




"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   str				)       ->   float:






      '''simple docstring'''


      def get_matched_characters(_snake_case				:   str							,    _snake_case				:   str				) -> str:
            _A     =  []
            _A     =  min(len(_stra				)							,    len(_stra				)				) // 2
            for i, l in enumerate(_stra				):
                  _A     =  int(max(0							,    i - limit				)				)
                  _A     =  int(min(i + limit + 1							,    len(_stra				)				)				)
                  if l in _stra[left:right]:
                        matched.append(_snake_case				)
                        _A     =  F'''{_stra[0:_stra.index(_snake_case				)]} {_stra[_stra.index(_snake_case				) + 1:]}'''

            return "".join(_snake_case				)

      # matching characters
      _A     =  get_matched_characters(_snake_case							,    _snake_case				)
      _A     =  get_matched_characters(_snake_case							,    _snake_case				)
      _A     =  len(_snake_case				)

      # transposition
      _A     =  (
          len([(ca, ca) for ca, ca in zip(_snake_case							,    _snake_case				) if ca != ca]				) // 2
      )

      if not match_count:
            _A     =  0.0
      else:
            _A     =  (
                1
                / 3
                * (
                    match_count / len(_snake_case				)
                    + match_count / len(_snake_case				)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      _A     =  0
      for ca, ca in zip(stra[:4]							,    stra[:4]				):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler('''hello''', '''world'''))







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

