



'''simple docstring'''





import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Dict:
  # Initialise PyTorch model
  __lowerCamelCase			:			List[str]       			=					BigBirdConfig.from_json_file(_lowerCAmelCase			)
  print(F'Building PyTorch model from configuration: {config}'			)

  if is_trivia_qa:
    __lowerCamelCase			:			Dict       			=					BigBirdForQuestionAnswering(_lowerCAmelCase			)
  else:
    __lowerCamelCase			:			Any       			=					BigBirdForPreTraining(_lowerCAmelCase			)

  # Load weights from tf checkpoint
  load_tf_weights_in_big_bird(_lowerCAmelCase			,_lowerCAmelCase			,is_trivia_qa=_lowerCAmelCase			)

  # Save pytorch-model
  print(F'Save PyTorch model to {pytorch_dump_path}'			)
  model.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
   _UpperCamelCase											=		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(
       '--big_bird_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.'
   )
   parser.add_argument(
       '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
   )
   _UpperCamelCase											=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
   )











'''simple docstring'''





import json
import os
from typing import Optional, Tuple

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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    return dict(self.vocab     ,      **self.added_tokens_encoder  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

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

    return (vocab_file,)






