





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					)						->			str:
 if hor == 128:
  UpperCamelCase     : List[str]	     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  UpperCamelCase     : Dict	     =       (32, 128, 256)
  UpperCamelCase     : Dict	     =       ("UpResnetBlock1D", "UpResnetBlock1D")

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

 UpperCamelCase     : Optional[Any]	     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"					)
 UpperCamelCase     : Optional[Any]	     =       model
 UpperCamelCase     : 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()					)}"""					)

 UpperCamelCase     : Optional[int]	     =       dict(zip(state_dict.keys()							,       hf_value_function.state_dict().keys()					)					)
 for k, v in mapping.items():
  UpperCamelCase     : Tuple	     =       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 __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
