



from collections import OrderedDict
from typing import Mapping

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


_snake_case				=					logging.get_logger(__name__)

_snake_case				=					{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}






class 		lowerCAmelCase_ (			_lowercase ):







		"""simple docstring"""
		UpperCAmelCase__													=       "roberta"
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE=50_265     ,    _SCREAMING_SNAKE_CASE=768     ,    _SCREAMING_SNAKE_CASE=12     ,    _SCREAMING_SNAKE_CASE=12     ,    _SCREAMING_SNAKE_CASE=3_072     ,    _SCREAMING_SNAKE_CASE="gelu"     ,    _SCREAMING_SNAKE_CASE=0.1     ,    _SCREAMING_SNAKE_CASE=0.1     ,    _SCREAMING_SNAKE_CASE=512     ,    _SCREAMING_SNAKE_CASE=2     ,    _SCREAMING_SNAKE_CASE=0.0_2     ,    _SCREAMING_SNAKE_CASE=1e-1_2     ,    _SCREAMING_SNAKE_CASE=1     ,    _SCREAMING_SNAKE_CASE=0     ,    _SCREAMING_SNAKE_CASE=2     ,    _SCREAMING_SNAKE_CASE="absolute"     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=None     ,    **_SCREAMING_SNAKE_CASE     ,    )     ->  Any:
									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,    bos_token_id=_SCREAMING_SNAKE_CASE     ,    eos_token_id=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE			)

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






class 		lowerCAmelCase_ (			_lowercase ):







		"""simple docstring"""
		@property
		def 						__lowercase(		self			)     ->  Any:





									if self.task == "multiple-choice":
																__UpperCamelCase          =  {0: "batch", 1: "choice", 2: "sequence"}
									else:
																__UpperCamelCase          =  {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]			)







'''simple docstring'''


import functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

