





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_      :			Dict,	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Tuple	):
   __lowerCAmelCase								=	BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
   print(F"""Building PyTorch model from configuration: {config}"""	)

   if is_trivia_qa:
      __lowerCAmelCase								=	BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE_	)
   else:
      __lowerCAmelCase								=	BigBirdForPreTraining(SCREAMING_SNAKE_CASE_	)

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

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


if __name__ == "__main__":
     _snake_case  :    List[str]						    =  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.'
     )
     _snake_case  :    Optional[Any]						    =  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
     )




import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DPTImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

