




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


A_			      =		logging.get_logger(__name__)

A_			      =		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"xmod"





     def __init__(     self     :					Dict     ,							__lowerCamelCase     :					Union[str, Any]=30522     ,							__lowerCamelCase     :					Any=768     ,							__lowerCamelCase     :					List[str]=12     ,							__lowerCamelCase     :					List[Any]=12     ,							__lowerCamelCase     :					str=3072     ,							__lowerCamelCase     :					Tuple="gelu"     ,							__lowerCamelCase     :					List[str]=0.1     ,							__lowerCamelCase     :					Dict=0.1     ,							__lowerCamelCase     :					Tuple=512     ,							__lowerCamelCase     :					Any=2     ,							__lowerCamelCase     :					Any=0.0_2     ,							__lowerCamelCase     :					Optional[Any]=1E-12     ,							__lowerCamelCase     :					str=1     ,							__lowerCamelCase     :					Dict=0     ,							__lowerCamelCase     :					Union[str, Any]=2     ,							__lowerCamelCase     :					List[Any]="absolute"     ,							__lowerCamelCase     :					Any=True     ,							__lowerCamelCase     :					Dict=None     ,							__lowerCamelCase     :					List[str]=False     ,							__lowerCamelCase     :					Dict=2     ,							__lowerCamelCase     :					List[Any]=False     ,							__lowerCamelCase     :					Optional[Any]=True     ,							__lowerCamelCase     :					Dict=True     ,							__lowerCamelCase     :					Union[str, Any]=("en_XX",)     ,							__lowerCamelCase     :					str=None     ,							**__lowerCamelCase     :					Any     ,							):
           super().__init__(pad_token_id=__lowerCamelCase     ,							bos_token_id=__lowerCamelCase     ,							eos_token_id=__lowerCamelCase     ,							**__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    vocab_size
           snake_case__       :   List[str]	      =    hidden_size
           snake_case__       :   Optional[Any]	      =    num_hidden_layers
           snake_case__       :   Optional[int]	      =    num_attention_heads
           snake_case__       :   Union[str, Any]	      =    hidden_act
           snake_case__       :   Union[str, Any]	      =    intermediate_size
           snake_case__       :   str	      =    hidden_dropout_prob
           snake_case__       :   int	      =    attention_probs_dropout_prob
           snake_case__       :   List[str]	      =    max_position_embeddings
           snake_case__       :   List[str]	      =    type_vocab_size
           snake_case__       :   Optional[int]	      =    initializer_range
           snake_case__       :   List[Any]	      =    layer_norm_eps
           snake_case__       :   List[str]	      =    position_embedding_type
           snake_case__       :   List[Any]	      =    use_cache
           snake_case__       :   Dict	      =    classifier_dropout
           snake_case__       :   List[Any]	      =    pre_norm
           snake_case__       :   int	      =    adapter_reduction_factor
           snake_case__       :   Union[str, Any]	      =    adapter_layer_norm
           snake_case__       :   Dict	      =    adapter_reuse_layer_norm
           snake_case__       :   List[Any]	      =    ln_before_adapter
           snake_case__       :   str	      =    list(__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    default_language






class        lowercase_      (						lowerCAmelCase_							):





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






'''simple docstring'''






import functools






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Any	      =    len(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   List[str]	      =    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
            snake_case__       :   Union[str, Any]	      =    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()
