



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__      (		_UpperCamelCase      :			Optional[int]				):
   if hor == 1_28:
      __lowerCamelCase    				=     ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
      __lowerCamelCase    				=     (32, 1_28, 2_56)
      __lowerCamelCase    				=     ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

   elif hor == 32:
      __lowerCamelCase    				=     ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
      __lowerCamelCase    				=     (32, 64, 1_28, 2_56)
      __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_55_36,
       '''mid_block_type''': '''MidResTemporalBlock1D''',
       '''act_fn''': '''mish''',
   }
   __lowerCamelCase    				=     UNetaDModel(**_UpperCamelCase				)
   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(_UpperCamelCase				)
   hf_value_function.load_state_dict(_UpperCamelCase				)

   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(_UpperCamelCase			,_UpperCamelCase				)





def 		a__      (		):
   __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, 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''',
   }

   __lowerCamelCase    				=     torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''				)
   __lowerCamelCase    				=     model
   __lowerCamelCase    				=     UNetaDModel(**_UpperCamelCase				)
   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(_UpperCamelCase				)

   hf_value_function.load_state_dict(_UpperCamelCase				)

   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(_UpperCamelCase			,_UpperCamelCase				)


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











import math





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * power_factor





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






