




"""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 				lowercase_       (     A__							):



 '''simple docstring'''
 UpperCAmelCase    :     Union[str, Any]        =	VOCAB_FILES_NAMES
 UpperCAmelCase    :     Dict        =	PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase    :     List[Any]        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase    :     int        =	['''input_ids''', '''attention_mask''']
 UpperCAmelCase    :     Tuple        =	BlenderbotTokenizer

 def __init__( self       :       List[Any]    ,							_UpperCAmelCase       :       Optional[int]=None    ,							_UpperCAmelCase       :       Dict=None    ,							_UpperCAmelCase       :       Union[str, Any]=None    ,							_UpperCAmelCase       :       Tuple="replace"    ,							_UpperCAmelCase       :       Union[str, Any]="<s>"    ,							_UpperCAmelCase       :       str="</s>"    ,							_UpperCAmelCase       :       str="</s>"    ,							_UpperCAmelCase       :       Tuple="<s>"    ,							_UpperCAmelCase       :       Union[str, Any]="<unk>"    ,							_UpperCAmelCase       :       str="<pad>"    ,							_UpperCAmelCase       :       Union[str, Any]="<mask>"    ,							_UpperCAmelCase       :       Any=False    ,							_UpperCAmelCase       :       List[Any]=True    ,							**_UpperCAmelCase       :       int    ,							):
       super().__init__(
           _UpperCAmelCase    ,							_UpperCAmelCase    ,							tokenizer_file=_UpperCAmelCase    ,							errors=_UpperCAmelCase    ,							bos_token=_UpperCAmelCase    ,							eos_token=_UpperCAmelCase    ,							sep_token=_UpperCAmelCase    ,							cls_token=_UpperCAmelCase    ,							unk_token=_UpperCAmelCase    ,							pad_token=_UpperCAmelCase    ,							mask_token=_UpperCAmelCase    ,							add_prefix_space=_UpperCAmelCase    ,							trim_offsets=_UpperCAmelCase    ,							**_UpperCAmelCase    ,							)

       _A					   =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
       if pre_tok_state.get('add_prefix_space'    ,							_UpperCAmelCase		) != add_prefix_space:
             _A					   =   getattr(_UpperCAmelCase    ,							pre_tok_state.pop('type'		)		)
             _A					   =   add_prefix_space
             _A					   =   pre_tok_class(**_UpperCAmelCase		)

       _A					   =   add_prefix_space

       _A					   =   "post_processor"
       _A					   =   getattr(self.backend_tokenizer    ,							_UpperCAmelCase    ,							_UpperCAmelCase		)
       if tokenizer_component_instance:
             _A					   =   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					   =   tuple(state['sep']		)
             if "cls" in state:
                   _A					   =   tuple(state['cls']		)

             _A					   =   False

             if state.get('add_prefix_space'    ,							_UpperCAmelCase		) != add_prefix_space:
                   _A					   =   add_prefix_space
                   _A					   =   True

             if state.get('trim_offsets'    ,							_UpperCAmelCase		) != trim_offsets:
                   _A					   =   trim_offsets
                   _A					   =   True

             if changes_to_apply:
                   _A					   =   getattr(_UpperCAmelCase    ,							state.pop('type'		)		)
                   _A					   =   component_class(**_UpperCAmelCase		)
                   setattr(self.backend_tokenizer    ,							_UpperCAmelCase    ,							_UpperCAmelCase		)

 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 			lowerCAmelCase_							( self       :       Union[str, Any]		):






       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 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Optional[int]		):
       _A					   =   AddedToken(_UpperCAmelCase    ,							lstrip=_UpperCAmelCase    ,							rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase    ,							_UpperCAmelCase		) else value
       _A					   =   value

 def 			lowerCAmelCase_							( self       :       Any    ,							*_UpperCAmelCase       :       List[str]    ,							**_UpperCAmelCase       :       Tuple		):
       _A					   =   kwargs.get('is_split_into_words'    ,							_UpperCAmelCase		)
       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(*_UpperCAmelCase    ,							**_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       Dict    ,							*_UpperCAmelCase       :       int    ,							**_UpperCAmelCase       :       Dict		):
       _A					   =   kwargs.get('is_split_into_words'    ,							_UpperCAmelCase		)

       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(*_UpperCAmelCase    ,							**_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       str    ,							_UpperCAmelCase       :       Optional[str] = None		):
       _A					   =   self._tokenizer.model.save(_UpperCAmelCase    ,							name=_UpperCAmelCase		)
       return tuple(_UpperCAmelCase		)

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       List[int]    ,							_UpperCAmelCase       :       Optional[List[int]] = None		):
       _A					   =   [self.sep_token_id]
       _A					   =   [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 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       List[int]    ,							_UpperCAmelCase       :       Optional[List[int]] = None		):
       return token_ids_a + [self.eos_token_id]



 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       "Conversation"		):
       _A					   =   []
       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(_UpperCAmelCase		)

       _A					   =   "  ".join(_UpperCAmelCase		)
       _A					   =   self.encode(_UpperCAmelCase		)
       if len(_UpperCAmelCase		) > self.model_max_length:
             _A					   =   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









def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     while b:
          UpperCamelCase__				,     UpperCamelCase__			:   int       				=  b, a % b
     return a







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase					,    a % b			)







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5			)}'			)
     print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3			)}'			)
     print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3			)}'			)
     print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6			)}'			)
     print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3			)}'			)

     print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5			)}'			)
     print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3			)}'			)
     print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3			)}'			)
     print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6			)}'			)
     print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3			)}'			)


if __name__ == "__main__":
 main()