




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:
     UpperCamelCase								=					None

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

UpperCamelCase								=					{
    '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',
    },
}

UpperCamelCase								=					{
    'google/rembert': 256,
}

UpperCamelCase								=					'▁'





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				VOCAB_FILES_NAMES
    snake_case__ =				PRETRAINED_VOCAB_FILES_MAP
    snake_case__ =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__ =				RemBertTokenizer


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

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

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


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[int]] = None       )				->	List[int]:
         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        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[int]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = False       )				->	List[int]:

         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(SCREAMING_SNAKE_CASE__       )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1]


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[int]] = None       )				->	List[int]:
         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        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       )				->	Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__       ):
              logger.error("Vocabulary path ({}) should be a directory".format(SCREAMING_SNAKE_CASE__       )       )
              return
         lowerCAmelCase__          =			os.path.join(
             SCREAMING_SNAKE_CASE__       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(SCREAMING_SNAKE_CASE__       ):
              copyfile(self.vocab_file       ,     SCREAMING_SNAKE_CASE__       )

         return (out_vocab_file,)






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


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