





"""simple docstring"""



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

a_					=     logging.get_logger(__name__)


a_					=     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

a_					=     {
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

a_					=     {"""facebook/blenderbot-3B""": 128}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	VOCAB_FILES_NAMES
 _lowerCamelCase								=	PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase								=	["""input_ids""", """attention_mask"""]
 _lowerCamelCase								=	BlenderbotTokenizer

 def __init__(       self   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase=None   ,		__lowerCamelCase="replace"   ,		__lowerCamelCase="<s>"   ,		__lowerCamelCase="</s>"   ,		__lowerCamelCase="</s>"   ,		__lowerCamelCase="<s>"   ,		__lowerCamelCase="<unk>"   ,		__lowerCamelCase="<pad>"   ,		__lowerCamelCase="<mask>"   ,		__lowerCamelCase=False   ,		__lowerCamelCase=True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(
         __lowerCamelCase   ,		__lowerCamelCase   ,		tokenizer_file=__lowerCamelCase   ,		errors=__lowerCamelCase   ,		bos_token=__lowerCamelCase   ,		eos_token=__lowerCamelCase   ,		sep_token=__lowerCamelCase   ,		cls_token=__lowerCamelCase   ,		unk_token=__lowerCamelCase   ,		pad_token=__lowerCamelCase   ,		mask_token=__lowerCamelCase   ,		add_prefix_space=__lowerCamelCase   ,		trim_offsets=__lowerCamelCase   ,		**__lowerCamelCase   ,		)

     __A       :		Dict							=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
     if pre_tok_state.get('''add_prefix_space'''   ,		__lowerCamelCase    ) != add_prefix_space:
         __A       :		int							=	getattr(__lowerCamelCase   ,		pre_tok_state.pop('''type'''    )    )
         __A       :		str							=	add_prefix_space
         __A       :		Optional[int]							=	pre_tok_class(**__lowerCamelCase    )

     __A       :		Optional[Any]							=	add_prefix_space

     __A       :		Any							=	'''post_processor'''
     __A       :		Optional[int]							=	getattr(self.backend_tokenizer   ,		__lowerCamelCase   ,		__lowerCamelCase    )
     if tokenizer_component_instance:
         __A       :		List[str]							=	json.loads(tokenizer_component_instance.__getstate__()    )

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
             __A       :		Dict							=	tuple(state['''sep''']    )
         if "cls" in state:
             __A       :		str							=	tuple(state['''cls''']    )

         __A       :		Optional[Any]							=	False

         if state.get('''add_prefix_space'''   ,		__lowerCamelCase    ) != add_prefix_space:
             __A       :		Tuple							=	add_prefix_space
             __A       :		Optional[int]							=	True

         if state.get('''trim_offsets'''   ,		__lowerCamelCase    ) != trim_offsets:
             __A       :		Any							=	trim_offsets
             __A       :		Optional[int]							=	True

         if changes_to_apply:
             __A       :		Optional[int]							=	getattr(__lowerCamelCase   ,		state.pop('''type'''    )    )
             __A       :		int							=	component_class(**__lowerCamelCase    )
             setattr(self.backend_tokenizer   ,		__lowerCamelCase   ,		__lowerCamelCase    )

 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     if self._mask_token is None:
         if self.verbose:
             logger.error('''Using mask_token, but it is not set yet.'''    )
         return None
     return str(self._mask_token    )

 @mask_token.setter
 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	AddedToken(__lowerCamelCase   ,		lstrip=__lowerCamelCase   ,		rstrip=__lowerCamelCase    ) if isinstance(__lowerCamelCase   ,		__lowerCamelCase    ) else value
     __A       :		Tuple							=	value

 def    UpperCamelCase__(       self   ,		*__lowerCamelCase   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[Any]							=	kwargs.get('''is_split_into_words'''   ,		__lowerCamelCase    )
     assert self.add_prefix_space or not is_split_into_words, (
         F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		*__lowerCamelCase   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Tuple							=	kwargs.get('''is_split_into_words'''   ,		__lowerCamelCase    )

     assert self.add_prefix_space or not is_split_into_words, (
         F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	self._tokenizer.model.save(__lowerCamelCase   ,		name=__lowerCamelCase    )
     return tuple(__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     __A       :		Dict							=	[self.sep_token_id]
     __A       :		Dict							=	[self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep    ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None    ):



     '''simple docstring'''




     return token_ids_a + [self.eos_token_id]







 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	[]
     for is_user, text in conversation.iter_texts():
         if is_user:
             # We need to space prefix as it's being done within blenderbot
             inputs.append(''' ''' + text    )
         else:
             # Generated responses should contain them already.
             inputs.append(__lowerCamelCase    )

     __A       :		int							=	'''  '''.join(__lowerCamelCase    )
     __A       :		Optional[Any]							=	self.encode(__lowerCamelCase    )
     if len(__lowerCamelCase    ) > self.model_max_length:
         __A       :		int							=	input_ids[-self.model_max_length :]
         logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
     return input_ids











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







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

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	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"""))




