






__lowerCamelCase :      Union[str, Any]										=				"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							bytes       ):
	# Make sure the supplied data is a bytes-like object
	if not isinstance(snake_case_					,  snake_case_       ):
		snake_case__			:				List[Any]       =  F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(snake_case_       )

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

	snake_case__			:				int       =  len(snake_case_       ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		snake_case__			:				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:
		snake_case__			:				Union[str, Any]       =  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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	# Make sure encoded_data is either a string or a bytes-like object
	if not isinstance(snake_case_					,  snake_case_       ) and not isinstance(snake_case_					,  snake_case_       ):
		snake_case__			:				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:
			snake_case__			:				Union[str, Any]       =  encoded_data.decode("utf-8"       )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"       )

	snake_case__			:				int       =  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
		snake_case__			:				Optional[int]       =  encoded_data[:-padding]

		snake_case__			:				Optional[Any]       =  "".join(
		    bin(B64_CHARSET.index(snake_case_       )       )[2:].zfill(6       ) for char in encoded_data       )[: -padding * 2]
	else:
		snake_case__			:				List[Any]       =  "".join(
		    bin(B64_CHARSET.index(snake_case_       )       )[2:].zfill(6       ) for char in encoded_data       )

	snake_case__			:				Optional[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()











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :      Optional[int]										=				logging.get_logger(__name__)

__lowerCamelCase :      str										=				{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"roberta-prelayernorm"



					def __init__( self					:  Tuple							,		__A					:  Any=5_0_2_6_5							,		__A					:  Optional[int]=7_6_8							,		__A					:  Dict=1_2							,		__A					:  Union[str, Any]=1_2							,		__A					:  List[Any]=3_0_7_2							,		__A					:  Optional[Any]="gelu"							,		__A					:  Optional[int]=0.1							,		__A					:  Tuple=0.1							,		__A					:  Optional[Any]=5_1_2							,		__A					:  List[str]=2							,		__A					:  Optional[int]=0.0_2							,		__A					:  Tuple=1e-1_2							,		__A					:  Any=1							,		__A					:  str=0							,		__A					:  int=2							,		__A					:  List[str]="absolute"							,		__A					:  Optional[Any]=True							,		__A					:  List[Any]=None							,		**__A					:  Optional[Any]							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Optional[Any]       =  hidden_size
						snake_case__			:				List[Any]       =  num_hidden_layers
						snake_case__			:				List[Any]       =  num_attention_heads
						snake_case__			:				Dict       =  hidden_act
						snake_case__			:				Union[str, Any]       =  intermediate_size
						snake_case__			:				List[Any]       =  hidden_dropout_prob
						snake_case__			:				Any       =  attention_probs_dropout_prob
						snake_case__			:				int       =  max_position_embeddings
						snake_case__			:				Tuple       =  type_vocab_size
						snake_case__			:				Optional[int]       =  initializer_range
						snake_case__			:				int       =  layer_norm_eps
						snake_case__			:				Dict       =  position_embedding_type
						snake_case__			:				int       =  use_cache
						snake_case__			:				Dict       =  classifier_dropout



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  Optional[int]	):
						if self.task == "multiple-choice":
							snake_case__			:				List[str]       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Tuple       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



