


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			:						Any        =  logging.get_logger(__name__)


A			:						Any        =  '▁'

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

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

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



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






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

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




							"""simple docstring"""


							lowercase__	     =						{} 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 ,   )

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

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




							"""simple docstring"""


							return self.sp_model.get_piece_size()

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




							"""simple docstring"""


							lowercase__	     =						{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]						)		->  Optional[int]:




							"""simple docstring"""


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

					def __setstate__(self       :		Dict ,   _UpperCAmelCase       :		List[str]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						d

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

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

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		str						)		->  List[str]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							return self.sp_model.piece_to_id(_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any]						)		->  Any:




							"""simple docstring"""


							if index < self.sp_model.get_piece_size():
									lowercase__	     =						self.sp_model.IdToPiece(_UpperCAmelCase						)
							return token

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		List[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						[]
							lowercase__	     =						""""""
							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
											lowercase__	     =						[]
									else:
											current_sub_tokens.append(_UpperCAmelCase						)
							out_string += self.sp_model.decode(_UpperCAmelCase						)
							return out_string.strip()


					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							if not os.path.isdir(_UpperCAmelCase						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							lowercase__	     =						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:
											lowercase__	     =						self.sp_model.serialized_model_proto()
											fi.write(_UpperCAmelCase						)

							return (out_vocab_file,)





import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

