






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_roberta import RobertaTokenizer


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase__       		=  {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Dict								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase				:					Optional[Any]								=			["input_ids", "attention_mask"]
       lowerCamelCase				:					str								=			RobertaTokenizer



       def __init__(				self      ,    a__=None      ,    a__=None      ,    a__=None      ,    a__="replace"      ,    a__="<s>"      ,    a__="</s>"      ,    a__="</s>"      ,    a__="<s>"      ,    a__="<unk>"      ,    a__="<pad>"      ,    a__="<mask>"      ,    a__=False      ,    a__=True      ,    **a__      ,    )			->   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__      ,    )

            __snake_case      :Dict 							=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
            if pre_tok_state.get("""add_prefix_space"""      ,    a__     ) != add_prefix_space:
                 __snake_case      :Optional[int] 							=    getattr(a__      ,    pre_tok_state.pop("""type"""     )     )
                 __snake_case      :Dict 							=    add_prefix_space
                 __snake_case      :Optional[int] 							=    pre_tok_class(**a__     )

            __snake_case      :str 							=    add_prefix_space

            __snake_case      :Union[str, Any] 							=    """post_processor"""
            __snake_case      :Optional[Any] 							=    getattr(self.backend_tokenizer      ,    a__      ,    a__     )
            if tokenizer_component_instance:
                 __snake_case      :Optional[Any] 							=    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:
                      __snake_case      :Union[str, Any] 							=    tuple(state["""sep"""]     )
                 if "cls" in state:
                      __snake_case      :Tuple 							=    tuple(state["""cls"""]     )

                 __snake_case      :Union[str, Any] 							=    False

                 if state.get("""add_prefix_space"""      ,    a__     ) != add_prefix_space:
                      __snake_case      :Dict 							=    add_prefix_space
                      __snake_case      :List[str] 							=    True

                 if state.get("""trim_offsets"""      ,    a__     ) != trim_offsets:
                      __snake_case      :List[str] 							=    trim_offsets
                      __snake_case      :Tuple 							=    True

                 if changes_to_apply:
                      __snake_case      :int 							=    getattr(a__      ,    state.pop("""type"""     )     )
                      __snake_case      :Optional[Any] 							=    component_class(**a__     )
                      setattr(self.backend_tokenizer      ,    a__      ,    a__     )



       @property
       def        __lowercase    (				self     )			->   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        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    AddedToken(a__      ,    lstrip=a__      ,    rstrip=a__     ) if isinstance(a__      ,    a__     ) else value
            __snake_case      :Optional[int] 							=    value



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   BatchEncoding:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    kwargs.get("""is_split_into_words"""      ,    a__     )
            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(*a__      ,    **a__     )



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   BatchEncoding:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    kwargs.get("""is_split_into_words"""      ,    a__     )

            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(*a__      ,    **a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            __snake_case      :int 							=    self._tokenizer.model.save(a__      ,    name=a__     )
            return tuple(a__     )



       def        __lowercase    (				self      ,    a__      ,    a__=None     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    [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        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :int 							=    [self.sep_token_id]
            __snake_case      :Dict 							=    [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 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


