


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    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=False ,     __lowerCamelCase=False ,     __lowerCamelCase=False       )		->int:


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

 snake_case   : Tuple	  =      []
 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   : Dict	  =      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`
    snake_case   : Optional[int]	  =      max(image_processor_tester.size_divisor ,     __lowerCamelCase       )
   snake_case  ,					snake_case   : Optional[Any]	  =      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
  snake_case   : List[Any]	  =      [Image.fromarray(np.moveaxis(__lowerCamelCase ,     0 ,     -1       )       ) for image in image_inputs]

 if torchify:
  snake_case   : Union[str, Any]	  =      [torch.from_numpy(__lowerCamelCase       ) for image in image_inputs]

 return image_inputs






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=10 ,     __lowerCamelCase=10 ,     __lowerCamelCase=False ,     __lowerCamelCase=False       )		->int:


 '''simple docstring'''
 snake_case   : List[str]	  =      []
 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   : str	  =      [Image.fromarray(np.moveaxis(__lowerCamelCase ,     0 ,     -1       )       ) for frame in video]

 if torchify:
  snake_case   : int	  =      [torch.from_numpy(__lowerCamelCase       ) for frame in video]

 return video






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase=False ,     __lowerCamelCase=False ,     __lowerCamelCase=False       )		->Optional[Any]:


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

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

 return video_inputs





class      lowerCamelCase	:
       _UpperCamelCase    		=      None

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[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   : Union[str, Any]	  =      self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE					)

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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Dict	  =      self.image_processing_class(**self.image_processor_dict					)

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

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

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      self.image_processing_class()
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)



       @require_torch
       @require_vision
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        if self.test_cast_dtype is not None:
         # Initialize image_processor
         snake_case   : List[Any]	  =      self.image_processing_class(**self.image_processor_dict					)

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

         snake_case   : List[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   : Dict	  =      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   : List[str]	  =      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   : Union[str, Any]	  =      image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt'''					).to(torch.bfloataa	,						'''cpu'''					)

         # Try with text + image feature
         snake_case   : Union[str, Any]	  =      image_processor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt'''					)
         encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
         snake_case   : str	  =      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 itertools import permutations






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : Any	  =      list(map(''''''.join ,     permutations('''0123456789'''       )       )       )
 return result[999999]


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






