



"""simple docstring"""







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


A		=						logging.get_logger(__name__)


A		=						'▁'

A		=						{'vocab_file': 'spiece.model'}

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

A		=						{
    'google/reformer-crime-and-punishment': 5_2_4_2_8_8,
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     VOCAB_FILES_NAMES
			__magic_name__     					=     PRETRAINED_VOCAB_FILES_MAP
			__magic_name__     					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__magic_name__     					=     ["""input_ids""", """attention_mask"""]



			def __init__(			self							:				List[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				List[str]="</s>"   ,	_lowercase							:				int="<unk>"   ,	_lowercase							:				Tuple=[]   ,	_lowercase							:				Optional[Dict[str, Any]] = None   ,	**_lowercase							:				Any   ,	)							->					None:
									snake_case   :			Optional[Any]						    = {} if sp_model_kwargs is None else sp_model_kwargs

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

									snake_case   :			int						    = vocab_file
									snake_case   :			str						    = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_lowercase		)



			@property
			def 				__lowercase					(			self							:				List[Any]		)							->					Optional[int]:
									return self.sp_model.get_piece_size()



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



			def __getstate__(			self							:				Optional[int]		)							->					str:
									snake_case   :			str						    = self.__dict__.copy()
									snake_case   :			List[str]						    = None
									return state



			def __setstate__(			self							:				List[Any]   ,	_lowercase							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			Dict						    = d

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

									snake_case   :			Dict						    = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)



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



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				Any		)							->					Tuple:
									return self.sp_model.piece_to_id(_lowercase		)



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Optional[int]		)							->					Optional[int]:
									if index < self.sp_model.get_piece_size():
															snake_case   :			int						    = self.sp_model.IdToPiece(_lowercase		)
									return token



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Optional[int]		)							->					List[Any]:
									snake_case   :			List[str]						    = []
									snake_case   :			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(_lowercase		) + token
																					snake_case   :			Optional[Any]						    = []
															else:
																					current_sub_tokens.append(_lowercase		)
									out_string += self.sp_model.decode(_lowercase		)
									return out_string.strip()



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

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

									return (out_vocab_file,)








"""simple docstring"""







A		=						8.31_4462  # Unit - J mol-1 K-1

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							float ):
						"""simple docstring"""






						if moles < 0 or kelvin < 0 or volume < 0:
												raise ValueError("Invalid inputs. Enter positive value." )
						return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							float     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							float ):
						"""simple docstring"""






						if moles < 0 or kelvin < 0 or pressure < 0:
												raise ValueError("Invalid inputs. Enter positive value." )
						return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
			from doctest import testmod

			testmod()



