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


__snake_case						=			logging.get_logger(__name__)

__snake_case						=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case						=			{
    '''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'''
        ),
    },
}

__snake_case						=			{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}







class 	__snake_case      (   A__      ):
 __lowerCamelCase			:      str           =					VOCAB_FILES_NAMES
 __lowerCamelCase			:      Tuple           =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase			:      str           =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase			:      Optional[Any]           =					["""input_ids""", """attention_mask"""]
 __lowerCamelCase			:      Tuple           =					RobertaTokenizer
 def __init__(     self						,      snake_case__=None						,      snake_case__=None						,      snake_case__=None						,      snake_case__="replace"						,      snake_case__="<s>"						,      snake_case__="</s>"						,      snake_case__="</s>"						,      snake_case__="<s>"						,      snake_case__="<unk>"						,      snake_case__="<pad>"						,      snake_case__="<mask>"						,      snake_case__=False						,      snake_case__=True						,      **snake_case__						,      )		->							Optional[int]:
       '''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						,      )

       UpperCAmelCase			:       Optional[int]			     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
       if pre_tok_state.get('''add_prefix_space'''						,      _A  ) != add_prefix_space:
             UpperCAmelCase			:       Union[str, Any]			     =getattr(_A						,      pre_tok_state.pop('''type'''  )  )
             UpperCAmelCase			:       Optional[int]			     =add_prefix_space
             UpperCAmelCase			:       Optional[int]			     =pre_tok_class(**_A  )

       UpperCAmelCase			:       List[Any]			     =add_prefix_space

       UpperCAmelCase			:       Optional[int]			     ="post_processor"
       UpperCAmelCase			:       Optional[int]			     =getattr(self.backend_tokenizer						,      _A						,      _A  )
       if tokenizer_component_instance:
             UpperCAmelCase			:       Dict			     =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:
                   UpperCAmelCase			:       Tuple			     =tuple(state['''sep''']  )
             if "cls" in state:
                   UpperCAmelCase			:       Optional[Any]			     =tuple(state['''cls''']  )

             UpperCAmelCase			:       Optional[int]			     =False

             if state.get('''add_prefix_space'''						,      _A  ) != add_prefix_space:
                   UpperCAmelCase			:       List[Any]			     =add_prefix_space
                   UpperCAmelCase			:       int			     =True

             if state.get('''trim_offsets'''						,      _A  ) != trim_offsets:
                   UpperCAmelCase			:       List[Any]			     =trim_offsets
                   UpperCAmelCase			:       List[Any]			     =True

             if changes_to_apply:
                   UpperCAmelCase			:       int			     =getattr(_A						,      state.pop('''type'''  )  )
                   UpperCAmelCase			:       str			     =component_class(**_A  )
                   setattr(self.backend_tokenizer						,      _A						,      _A  )
 @property
 def 		UpperCAmelCase__				(     self  )		->							Union[str, Any]:
       '''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 		UpperCAmelCase__				(     self						,      snake_case__  )		->							int:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =AddedToken(_A						,      lstrip=_A						,      rstrip=_A  ) if isinstance(_A						,      _A  ) else value
       UpperCAmelCase			:       Union[str, Any]			     =value
 def 		UpperCAmelCase__				(     self						,      *snake_case__						,      **snake_case__  )		->							str:
       '''simple docstring'''





       UpperCAmelCase			:       Any			     =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 		UpperCAmelCase__				(     self						,      *snake_case__						,      **snake_case__  )		->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase			:       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 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = None  )		->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     =self._tokenizer.model.save(_A						,      name=_A  )
       return tuple(_A  )
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__=None  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     =[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 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = None  )		->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =[self.sep_token_id]
       UpperCAmelCase			:       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]

from functools import lru_cache




@lru_cache
def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		if num < 0:
				raise ValueError("Number should not be negative."	)

		return 1 if num in (0, 1) else num * factorial(num - 1	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
