
"""simple docstring"""





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_       :    Tuple						      =			logging.get_logger(__name__)

UpperCamelCase_       :    Dict						      =			{'''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_       :    List[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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			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    :       int							, _snake_case    :       Tuple=None							, _snake_case    :       List[Any]=None							, _snake_case    :       Any=None							, _snake_case    :       List[Any]="replace"							, _snake_case    :       Dict="<s>"							, _snake_case    :       Dict="</s>"							, _snake_case    :       Optional[Any]="</s>"							, _snake_case    :       List[str]="<s>"							, _snake_case    :       str="<unk>"							, _snake_case    :       Optional[int]="<pad>"							, _snake_case    :       Union[str, Any]="<mask>"							, _snake_case    :       List[str]=False							, _snake_case    :       Tuple=True							, **_snake_case    :       List[Any]							, )   ->			int:







								"""simple docstring"""
								super().__init__(
								    _snake_case							, _snake_case							, tokenizer_file=_snake_case							, errors=_snake_case							, bos_token=_snake_case							, eos_token=_snake_case							, sep_token=_snake_case							, cls_token=_snake_case							, unk_token=_snake_case							, pad_token=_snake_case							, mask_token=_snake_case							, add_prefix_space=_snake_case							, trim_offsets=_snake_case							, **_snake_case							, )

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

								A_								=    add_prefix_space

								A_								=    "post_processor"
								A_								=    getattr(self.backend_tokenizer							, _snake_case							, _snake_case						)
								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"							, _snake_case						) != add_prefix_space:
																		A_								=    add_prefix_space
																		A_								=    True

													if state.get("trim_offsets"							, _snake_case						) != trim_offsets:
																		A_								=    trim_offsets
																		A_								=    True

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




			@property
			def   lowerCamelCase__  (					self    :       Optional[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    :       int							, _snake_case    :       str						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    AddedToken(_snake_case							, lstrip=_snake_case							, rstrip=_snake_case						) if isinstance(_snake_case							, _snake_case						) else value
								A_								=    value




			def   lowerCamelCase__  (					self    :       List[str]							, *_snake_case    :       str							, **_snake_case    :       List[str]						)   ->			BatchEncoding:







								"""simple docstring"""
								A_								=    kwargs.get("is_split_into_words"							, _snake_case						)
								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(*_snake_case							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Dict							, *_snake_case    :       Union[str, Any]							, **_snake_case    :       List[str]						)   ->			BatchEncoding:







								"""simple docstring"""
								A_								=    kwargs.get("is_split_into_words"							, _snake_case						)

								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(*_snake_case							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       str							, _snake_case    :       Optional[str] = None						)   ->			Tuple[str]:







								"""simple docstring"""
								A_								=    self._tokenizer.model.save(_snake_case							, name=_snake_case						)
								return tuple(_snake_case						)




			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       Optional[Any]							, _snake_case    :       Union[str, Any]=None						)   ->			str:







								"""simple docstring"""
								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   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       List[int]							, _snake_case    :       Optional[List[int]] = None						)   ->			List[int]:







								"""simple docstring"""
								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]






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_       :    Dict						      =			{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    int						      =			['''PLBartTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Optional[int]						      =			[
		    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PLBartForCausalLM''',
		    '''PLBartForConditionalGeneration''',
		    '''PLBartForSequenceClassification''',
		    '''PLBartModel''',
		    '''PLBartPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_plbart import PLBartTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_plbart import (
				    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PLBartForCausalLM,
				    PLBartForConditionalGeneration,
				    PLBartForSequenceClassification,
				    PLBartModel,
				    PLBartPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    Optional[int]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




