


"""simple docstring"""
import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class      _UpperCAmelCase					(				utils.Parser							):






		'''simple docstring'''

		lowerCamelCase__           ="halfcheetah-medium-expert-v2"
		lowerCamelCase__           ="config.offline"






def 						lowercase				(	_snake_case		: Optional[int]	,  _snake_case		: Any	,  _snake_case		: str	,  _snake_case		: str	,  _snake_case		: List[str]	)	->Tuple:
	"""simple docstring"""





	__snake_case      ,				__snake_case      :		List[Any]         =							utils.load_model(_snake_case	,  _snake_case	,  _snake_case	,  epoch=_snake_case	,  device=_snake_case	)
	__snake_case      :		Optional[Any]         =							TrajectoryTransformerModel(gpt.config	)

	trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()	)
	__snake_case      :		Tuple         =							gpt.pos_emb
	trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()	)
	trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()	)
	trajectory_transformer.head.load_state_dict(gpt.head.state_dict()	)

	for i, block in enumerate(gpt.blocks	):
		trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()	)
		trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()	)
		trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()	)

		trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()	)
		trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()	)
		trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()	)
		trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()	)

	torch.save(trajectory_transformer.state_dict()	,  '''pytorch_model.bin'''	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Any							   =							Parser().parse_args("""plan""")
			convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
			    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
			)









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

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


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

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

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


