


'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


UpperCAmelCase_			:  Optional[Any]										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_			:  Any										=   {
    'tokenizer_file': {
        'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
        'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
        'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
        'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
        'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
        'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
        'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
    },
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    VOCAB_FILES_NAMES
 __lowercase					:		Dict     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		Any     				=    ['''input_ids''', '''attention_mask''']
 __lowercase					:		List[str]     				=    None


 def __init__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase="<unk>"   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="<pad>"   ,   __lowercase=False   ,   __lowercase=False   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   __lowercase   ,   tokenizer_file=__lowercase   ,   unk_token=__lowercase   ,   bos_token=__lowercase   ,   eos_token=__lowercase   ,   pad_token=__lowercase   ,   add_prefix_space=__lowercase   ,   clean_up_tokenization_spaces=__lowercase   ,   **__lowercase   ,   )
  __A	:		Tuple	  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
   __A	:		Tuple	  =      getattr(__lowercase   ,   pre_tok_state.pop('type'    )    )
   __A	:		Optional[Any]	  =      add_prefix_space
   __A	:		Dict	  =      pre_tok_class(**__lowercase    )

  __A	:		int	  =      add_prefix_space


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







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

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


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )

  if not (self.add_prefix_space or not is_split_into_words):
   raise Exception(
       F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
       ' pretokenized inputs.'    )

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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







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







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(__lowercase   ,   add_special_tokens=__lowercase    ) + [self.eos_token_id]    )

  if len(__lowercase    ) > self.model_max_length:
   __A	:		Optional[Any]	  =      input_ids[-self.model_max_length :]
  return input_ids










'''simple docstring'''
def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Dict	  =      set()
 # edges = list of graph's edges
 __A	:		Any	  =      get_edges(UpperCamelCase__      )

 # While there are still elements in edges list, take an arbitrary edge
 # (from_node, to_node) and add his extremity to chosen_vertices and then
 # remove all arcs adjacent to the from_node and to_node
 while edges:
  __A  ,__A	:		List[Any]	  =      edges.pop()
  chosen_vertices.add(UpperCamelCase__      )
  chosen_vertices.add(UpperCamelCase__      )
  for edge in edges.copy():
   if from_node in edge or to_node in edge:
    edges.discard(UpperCamelCase__      )
 return chosen_vertices


def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Any	  =      set()
 for from_node, to_nodes in graph.items():
  for to_node in to_nodes:
   edges.add((from_node, to_node)      )
 return edges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")






