
from torch import nn




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Union[str, Any])					->    int:






							'''simple docstring'''





							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(F'Unsupported activation function: {act_fn}')

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


lowercase  :					int													=       logging.get_logger(__name__)


lowercase  :					Union[str, Any]													=       '▁'

lowercase  :					Tuple													=       {'vocab_file': 'spiece.model'}

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

lowercase  :					Any													=       {
    'google/reformer-crime-and-punishment': 524288,
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







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

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

								__UpperCamelCase	:   Optional[Any]			   =					vocab_file
								__UpperCamelCase	:   Dict			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)







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







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Dict[str, int]:
								__UpperCamelCase	:   str			   =					{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		:str						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.__dict__.copy()
								__UpperCamelCase	:   Optional[Any]			   =					None
								return state







	def __setstate__(					self		:int     ,   a		:List[str]						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					d

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

								__UpperCamelCase	:   Any			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(self.vocab_file						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[Any]						)		-> str:
								return self.sp_model.piece_to_id(a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Union[str, Any]						)		-> Optional[int]:
								if index < self.sp_model.get_piece_size():
															__UpperCamelCase	:   Optional[int]			   =					self.sp_model.IdToPiece(a						)
								return token







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[]
								__UpperCamelCase	:   str			   =					""
								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(a						) + token
																						__UpperCamelCase	:   List[Any]			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:Optional[str] = None						)		-> Tuple[str]:
								if not os.path.isdir(a						):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
															return
								__UpperCamelCase	:   List[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,)