
"""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


_snake_case        =    logging.get_logger(__name__)


_snake_case        =    "▁"

_snake_case        =    {"vocab_file": "spiece.model"}

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

_snake_case        =    {
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}
class 							_SCREAMING_SNAKE_CASE	(	_a				):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:				Optional[Any]				=       VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_:				Dict				=       PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_:				Any				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_:				int				=       ["""input_ids""", """attention_mask"""]


				def __init__(       self						:      int					,  UpperCAmelCase_						:      List[Any]					,  UpperCAmelCase_						:      Optional[Any]="</s>"					,  UpperCAmelCase_						:      Optional[Any]="<unk>"					,  UpperCAmelCase_						:      Dict=[]					,  UpperCAmelCase_						:      List[str] = None					,  **UpperCAmelCase_						:      Any					,  )		->	None:







											"""simple docstring"""




											_lowerCAmelCase							=      {} if sp_model_kwargs is None else sp_model_kwargs

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

											_lowerCAmelCase							=      vocab_file
											_lowerCAmelCase							=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(UpperCAmelCase_	)


				@property
				def 					__lowerCamelCase						(       self						:      str	)		->	Dict:







											"""simple docstring"""




											return self.sp_model.get_piece_size()


				def 					__lowerCamelCase						(       self						:      Dict	)		->	Dict[str, int]:







											"""simple docstring"""




											_lowerCAmelCase							=      {self.convert_ids_to_tokens(UpperCAmelCase_	): i for i in range(self.vocab_size	)}
											vocab.update(self.added_tokens_encoder	)
											return vocab


				def __getstate__(       self						:      Any	)		->	Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.__dict__.copy()
											_lowerCAmelCase							=      None
											return state


				def __setstate__(       self						:      Any					,  UpperCAmelCase_						:      int	)		->	int:







											"""simple docstring"""




											_lowerCAmelCase							=      d

											# for backward compatibility
											if not hasattr(self					,  'sp_model_kwargs'	):
																		_lowerCAmelCase							=      {}

											_lowerCAmelCase							=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(self.vocab_file	)


				def 					__lowerCamelCase						(       self						:      List[Any]					,  UpperCAmelCase_						:      int	)		->	List[str]:







											"""simple docstring"""




											return self.sp_model.encode(UpperCAmelCase_					,  out_type=UpperCAmelCase_	)


				def 					__lowerCamelCase						(       self						:      List[str]					,  UpperCAmelCase_						:      str	)		->	List[Any]:







											"""simple docstring"""




											return self.sp_model.piece_to_id(UpperCAmelCase_	)


				def 					__lowerCamelCase						(       self						:      List[Any]					,  UpperCAmelCase_						:      str	)		->	Tuple:







											"""simple docstring"""




											if index < self.sp_model.get_piece_size():
																		_lowerCAmelCase							=      self.sp_model.IdToPiece(UpperCAmelCase_	)
											return token


				def 					__lowerCamelCase						(       self						:      int					,  UpperCAmelCase_						:      List[Any]	)		->	Dict:







											"""simple docstring"""




											_lowerCAmelCase							=      []
											_lowerCAmelCase							=      ''
											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(UpperCAmelCase_	) + token
																									_lowerCAmelCase							=      []
																		else:
																									current_sub_tokens.append(UpperCAmelCase_	)
											out_string += self.sp_model.decode(UpperCAmelCase_	)
											return out_string.strip()







				def 					__lowerCamelCase						(       self						:      Dict					,  UpperCAmelCase_						:      List[str]					,  UpperCAmelCase_						:      List[str] = None	)		->	Tuple[str]:







											"""simple docstring"""




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

											if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCAmelCase_	) and os.path.isfile(self.vocab_file	):
																		copyfile(self.vocab_file					,  UpperCAmelCase_	)
											elif not os.path.isfile(self.vocab_file	):
																		with open(UpperCAmelCase_					,  'wb'	) as fi:
																									_lowerCAmelCase							=      self.sp_model.serialized_model_proto()
																									fi.write(UpperCAmelCase_	)

											return (out_vocab_file,)










from __future__ import annotations

import numpy as np



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    return np.maximum(0				,					lowerCamelCase_					)


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


