




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

_lowerCamelCase      			=				logging.get_logger(__name__)


_lowerCamelCase      			=				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_lowerCamelCase      			=				{
    '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'
    },
}

_lowerCamelCase      			=				{'facebook/blenderbot-3B': 1_28}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  VOCAB_FILES_NAMES
     lowerCAmelCase    :	int       		=  PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase    :	Optional[Any]       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase    :	Dict       		=  ['input_ids', 'attention_mask']
     lowerCAmelCase    :	Optional[int]       		=  BlenderbotTokenizer
     def __init__(     self       :      str ,   __snake_case       :      Any=None ,   __snake_case       :      str=None ,   __snake_case       :      int=None ,   __snake_case       :      int="replace" ,   __snake_case       :      int="<s>" ,   __snake_case       :      str="</s>" ,   __snake_case       :      str="</s>" ,   __snake_case       :      int="<s>" ,   __snake_case       :      str="<unk>" ,   __snake_case       :      int="<pad>" ,   __snake_case       :      str="<mask>" ,   __snake_case       :      int=False ,   __snake_case       :      Dict=True ,   **__snake_case       :      Union[str, Any] ,   ):
         super().__init__(
             __snake_case ,   __snake_case ,   tokenizer_file=__snake_case ,   errors=__snake_case ,   bos_token=__snake_case ,   eos_token=__snake_case ,   sep_token=__snake_case ,   cls_token=__snake_case ,   unk_token=__snake_case ,   pad_token=__snake_case ,   mask_token=__snake_case ,   add_prefix_space=__snake_case ,   trim_offsets=__snake_case ,   **__snake_case ,   )

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

         UpperCAmelCase_										=					add_prefix_space

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

             UpperCAmelCase_										=					False

             if state.get('''add_prefix_space''' ,   __snake_case    ) != add_prefix_space:
                 UpperCAmelCase_										=					add_prefix_space
                 UpperCAmelCase_										=					True

             if state.get('''trim_offsets''' ,   __snake_case    ) != trim_offsets:
                 UpperCAmelCase_										=					trim_offsets
                 UpperCAmelCase_										=					True

             if changes_to_apply:
                 UpperCAmelCase_										=					getattr(__snake_case ,   state.pop('''type'''    )    )
                 UpperCAmelCase_										=					component_class(**__snake_case    )
                 setattr(self.backend_tokenizer ,   __snake_case ,   __snake_case    )
     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def   lowerCamelCase_       (     self       :      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       :      Dict ,   __snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					AddedToken(__snake_case ,   lstrip=__snake_case ,   rstrip=__snake_case    ) if isinstance(__snake_case ,   __snake_case    ) else value
         UpperCAmelCase_										=					value
     def   lowerCamelCase_       (     self       :      str ,   *__snake_case       :      Optional[int] ,   **__snake_case       :      Union[str, Any]    ):
         UpperCAmelCase_										=					kwargs.get('''is_split_into_words''' ,   __snake_case    )
         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(*__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      int ,   *__snake_case       :      Optional[int] ,   **__snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					kwargs.get('''is_split_into_words''' ,   __snake_case    )

         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(*__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      str ,   __snake_case       :      Optional[str] = None    ):
         UpperCAmelCase_										=					self._tokenizer.model.save(__snake_case ,   name=__snake_case    )
         return tuple(__snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
         UpperCAmelCase_										=					[self.sep_token_id]
         UpperCAmelCase_										=					[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       :      Any ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
         return token_ids_a + [self.eos_token_id]




     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      "Conversation"    ):
         UpperCAmelCase_										=					[]
         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(__snake_case    )

         UpperCAmelCase_										=					'''  '''.join(__snake_case    )
         UpperCAmelCase_										=					self.encode(__snake_case    )
         if len(__snake_case    ) > self.model_max_length:
             UpperCAmelCase_										=					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











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					max(self.edges.values()    ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


if __name__ == "__main__":
 print(F"{solution() = }")





