





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

_UpperCAmelCase				:       int	   =				{
    """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				:       Optional[int]	   =				{
    """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    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		["""input_ids""", """attention_mask"""]
 UpperCAmelCase__													=		RobertaTokenizer
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=None			,     UpperCAmelCase  :      Dict=None			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      List[Any]="replace"			,     UpperCAmelCase  :      Any="<s>"			,     UpperCAmelCase  :      Union[str, Any]="</s>"			,     UpperCAmelCase  :      int="</s>"			,     UpperCAmelCase  :      Optional[Any]="<s>"			,     UpperCAmelCase  :      Optional[Any]="<unk>"			,     UpperCAmelCase  :      List[Any]="<pad>"			,     UpperCAmelCase  :      int="<mask>"			,     UpperCAmelCase  :      int=False			,     UpperCAmelCase  :      str=True			,     **UpperCAmelCase  :      Tuple			,     )			->	List[str]:
     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__    :      Dict					 =		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__    :      Tuple					 =		pre_tok_class(**UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		add_prefix_space

     lowerCamelCase__    :      str					 =		'post_processor'
     lowerCamelCase__    :      Union[str, Any]					 =		getattr(self.backend_tokenizer			,     UpperCAmelCase			,     UpperCAmelCase	)
     if tokenizer_component_instance:
         lowerCamelCase__    :      str					 =		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__    :      int					 =		tuple(state['sep']	)
         if "cls" in state:
             lowerCamelCase__    :      str					 =		tuple(state['cls']	)

         lowerCamelCase__    :      Union[str, Any]					 =		False

         if state.get('add_prefix_space'			,     UpperCAmelCase	) != add_prefix_space:
             lowerCamelCase__    :      int					 =		add_prefix_space
             lowerCamelCase__    :      int					 =		True

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

         if changes_to_apply:
             lowerCamelCase__    :      Optional[int]					 =		getattr(UpperCAmelCase			,     state.pop('type'	)	)
             lowerCamelCase__    :      str					 =		component_class(**UpperCAmelCase	)
             setattr(self.backend_tokenizer			,     UpperCAmelCase			,     UpperCAmelCase	)
 @property
 def      A_	(      self  :      Optional[int]	)			->	str:
     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  :      Optional[int]			,     UpperCAmelCase  :      List[str]	)			->	str:
     lowerCamelCase__    :      List[str]					 =		AddedToken(UpperCAmelCase			,     lstrip=UpperCAmelCase			,     rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase			,     UpperCAmelCase	) else value
     lowerCamelCase__    :      Union[str, Any]					 =		value
 def      A_	(      self  :      int			,     *UpperCAmelCase  :      Union[str, Any]			,     **UpperCAmelCase  :      Any	)			->	BatchEncoding:
     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()._batch_encode_plus(*UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]			,     *UpperCAmelCase  :      int			,     **UpperCAmelCase  :      Optional[Any]	)			->	BatchEncoding:
     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      A_	(      self  :      Any			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     lowerCamelCase__    :      int					 =		self._tokenizer.model.save(UpperCAmelCase			,     name=UpperCAmelCase	)
     return tuple(UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[Any]=None	)			->	Dict:
     lowerCamelCase__    :      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      A_	(      self  :      Any			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      List[str]					 =		[self.sep_token_id]
     lowerCamelCase__    :      List[Any]					 =		[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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





