




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


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_						=			{
    '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 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Dict			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      int			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      ['''input_ids''', '''attention_mask''']
  _UpperCamelCase       :      Dict			=      None






  def __init__(  self		:     Any				,						_A		:     List[str]=None				,						_A		:     Optional[Any]=None				,						_A		:     int=None				,						_A		:     str="<unk>"				,						_A		:     List[Any]="<s>"				,						_A		:     Optional[int]="</s>"				,						_A		:     Optional[int]="<pad>"				,						_A		:     Union[str, Any]=False				,						_A		:     Union[str, Any]=False				,						**_A		:     List[Any]				,						)		->   str:






       """simple docstring"""







       super().__init__(
           _A				,						_A				,						tokenizer_file=_A				,						unk_token=_A				,						bos_token=_A				,						eos_token=_A				,						pad_token=_A				,						add_prefix_space=_A				,						clean_up_tokenization_spaces=_A				,						**_A				,						)
       lowercase :       str       =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''				,						_A				) != add_prefix_space:
            lowercase :       List[str]       =			getattr(_A				,						pre_tok_state.pop('''type'''				)				)
            lowercase :       Dict       =			add_prefix_space
            lowercase :       Union[str, Any]       =			pre_tok_class(**_A				)

       lowercase :       Union[str, Any]       =			add_prefix_space






  def  __a      (  self		:     Optional[int]				,						*_A		:     str				,						**_A		:     Any				)		->   BatchEncoding:






       """simple docstring"""







       lowercase :       Any       =			kwargs.get('''is_split_into_words'''				,						_A				)
       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(*_A				,						**_A				)






  def  __a      (  self		:     str				,						*_A		:     str				,						**_A		:     List[Any]				)		->   BatchEncoding:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			kwargs.get('''is_split_into_words'''				,						_A				)

       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(*_A				,						**_A				)






  def  __a      (  self		:     List[Any]				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       lowercase :       List[str]       =			self._tokenizer.model.save(_A				,						name=_A				)
       return tuple(_A				)






  def  __a      (  self		:     List[Any]				,						_A		:     "Conversation"				)		->   List[int]:






       """simple docstring"""







       lowercase :       Tuple       =			[]
       for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(_A				,						add_special_tokens=_A				) + [self.eos_token_id]				)

       if len(_A				) > self.model_max_length:
            lowercase :       int       =			input_ids[-self.model_max_length :]
       return input_ids





import math






def  snake_case(    __magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

     return exponent == int(__magic_name__   )






def  snake_case(    __magic_name__ = 1 / 1_23_45   )			->							int:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			0
     lowercase :       str       =			0

     lowercase :       Optional[int]       =			3
     while True:
          lowercase :       Any       =			(integer**2 - 1) / 4
          # if candidate is an integer, then there is a partition for k
          if partition_candidate == int(__magic_name__   ):
               lowercase :       Any       =			int(__magic_name__   )
               total_partitions += 1
               if check_partition_perfect(__magic_name__   ):
                    perfect_partitions += 1
          if perfect_partitions > 0:
               if perfect_partitions / total_partitions < max_proportion:
                    return int(__magic_name__   )
          integer += 1


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