




import json
from typing import 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_mvp import MvpTokenizer


lowerCamelCase       :	str									=     logging.get_logger(__name__)


lowerCamelCase       :	Optional[Any]									=     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase       :	Dict									=     {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

lowerCamelCase       :	Dict									=     {
    "RUCAIBox/mvp": 1_024,
}







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		VOCAB_FILES_NAMES
 UpperCamelCase		     =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		     =		['''input_ids''', '''attention_mask''']
 UpperCamelCase		     =		MvpTokenizer

 def __init__(       self      :						List[str]		,							A_      :						Optional[Any]=None		,							A_      :						str=None		,							A_      :						Dict=None		,							A_      :						Optional[int]="replace"		,							A_      :						str="<s>"		,							A_      :						Any="</s>"		,							A_      :						Optional[Any]="</s>"		,							A_      :						int="<s>"		,							A_      :						Optional[Any]="<unk>"		,							A_      :						Tuple="<pad>"		,							A_      :						List[Any]="<mask>"		,							A_      :						Optional[Any]=False		,							A_      :						Union[str, Any]=True		,							**A_      :						Any		,							)     ->			List[Any]:

       """simple docstring"""






       super().__init__(
           A_		,							A_		,							tokenizer_file=A_		,							errors=A_		,							bos_token=A_		,							eos_token=A_		,							sep_token=A_		,							cls_token=A_		,							unk_token=A_		,							pad_token=A_		,							mask_token=A_		,							add_prefix_space=A_		,							trim_offsets=A_		,							**A_		,							)

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

       lowerCamelCase_	  =					add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       lowerCamelCase_	  =					'post_processor'
       lowerCamelCase_	  =					getattr(self.backend_tokenizer		,							A_		,							A_						)
       if tokenizer_component_instance:
             lowerCamelCase_	  =					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:
                   lowerCamelCase_	  =					tuple(state['sep']						)
             if "cls" in state:
                   lowerCamelCase_	  =					tuple(state['cls']						)

             lowerCamelCase_	  =					False

             if state.get('add_prefix_space'		,							A_						) != add_prefix_space:
                   lowerCamelCase_	  =					add_prefix_space
                   lowerCamelCase_	  =					True

             if state.get('trim_offsets'		,							A_						) != trim_offsets:
                   lowerCamelCase_	  =					trim_offsets
                   lowerCamelCase_	  =					True

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

 @property
 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       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        a__				(       self      :						List[str]		,							A_      :						Any						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					AddedToken(A_		,							lstrip=A_		,							rstrip=A_						) if isinstance(A_		,							A_						) else value
       lowerCamelCase_	  =					value

 def        a__				(       self      :						Optional[int]		,							*A_      :						Optional[int]		,							**A_      :						int						)     ->			BatchEncoding:

       """simple docstring"""






       lowerCamelCase_	  =					kwargs.get('is_split_into_words'		,							A_						)

       if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 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_      :						List[Any]		,							**A_      :						Tuple						)     ->			BatchEncoding:

       """simple docstring"""






       lowerCamelCase_	  =					kwargs.get('is_split_into_words'		,							A_						)

       if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 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      :						Union[str, Any]		,							A_      :						str		,							A_      :						Optional[str] = None						)     ->			Tuple[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self._tokenizer.model.save(A_		,							name=A_						)
       return tuple(A_						)

 def        a__				(       self      :						List[str]		,							A_      :						int		,							A_      :						List[Any]=None						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
             return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





 def        a__				(       self      :						Dict		,							A_      :						List[int]		,							A_      :						Optional[List[int]] = None						)     ->			List[int]:

       """simple docstring"""






       lowerCamelCase_	  =					[self.sep_token_id]
       lowerCamelCase_	  =					[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     _SCREAMING_SNAKE_CASE    (							lowercase			: list[int]			,							lowercase			: list[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					len(lowercase  )
      print('The following activities are selected:'  )

      # The first activity is always selected
      lowerCamelCase_	  =					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=','  )
                  lowerCamelCase_	  =					j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase       :	Tuple									=     [1, 3, 0, 5, 8, 5]
 lowerCamelCase       :	int									=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)



