



'''simple docstring'''




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a     :    Dict      						=					logging.get_logger(__name__)

a     :    Tuple      						=					{"""vocab_file""": """spiece.model"""}

a     :    int      						=					{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

a     :    Tuple      						=					{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}

class     UpperCamelCase_				(							UpperCamelCase_    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				['input_ids', 'attention_mask']
	lowercase    =				[]





	def __init__(						self	,	A	,	A="<unk>"	,	A="<s>"	,	A="</s>"	,	A="<pad>"	,	A="[SEP]"	,	A="[MASK]"	,	A="[CLS]"	,	A = None	,	**A	,	)    ->    None:
								UpperCAmelCase  :	Tuple							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else bos_token
								UpperCAmelCase  :	List[str]							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else eos_token
								UpperCAmelCase  :	Optional[int]							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else unk_token
								UpperCAmelCase  :	List[str]							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else pad_token
								UpperCAmelCase  :	Tuple							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else cls_token
								UpperCAmelCase  :	Tuple							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else sep_token

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase  :	Tuple							  =	AddedToken(__a	,	lstrip=__a	,	rstrip=__a					) if isinstance(__a	,	__a					) else mask_token

								UpperCAmelCase  :	str							  =	{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__a	,	eos_token=__a	,	unk_token=__a	,	pad_token=__a	,	sep_token=__a	,	mask_token=__a	,	cls_token=__a	,	sp_model_kwargs=self.sp_model_kwargs	,	**__a	,	)

								UpperCAmelCase  :	Optional[int]							  =	vocab_file

								UpperCAmelCase  :	Dict							  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(__a					)





	@property
	def 				_lowercase(						self					)    ->    List[str]:
								return self.sp_model.get_piece_size()





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	{self.convert_ids_to_tokens(__a					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab





	def __getstate__(						self					)    ->    Tuple:
								UpperCAmelCase  :	str							  =	self.__dict__.copy()
								UpperCAmelCase  :	Optional[Any]							  =	None
								return state





	def __setstate__(						self	,	A					)    ->    Tuple:
								UpperCAmelCase  :	int							  =	d

								# for backward compatibility
								if not hasattr(self	,	"""sp_model_kwargs"""					):
															UpperCAmelCase  :	int							  =	{}

								UpperCAmelCase  :	Optional[int]							  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.vocab_file					)





	def 				_lowercase(						self	,	A					)    ->    List[str]:
								return self.sp_model.encode(__a	,	out_type=__a					)





	def 				_lowercase(						self	,	A					)    ->    List[str]:
								return self.sp_model.piece_to_id(__a					)





	def 				_lowercase(						self	,	A					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	self.sp_model.IdToPiece(__a					)
								return token





	def 				_lowercase(						self	,	A					)    ->    Optional[int]:
								UpperCAmelCase  :	str							  =	[]
								UpperCAmelCase  :	Dict							  =	''
								UpperCAmelCase  :	Union[str, Any]							  =	False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__a					) + token
																						UpperCAmelCase  :	int							  =	True
																						UpperCAmelCase  :	str							  =	[]
															else:
																						current_sub_tokens.append(__a					)
																						UpperCAmelCase  :	Any							  =	False
								out_string += self.sp_model.decode(__a					)
								return out_string.strip()





	def 				_lowercase(						self	,	A	,	A = False	,	A = None	,	A = True	,	**A	,	)    ->    str:
								UpperCAmelCase  :	Optional[Any]							  =	kwargs.pop("""use_source_tokenizer"""	,	__a					)

								UpperCAmelCase  :	List[Any]							  =	self.convert_ids_to_tokens(__a	,	skip_special_tokens=__a					)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								UpperCAmelCase  :	Tuple							  =	[]
								UpperCAmelCase  :	str							  =	[]
								for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																						continue
															if token in self.added_tokens_encoder:
																						if current_sub_text:
																													sub_texts.append(self.convert_tokens_to_string(__a					)					)
																													UpperCAmelCase  :	Optional[int]							  =	[]
																						sub_texts.append(__a					)
															else:
																						current_sub_text.append(__a					)
								if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(__a					)					)

								# Mimic the behavior of the Rust tokenizer:
								# No space before [MASK] and [SEP]
								if spaces_between_special_tokens:
															UpperCAmelCase  :	List[str]							  =	re.sub(r""" (\[(MASK|SEP)\])"""	,	r"""\1"""	,	""" """.join(__a					)					)
								else:
															UpperCAmelCase  :	Optional[Any]							  =	''.join(__a					)

								UpperCAmelCase  :	Optional[int]							  =	(
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
															UpperCAmelCase  :	Union[str, Any]							  =	self.clean_up_tokenization(__a					)
															return clean_text
								else:
															return text





	def 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:






								if not os.path.isdir(__a					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	Any							  =	os.path.join(
								    __a	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(__a					) and os.path.isfile(self.vocab_file					):
															copyfile(self.vocab_file	,	__a					)
								elif not os.path.isfile(self.vocab_file					):
															with open(__a	,	"""wb"""					) as fi:
																						UpperCAmelCase  :	int							  =	self.sp_model.serialized_model_proto()
																						fi.write(__a					)

								return (out_vocab_file,)





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:






								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase  :	Any							  =	[self.cls_token_id]
								UpperCAmelCase  :	int							  =	[self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep





	def 				_lowercase(						self	,	A	,	A = None	,	A = False					)    ->    List[int]:






								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__a	,	token_ids_a=__a	,	already_has_special_tokens=__a					)

								if token_ids_a is None:
															return [1] + ([0] * len(__a					)) + [1]
								return [1] + ([0] * len(__a					)) + [1] + ([0] * len(__a					)) + [1]






	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Dict							  =	[self.sep_token_id]
								UpperCAmelCase  :	Union[str, 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					) * [0] + len(token_ids_a + sep					) * [1]






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

