
'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__lowerCAmelCase   =       logging.get_logger(__name__)


__lowerCAmelCase   =       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCAmelCase   =       {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowerCAmelCase   =       {"facebook/blenderbot-3B": 1_2_8}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    UpperCAmelCase_						():






   """simple docstring"""

   _a							:      Dict     =  (
       list(range(ord('!'					)					,      ord('~'					) + 1					)					) + list(range(ord('¡'					)					,      ord('¬'					) + 1					)					) + list(range(ord('®'					)					,      ord('ÿ'					) + 1					)					)
   )
   _a							:      Any     =  bs[:]
   _a							:      Tuple     =  0
   for b in range(2**8					):
      if b not in bs:
         bs.append(UpperCamelCase__					)
         cs.append(2**8 + n					)
         n += 1
   _a							:      Optional[int]     =  [chr(UpperCamelCase__					) for n in cs]
   return dict(zip(UpperCamelCase__					,      UpperCamelCase__					)					)





def    UpperCAmelCase_						(__a :       int					):






   """simple docstring"""

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







class UpperCAmelCase__      (					lowercase__      ):





  """simple docstring"""

  __UpperCAmelCase							:							Tuple    					=   VOCAB_FILES_NAMES
  __UpperCAmelCase							:							str    					=   PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase							:							Any    					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase							:							Optional[Any]    					=   ['''input_ids''', '''attention_mask''']


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


     '''simple docstring'''






     _a							:      Tuple     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else bos_token
     _a							:      Any     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else eos_token
     _a							:      Dict     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else sep_token
     _a							:      Optional[int]     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else cls_token
     _a							:      Union[str, Any]     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else unk_token
     _a							:      Optional[Any]     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     _a							:      str     =  AddedToken(__a				,lstrip=__a				,rstrip=__a			) if isinstance(__a				,__a			) else mask_token

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

     with open(__a				,encoding='utf-8'			) as vocab_handle:
        _a							:      int     =  json.load(__a			)
     _a							:      Union[str, Any]     =  {v: k for k, v in self.encoder.items()}
     _a							:      Optional[Any]     =  errors  # how to handle errors in decoding
     _a							:      List[str]     =  bytes_to_unicode()
     _a							:      Any     =  {v: k for k, v in self.byte_encoder.items()}
     with open(__a				,encoding='utf-8'			) as merges_handle:
        _a							:      List[str]     =  merges_handle.read().split('\n'			)[1:-1]
     _a							:      Any     =  [tuple(merge.split()			) for merge in bpe_merges]
     _a							:      Any     =  dict(zip(__a				,range(len(__a			)			)			)			)
     _a							:      int     =  {}
     _a							:      Tuple     =  add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     _a							:      Optional[int]     =  re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'			)


  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def     __lowercase						(						self :	List[Any]			):


     '''simple docstring'''






     return len(self.encoder			)


  def     __lowercase						(						self :	str			):


     '''simple docstring'''






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


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


     '''simple docstring'''






     if token in self.cache:
        return self.cache[token]
     _a							:      Any     =  tuple(__a			)
     _a							:      int     =  get_pairs(__a			)

     if not pairs:
        return token

     while True:
        _a							:      List[Any]     =  min(__a				,key=lambda _a			: self.bpe_ranks.get(__a				,float('inf'			)			)			)
        if bigram not in self.bpe_ranks:
           break
        _a,    _a							:      int     =  bigram
        _a							:      List[Any]     =  []
        _a							:      str     =  0
        while i < len(__a			):
           try:
              _a							:      int     =  word.index(__a				,__a			)
           except ValueError:
              new_word.extend(word[i:]			)
              break
           else:
              new_word.extend(word[i:j]			)
              _a							:      Dict     =  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							:      Optional[int]     =  tuple(__a			)
        _a							:      List[Any]     =  new_word
        if len(__a			) == 1:
           break
        else:
           _a							:      Tuple     =  get_pairs(__a			)
     _a							:      int     =  ' '.join(__a			)
     _a							:      Union[str, Any]     =  word
     return word


  def     __lowercase						(						self :	int				,_a :	int			):


     '''simple docstring'''






     _a							:      Optional[Any]     =  []
     for token in re.findall(self.pat				,__a			):
        _a							:      int     =  ''.join(
            self.byte_encoder[b] for b in token.encode('utf-8'			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__a			).split(' '			)			)
     return bpe_tokens


  def     __lowercase						(						self :	Optional[Any]				,_a :	Tuple			):


     '''simple docstring'''






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


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


     '''simple docstring'''






     return self.decoder.get(__a			)


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


     '''simple docstring'''






     _a							:      Dict     =  ''.join(__a			)
     _a							:      Union[str, Any]     =  bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'				,errors=self.errors			)
     return text


  def     __lowercase						(						self :	Union[str, Any]				,_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							:      Union[str, Any]     =  os.path.join(
         __a				,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
     _a							:      Tuple     =  os.path.join(
         __a				,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

     with open(__a				,'w'				,encoding='utf-8'			) as f:
        f.write(json.dumps(self.encoder				,indent=2				,sort_keys=__a				,ensure_ascii=__a			) + '\n'			)

     _a							:      str     =  0
     with open(__a				,'w'				,encoding='utf-8'			) as writer:
        writer.write('#version: 0.2\n'			)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,key=lambda _a			: kv[1]			):
           if index != token_index:
              logger.warning(
                  F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  ' Please check that the tokenizer is not corrupted!'			)
              _a							:      Optional[int]     =  token_index
           writer.write(' '.join(__a			) + '\n'			)
           index += 1

     return vocab_file, merge_file


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


     '''simple docstring'''






     _a							:      Dict     =  [self.sep_token_id]
     _a							:      List[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]


  def     __lowercase						(						self :	int				,_a :	Optional[int]				,_a :	int=False				,**_a :	Union[str, Any]			):


     '''simple docstring'''






     _a							:      Any     =  kwargs.pop('add_prefix_space'				,self.add_prefix_space			)
     if (is_split_into_words or add_prefix_space) and (len(__a			) > 0 and not text[0].isspace()):
        _a							:      Tuple     =  ' ' + text
     return (text, kwargs)


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


     '''simple docstring'''






     return token_ids_a + [self.eos_token_id]


  def     __lowercase						(						self :	Optional[Any]				,_a :	"Conversation"			):


     '''simple docstring'''






     _a							:      List[Any]     =  []
     for is_user, text in conversation.iter_texts():
        if is_user:
           # We need to space prefix as it's being done within blenderbot
           inputs.append(' ' + text			)
        else:
           # Generated responses should contain them already.
           inputs.append(__a			)

     _a							:      Optional[int]     =  '  '.join(__a			)
     _a							:      Optional[int]     =  self.encode(__a			)
     if len(__a			) > self.model_max_length:
        _a							:      str     =  input_ids[-self.model_max_length :]
        logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""			)
     return input_ids




'''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)
