






'''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


SCREAMING_SNAKE_CASE_					   =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					   =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE_					   =				{
    '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
SCREAMING_SNAKE_CASE_					   =				{
    '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,
}

SCREAMING_SNAKE_CASE_					   =				'▁'




class 				lowerCAmelCase       (				_lowerCamelCase ):







   """simple docstring"""







   _A							= VOCAB_FILES_NAMES
   _A							= PRETRAINED_VOCAB_FILES_MAP
   _A							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A							= ['''input_ids''', '''attention_mask''']



   def __init__(			self , _A , _A="</s>" , _A="<unk>" , _A="<pad>" , _A=100 , _A=None , _A = None , _A=True , **_A , )     -> None:


    if extra_ids > 0 and additional_special_tokens is None:
     __a     :						Dict          =    [f'''<extra_id_{i}>''' for i in range(_SCREAMING_SNAKE_CASE      )]
    elif extra_ids > 0 and additional_special_tokens is not None:
     # Check that we have the right number of extra_id special tokens
     __a     :						Optional[int]          =    len(set(filter(lambda _A      : bool('extra_id' in str(_SCREAMING_SNAKE_CASE      )      ) , _SCREAMING_SNAKE_CASE      )      )      )
     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     :						Optional[Any]          =    legacy
    __a     :						List[str]          =    {} if sp_model_kwargs is None else sp_model_kwargs

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

    __a     :						List[str]          =    vocab_file
    __a     :						Dict          =    extra_ids

    __a     :						Any          =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(_SCREAMING_SNAKE_CASE      )



   @staticmethod
   def  __magic_name__     (			_A , _A , _A      )     -> Optional[int]:


    if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
     __a     :						List[Any]          =    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.' , _SCREAMING_SNAKE_CASE , )

    return max_model_length



   @property
   def  __magic_name__     (			self      )     -> Any:
    return self.sp_model.get_piece_size() + self._extra_ids



   def  __magic_name__     (			self      )     -> Tuple:
    __a     :						List[str]          =    {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab



   def  __magic_name__     (			self , _A , _A = None , _A = False      )     -> List[int]:


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

    # normal case: some special tokens
    if token_ids_a is None:
     return ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1]
    return ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1]



   def  __magic_name__     (			self      )     -> int:
    return list(
        set(filter(lambda _A      : bool(re.search(r'<extra_id_\d+>' , _SCREAMING_SNAKE_CASE      )      ) is not None , self.additional_special_tokens      )      )      )



   def  __magic_name__     (			self      )     -> Optional[int]:
    return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE      ) for token in self.get_sentinel_tokens()]



   def  __magic_name__     (			self , _A      )     -> List[int]:


    if len(_SCREAMING_SNAKE_CASE      ) > 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  __magic_name__     (			self , _A , _A = None      )     -> List[int]:
    __a     :						Optional[int]          =    [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  __magic_name__     (			self , _A , _A = None      )     -> List[int]:
    __a     :						Union[str, Any]          =    self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE      )
    if token_ids_a is None:
     return token_ids_a
    else:
     __a     :						Tuple          =    self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE      )
     return token_ids_a + token_ids_a



   def __getstate__(			self      )     -> str:
    __a     :						Any          =    self.__dict__.copy()
    __a     :						Dict          =    None
    return state



   def __setstate__(			self , _A      )     -> str:
    __a     :						Tuple          =    d

    # for backward compatibility
    if not hasattr(self , 'sp_model_kwargs'      ):
     __a     :						Optional[Any]          =    {}

    __a     :						int          =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(self.vocab_file      )



   def  __magic_name__     (			self , _A , **_A      )     -> List[str]:


    if not self.legacy:
     __a     :						List[Any]          =    SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE , ' '      )
    return super().tokenize(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE      )



   def  __magic_name__     (			self , _A , **_A      )     -> Any:


    if not self.legacy:
     __a     :						int          =    text.startswith(_SCREAMING_SNAKE_CASE      )
     if is_first:
      __a     :						Tuple          =    text[1:]

    __a     :						Any          =    self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE      )

    if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(_SCREAMING_SNAKE_CASE      ):
     __a     :						Dict          =    ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
    return tokens



   def  __magic_name__     (			self , _A      )     -> Tuple:


    if token.startswith('<extra_id_'      ):
     __a     :						Any          =    re.match(r'<extra_id_(\d+)>' , _SCREAMING_SNAKE_CASE      )
     __a     :						str          =    int(match.group(1      )      )
     return self.vocab_size - num - 1
    return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE      )



   def  __magic_name__     (			self , _A      )     -> Tuple:


    if index < self.sp_model.get_piece_size():
     __a     :						List[str]          =    self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE      )
    else:
     __a     :						Optional[Any]          =    f'''<extra_id_{self.vocab_size - 1 - index}>'''
    return token



   def  __magic_name__     (			self , _A      )     -> Union[str, Any]:
    __a     :						int          =    []
    __a     :						Optional[Any]          =    ''
    __a     :						int          =    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(_SCREAMING_SNAKE_CASE      ) + token
      __a     :						str          =    True
      __a     :						Optional[int]          =    []
     else:
      current_sub_tokens.append(_SCREAMING_SNAKE_CASE      )
      __a     :						Dict          =    False
    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE      )
    return out_string.strip()



   def  __magic_name__     (			self , _A , _A = None      )     -> Tuple[str]:


    if not os.path.isdir(_SCREAMING_SNAKE_CASE      ):
     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
     return
    __a     :						Dict          =    os.path.join(
        _SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

    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  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Any							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     BertConfig.from_json_file(__A     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     BertForPreTraining(__A     )

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

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


if __name__ == "__main__":
							A_						:    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_						:    str				=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





