



from cva import destroyAllWindows, imread, imshow, waitKey




def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			a__				,       a__					=  img.shape[0], img.shape[1]

			def to_grayscale(lowercase__						:					Union[str, Any]				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]	):
						return 0.2126 * red + 0.587 * green + 0.114 * blue

			def normalize(lowercase__						:					List[str]	):
						return min(_SCREAMING_SNAKE_CASE				,					255	)

			for i in range(_SCREAMING_SNAKE_CASE	):
						for j in range(_SCREAMING_SNAKE_CASE	):
									a__					=  int(to_grayscale(*img[i][j]	)	)
									a__					=  [
									    normalize(_SCREAMING_SNAKE_CASE	),
									    normalize(greyscale + factor	),
									    normalize(greyscale + 2 * factor	),
									]

			return img


if __name__ == "__main__":
	# read original image
	_lowercase				:					str     					={
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (10, 20, 30, 40)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(f'''Original image with sepia (factor: {percentage})''', img)

	waitKey(0)
	destroyAllWindows()








import argparse

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


logging.set_verbosity_info()



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			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."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


