
"""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


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 			A__	(     _lowerCamelCase):
		A_						:	List[Any]           =     VOCAB_FILES_NAMES
		A_						:	List[Any]           =     PRETRAINED_VOCAB_FILES_MAP
		A_						:	Optional[int]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	str           =     ['input_ids', 'attention_mask']
		A_						:	Optional[Any]           =     RobertaTokenizer

		def __init__(				self	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE="replace"	,		_SCREAMING_SNAKE_CASE="<s>"	,		_SCREAMING_SNAKE_CASE="</s>"	,		_SCREAMING_SNAKE_CASE="</s>"	,		_SCREAMING_SNAKE_CASE="<s>"	,		_SCREAMING_SNAKE_CASE="<unk>"	,		_SCREAMING_SNAKE_CASE="<pad>"	,		_SCREAMING_SNAKE_CASE="<mask>"	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=True	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(
			    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		tokenizer_file=_SCREAMING_SNAKE_CASE	,		errors=_SCREAMING_SNAKE_CASE	,		bos_token=_SCREAMING_SNAKE_CASE	,		eos_token=_SCREAMING_SNAKE_CASE	,		sep_token=_SCREAMING_SNAKE_CASE	,		cls_token=_SCREAMING_SNAKE_CASE	,		unk_token=_SCREAMING_SNAKE_CASE	,		pad_token=_SCREAMING_SNAKE_CASE	,		mask_token=_SCREAMING_SNAKE_CASE	,		add_prefix_space=_SCREAMING_SNAKE_CASE	,		trim_offsets=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)

			__lowerCAmelCase      :						Optional[int]						      =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
			if pre_tok_state.get('add_prefix_space'	,		_SCREAMING_SNAKE_CASE			) != add_prefix_space:
				__lowerCAmelCase      :						Dict						      =				getattr(_SCREAMING_SNAKE_CASE	,		pre_tok_state.pop('type'			)			)
				__lowerCAmelCase      :						List[str]						      =				add_prefix_space
				__lowerCAmelCase      :						List[str]						      =				pre_tok_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				add_prefix_space

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

				__lowerCAmelCase      :						List[str]						      =				False

				if state.get('add_prefix_space'	,		_SCREAMING_SNAKE_CASE			) != add_prefix_space:
					__lowerCAmelCase      :						List[Any]						      =				add_prefix_space
					__lowerCAmelCase      :						Optional[int]						      =				True

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

				if changes_to_apply:
					__lowerCAmelCase      :						List[Any]						      =				getattr(_SCREAMING_SNAKE_CASE	,		state.pop('type'			)			)
					__lowerCAmelCase      :						Optional[Any]						      =				component_class(**_SCREAMING_SNAKE_CASE			)
					setattr(self.backend_tokenizer	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

		@property
		def 							__lowerCamelCase							(				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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						int						      =				AddedToken(_SCREAMING_SNAKE_CASE	,		lstrip=_SCREAMING_SNAKE_CASE	,		rstrip=_SCREAMING_SNAKE_CASE			) if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) else value
			__lowerCAmelCase      :						Optional[int]						      =				value

		def 							__lowerCamelCase							(				self	,		*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						str						      =				kwargs.get('is_split_into_words'	,		_SCREAMING_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(*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						int						      =				kwargs.get('is_split_into_words'	,		_SCREAMING_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(*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						int						      =				self._tokenizer.model.save(_SCREAMING_SNAKE_CASE	,		name=_SCREAMING_SNAKE_CASE			)
			return tuple(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None			):
			__lowerCAmelCase      :						str						      =				[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	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						Dict						      =				[self.sep_token_id]
			__lowerCAmelCase      :						Tuple						      =				[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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels