

"""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  __lowerCAmelCase (  A    ,       A    ,       A      ):
	# Initialise PyTorch model
	UpperCAmelCase_    			=     json.load(open(A    ,       "r"      )      )
	UpperCAmelCase_    			=     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}"      )
	UpperCAmelCase_    			=     GPTNeoForCausalLM(A      )

	# Load weights from tf checkpoint
	load_tf_weights_in_gpt_neo(A    ,       A    ,       A      )

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


if __name__ == "__main__":
				_a:      List[Any]								=     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:      Tuple								=     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


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



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


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

				# get values with two different mask size
				_a:      Tuple								=     median_filter(gray, 3)
				_a:      Dict								=     median_filter(gray, 5)

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