ,       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(_SCREAMING_SNAKE_CASE ,       0 ,       -1     )     ) for image in image_inputs]

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

  return image_inputs



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase=10 ,       _UpperCamelCase=10 ,       _UpperCamelCase=False ,       _UpperCamelCase=False     )							->	Optional[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(_SCREAMING_SNAKE_CASE ,       0 ,       -1     )     ) for frame in video]

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

  return video



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase=False ,       _UpperCamelCase=False ,       _UpperCamelCase=False     )							->	Dict:



  """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						=							image_processor_tester.max_resolution
    else:
      _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=_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   lowercase_    :
      UpperCamelCase_			:		str						      =				None



      def 						UpperCamelCase_		(			self      :				List[str]		)    ->     Optional[Any]:
        _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]			,					_snake_case		)



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

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

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



      def 						UpperCamelCase_		(			self      :				Union[str, Any]		)    ->     Dict:
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)

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

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



      def 						UpperCamelCase_		(			self      :				Optional[Any]		)    ->     Union[str, Any]:
        _snake_case						=							self.image_processing_class()
        self.assertIsNotNone(_snake_case		)







      @require_torch
      @require_vision
      def 						UpperCamelCase_		(			self      :				Any		)    ->     Tuple:
        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=_snake_case			,					torchify=_snake_case		)

          _snake_case						=							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		)

          _snake_case						=							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		)

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

          # Try with text + image feature
          _snake_case						=							image_processor(_snake_case			,					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		)




'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





	def alphanum_key(_SCREAMING_SNAKE_CASE					:    Optional[Any]							):
		return [int(_SCREAMING_SNAKE_CASE							) if s.isdigit() else s.lower() for s in re.split("([0-9]+)"     ,							_SCREAMING_SNAKE_CASE							)]

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




