


'''simple docstring'''






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
 from ...tokenization_utils_base import TextInput
from ...utils import logging


a_					:    int          =    logging.get_logger(__name__)

a_					:    Any          =    {"vocab_file": "spiece.model"}

a_					:    int          =    {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a_					:    Union[str, Any]          =    {
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}

a_					:    List[Any]          =    "▁"




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase 					=	["""input_ids""", """attention_mask"""]
 def __init__( self      ,				__magic_name__      ,				__magic_name__="</s>"      ,				__magic_name__="<unk>"      ,				__magic_name__="<pad>"      ,				__magic_name__=1_00      ,				__magic_name__=None      ,				__magic_name__ = None      ,				__magic_name__=True      ,				**__magic_name__      ,				)					->							None:
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
           _a			      =							[f'<extra_id_{i}>' for i in range(__magic_name__						)]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           _a			      =							len(set(filter(lambda __magic_name__						: bool('extra_id' in str(__magic_name__						)						)      ,				__magic_name__						)						)						)
           if extra_tokens != extra_ids:
                raise ValueError(
                    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                    ' tokens'						)
      if legacy:
           logger.warning_once(
               f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'						)

      _a			      =							legacy
      _a			      =							{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=__magic_name__      ,				unk_token=__magic_name__      ,				pad_token=__magic_name__      ,				extra_ids=__magic_name__      ,				additional_special_tokens=__magic_name__      ,				sp_model_kwargs=self.sp_model_kwargs      ,				legacy=__magic_name__      ,				**__magic_name__      ,				)

      _a			      =							vocab_file
      _a			      =							extra_ids

      _a			      =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(__magic_name__						)
 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							str:
      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           _a			      =							TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    'This tokenizer was incorrectly instantiated with a model max length of'
                    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                    f' {pretrained_model_name_or_path} automatically truncating your input to'
                    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,				__magic_name__      ,				)

      return max_model_length
 @property
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return self.sp_model.get_piece_size() + self._extra_ids
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							{self.convert_ids_to_tokens(__magic_name__						): i for i in range(self.vocab_size						)}
      vocab.update(self.added_tokens_encoder						)
      return vocab
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = False						)					->							List[int]:
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__      ,				token_ids_a=__magic_name__      ,				already_has_special_tokens=__magic_name__						)

      # normal case: some special tokens
      if token_ids_a is None:
           return ([0] * len(__magic_name__						)) + [1]
      return ([0] * len(__magic_name__						)) + [1] + ([0] * len(__magic_name__						)) + [1]
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      return list(
          set(filter(lambda __magic_name__						: bool(re.search(r'<extra_id_\d+>'      ,				__magic_name__						)						) is not None      ,				self.additional_special_tokens						)						)						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      return [self._convert_token_to_id(__magic_name__						) for token in self.get_sentinel_tokens()]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[int]:
      if len(__magic_name__						) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'						)
           return token_ids
      else:
           return token_ids + [self.eos_token_id]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[int]:
      _a			      =							[self.eos_token_id]

      if token_ids_a is None:
           return len(token_ids_a + eos						) * [0]
      return len(token_ids_a + eos + token_ids_a + eos						) * [0]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[int]:
      _a			      =							self._add_eos_if_not_present(__magic_name__						)
      if token_ids_a is None:
           return token_ids_a
      else:
           _a			      =							self._add_eos_if_not_present(__magic_name__						)
           return token_ids_a + token_ids_a
 def __getstate__( self						)					->							Optional[Any]:
      _a			      =							self.__dict__.copy()
      _a			      =							None
      return state
 def __setstate__( self      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							d

      # for backward compatibility
      if not hasattr(self      ,				'sp_model_kwargs'						):
           _a			      =							{}

      _a			      =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(self.vocab_file						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				**__magic_name__						)					->							List[str]:
      # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
      # the beginning of the text
      if not self.legacy:
           _a			      =							SPIECE_UNDERLINE + text.replace(__magic_name__      ,				' '						)
      return super().tokenize(__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				**__magic_name__						)					->							Dict:
      if not self.legacy:
           _a			      =							text.startswith(__magic_name__						)
           if is_first:
                _a			      =							text[1:]

      _a			      =							self.sp_model.encode(__magic_name__      ,				out_type=__magic_name__						)

      if not self.legacy and not is_first and not text.startswith(' '						) and tokens[0].startswith(__magic_name__						):
           _a			      =							([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
      return tokens
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if token.startswith('<extra_id_'						):
           _a			      =							re.match(r'<extra_id_(\d+)>'      ,				__magic_name__						)
           _a			      =							int(match.group(1						)						)
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      if index < self.sp_model.get_piece_size():
           _a			      =							self.sp_model.IdToPiece(__magic_name__						)
      else:
           _a			      =							f'<extra_id_{self.vocab_size - 1 - index}>'
      return token
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      _a			      =							[]
      _a			      =							''
      _a			      =							False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special:
                     out_string += " "
                out_string += self.sp_model.decode(__magic_name__						) + token
                _a			      =							True
                _a			      =							[]
           else:
                current_sub_tokens.append(__magic_name__						)
                _a			      =							False
      out_string += self.sp_model.decode(__magic_name__						)
      return out_string.strip()




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							Tuple[str]:
      if not os.path.isdir(__magic_name__						):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
           return
      _a			      =							os.path.join(
          __magic_name__      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

      if os.path.abspath(self.vocab_file						) != os.path.abspath(__magic_name__						) and os.path.isfile(self.vocab_file						):
           copyfile(self.vocab_file      ,				__magic_name__						)
      elif not os.path.isfile(self.vocab_file						):
           with open(__magic_name__      ,				'wb'						) as fi:
                _a			      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__						)

      return (out_vocab_file,)





'''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 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

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

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


if __name__ == "__main__":
 a_					:    Optional[int]          =    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_					:    Optional[int]          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

