




"""simple docstring"""

import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)




def     _A					( __lowercase    ):





       """simple docstring"""



       if hor == 128:
              lowerCamelCase__          =						('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
              lowerCamelCase__          =						(32, 128, 256)
              lowerCamelCase__          =						('''UpResnetBlock1D''', '''UpResnetBlock1D''')

       elif hor == 32:
              lowerCamelCase__          =						('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
              lowerCamelCase__          =						(32, 64, 128, 256)
              lowerCamelCase__          =						('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
       lowerCamelCase__          =						torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""    )
       lowerCamelCase__          =						model.state_dict()
       lowerCamelCase__          =						{
           '''down_block_types''': down_block_types,
           '''block_out_channels''': block_out_channels,
           '''up_block_types''': up_block_types,
           '''layers_per_block''': 1,
           '''use_timestep_embedding''': True,
           '''out_block_type''': '''OutConv1DBlock''',
           '''norm_num_groups''': 8,
           '''downsample_each_block''': False,
           '''in_channels''': 14,
           '''out_channels''': 14,
           '''extra_in_channels''': 0,
           '''time_embedding_type''': '''positional''',
           '''flip_sin_to_cos''': False,
           '''freq_shift''': 1,
           '''sample_size''': 6_5536,
           '''mid_block_type''': '''MidResTemporalBlock1D''',
           '''act_fn''': '''mish''',
       }
       lowerCamelCase__          =						UNetaDModel(**__lowercase    )
       print(f"""length of state dict: {len(state_dict.keys()    )}"""    )
       print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()    )}"""    )
       lowerCamelCase__          =						dict(zip(model.state_dict().keys()							,     hf_value_function.state_dict().keys()    )    )
       for k, v in mapping.items():
              lowerCamelCase__          =						state_dict.pop(__lowercase    )
       hf_value_function.load_state_dict(__lowercase    )

       torch.save(hf_value_function.state_dict()							,     f"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""    )
       with open(f"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""							,     """w"""    ) as f:
              json.dump(__lowercase							,     __lowercase    )




def     _A					( ):





       """simple docstring"""



       lowerCamelCase__          =						{
           '''in_channels''': 14,
           '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
           '''up_block_types''': (),
           '''out_block_type''': '''ValueFunction''',
           '''mid_block_type''': '''ValueFunctionMidBlock1D''',
           '''block_out_channels''': (32, 64, 128, 256),
           '''layers_per_block''': 1,
           '''downsample_each_block''': True,
           '''sample_size''': 6_5536,
           '''out_channels''': 14,
           '''extra_in_channels''': 0,
           '''time_embedding_type''': '''positional''',
           '''use_timestep_embedding''': True,
           '''flip_sin_to_cos''': False,
           '''freq_shift''': 1,
           '''norm_num_groups''': 8,
           '''act_fn''': '''mish''',
       }

       lowerCamelCase__          =						torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""    )
       lowerCamelCase__          =						model
       lowerCamelCase__          =						UNetaDModel(**__lowercase    )
       print(f"""length of state dict: {len(state_dict.keys()    )}"""    )
       print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()    )}"""    )

       lowerCamelCase__          =						dict(zip(state_dict.keys()							,     hf_value_function.state_dict().keys()    )    )
       for k, v in mapping.items():
              lowerCamelCase__          =						state_dict.pop(__lowercase    )

       hf_value_function.load_state_dict(__lowercase    )

       torch.save(hf_value_function.state_dict()							,     """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""    )
       with open("""hub/hopper-medium-v2/value_function/config.json"""							,     """w"""    ) as f:
              json.dump(__lowercase							,     __lowercase    )


if __name__ == "__main__":
  unet(32)
  # unet(128)
  value_function()









'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										if dst_width < 0 or dst_height < 0:
																raise ValueError('''Destination width/height should be > 0'''   )

										lowercase :							Union[str, Any]    		=img
										lowercase :							Union[str, Any]    		=img.shape[1]
										lowercase :							str    		=img.shape[0]
										lowercase :							Union[str, Any]    		=dst_width
										lowercase :							str    		=dst_height

										lowercase :							str    		=self.src_w / self.dst_w
										lowercase :							Optional[Any]    		=self.src_h / self.dst_h

										lowercase :							int    		=(
										    np.ones((self.dst_h, self.dst_w, 3)		,					np.uinta   ) * 255
										)







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										for i in range(self.dst_h   ):
																for j in range(self.dst_w   ):
																						lowercase :							List[Any]    		=self.img[self.get_y(UpperCAmelCase__   )][self.get_x(UpperCAmelCase__   )]







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_x * x   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_y * y   )


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       800, 600
							UpperCamelCase_    						=       imread("""image_data/lena.jpg""", 1)
							UpperCamelCase_    						=       NearestNeighbour(im, dst_w, dst_h)
							n.process()

							imshow(
							    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
							)
							waitKey(0)
							destroyAllWindows()





