






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase							=logging.get_logger(__name__)

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




class     A    (      SCREAMING_SNAKE_CASE__						):






	"""simple docstring"""





	__a			:						Dict      =						'''roberta-prelayernorm'''
	def __init__(							self			,     __lowerCAmelCase=5_02_65			,     __lowerCAmelCase=7_68			,     __lowerCAmelCase=12			,     __lowerCAmelCase=12			,     __lowerCAmelCase=30_72			,     __lowerCAmelCase="gelu"			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=5_12			,     __lowerCAmelCase=2			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1E-12			,     __lowerCAmelCase=1			,     __lowerCAmelCase=0			,     __lowerCAmelCase=2			,     __lowerCAmelCase="absolute"			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
							super().__init__(pad_token_id=__lowerCAmelCase			,     bos_token_id=__lowerCAmelCase			,     eos_token_id=__lowerCAmelCase			,     **__lowerCAmelCase )

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






class     A    (      SCREAMING_SNAKE_CASE__						):






	"""simple docstring"""





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














'''simple docstring'''

def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      assert (
          isinstance(a_    ,   a_	) and number_of_steps > 0
      ), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
      if number_of_steps == 1:
            return 1
      UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							1, 1
      for _ in range(number_of_steps - 1	):
            UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							current + previous, current
      return current


if __name__ == "__main__":
      import doctest

      doctest.testmod()






