
"""simple docstring"""




import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	Dict					,			__snake_case    :	Union[str, Any]  )			->    Tuple:
       # Initialise PyTorch model
       lowercase   :    str			=							TaConfig.from_json_file(__snake_case  )
       print(f"""Building PyTorch model from configuration: {config}"""  )
       lowercase   :    Optional[int]			=							TaForConditionalGeneration(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(__snake_case					,			__snake_case					,			__snake_case  )

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


if __name__ == "__main__":
   _A      :	Union[str, Any]       		=	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(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A      :	Optional[int]       		=	parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_A      :	List[str]       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


