

'''simple docstring'''



import numpy as np
from PIL import Image







def      a__ (						a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b







def      a__ (						a__		):




     """simple docstring"""


     return (gray > 1_27) & (gray <= 2_55)







def      a__ (						a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   np.zeros_like(a__		)
     __SCREAMING_SNAKE_CASE						     =   np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)		)

     # Copy image to padded image
     __SCREAMING_SNAKE_CASE						     =   image

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


# kernel to be applied
UpperCAmelCase				: Union[str, Any]         =  np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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









'''simple docstring'''



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		lowerCAmelCase__     (   utils.Parser      ):



 """simple docstring"""



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







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   utils.load_model(a__   ,					a__   ,					a__   ,					epoch=a__   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   TrajectoryTransformerModel(gpt.config		)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
     __SCREAMING_SNAKE_CASE						     =   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__":
  UpperCAmelCase				: Dict         =  Parser().parse_args('plan')
  convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
      args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
  )






