




'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


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

a  :				Optional[int]									=  {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"roberta-prelayernorm"


				def __init__(		self    :					List[str]       ,				lowercase_    :					Union[str, Any]=5_0265       ,				lowercase_    :					Any=768       ,				lowercase_    :					Tuple=12       ,				lowercase_    :					int=12       ,				lowercase_    :					Dict=3072       ,				lowercase_    :					Any="gelu"       ,				lowercase_    :					Dict=0.1       ,				lowercase_    :					int=0.1       ,				lowercase_    :					List[Any]=512       ,				lowercase_    :					Any=2       ,				lowercase_    :					str=0.02       ,				lowercase_    :					Union[str, Any]=1e-12       ,				lowercase_    :					Tuple=1       ,				lowercase_    :					Optional[int]=0       ,				lowercase_    :					List[str]=2       ,				lowercase_    :					str="absolute"       ,				lowercase_    :					Tuple=True       ,				lowercase_    :					Any=None       ,				**lowercase_    :					Union[str, Any]       ,				):
									super().__init__(pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				**lowercase_					)

									snake_case_									=				vocab_size
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				hidden_act
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				max_position_embeddings
									snake_case_									=				type_vocab_size
									snake_case_									=				initializer_range
									snake_case_									=				layer_norm_eps
									snake_case_									=				position_embedding_type
									snake_case_									=				use_cache
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):


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



"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):

		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'float' object cannot be interpreted as an integer"""				)
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'str' object cannot be interpreted as an integer"""				)

		if num == 0:
				return "0b0"

		__SCREAMING_SNAKE_CASE            = False

		if num < 0:
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = -num

		__SCREAMING_SNAKE_CASE            = []
		while num > 0:
				binary.insert(0  ,				num % 2				)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(UpperCamelCase_				) for e in binary				)

		return "0b" + "".join(str(UpperCamelCase_				) for e in binary				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




