





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      __SCREAMING_SNAKE_CASE    (						a__   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:str	     =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__							:Dict	     =					None

  def __init__(		self   :       Optional[int]					,   __a   :       Optional[int]=None					,   __a   :       Tuple=None					,   __a   :       int=None					,   __a   :       str="<unk>"					,   __a   :       Optional[Any]="<s>"					,   __a   :       List[Any]="</s>"					,   __a   :       Optional[int]="<pad>"					,   __a   :       str=False					,   __a   :       Tuple=False					,   **__a   :       Optional[int]					,   )		->				Optional[int]:
        super().__init__(
            lowerCAmelCase__					,   lowerCAmelCase__					,   tokenizer_file=lowerCAmelCase__					,   unk_token=lowerCAmelCase__					,   bos_token=lowerCAmelCase__					,   eos_token=lowerCAmelCase__					,   pad_token=lowerCAmelCase__					,   add_prefix_space=lowerCAmelCase__					,   clean_up_tokenization_spaces=lowerCAmelCase__					,   **lowerCAmelCase__					,   )
        _UpperCamelCase      :	Any						=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"					,   lowerCAmelCase__	) != add_prefix_space:
              _UpperCamelCase      :	Union[str, Any]						=       getattr(lowerCAmelCase__					,   pre_tok_state.pop("type"	)	)
              _UpperCamelCase      :	Optional[int]						=       add_prefix_space
              _UpperCamelCase      :	Any						=       pre_tok_class(**lowerCAmelCase__	)

        _UpperCamelCase      :	Optional[int]						=       add_prefix_space

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   *__a   :       List[Any]					,   **__a   :       Optional[int]	)		->				BatchEncoding:
        _UpperCamelCase      :	Dict						=       kwargs.get("is_split_into_words"					,   lowerCAmelCase__	)
        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(*lowerCAmelCase__					,   **lowerCAmelCase__	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   *__a   :       str					,   **__a   :       List[Any]	)		->				BatchEncoding:
        _UpperCamelCase      :	List[Any]						=       kwargs.get("is_split_into_words"					,   lowerCAmelCase__	)

        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(*lowerCAmelCase__					,   **lowerCAmelCase__	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       List[Any]					,   __a   :       int = None	)		->				Tuple[str]:
        _UpperCamelCase      :	Union[str, Any]						=       self._tokenizer.model.save(lowerCAmelCase__					,   name=lowerCAmelCase__	)
        return tuple(lowerCAmelCase__	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       str	)		->				List[int]:
        _UpperCamelCase      :	Dict						=       []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowerCAmelCase__					,   add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

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







"""simple docstring"""


def 					lowercase__    (					lowercase_		,lowercase_			)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       len(lowercase_			)
      print("The following activities are selected:"			)

      # The first activity is always selected
      _UpperCamelCase      :	List[Any]						=       0
      print(lowercase_		,end=","			)

      # Consider rest of the activities
      for j in range(lowercase_			):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase_		,end=","			)
                  _UpperCamelCase      :	Optional[Any]						=       j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase__				  =   [1, 3, 0, 5, 8, 5]
  lowerCamelCase__				  =   [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)
