



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  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					# Construct configuration depending on size
					UpperCamelCase_			= {"small": (5_12, 8, 24), "medium": (10_24, 8, 36), "large": (15_36, 16, 48)}
					UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= MODELS[model_size]  # set model hyperparameters
					UpperCamelCase_			= ImageGPTConfig(n_embd=_lowerCAmelCase ,						n_layer=_lowerCAmelCase ,						n_head=_lowerCAmelCase)
					UpperCamelCase_			= ImageGPTForCausalLM(_lowerCAmelCase)

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

					# 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() ,						_lowerCAmelCase)
					print(f"""Save configuration file to {pytorch_config_dump_path}""")
					with open(_lowerCAmelCase ,						"w" ,						encoding="utf-8") as f:
										f.write(config.to_json_string())


if __name__ == "__main__":
	UpperCAmelCase :				Optional[int]							=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."""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)



"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def     _A    (    UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
   __lowercase								=		np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   # modify the pix within [0, 255]
   __lowercase								=		dst_x * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_y * 255 / np.max(UpperCamelCase_)

   __lowercase								=		np.sqrt((np.square(UpperCamelCase_)) + (np.square(UpperCamelCase_)))
   __lowercase								=		dst_xy * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_xy.astype(np.uinta)

   __lowercase								=		np.arctana(UpperCamelCase_,     UpperCamelCase_)
   return dst, theta


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

   _a ,		_a					 =				sobel_filter(gray)

   # show result images
   imshow('sobel filter', sobel_grad)
   imshow('sobel theta', sobel_theta)
   waitKey(0)




