





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


lowerCAmelCase_						   =    logging.get_logger(__name__)

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

lowerCAmelCase_						   =    {
    """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"""
        ),
    },
}

lowerCAmelCase_						   =    {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       List[Any]      					=VOCAB_FILES_NAMES
  a_							:       Dict      					=PRETRAINED_VOCAB_FILES_MAP
  a_							:       str      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_							:       Tuple      					=["""input_ids""", """attention_mask"""]
  a_							:       Dict      					=RobertaTokenizer







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


    '''simple docstring'''





    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      ,				)

    _snake_case   :				List[str]														=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
    if pre_tok_state.get('add_prefix_space'      ,				UpperCamelCase							) != add_prefix_space:
      _snake_case   :				List[Any]														=	getattr(UpperCamelCase      ,				pre_tok_state.pop('type'							)							)
      _snake_case   :				List[str]														=	add_prefix_space
      _snake_case   :				Union[str, Any]														=	pre_tok_class(**UpperCamelCase							)

    _snake_case   :				str														=	add_prefix_space

    _snake_case   :				str														=	'post_processor'
    _snake_case   :				str														=	getattr(self.backend_tokenizer      ,				UpperCamelCase      ,				UpperCamelCase							)
    if tokenizer_component_instance:
      _snake_case   :				List[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:
        _snake_case   :				Optional[int]														=	tuple(state['sep']							)
      if "cls" in state:
        _snake_case   :				Any														=	tuple(state['cls']							)

      _snake_case   :				Optional[Any]														=	False

      if state.get('add_prefix_space'      ,				UpperCamelCase							) != add_prefix_space:
        _snake_case   :				Union[str, Any]														=	add_prefix_space
        _snake_case   :				Any														=	True

      if state.get('trim_offsets'      ,				UpperCamelCase							) != trim_offsets:
        _snake_case   :				Union[str, Any]														=	trim_offsets
        _snake_case   :				Optional[Any]														=	True

      if changes_to_apply:
        _snake_case   :				int														=	getattr(UpperCamelCase      ,				state.pop('type'							)							)
        _snake_case   :				Any														=	component_class(**UpperCamelCase							)
        setattr(self.backend_tokenizer      ,				UpperCamelCase      ,				UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''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 UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	AddedToken(UpperCamelCase      ,				lstrip=UpperCamelCase      ,				rstrip=UpperCamelCase							) if isinstance(UpperCamelCase      ,				UpperCamelCase							) else value
    _snake_case   :				Tuple														=	value







  def UpperCamelCase_ (      self      :					List[str]      ,				*UpperCamelCase      :					List[Any]      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				Dict														=	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      :					List[str]      ,				*UpperCamelCase      :					List[Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	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      :					Any      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[str] = None							):


    '''simple docstring'''





    _snake_case   :				Any														=	self._tokenizer.model.save(UpperCamelCase      ,				name=UpperCamelCase							)
    return tuple(UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[Any]=None							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	[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      :					Tuple      ,				UpperCamelCase      :					List[int]      ,				UpperCamelCase      :					Optional[List[int]] = None							):


    '''simple docstring'''





    _snake_case   :				Any														=	[self.sep_token_id]
    _snake_case   :				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]











from random import randint, random


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	int = 5    ,							)->		list:
  _snake_case   :				Dict														=	[[-1] * number_of_cells]  # Create a highway without any car
  _snake_case   :				List[str]														=	0
  _snake_case   :				List[str]														=	max(lowerCAmelCase    ,							0				)
  while i < number_of_cells:
    _snake_case   :				Optional[Any]														=	(
        randint(0    ,							lowerCAmelCase				) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1    ,							max_speed * 2				) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int				)->		int:
  _snake_case   :				Dict														=	0
  _snake_case   :				Optional[Any]														=	highway_now[car_index + 1 :]
  for cell in range(len(lowerCAmelCase				)				):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(lowerCAmelCase    ,							-1				)


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	len(lowerCAmelCase				)
  # Beforce calculations, the highway is empty
  _snake_case   :				List[Any]														=	[-1] * number_of_cells

  for car_index in range(lowerCAmelCase				):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _snake_case   :				int														=	min(highway_now[car_index] + 1    ,							lowerCAmelCase				)
      # Number of empty cell before the next car
      _snake_case   :				Tuple														=	get_distance(lowerCAmelCase    ,							lowerCAmelCase				) - 1
      # We can't have the car causing an accident
      _snake_case   :				Union[str, Any]														=	min(next_highway[car_index]    ,							lowerCAmelCase				)
      if random() < probability:
        # Randomly, a driver will slow down
        _snake_case   :				int														=	max(next_highway[car_index] - 1    ,							0				)
  return next_highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				Dict														=	len(highway[0]				)

  for i in range(lowerCAmelCase				):
    _snake_case   :				Any														=	update(highway[i]    ,							lowerCAmelCase    ,							lowerCAmelCase				)
    _snake_case   :				Tuple														=	[-1] * number_of_cells

    for car_index in range(lowerCAmelCase				):
      _snake_case   :				Union[str, Any]														=	next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _snake_case   :				Union[str, Any]														=	(car_index + speed) % number_of_cells
        # Commit the change of position
        _snake_case   :				Tuple														=	speed
    highway.append(lowerCAmelCase				)

  return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




