
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 			__A( a			):
				snake_case_						   =    None
				snake_case_						   =    None
				snake_case_						   =    None
				snake_case_						   =    None







class 			__A( a			):




				def __init__(     self		,     _snake_case=1		,     _snake_case=0		,     _snake_case=2		,     _snake_case=512		,     _snake_case="cls"		,     _snake_case=False		,     _snake_case=True		,     **_snake_case		,     )    ->      Dict:


								'''simple docstring'''




								super().__init__(pad_token_id=_snake_case		,     bos_token_id=_snake_case		,     eos_token_id=_snake_case		,     **_snake_case				)
								__a										=    project_dim
								__a										=    pooler_fn
								__a										=    learn_encoder
								__a										=    use_attention_mask







class 			__A( a			):
				snake_case_						   =    [r'''pooler''', r'''logit_scale''']
				snake_case_						   =    [r'''position_ids''', r'''predictions.decoder.bias''']
				snake_case_						   =    '''roberta'''
				snake_case_						   =    RobertaSeriesConfig




				def __init__(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								super().__init__(_snake_case				)
								__a										=    XLMRobertaModel(_snake_case				)
								__a										=    nn.Linear(config.hidden_size		,     config.project_dim				)
								__a										=    getattr(_snake_case		,     '''has_pre_transformation'''		,     _snake_case				)
								if self.has_pre_transformation:
												__a										=    nn.Linear(config.hidden_size		,     config.project_dim				)
												__a										=    nn.LayerNorm(config.hidden_size		,     eps=config.layer_norm_eps				)
								self.post_init()




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     )    ->      int:


								'''simple docstring'''




								__a										=    return_dict if return_dict is not None else self.config.use_return_dict

								__a										=    self.base_model(
								    input_ids=_snake_case		,     attention_mask=_snake_case		,     token_type_ids=_snake_case		,     position_ids=_snake_case		,     head_mask=_snake_case		,     inputs_embeds=_snake_case		,     encoder_hidden_states=_snake_case		,     encoder_attention_mask=_snake_case		,     output_attentions=_snake_case		,     output_hidden_states=True if self.has_pre_transformation else output_hidden_states		,     return_dict=_snake_case		,     )

								if self.has_pre_transformation:
												__a										=    outputs['''hidden_states'''][-2]
												__a										=    self.pre_LN(_snake_case				)
												__a										=    self.transformation_pre(_snake_case				)

												return TransformationModelOutput(
												    projection_state=_snake_case		,     last_hidden_state=outputs.last_hidden_state		,     hidden_states=outputs.hidden_states		,     attentions=outputs.attentions		,     )
								else:
												__a										=    self.transformation(outputs.last_hidden_state				)
												return TransformationModelOutput(
												    projection_state=_snake_case		,     last_hidden_state=outputs.last_hidden_state		,     hidden_states=outputs.hidden_states		,     attentions=outputs.attentions		,     )

def     __lowerCAmelCase			(			a__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> 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(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

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

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (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'))