


"""simple docstring"""



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		(		lowercase    :    List[Any]					)      ->		Dict:


    """simple docstring"""





    if hor == 128:
        snake_case       : List[str]    =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        snake_case       : Union[str, Any]    =       (32, 128, 256)
        snake_case       : Any    =       ("UpResnetBlock1D", "UpResnetBlock1D")

    elif hor == 32:
        snake_case       : List[str]    =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
        snake_case       : Any    =       (32, 64, 128, 256)
        snake_case       : List[Any]    =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
    snake_case       : Optional[Any]    =       torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'					)
    snake_case       : Any    =       model.state_dict()
    snake_case       : Optional[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",
    }
    snake_case       : Optional[Any]    =       UNetaDModel(**lowercase					)
    print(F'length of state dict: {len(state_dict.keys()					)}'					)
    print(F'length of value function dict: {len(hf_value_function.state_dict().keys()					)}'					)
    snake_case       : Any    =       dict(zip(model.state_dict().keys()		,				hf_value_function.state_dict().keys()					)					)
    for k, v in mapping.items():
        snake_case       : Union[str, Any]    =       state_dict.pop(lowercase					)
    hf_value_function.load_state_dict(lowercase					)

    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(lowercase		,				lowercase					)







def     __lowerCAmelCase		(		)      ->		Tuple:


    """simple docstring"""





    snake_case       : Dict    =       {
        "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",
    }

    snake_case       : List[Any]    =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"					)
    snake_case       : List[Any]    =       model
    snake_case       : Optional[Any]    =       UNetaDModel(**lowercase					)
    print(F'length of state dict: {len(state_dict.keys()					)}'					)
    print(F'length of value function dict: {len(hf_value_function.state_dict().keys()					)}'					)

    snake_case       : str    =       dict(zip(state_dict.keys()		,				hf_value_function.state_dict().keys()					)					)
    for k, v in mapping.items():
        snake_case       : str    =       state_dict.pop(lowercase					)

    hf_value_function.load_state_dict(lowercase					)

    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(lowercase		,				lowercase					)


if __name__ == "__main__":
  unet(32)
  # unet(128)
  value_function()









"""simple docstring"""



import string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





