





"""simple docstring"""


import numpy as np
from PIL import Image




def     lowercase__			(    lowercase_	)    ->						np.array:




      """simple docstring"""

      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
      return 0.2989 * r + 0.5870 * g + 0.1140 * b




def     lowercase__			(    lowercase_	)    ->						np.array:




      """simple docstring"""

      return (gray > 127) & (gray <= 255)




def     lowercase__			(    lowercase_						,lowercase_	)    ->						np.array:




      """simple docstring"""

      _UpperCamelCase					:		List[str]  =				np.zeros_like(lowercase_	)
      _UpperCamelCase					:		Dict  =				np.zeros(
          (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

      # Copy image to padded image
      _UpperCamelCase					:		str  =				image

      # Iterate over image & apply kernel
      for x in range(image.shape[1]	):
            for y in range(image.shape[0]	):
                  _UpperCamelCase					:		List[Any]  =				(
                      kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                  ).sum()
                  _UpperCamelCase					:		Any  =				int(summation == 5	)
      return output


# kernel to be applied
lowerCamelCase__      =      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
  # read original image
  lowerCamelCase__      =      np.array(Image.open(R"..\image_data\lena.jpg"))
  # Apply erosion operation to a binary image
  lowerCamelCase__      =      erosion(grayabinary(rgbagray(image)), structuring_element)
  # Save the output image
  lowerCamelCase__      =      Image.fromarray(output).convert("RGB")
  pil_img.save("result_erosion.png")







"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

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


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
