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


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		str							      =  {
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'pegasus'
			UpperCamelCase__          =    ['past_key_values']
			UpperCamelCase__          =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







			def __init__(				self						,   _a=50_265						,   _a=1_024						,   _a=12						,   _a=4_096						,   _a=16						,   _a=12						,   _a=4_096						,   _a=16						,   _a=0.0						,   _a=0.0						,   _a=True						,   _a=True						,   _a="gelu"						,   _a=1_024						,   _a=0.1						,   _a=0.0						,   _a=0.0						,   _a=0.02						,   _a=0						,   _a=False						,   _a=0						,   _a=1						,   _a=1						,   **_a						,   ):
				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     max_position_embeddings
				__magic_name__		:		Union[str, Any]	   =     d_model
				__magic_name__		:		Tuple	   =     encoder_ffn_dim
				__magic_name__		:		Union[str, Any]	   =     encoder_layers
				__magic_name__		:		List[Any]	   =     encoder_attention_heads
				__magic_name__		:		Any	   =     decoder_ffn_dim
				__magic_name__		:		int	   =     decoder_layers
				__magic_name__		:		str	   =     decoder_attention_heads
				__magic_name__		:		Union[str, Any]	   =     dropout
				__magic_name__		:		List[Any]	   =     attention_dropout
				__magic_name__		:		Dict	   =     activation_dropout
				__magic_name__		:		Any	   =     activation_function
				__magic_name__		:		str	   =     init_std
				__magic_name__		:		Union[str, Any]	   =     encoder_layerdrop
				__magic_name__		:		Union[str, Any]	   =     decoder_layerdrop
				__magic_name__		:		List[str]	   =     use_cache
				__magic_name__		:		List[Any]	   =     encoder_layers
				__magic_name__		:		Tuple	   =     scale_embedding  # scale factor will be sqrt(d_model) if True
				super().__init__(
				    pad_token_id=_a						,   eos_token_id=_a						,   is_encoder_decoder=_a						,   decoder_start_token_id=_a						,   forced_eos_token_id=_a						,   **_a						,   )







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.encoder_attention_heads







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self.d_model


snake_case :		Optional[int]							      =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def   lowerCAmelCase_						(					_snake_case      :  bytes   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Tuple	   =     F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(_snake_case   )

	__magic_name__		:		Optional[int]	   =     "".join(bin(_snake_case   )[2:].zfill(8   ) for byte in data   )

	__magic_name__		:		List[Any]	   =     len(_snake_case   ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__magic_name__		:		List[str]	   =     B"=" * ((6 - len(_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(_snake_case   ) % 6)
	else:
		__magic_name__		:		List[str]	   =     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(_snake_case   )      ,   6   )   ).encode()
	    + padding
	)






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ) and not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		List[str]	   =     (
		    "argument should be a bytes-like object or ASCII string, "
		    F'''not \'{encoded_data.__class__.__name__}\''''
		)
		raise TypeError(_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(_snake_case      ,   _snake_case   ):
		try:
			__magic_name__		:		List[Any]	   =     encoded_data.decode("utf-8"   )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"   )

	__magic_name__		:		List[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(_snake_case   ) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__magic_name__		:		Optional[int]	   =     encoded_data[:-padding]

		__magic_name__		:		Dict	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
	else:
		__magic_name__		:		Union[str, Any]	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )

	__magic_name__		:		List[Any]	   =     [
	    int(binary_stream[index : index + 8]      ,   2   )
	    for index in range(0      ,   len(_snake_case   )      ,   8   )
	]

	return bytes(_snake_case   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
