
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


_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      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'
        ),
    },
}

_SCREAMING_SNAKE_CASE    :      Any          =			{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]
	_lowerCAmelCase							       =				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__  :	Any         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	int         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	str         =							add_prefix_space
									A__  :	int         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	Any         =							add_prefix_space

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

									A__  :	Any         =							False

									if state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
													A__  :	Dict         =							add_prefix_space
													A__  :	Optional[Any]         =							True

									if state.get('''trim_offsets'''				,       UpperCamelCase__		) != trim_offsets:
													A__  :	Tuple         =							trim_offsets
													A__  :	Union[str, Any]         =							True

									if changes_to_apply:
													A__  :	Optional[Any]         =							getattr(UpperCamelCase__				,       state.pop('''type'''		)		)
													A__  :	Union[str, Any]         =							component_class(**UpperCamelCase__		)
													setattr(self.backend_tokenizer				,       UpperCamelCase__				,       UpperCamelCase__		)





	@property
	def   __snake_case				(			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   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							AddedToken(UpperCamelCase__				,       lstrip=UpperCamelCase__				,       rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__				,       UpperCamelCase__		) else value
					A__  :	List[str]         =							value





	def   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					A__  :	List[str]         =							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   __snake_case				(			self				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					A__  :	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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Union[str, Any]         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	Optional[int]         =							[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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	List[str]         =							[self.sep_token_id]
					A__  :	Optional[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]

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def   __snake_case				(			UpperCamelCase__		):
					raise NotImplementedError()





	@abstractmethod
	def   __snake_case				(			self		):
					raise NotImplementedError()