
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": 52_42_88,
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=VOCAB_FILES_NAMES
				_lowercase					=PRETRAINED_VOCAB_FILES_MAP
				_lowercase					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase					=['''input_ids''', '''attention_mask''']




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase="</s>"				,				_UpperCamelCase="<unk>"				,				_UpperCamelCase=[]				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      None:
							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      __a						(					self     )				->      Optional[Any]:
							return self.sp_model.get_piece_size()




				def      __a						(					self     )				->      Dict[str, int]:
							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     )				->      List[str]:
							lowerCAmelCase_						=	self.__dict__.copy()
							lowerCAmelCase_						=	None
							return state




				def __setstate__(					self				,				_UpperCamelCase     )				->      Any:
							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      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							return self.sp_model.encode(_UpperCamelCase				,				out_type=_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      Union[str, Any]:
							return self.sp_model.piece_to_id(_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[Any]:
							if index < self.sp_model.get_piece_size():
										lowerCAmelCase_						=	self.sp_model.IdToPiece(_UpperCamelCase     )
							return token




				def      __a						(					self				,				_UpperCamelCase     )				->      str:
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None     )				->      Tuple[str]:
							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 pathlib import Path

import fire






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			dest_dir.mkdir(exist_ok=__lowerCAmelCase				)
			for path in src_dir.iterdir():
						lowerCAmelCase_						=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
						lowerCAmelCase_						=	dest_dir.joinpath(path.name				)
						print(__lowerCAmelCase				)
						dest_path.open("w"				).write("\n".join(__lowerCAmelCase				)				)


if __name__ == "__main__":
			fire.Fire(minify)



