






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 				snake_case__							(							UpperCAmelCase							:  int       ):
    if hor == 1_2_8:
        lowerCAmelCase__   :Optional[Any]      							=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        lowerCAmelCase__   :List[Any]      							=				(3_2, 1_2_8, 2_5_6)
        lowerCAmelCase__   :Optional[Any]      							=				("UpResnetBlock1D", "UpResnetBlock1D")

    elif hor == 3_2:
        lowerCAmelCase__   :int      							=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        lowerCAmelCase__   :int      							=				(3_2, 6_4, 1_2_8, 2_5_6)
        lowerCAmelCase__   :List[Any]      							=				("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
    lowerCAmelCase__   :str      							=				torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
    lowerCAmelCase__   :str      							=				model.state_dict()
    lowerCAmelCase__   :Union[str, Any]      							=				{
        "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": 1_4,
        "out_channels": 1_4,
        "extra_in_channels": 0,
        "time_embedding_type": "positional",
        "flip_sin_to_cos": False,
        "freq_shift": 1,
        "sample_size": 6_5_5_3_6,
        "mid_block_type": "MidResTemporalBlock1D",
        "act_fn": "mish",
    }
    lowerCAmelCase__   :Tuple      							=				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__   :Optional[int]      							=				dict(zip(model.state_dict().keys()    ,			hf_value_function.state_dict().keys()       )       )
    for k, v in mapping.items():
        lowerCAmelCase__   :Any      							=				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 				snake_case__							(							):
    lowerCAmelCase__   :List[Any]      							=				{
        "in_channels": 1_4,
        "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
        "up_block_types": (),
        "out_block_type": "ValueFunction",
        "mid_block_type": "ValueFunctionMidBlock1D",
        "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
        "layers_per_block": 1,
        "downsample_each_block": True,
        "sample_size": 6_5_5_3_6,
        "out_channels": 1_4,
        "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__   :str      							=				torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"       )
    lowerCAmelCase__   :Union[str, Any]      							=				model
    lowerCAmelCase__   :Optional[int]      							=				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__   :Union[str, Any]      							=				dict(zip(state_dict.keys()    ,			hf_value_function.state_dict().keys()       )       )
    for k, v in mapping.items():
        lowerCAmelCase__   :List[str]      							=				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()











def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return "".join(chr(ord(UpperCAmelCase       ) - 3_2       ) if "a" <= char <= "z" else char for char in word       )


if __name__ == "__main__":
 from doctest import testmod

 testmod()



