


"""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      (   __snake_case							:      List[Any]						, __snake_case							:      str						, __snake_case							:      int						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		BigBirdConfig.from_json_file(__snake_case   )
   print(F'Building PyTorch model from configuration: {config}'   )

   if is_trivia_qa:
      _lowerCamelCase	:					List[str]   =		BigBirdForQuestionAnswering(__snake_case   )
   else:
      _lowerCamelCase	:					Union[str, Any]   =		BigBirdForPreTraining(__snake_case   )

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

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


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"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

