


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_1_2,
    '''roberta-large''': 5_1_2,
    '''roberta-large-mnli''': 5_1_2,
    '''distilroberta-base''': 5_1_2,
    '''roberta-base-openai-detector''': 5_1_2,
    '''roberta-large-openai-detector''': 5_1_2,
}



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     ["""input_ids""", """attention_mask"""]
       _lowercase											=     RobertaTokenizer







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

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

            __UpperCamelCase =			add_prefix_space

            __UpperCamelCase =			'post_processor'
            __UpperCamelCase =			getattr(self.backend_tokenizer,A_,A_					)
            if tokenizer_component_instance:
                 __UpperCamelCase =			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(state['sep']					)
                 if "cls" in state:
                      __UpperCamelCase =			tuple(state['cls']					)

                 __UpperCamelCase =			False

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

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

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







       @property
       def 		snake_case_	(			self: Optional[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 		snake_case_	(			self: Dict,A_: List[str]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[int],*A_: Dict,**A_: str					):






            '''simple docstring'''




            __UpperCamelCase =			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 		snake_case_	(			self: Tuple,*A_: Dict,**A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			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 		snake_case_	(			self: Dict,A_: str,A_: Optional[str] = None					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any,A_: Optional[int],A_: Optional[int]=None					):






            '''simple docstring'''




            __UpperCamelCase =			[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 		snake_case_	(			self: Union[str, Any],A_: List[int],A_: Optional[List[int]] = None					):






            '''simple docstring'''




            __UpperCamelCase =			[self.sep_token_id]
            __UpperCamelCase =			[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]






import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


