



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 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any]):





    '''simple docstring'''

    if hor == 128:
        lowerCAmelCase__  :       int				=  ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        lowerCAmelCase__  :       Tuple				=  (32, 128, 256)
        lowerCAmelCase__  :       List[str]				=  ("UpResnetBlock1D", "UpResnetBlock1D")

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





    '''simple docstring'''

    lowerCAmelCase__  :       Tuple				=  {
        "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",
    }

    lowerCAmelCase__  :       Tuple				=  torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch''')
    lowerCAmelCase__  :       int				=  model
    lowerCAmelCase__  :       Tuple				=  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())}""")

    lowerCAmelCase__  :       List[str]				=  dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys()))
    for k, v in mapping.items():
        lowerCAmelCase__  :       Union[str, 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(3_2)
       # unet(128)
       value_function()






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  [0 for i in range(r + 1)]
				# nc0 = 1
				lowerCAmelCase__  :       Tuple				=  1
				for i in range(1 ,n + 1):
								# to compute current row from previous row.
								lowerCAmelCase__  :       Optional[int]				=  min(lowerCamelCase_ ,lowerCamelCase_)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]

print(binomial_coefficient(n=1_0, r=5))

