

'''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					:      str 		=     logging.get_logger(__name__)


__snake_case					:      Optional[Any] 		=     '▁'

__snake_case					:      Dict 		=     {'vocab_file': 'spiece.model'}

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

__snake_case					:      Union[str, Any] 		=     {
    'google/reformer-crime-and-punishment': 52_4288,
}



class lowerCamelCase					(    SCREAMING_SNAKE_CASE_		):



			'''simple docstring'''






			__snake_case             =			VOCAB_FILES_NAMES
			__snake_case             =			PRETRAINED_VOCAB_FILES_MAP
			__snake_case             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case             =			["""input_ids""", """attention_mask"""]


			def __init__( self			:		Optional[int]						,     lowerCAmelCase_			:		Optional[Any]						,     lowerCAmelCase_			:		int="</s>"						,     lowerCAmelCase_			:		Any="<unk>"						,     lowerCAmelCase_			:		Tuple=[]						,     lowerCAmelCase_			:		Optional[Any] = None						,     **lowerCAmelCase_			:		Any						,     )     ->						Union[str, Any]:





									'''simple docstring'''
									A__	:  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_						,     )

									A__	:  Dict           =vocab_file
									A__	:  List[Any]           =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(lowerCAmelCase_					)


			@property
			def lowercase__		( self			:		Any					)     ->						List[Any]:





									'''simple docstring'''
									return self.sp_model.get_piece_size()


			def lowercase__		( self			:		str					)     ->						List[Any]:





									'''simple docstring'''
									A__	:  int           ={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			:		Dict					)     ->						str:





									'''simple docstring'''
									A__	:  Optional[int]           =self.__dict__.copy()
									A__	:  Union[str, Any]           =None
									return state


			def __setstate__( self			:		List[str]						,     lowerCAmelCase_			:		List[str]					)     ->						int:





									'''simple docstring'''
									A__	:  int           =d

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

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


			def lowercase__		( self			:		Optional[Any]						,     lowerCAmelCase_			:		List[str]					)     ->						List[Any]:





									'''simple docstring'''
									return self.sp_model.encode(lowerCAmelCase_						,     out_type=lowerCAmelCase_					)


			def lowercase__		( self			:		Optional[Any]						,     lowerCAmelCase_			:		int					)     ->						Dict:





									'''simple docstring'''
									return self.sp_model.piece_to_id(lowerCAmelCase_					)


			def lowercase__		( self			:		List[str]						,     lowerCAmelCase_			:		Tuple					)     ->						List[str]:





									'''simple docstring'''
									if index < self.sp_model.get_piece_size():
															A__	:  Dict           =self.sp_model.IdToPiece(lowerCAmelCase_					)
									return token


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





									'''simple docstring'''
									A__	:  Tuple           =[]
									A__	:  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
																					A__	:  Dict           =[]
															else:
																					current_sub_tokens.append(lowerCAmelCase_					)
									out_string += self.sp_model.decode(lowerCAmelCase_					)
									return out_string.strip()



			def lowercase__		( self			:		Optional[int]						,     lowerCAmelCase_			:		Union[str, Any]						,     lowerCAmelCase_			:		Union[str, Any] = None					)     ->						Union[str, Any]:





									'''simple docstring'''
									if not os.path.isdir(lowerCAmelCase_					):
															logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
															return
									A__	:  Any           =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:
																					A__	:  List[str]           =self.sp_model.serialized_model_proto()
																					fi.write(lowerCAmelCase_					)

									return (out_vocab_file,)






"""simple docstring"""







from typing import TYPE_CHECKING

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


a_							=    {
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





