
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_rembert import RemBertTokenizer
else:
		lowercase   =	None

lowercase   =	logging.get_logger(__name__)
lowercase   =	{'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase   =	{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

lowercase   =	{
    '''google/rembert''': 2_5_6,
}

lowercase   =	'''▁'''





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE           =     RemBertTokenizer

					def __init__(						self       :  Optional[Any] ,       __UpperCamelCase       :  Union[str, Any]=None ,       __UpperCamelCase       :  str=None ,       __UpperCamelCase       :  List[str]=True ,       __UpperCamelCase       :  Optional[Any]=True ,       __UpperCamelCase       :  List[str]=False ,       __UpperCamelCase       :  List[Any]="[CLS]" ,       __UpperCamelCase       :  str="[SEP]" ,       __UpperCamelCase       :  Union[str, Any]="<unk>" ,       __UpperCamelCase       :  Optional[int]="[SEP]" ,       __UpperCamelCase       :  List[str]="<pad>" ,       __UpperCamelCase       :  Optional[int]="[CLS]" ,       __UpperCamelCase       :  Tuple="[MASK]" ,       **__UpperCamelCase       :  str ,       ):
												# Mask token behave like a normal word, i.e. include the space before it
												lowerCamelCase_					=       AddedToken(__UpperCamelCase ,       lstrip=__UpperCamelCase ,       rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else mask_token

												super().__init__(
												    __UpperCamelCase ,       tokenizer_file=__UpperCamelCase ,       do_lower_case=__UpperCamelCase ,       remove_space=__UpperCamelCase ,       keep_accents=__UpperCamelCase ,       bos_token=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       sep_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       cls_token=__UpperCamelCase ,       mask_token=__UpperCamelCase ,       **__UpperCamelCase ,       )

												lowerCamelCase_					=       do_lower_case
												lowerCamelCase_					=       remove_space
												lowerCamelCase_					=       keep_accents
												lowerCamelCase_					=       vocab_file
												lowerCamelCase_					=       False if not self.vocab_file else True

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]
												if token_ids_a is None:
																			return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ,       __UpperCamelCase       :  bool = False ):

												if already_has_special_tokens:
																			if token_ids_a is not None:
																										raise ValueError(
																										    """You should not supply a second sequence if the provided sequence of """
																										    """ids is already formatted with special tokens for the model.""" )
																			return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

												if token_ids_a is not None:
																			return [1] + ([0] * len(__UpperCamelCase )) + [1] + ([0] * len(__UpperCamelCase )) + [1]
												return [1] + ([0] * len(__UpperCamelCase )) + [1]

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not os.path.isdir(__UpperCamelCase ):
																			logger.error("""Vocabulary path ({}) should be a directory""".format(__UpperCamelCase ) )
																			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 ):
																			copyfile(self.vocab_file ,       __UpperCamelCase )

												return (out_vocab_file,)







import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str      ,					**UpperCAmelCase__					:   List[str]							)	->     Tuple:
							lowerCamelCase_					=       AutoConfig.from_pretrained(UpperCAmelCase__      ,					**UpperCAmelCase__							)
							lowerCamelCase_					=       AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__							)
							model.save_pretrained(UpperCAmelCase__							)
							AutoTokenizer.from_pretrained(UpperCAmelCase__							).save_pretrained(UpperCAmelCase__							)
							return model


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





