





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
    __lowerCamelCase			:	Dict					    =	{
        """linear""": PIL.Image.Resampling.BILINEAR,
        """bilinear""": PIL.Image.Resampling.BILINEAR,
        """bicubic""": PIL.Image.Resampling.BICUBIC,
        """lanczos""": PIL.Image.Resampling.LANCZOS,
        """nearest""": PIL.Image.Resampling.NEAREST,
    }
else:
    __lowerCamelCase			:	List[str]					    =	{
        """linear""": PIL.Image.LINEAR,
        """bilinear""": PIL.Image.BILINEAR,
        """bicubic""": PIL.Image.BICUBIC,
        """lanczos""": PIL.Image.LANCZOS,
        """nearest""": PIL.Image.NEAREST,
    }



def 						A_							(	_lowerCAmelCase	)		->					Optional[int]:
 UpperCamelCase      :   int			      =  (images / 2 + 0.5).clamp(0				,  1	)
 UpperCamelCase      :   List[str]			      =  images.cpu().permute(0				,  2				,  3				,  1	).float().numpy()
 UpperCamelCase      :   Tuple			      =  numpy_to_pil(_lowerCAmelCase	)
 return images



def 						A_							(	_lowerCAmelCase	)		->					Union[str, Any]:
 if images.ndim == 3:
  UpperCamelCase      :   Any			      =  images[None, ...]
 UpperCamelCase      :   Dict			      =  (images * 255).round().astype("uint8"	)
 if images.shape[-1] == 1:
  # special case for grayscale (single channel) images
  UpperCamelCase      :   List[str]			      =  [Image.fromarray(image.squeeze()				,  mode="L"	) for image in images]
 else:
  UpperCamelCase      :   Union[str, Any]			      =  [Image.fromarray(_lowerCAmelCase	) for image in images]

 return pil_images







import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
