
import argparse

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


logging.set_verbosity_info()






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Optional[int]	,		__a      :	List[Any]	,		__a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    BigBirdConfig.from_json_file(__a						)
     print(f"Building PyTorch model from configuration: {config}"						)

     if is_trivia_qa:
          UpperCamelCase__				=    BigBirdForQuestionAnswering(__a						)
     else:
          UpperCamelCase__				=    BigBirdForPreTraining(__a						)

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

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


if __name__ == "__main__":
    lowerCamelCase_						     =					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.'''
    )
    lowerCamelCase_						     =					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_segformer import SegformerImageProcessor


lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""	,      SCREAMING_SNAKE_CASE_	,      )
       super().__init__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)





