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 			lowercase_				(			_lowerCamelCase							:       Any):
    if hor == 128:
        lowercase__ :   List[str]    					=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        lowercase__ :   Union[str, Any]    					=				(32, 128, 256)
        lowercase__ :   Dict    					=				("UpResnetBlock1D", "UpResnetBlock1D")

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

    lowercase__ :   Optional[int]    					=				torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
    lowercase__ :   List[Any]    					=				model
    lowercase__ :   Union[str, 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(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()		,	"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()




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



