






'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase   :							Any         =   logging.get_logger(__name__)

__UpperCamelCase   :							Any         =   {
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="xglm"
      __a	       =["past_key_values"]

      __a	       ={
          "num_attention_heads": "attention_heads",
          "hidden_size": "d_model",
          "num_hidden_layers": "num_layers",
      }




      def __init__(	self			, lowerCamelCase=25_6008			, lowerCamelCase=2048			, lowerCamelCase=1024			, lowerCamelCase=4096			, lowerCamelCase=24			, lowerCamelCase=16			, lowerCamelCase="gelu"			, lowerCamelCase=0.1			, lowerCamelCase=0.1			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=2			, lowerCamelCase=1			, lowerCamelCase=0			, lowerCamelCase=2			, **lowerCamelCase			, )				->int:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     max_position_embeddings
         __a					  =     d_model
         __a					  =     ffn_dim
         __a					  =     num_layers
         __a					  =     attention_heads
         __a					  =     activation_function
         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     layerdrop
         __a					  =     init_std
         __a					  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         __a					  =     use_cache

         super().__init__(
             pad_token_id=lowerCamelCase			, bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, **lowerCamelCase			, )







'''simple docstring'''



__UpperCamelCase   :							List[Any]         =   """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						bytes     )	->      bytes:

   """simple docstring"""
   # Make sure the supplied data is a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   __a					  =     ''.join(bin(SCREAMING_SNAKE_CASE__     )[2:].zfill(8     ) for byte in data     )

   __a					  =     len(SCREAMING_SNAKE_CASE__     ) % 6 != 0

   if padding_needed:
      # The padding that will be added later
      __a					  =     b'=' * ((6 - len(SCREAMING_SNAKE_CASE__     ) % 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(SCREAMING_SNAKE_CASE__     ) % 6)
   else:
      __a					  =     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(SCREAMING_SNAKE_CASE__     ),       6     )     ).encode()
       + padding
   )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bytes:

   """simple docstring"""
   # Make sure encoded_data is either a string or a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ) and not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     (
          'argument should be a bytes-like object or ASCII string, '
          f"""not '{encoded_data.__class__.__name__}'"""
      )
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   # 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(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      try:
         __a					  =     encoded_data.decode('utf-8'     )
      except UnicodeDecodeError:
         raise ValueError('base64 encoded data should only contain ASCII characters'     )

   __a					  =     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(SCREAMING_SNAKE_CASE__     ) % 4 == 0 and padding < 3, "Incorrect padding"

   if padding:
      # Remove padding if there is one
      __a					  =     encoded_data[:-padding]

      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
   else:
      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )

   __a					  =     [
       int(binary_stream[index : index + 8],       2     )
       for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       8     )
   ]

   return bytes(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()