




"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Optional[int]   ,     *UpperCamelCase       :		str   ,     **UpperCamelCase       :		List[str]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )











"""simple docstring"""


import argparse

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


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

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

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


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(
      """--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													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





