




'''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 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Dict					,     _a			:							List[Any]					,     _a			:							Optional[Any]  ):
    # Initialise PyTorch model
    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__":
   A									=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.'
   )
   A									=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 warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


A									=logging.get_logger(__name__)


class     _a						(			__a				):
       def __init__(			self		:			List[str]     ,       *lowercase		:			List[str]     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           warnings.warn(
               '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use CLIPImageProcessor instead.'''     ,       lowercase     ,       )
           super().__init__(*lowercase     ,       **lowercase       )




