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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__:					Dict							=     logging.get_logger(__name__)


__magic_name__:					List[Any]							=     "▁"

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

__magic_name__:					Dict							=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

__magic_name__:					Optional[Any]							=     {
    "google/reformer-crime-and-punishment": 524_288,
}





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					List[str]											=   VOCAB_FILES_NAMES
	lowercase__						:					str											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Optional[int]											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					Union[str, Any]											=   ['''input_ids''', '''attention_mask''']
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__=[]      ,       lowerCAmelCase__ = None      ,       **lowerCAmelCase__      ,       )							->				None:
							__magic_name__      :       Union[str, Any]       						= {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=lowerCAmelCase__      ,       unk_token=lowerCAmelCase__      ,       additional_special_tokens=lowerCAmelCase__      ,       sp_model_kwargs=self.sp_model_kwargs      ,       **lowerCAmelCase__      ,       )

							__magic_name__      :       Optional[int]       						= vocab_file
							__magic_name__      :       Dict       						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
							self.sp_model.Load(lowerCAmelCase__			)
	@property
	def       __magic_name__    (    self			)							->				Tuple:
							return self.sp_model.get_piece_size()
	def       __magic_name__    (    self			)							->				Dict[str, int]:
							__magic_name__      :       str       						= {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
							vocab.update(self.added_tokens_encoder			)
							return vocab
	def __getstate__(    self			)							->				List[Any]:
							__magic_name__      :       Tuple       						= self.__dict__.copy()
							__magic_name__      :       List[Any]       						= None
							return state
	def __setstate__(    self      ,       lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       int       						= d

							# for backward compatibility
							if not hasattr(self      ,       """sp_model_kwargs"""			):
													__magic_name__      :       Optional[Any]       						= {}

							__magic_name__      :       Union[str, Any]       						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
							self.sp_model.Load(self.vocab_file			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
							return self.sp_model.encode(lowerCAmelCase__      ,       out_type=lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Optional[Any]:
							return self.sp_model.piece_to_id(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Tuple:
							if index < self.sp_model.get_piece_size():
													__magic_name__      :       List[str]       						= self.sp_model.IdToPiece(lowerCAmelCase__			)
							return token
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Any       						= []
							__magic_name__      :       Optional[int]       						= """"""
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(lowerCAmelCase__			) + token
																			__magic_name__      :       str       						= []
													else:
																			current_sub_tokens.append(lowerCAmelCase__			)
							out_string += self.sp_model.decode(lowerCAmelCase__			)
							return out_string.strip()







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							if not os.path.isdir(lowerCAmelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
													return
							__magic_name__      :       Tuple       						= os.path.join(
							    lowerCAmelCase__      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

							if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			) and os.path.isfile(self.vocab_file			):
													copyfile(self.vocab_file      ,       lowerCAmelCase__			)
							elif not os.path.isfile(self.vocab_file			):
													with open(lowerCAmelCase__      ,       """wb"""			) as fi:
																			__magic_name__      :       List[Any]       						= self.sp_model.serialized_model_proto()
																			fi.write(lowerCAmelCase__			)

							return (out_vocab_file,)








from typing import TYPE_CHECKING

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


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
