



"""simple docstring"""






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Tuple:
  # Initialise PyTorch model
  _snake_case   	=      RemBertConfig.from_json_file(lowerCAmelCase_	)
  print('''Building PyTorch model from configuration: {}'''.format(str(lowerCAmelCase_	)	)	)
  _snake_case   	=      RemBertModel(lowerCAmelCase_	)

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

  # Save pytorch-model
  print('''Save PyTorch model to {}'''.format(lowerCAmelCase_	)	)
  torch.save(model.state_dict()  ,						lowerCAmelCase_	)


if __name__ == "__main__":
     snake_case   					=				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(
         '''--rembert_config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained RemBERT 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.'''
     )
     snake_case   					=				parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)










"""simple docstring"""






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt'''}

snake_case   					=				{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

snake_case   					=				{
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  _snake_case   	=      collections.OrderedDict()
  with open(lowerCAmelCase_  ,						'''r'''  ,						encoding='''utf-8'''	) as reader:
    _snake_case   	=      reader.readlines()
  for index, token in enumerate(lowerCAmelCase_	):
    _snake_case   	=      token.rstrip('''\n'''	)
    _snake_case   	=      index
  return vocab



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	List[str]="<unk>"							,       __lowerCamelCase    :	Tuple=2_0_0				):



     """simple docstring"""
     _snake_case   	=      vocab
     _snake_case   	=      unk_token
     _snake_case   	=      max_input_chars_per_word





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      list(__lowerCamelCase				)
     if len(__lowerCamelCase				) > self.max_input_chars_per_word:
       return [self.unk_token]

     _snake_case   	=      0
     _snake_case   	=      []
     while start < len(__lowerCamelCase				):
       _snake_case   	=      len(__lowerCamelCase				)
       _snake_case   	=      None
       while start < end:
         _snake_case   	=      ''''''.join(chars[start:end]				)
         if substr in self.vocab:
           _snake_case   	=      substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token				)
         start += 1
       else:
         sub_tokens.append(__lowerCamelCase				)
         _snake_case   	=      end

     return sub_tokens



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	List[str]           =  VOCAB_FILES_NAMES
   A__		:	str           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	str           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Union[str, Any]           =  ['''input_ids''', '''attention_mask''']
   A__		:	Optional[int]           =  False



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	str="<d>"							,       __lowerCamelCase    :	Tuple="</d>"							,       __lowerCamelCase    :	Tuple="<s>"							,       __lowerCamelCase    :	int="</s>"							,       __lowerCamelCase    :	List[str]="<pad>"							,       __lowerCamelCase    :	int="<unk>"							,       __lowerCamelCase    :	int="</n>"							,       __lowerCamelCase    :	Tuple="</_>"							,       __lowerCamelCase    :	Optional[Any]="left"							,       **__lowerCamelCase    :	str							,       ):



     """simple docstring"""
     requires_backends(self							,       ['''jieba''']				)
     super().__init__(
         bod_token=__lowerCamelCase							,       eod_token=__lowerCamelCase							,       bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       line_token=__lowerCamelCase							,       space_token=__lowerCamelCase							,       padding_side=__lowerCamelCase							,       **__lowerCamelCase							,       )
     _snake_case   	=      bod_token
     _snake_case   	=      eod_token
     _snake_case   	=      load_vocab(__lowerCamelCase				)
     _snake_case   	=      self.encoder[space_token]
     _snake_case   	=      self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     _snake_case   	=      {v: k for k, v in self.encoder.items()}

     _snake_case   	=      WordpieceTokenizer(vocab=self.encoder							,       unk_token=self.unk_token				)



   @property
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return self.encoder[self.bod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return self.encoder[self.eod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     return self.encoder["\n"]



   @property
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     return len(self.encoder				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return dict(self.encoder							,       **self.added_tokens_encoder				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      []
     for x in jieba.cut(__lowerCamelCase							,       cut_all=__lowerCamelCase				):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCamelCase				)				)
     return output_tokens



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      [i for i in token_ids if i >= 0]
     _snake_case   	=      [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return token in self.encoder



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return "".join(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.encoder.get(__lowerCamelCase							,       self.encoder.get(self.unk_token				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.decoder.get(__lowerCamelCase							,       self.unk_token				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if os.path.isdir(__lowerCamelCase				):
       _snake_case   	=      os.path.join(
           __lowerCamelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
     else:
       _snake_case   	=      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
     _snake_case   	=      0
     if " " in self.encoder:
       _snake_case   	=      self.encoder[''' ''']
       del self.encoder[" "]
     if "\n" in self.encoder:
       _snake_case   	=      self.encoder['''\n''']
       del self.encoder["\n"]
     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               ''' Please check that the vocabulary is not corrupted!'''				)
           _snake_case   	=      token_index
         writer.write(token + '''\n'''				)
         index += 1
     return (vocab_file,)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	List[int] = None				):



     """simple docstring"""
     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None							,       __lowerCamelCase    :	bool = False				):



     """simple docstring"""

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCamelCase							,       token_ids_a=__lowerCamelCase							,       already_has_special_tokens=__lowerCamelCase				)

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





