


"""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_   (   _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     if hor == 1_28:
          _a    	=				('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          _a    	=				(32, 1_28, 2_56)
          _a    	=				('''UpResnetBlock1D''', '''UpResnetBlock1D''')

     elif hor == 32:
          _a    	=				('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          _a    	=				(32, 64, 1_28, 2_56)
          _a    	=				('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
     _a    	=				torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'       )
     _a    	=				model.state_dict()
     _a    	=				{
         '''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_55_36,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     _a    	=				UNetaDModel(**_lowerCAmelCase       )
     print(f'length of state dict: {len(state_dict.keys()       )}'       )
     print(f'length of value function dict: {len(hf_value_function.state_dict().keys()       )}'       )
     _a    	=				dict(zip(model.state_dict().keys(),     hf_value_function.state_dict().keys()       )       )
     for k, v in mapping.items():
          _a    	=				state_dict.pop(_lowerCAmelCase       )
     hf_value_function.load_state_dict(_lowerCAmelCase       )

     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(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   ):






     """simple docstring"""

     _a    	=				{
         '''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, 1_28, 2_56),
         '''layers_per_block''': 1,
         '''downsample_each_block''': True,
         '''sample_size''': 6_55_36,
         '''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''',
     }

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

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

     hf_value_function.load_state_dict(_lowerCAmelCase       )

     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(_lowerCAmelCase,     _lowerCAmelCase       )


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



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					list,     _lowerCAmelCase     :					list,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				len(_lowerCAmelCase       )
     _a    	=				[[0] * n for i in range(_lowerCAmelCase       )]
     for i in range(_lowerCAmelCase       ):
          _a    	=				y_points[i]

     for i in range(2,     _lowerCAmelCase       ):
          for j in range(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				(
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()