




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       UpperCAmelCase__  (     lowerCamelCase   ):
  if hor == 128:
    lowercase    :Optional[int]      	=						("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
    lowercase    :List[Any]      	=						(32, 128, 256)
    lowercase    :Any      	=						("UpResnetBlock1D", "UpResnetBlock1D")

  elif hor == 32:
    lowercase    :List[str]      	=						("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
    lowercase    :Optional[Any]      	=						(32, 64, 128, 256)
    lowercase    :Optional[int]      	=						("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
  lowercase    :str      	=						torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"   )
  lowercase    :str      	=						model.state_dict()
  lowercase    :int      	=						{
      "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": 65536,
      "mid_block_type": "MidResTemporalBlock1D",
      "act_fn": "mish",
  }
  lowercase    :List[Any]      	=						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()   )}"   )
  lowercase    :Union[str, Any]      	=						dict(zip(model.state_dict().keys(),			hf_value_function.state_dict().keys()   )   )
  for k, v in mapping.items():
    lowercase    :List[Any]      	=						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       UpperCAmelCase__  (     ):
  lowercase    :Optional[int]      	=						{
      "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": 65536,
      "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",
  }

  lowercase    :Dict      	=						torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"   )
  lowercase    :str      	=						model
  lowercase    :str      	=						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()   )}"   )

  lowercase    :List[str]      	=						dict(zip(state_dict.keys(),			hf_value_function.state_dict().keys()   )   )
  for k, v in mapping.items():
    lowercase    :Dict      	=						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()









from abc import ABC, abstractmethod
from argparse import ArgumentParser




class    __lowerCAmelCase       (							lowerCAmelCase):






       @staticmethod
       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					ArgumentParser				):
         raise NotImplementedError()






       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         raise NotImplementedError()



