

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  )							->			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(_A				, '''size_divisor'''				, _A  ):
        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
        _snake_case		=							max(image_processor_tester.size_divisor				, _A  )
      _snake_case,  _snake_case		=							np.random.choice(np.arange(_A				, 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(_A				, 0				, -1  )  ) for image in image_inputs]

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

  return image_inputs



def    snake_case_(_UpperCamelCase				, _UpperCamelCase=10				, _UpperCamelCase=10				, _UpperCamelCase=False				, _UpperCamelCase=False  )							->			Union[str, Any]:



  """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(_A				, 0				, -1  )  ) for frame in video]

  if torchify:
    _snake_case		=							[torch.from_numpy(_A  ) 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		=							_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=_A				, width=_A				, height=_A				, numpify=_A				, torchify=_A				, )
    video_inputs.append(_A  )

  return video_inputs



class 						UpperCAmelCase					:
      UpperCamelCase_    :	Dict		   =       None



      def 		UpperCamelCase_	(    self   :       Dict	)					->  int:
        _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]    ,      lowercase_	)



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  List[Any]:
        _snake_case		=							self.image_processing_class(**self.image_processor_dict	)

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

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



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

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

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



      def 		UpperCamelCase_	(    self   :       List[Any]	)					->  Optional[Any]:
        _snake_case		=							self.image_processing_class()
        self.assertIsNotNone(lowercase_	)







      @require_torch
      @require_vision
      def 		UpperCamelCase_	(    self   :       Dict	)					->  Dict:






        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=lowercase_    ,      torchify=lowercase_	)

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

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




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

