




'''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 UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    # Construct configuration depending on size
    _a							:       Dict       					=			{"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
    _a					,   _a					,   _a							:       List[Any]       					=			MODELS[model_size]  # set model hyperparameters
    _a							:       Dict       					=			ImageGPTConfig(n_embd=UpperCAmelCase_      ,  n_layer=UpperCAmelCase_      ,  n_head=UpperCAmelCase_	)
    _a							:       Dict       					=			ImageGPTForCausalLM(UpperCAmelCase_	)

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

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


if __name__ == "__main__":
  a		:  List[Any]   			=			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.'''
  )
  a		:  Dict   			=			parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )








'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


