
'''simple docstring'''

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


UpperCamelCase_        =							logging.get_logger(__name__)

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

UpperCamelCase_        =							{
    """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"""
        ),
    },
}

UpperCamelCase_        =							{
    """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 	a_	(__SCREAMING_SNAKE_CASE     ):
       __lowerCAmelCase     :					Tuple     =   VOCAB_FILES_NAMES
       __lowerCAmelCase     :					Union[str, Any]     =   PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase     :					Optional[Any]     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase     :					str     =   ["""input_ids""", """attention_mask"""]
       __lowerCAmelCase     :					Any     =   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_ , ):
         super().__init__(
             UpperCamelCase__ , UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , errors=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ , **UpperCamelCase__ , )

         _lowerCAmelCase							:	List[Any] 					=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
         if pre_tok_state.get("""add_prefix_space""" , UpperCamelCase__	) != add_prefix_space:
           _lowerCAmelCase							:	int 					=       getattr(UpperCamelCase__ , pre_tok_state.pop("""type"""	)	)
           _lowerCAmelCase							:	int 					=       add_prefix_space
           _lowerCAmelCase							:	List[Any] 					=       pre_tok_class(**UpperCamelCase__	)

         _lowerCAmelCase							:	Dict 					=       add_prefix_space

         _lowerCAmelCase							:	List[Any] 					=       '''post_processor'''
         _lowerCAmelCase							:	Any 					=       getattr(self.backend_tokenizer , UpperCamelCase__ , UpperCamelCase__	)
         if tokenizer_component_instance:
           _lowerCAmelCase							:	Tuple 					=       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 					=       tuple(state["""sep"""]	)
           if "cls" in state:
             _lowerCAmelCase							:	Optional[Any] 					=       tuple(state["""cls"""]	)

           _lowerCAmelCase							:	Any 					=       False

           if state.get("""add_prefix_space""" , UpperCamelCase__	) != add_prefix_space:
             _lowerCAmelCase							:	Dict 					=       add_prefix_space
             _lowerCAmelCase							:	Optional[int] 					=       True

           if state.get("""trim_offsets""" , UpperCamelCase__	) != trim_offsets:
             _lowerCAmelCase							:	Optional[int] 					=       trim_offsets
             _lowerCAmelCase							:	Optional[Any] 					=       True

           if changes_to_apply:
             _lowerCAmelCase							:	List[Any] 					=       getattr(UpperCamelCase__ , state.pop("""type"""	)	)
             _lowerCAmelCase							:	List[str] 					=       component_class(**UpperCamelCase__	)
             setattr(self.backend_tokenizer , UpperCamelCase__ , UpperCamelCase__	)




       @property
       def 				__UpperCamelCase							(  self	):







         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_	):
         _lowerCAmelCase							:	Union[str, Any] 					=       AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__ , UpperCamelCase__	) else value
         _lowerCAmelCase							:	Tuple 					=       value




       def 				__UpperCamelCase							(  self , *snake_case_ , **snake_case_	):
         _lowerCAmelCase							:	Any 					=       kwargs.get("""is_split_into_words""" , UpperCamelCase__	)
         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(*UpperCamelCase__ , **UpperCamelCase__	)




       def 				__UpperCamelCase							(  self , *snake_case_ , **snake_case_	):
         _lowerCAmelCase							:	Optional[Any] 					=       kwargs.get("""is_split_into_words""" , UpperCamelCase__	)

         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(*UpperCamelCase__ , **UpperCamelCase__	)




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_ = None	):
         _lowerCAmelCase							:	Optional[Any] 					=       self._tokenizer.model.save(UpperCamelCase__ , name=UpperCamelCase__	)
         return tuple(UpperCamelCase__	)




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_=None	):
         _lowerCAmelCase							:	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	):
         _lowerCAmelCase							:	List[Any] 					=       [self.sep_token_id]
         _lowerCAmelCase							:	str 					=       [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]




'''simple docstring'''
import numpy as np




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							np.ndarray:


		'''simple docstring'''


		return 1 / (1 + np.exp(-vector			))




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							np.ndarray:


		'''simple docstring'''


		return vector * sigmoid(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


