





import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Optional[Any]:


      '''simple docstring'''


      if gpta_config_file == "":
            SCREAMING_SNAKE_CASE	=		GPTaConfig()
      else:
            SCREAMING_SNAKE_CASE	=		GPTaConfig.from_json_file(_A			)
      SCREAMING_SNAKE_CASE	=		GPTaModel(_A			)

      # Load weights from numpy
      load_tf_weights_in_gpta(_A				,		_A				,		_A			)

      # Save pytorch-model
      SCREAMING_SNAKE_CASE	=		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
      SCREAMING_SNAKE_CASE	=		pytorch_dump_folder_path + """/""" + CONFIG_NAME
      print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""			)
      torch.save(model.state_dict()				,		_A			)
      print(F"""Save configuration file to {pytorch_config_dump_path}"""			)
      with open(_A				,		"""w"""				,		encoding="""utf-8"""			) as f:
            f.write(config.to_json_string()			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--gpt2_config_file""",
          default="""""",
          type=str,
          help=(
              """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
              """This specifies the model architecture."""
          ),
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()
      convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)










import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					int		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					List[Any] )  ->	None:




           '''simple docstring'''


           warnings.warn(
               """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
               """ Please use DeformableDetrImageProcessor instead."""		,lowerCamelCase__		,)
           super().__init__(*lowerCamelCase__		,**lowerCamelCase__ )


