
"""simple docstring"""





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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					if gpta_config_file == "":
										_UpperCamelCase									=    GPTaConfig()
					else:
										_UpperCamelCase									=    GPTaConfig.from_json_file(__snake_case      )
					_UpperCamelCase									=    GPTaModel(__snake_case      )

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

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


if __name__ == "__main__":
				_a								=						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."""
				    ),
				)
				_a								=						parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)













'''simple docstring'''

from PIL import Image

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Image:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(259 * (level + 255)) / (255 * (259 - level))

   def contrast(__UpperCAmelCase   ) -> int:
      return int(128 + factor * (c - 128)   )

   return img.point(__UpperCAmelCase   )


if __name__ == "__main__":
    # Load image
    with Image.open("image_data/lena.jpg") as img:
        # Change contrast to 170
        a   	=    change_contrast(img, 170)
        cont_img.save("image_data/lena_high_contrast.png", format="png")





