



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__ =						logging.get_logger(__name__)

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

UpperCamelCase__ =						{
    "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__ =						{
    "roberta-base": 5_12,
    "roberta-large": 5_12,
    "roberta-large-mnli": 5_12,
    "distilroberta-base": 5_12,
    "roberta-base-openai-detector": 5_12,
    "roberta-large-openai-detector": 5_12,
}
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Dict      =			VOCAB_FILES_NAMES
			lowercase__      :  str      =			PRETRAINED_VOCAB_FILES_MAP
			lowercase__      :  Tuple      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__      :  Optional[Any]      =			["""input_ids""", """attention_mask"""]
			lowercase__      :  Tuple      =			RobertaTokenizer





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







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

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

									__lowercase					=				add_prefix_space

									__lowercase					=				"""post_processor"""
									__lowercase					=				getattr(self.backend_tokenizer			,     lowercase			,     lowercase   )
									if tokenizer_component_instance:
															__lowercase					=				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:
																					__lowercase					=				tuple(state["""sep"""]   )
															if "cls" in state:
																					__lowercase					=				tuple(state["""cls"""]   )

															__lowercase					=				False

															if state.get("""add_prefix_space"""			,     lowercase   ) != add_prefix_space:
																					__lowercase					=				add_prefix_space
																					__lowercase					=				True

															if state.get("""trim_offsets"""			,     lowercase   ) != trim_offsets:
																					__lowercase					=				trim_offsets
																					__lowercase					=				True

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





			@property
			def 		snake_case__			(				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 		snake_case__			(				self	:       Any			,     lowercase	:       Tuple   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				AddedToken(lowercase			,     lstrip=lowercase			,     rstrip=lowercase   ) if isinstance(lowercase			,     lowercase   ) else value
									__lowercase					=				value





			def 		snake_case__			(				self	:       List[Any]			,     *lowercase	:       Tuple			,     **lowercase	:       Dict   )						->					BatchEncoding:
									"""simple docstring"""







									__lowercase					=				kwargs.get("""is_split_into_words"""			,     lowercase   )
									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(*lowercase			,     **lowercase   )





			def 		snake_case__			(				self	:       Any			,     *lowercase	:       int			,     **lowercase	:       Optional[int]   )						->					BatchEncoding:
									"""simple docstring"""







									__lowercase					=				kwargs.get("""is_split_into_words"""			,     lowercase   )

									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(*lowercase			,     **lowercase   )





			def 		snake_case__			(				self	:       int			,     lowercase	:       str			,     lowercase	:       Optional[str] = None   )						->					Tuple[str]:
									"""simple docstring"""







									__lowercase					=				self._tokenizer.model.save(lowercase			,     name=lowercase   )
									return tuple(lowercase   )





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       Dict			,     lowercase	:       Optional[Any]=None   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				[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	:       Tuple			,     lowercase	:       List[int]			,     lowercase	:       Optional[List[int]] = None   )						->					List[int]:
									"""simple docstring"""







									__lowercase					=				[self.sep_token_id]
									__lowercase					=				[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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ =						{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "LukeForEntityClassification",
			    "LukeForEntityPairClassification",
			    "LukeForEntitySpanClassification",
			    "LukeForMultipleChoice",
			    "LukeForQuestionAnswering",
			    "LukeForSequenceClassification",
			    "LukeForTokenClassification",
			    "LukeForMaskedLM",
			    "LukeModel",
			    "LukePreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			UpperCamelCase__ =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





