



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_							:      int      =  logging.get_logger(__name__)

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

UpperCAmelCase_							:      Dict      =  {
    "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_							:      Dict      =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
 lowerCAmelCase	:   List[str]				=	VOCAB_FILES_NAMES
 lowerCAmelCase	:   Optional[Any]				=	PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase	:   List[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase	:   Optional[int]				=	["""input_ids""", """attention_mask"""]
 lowerCAmelCase	:   Tuple				=	RobertaTokenizer



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

       A__							=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
       if pre_tok_state.get("add_prefix_space"			,	UpperCAmelCase__		) != add_prefix_space:
             A__							=       getattr(UpperCAmelCase__			,	pre_tok_state.pop("type"		)		)
             A__							=       add_prefix_space
             A__							=       pre_tok_class(**UpperCAmelCase__		)

       A__							=       add_prefix_space

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

             A__							=       False

             if state.get("add_prefix_space"			,	UpperCAmelCase__		) != add_prefix_space:
                   A__							=       add_prefix_space
                   A__							=       True

             if state.get("trim_offsets"			,	UpperCAmelCase__		) != trim_offsets:
                   A__							=       trim_offsets
                   A__							=       True

             if changes_to_apply:
                   A__							=       getattr(UpperCAmelCase__			,	state.pop("type"		)		)
                   A__							=       component_class(**UpperCAmelCase__		)
                   setattr(self.backend_tokenizer			,	UpperCAmelCase__			,	UpperCAmelCase__		)



 @property
 def 			__A							(       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 			__A							(       self			,	UpperCAmelCase__		):
       A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else value
       A__							=       value



 def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
       A__							=       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 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
       A__							=       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
       A__							=       self._tokenizer.model.save(UpperCAmelCase__			,	name=UpperCAmelCase__		)
       return tuple(UpperCAmelCase__		)



 def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=None		):
       A__							=       [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			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
       A__							=       [self.sep_token_id]
       A__							=       [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 __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





						if not elements_list:
												raise Exception("The Elements List is empty"    )

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





