

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


__lowercase  :   int						      =logging.get_logger(__name__)

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

__lowercase  :   Any						      ={
    """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"""
        ),
    },
}

__lowercase  :   Union[str, Any]						      ={
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}

class      A       (	__lowercase						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case							     =['''input_ids''', '''attention_mask''']
      _snake_case							     =RobertaTokenizer



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						Optional[int]=None		,  _lowerCAmelCase:						Dict=None		,  _lowerCAmelCase:						Union[str, Any]=None		,  _lowerCAmelCase:						Optional[Any]="replace"		,  _lowerCAmelCase:						Optional[Any]="<s>"		,  _lowerCAmelCase:						Union[str, Any]="</s>"		,  _lowerCAmelCase:						Dict="</s>"		,  _lowerCAmelCase:						Dict="<s>"		,  _lowerCAmelCase:						Optional[Any]="<unk>"		,  _lowerCAmelCase:						str="<pad>"		,  _lowerCAmelCase:						str="<mask>"		,  _lowerCAmelCase:						int=False		,  _lowerCAmelCase:						int=True		,  **_lowerCAmelCase:						Optional[int]		,  )						->    Optional[int]:
         '''simple docstring'''

         super().__init__(
             _lowerCAmelCase		,  _lowerCAmelCase		,  tokenizer_file=_lowerCAmelCase		,  errors=_lowerCAmelCase		,  bos_token=_lowerCAmelCase		,  eos_token=_lowerCAmelCase		,  sep_token=_lowerCAmelCase		,  cls_token=_lowerCAmelCase		,  unk_token=_lowerCAmelCase		,  pad_token=_lowerCAmelCase		,  mask_token=_lowerCAmelCase		,  add_prefix_space=_lowerCAmelCase		,  trim_offsets=_lowerCAmelCase		,  **_lowerCAmelCase		,  )

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

         UpperCAmelCase_											=add_prefix_space

         UpperCAmelCase_											="post_processor"
         UpperCAmelCase_											=getattr(self.backend_tokenizer		,  _lowerCAmelCase		,  _lowerCAmelCase       )
         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"		,  _lowerCAmelCase       ) != add_prefix_space:
               UpperCAmelCase_											=add_prefix_space
               UpperCAmelCase_											=True

            if state.get("trim_offsets"		,  _lowerCAmelCase       ) != trim_offsets:
               UpperCAmelCase_											=trim_offsets
               UpperCAmelCase_											=True

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



      @property
      def 	lowerCAmelCase__    (	self:						Any       )						->    str:
         '''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 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else value
         UpperCAmelCase_											=value



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  *_lowerCAmelCase:						List[str]		,  **_lowerCAmelCase:						List[Any]       )						->    BatchEncoding:
         '''simple docstring'''

         UpperCAmelCase_											=kwargs.get("is_split_into_words"		,  _lowerCAmelCase       )
         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(*_lowerCAmelCase		,  **_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Dict		,  *_lowerCAmelCase:						Union[str, Any]		,  **_lowerCAmelCase:						List[Any]       )						->    BatchEncoding:
         '''simple docstring'''

         UpperCAmelCase_											=kwargs.get("is_split_into_words"		,  _lowerCAmelCase       )

         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(*_lowerCAmelCase		,  **_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    Tuple[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self._tokenizer.model.save(_lowerCAmelCase		,  name=_lowerCAmelCase       )
         return tuple(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Optional[Any]=None       )						->    str:
         '''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 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''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 sys
import turtle




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    ):






   '''simple docstring'''






   my_pen.up()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.down()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )

   if depth == 0:
      return

   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )


if __name__ == "__main__":
     if len(sys.argv) != 2:
          raise ValueError(
              """Correct format for using this script: """
              """python fractals.py <int:depth_for_fractal>"""
          )
     __lowercase  :   Any						      =turtle.Turtle()
     my_pen.ht()
     my_pen.speed(5)
     my_pen.pencolor("""red""")

     __lowercase  :   str						      =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
     triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




