




__a							     =					'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Union[str, Any]  =     f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(_lowercase )

     UpperCAmelCase_     :				Any  =     ''''''.join(bin(_lowercase )[2:].zfill(8 ) for byte in data )

     UpperCAmelCase_     :				Any  =     len(_lowercase ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          UpperCAmelCase_     :				Union[str, Any]  =     B'''=''' * ((6 - len(_lowercase ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(_lowercase ) % 6)
     else:
          UpperCAmelCase_     :				int  =     B''''''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]					,		2 )]
             for index in range(0					,		len(_lowercase )					,		6 ) ).encode()
         + padding
     )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ) and not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Tuple  =     (
              '''argument should be a bytes-like object or ASCII string, '''
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(_lowercase )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(_lowercase					,		_lowercase ):
          try:
               UpperCAmelCase_     :				Any  =     encoded_data.decode('''utf-8''' )
          except UnicodeDecodeError:
               raise ValueError('''base64 encoded data should only contain ASCII characters''' )

     UpperCAmelCase_     :				str  =     encoded_data.count('''=''' )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(_lowercase ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          UpperCAmelCase_     :				List[Any]  =     encoded_data[:-padding]

          UpperCAmelCase_     :				List[Any]  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
     else:
          UpperCAmelCase_     :				Tuple  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )

     UpperCAmelCase_     :				str  =     [
         int(binary_stream[index : index + 8]					,		2 )
         for index in range(0					,		len(_lowercase )					,		8 )
     ]

     return bytes(_lowercase )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )