






'''simple docstring'''







import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__							):
      # Initialise PyTorch model
      __A		   =      json.load(open(snake_case__							,			"r"							)							)
      __A		   =      GPTNeoConfig(
          hidden_size=config_json["n_embd"]							,			num_layers=config_json["n_layer"]							,			num_heads=config_json["n_head"]							,			attention_types=config_json["attention_types"]							,			max_position_embeddings=config_json["n_positions"]							,			resid_dropout=config_json["res_dropout"]							,			embed_dropout=config_json["embed_dropout"]							,			attention_dropout=config_json["attn_dropout"]							,			)
      print(f"Building PyTorch model from configuration: {config}"							)
      __A		   =      GPTNeoForCausalLM(snake_case__							)

      # Load weights from tf checkpoint
      load_tf_weights_in_gpt_neo(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      :       int      =	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 mesh-tf model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __A      :       Any      =	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      __A   ,				__A		   =      img.shape[0], img.shape[1]

      def to_grayscale(snake_case__							,			snake_case__							,			snake_case__							):
            return 0.2126 * red + 0.587 * green + 0.114 * blue

      def normalize(snake_case__							):
            return min(snake_case__							,			255							)

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

      return img


if __name__ == "__main__":
       # read original image
       __A      :       Dict      =	{
           percentage: imread('image_data/lena.jpg', 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       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()


