





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:
       a__:      int									=		None

a__:      Optional[int]									=		logging.get_logger(__name__)
a__:      Optional[Any]									=		{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

a__:      List[Any]									=		{
    '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',
    },
}

a__:      Tuple									=		{
    'google/rembert': 256,
}

a__:      Tuple									=		'▁'




class     SCREAMING_SNAKE_CASE__ (						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,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase="[CLS]",__lowerCamelCase="[SEP]",__lowerCamelCase="<unk>",__lowerCamelCase="[SEP]",__lowerCamelCase="<pad>",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",**__lowerCamelCase,):
        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

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

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = 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(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]
        return [1] + ([0] * len(__lowerCamelCase )) + [1]



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not os.path.isdir(__lowerCamelCase ):
               logger.error('''Vocabulary path ({}) should be a directory'''.format(__lowerCamelCase ) )
               return
        A__											=						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 ):
               copyfile(self.vocab_file,__lowerCamelCase )

        return (out_vocab_file,)












import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"  )
       torch.save(model.state_dict() ,     UpperCamelCase__  )


if __name__ == "__main__":
       a__:      str									=		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





