
'''simple docstring'''







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

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


__lowerCAmelCase   =       logging.get_logger(__name__)

__lowerCAmelCase   =       {
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

__lowerCAmelCase   =       {
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

__lowerCAmelCase   =       {
    "vinai/phobert-base": 2_5_6,
    "vinai/phobert-large": 2_5_6,
}





def    UpperCAmelCase_						(__a :       Any					):






   """simple docstring"""

   _a							:      Optional[Any]     =  set()
   _a							:      Tuple     =  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)					)
      _a							:      int     =  char

   _a							:      Tuple     =  set(UpperCamelCase__					)
   return pairs







class UpperCAmelCase__      (					lowercase__      ):





  """simple docstring"""

  __UpperCAmelCase							:							int    					=   VOCAB_FILES_NAMES
  __UpperCAmelCase							:							List[Any]    					=   PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase							:							Tuple    					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


  def __init__(						self :	Any				,_a :	List[str]				,_a :	int				,_a :	List[str]="<s>"				,_a :	Tuple="</s>"				,_a :	int="</s>"				,_a :	Optional[Any]="<s>"				,_a :	List[str]="<unk>"				,_a :	Optional[int]="<pad>"				,_a :	List[Any]="<mask>"				,**_a :	Optional[Any]				,):


     '''simple docstring'''






     super().__init__(
         bos_token=__a				,eos_token=__a				,unk_token=__a				,sep_token=__a				,cls_token=__a				,pad_token=__a				,mask_token=__a				,**__a				,)

     _a							:      str     =  vocab_file
     _a							:      int     =  merges_file

     _a							:      Tuple     =  {}
     _a							:      Optional[Any]     =  0
     _a							:      str     =  1
     _a							:      Any     =  2
     _a							:      str     =  3

     self.add_from_file(__a			)

     _a							:      List[Any]     =  {v: k for k, v in self.encoder.items()}

     with open(__a				,encoding='utf-8'			) as merges_handle:
        _a							:      List[Any]     =  merges_handle.read().split('\n'			)[:-1]
     _a							:      Tuple     =  [tuple(merge.split()[:-1]			) for merge in merges]
     _a							:      Optional[int]     =  dict(zip(__a				,range(len(__a			)			)			)			)
     _a							:      Any     =  {}


  def     __lowercase						(						self :	str				,_a :	List[int]				,_a :	Optional[List[int]] = None			):


     '''simple docstring'''






     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _a							:      Optional[int]     =  [self.cls_token_id]
     _a							:      int     =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


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


     '''simple docstring'''






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

     if token_ids_a is None:
        return [1] + ([0] * len(__a			)) + [1]
     return [1] + ([0] * len(__a			)) + [1, 1] + ([0] * len(__a			)) + [1]


  def     __lowercase						(						self :	Optional[Any]				,_a :	List[int]				,_a :	Optional[List[int]] = None			):


     '''simple docstring'''






     _a							:      List[Any]     =  [self.sep_token_id]
     _a							:      Optional[Any]     =  [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 + sep + token_ids_a + sep			) * [0]


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


     '''simple docstring'''






     return len(self.encoder			)


  def     __lowercase						(						self :	int			):


     '''simple docstring'''






     return dict(self.encoder				,**self.added_tokens_encoder			)


  def     __lowercase						(						self :	str				,_a :	List[Any]			):


     '''simple docstring'''






     if token in self.cache:
        return self.cache[token]
     _a							:      Tuple     =  tuple(__a			)
     _a							:      Tuple     =  tuple(list(word[:-1]			) + [word[-1] + '</w>']			)
     _a							:      List[str]     =  get_pairs(__a			)

     if not pairs:
        return token

     while True:
        _a							:      int     =  min(__a				,key=lambda _a			: self.bpe_ranks.get(__a				,float('inf'			)			)			)
        if bigram not in self.bpe_ranks:
           break
        _a,    _a							:      str     =  bigram
        _a							:      Tuple     =  []
        _a							:      Tuple     =  0
        while i < len(__a			):
           try:
              _a							:      Union[str, Any]     =  word.index(__a				,__a			)
           except ValueError:
              new_word.extend(word[i:]			)
              break
           else:
              new_word.extend(word[i:j]			)
              _a							:      Optional[int]     =  j

           if word[i] == first and i < len(__a			) - 1 and word[i + 1] == second:
              new_word.append(first + second			)
              i += 2
           else:
              new_word.append(word[i]			)
              i += 1
        _a							:      Any     =  tuple(__a			)
        _a							:      List[Any]     =  new_word
        if len(__a			) == 1:
           break
        else:
           _a							:      Dict     =  get_pairs(__a			)
     _a							:      Optional[int]     =  '@@ '.join(__a			)
     _a							:      Dict     =  word[:-4]
     _a							:      Dict     =  word
     return word


  def     __lowercase						(						self :	List[Any]				,_a :	Union[str, Any]			):


     '''simple docstring'''






     _a							:      Union[str, Any]     =  []

     _a							:      Optional[Any]     =  re.findall(R'\S+\n?'				,__a			)

     for token in words:
        split_tokens.extend(list(self.bpe(__a			).split(' '			)			)			)
     return split_tokens


  def     __lowercase						(						self :	List[Any]				,_a :	str			):


     '''simple docstring'''






     return self.encoder.get(__a				,self.encoder.get(self.unk_token			)			)


  def     __lowercase						(						self :	Union[str, Any]				,_a :	List[str]			):


     '''simple docstring'''






     return self.decoder.get(__a				,self.unk_token			)


  def     __lowercase						(						self :	Optional[int]				,_a :	int			):


     '''simple docstring'''






     _a							:      List[Any]     =  ' '.join(__a			).replace('@@ '				,''			).strip()
     return out_string


  def     __lowercase						(						self :	Dict				,_a :	str				,_a :	Optional[str] = None			):


     '''simple docstring'''






     if not os.path.isdir(__a			):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
        return
     _a							:      List[Any]     =  os.path.join(
         __a				,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
     _a							:      str     =  os.path.join(
         __a				,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

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

     if os.path.abspath(self.merges_file			) != os.path.abspath(__a			):
        copyfile(self.merges_file				,__a			)

     return out_vocab_file, out_merge_file


  def     __lowercase						(						self :	int				,_a :	Union[str, Any]			):


     '''simple docstring'''






     if isinstance(__a				,__a			):
        try:
           with open(__a				,'r'				,encoding='utf-8'			) as fd:
              self.add_from_file(__a			)
        except FileNotFoundError as fnfe:
           raise fnfe
        except UnicodeError:
           raise Exception(F"""Incorrect encoding detected in {f}, please rebuild the dataset"""			)
        return

     _a							:      Union[str, Any]     =  f.readlines()
     for lineTmp in lines:
        _a							:      List[str]     =  lineTmp.strip()
        _a							:      Tuple     =  line.rfind(' '			)
        if idx == -1:
           raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''			)
        _a							:      Tuple     =  line[:idx]
        _a							:      Any     =  len(self.encoder			)




'''simple docstring'''


import argparse

import torch

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


logging.set_verbosity_info()





def    lowerCAmelCase       (		UpperCamelCase__					: Optional[int]     ,       UpperCamelCase__					: List[str]     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	# Initialise PyTorch model
	__UpperCAmelCase      					=      BertConfig.from_json_file(UpperCamelCase__							)
	print(f"""Building PyTorch model from configuration: {config}"""							)
	__UpperCAmelCase      					=      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__":
					__lowerCAmelCase						:							Tuple    							=						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."
					)
					__lowerCAmelCase						:							Optional[Any]    							=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
