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__  (					__A	):







    '''simple docstring'''
    if hor == 128:
        _lowerCamelCase     :		int 						=						("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
        _lowerCamelCase     :		Any 						=						(32, 128, 256)
        _lowerCamelCase     :		Optional[int] 						=						("""UpResnetBlock1D""", """UpResnetBlock1D""")

    elif hor == 32:
        _lowerCamelCase     :		str 						=						("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
        _lowerCamelCase     :		Optional[int] 						=						(32, 64, 128, 256)
        _lowerCamelCase     :		List[str] 						=						("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
    _lowerCamelCase     :		int 						=						torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
    _lowerCamelCase     :		List[Any] 						=						model.state_dict()
    _lowerCamelCase     :		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""": 65_536,
        """mid_block_type""": """MidResTemporalBlock1D""",
        """act_fn""": """mish""",
    }
    _lowerCamelCase     :		Dict 						=						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     :		List[str] 						=						dict(zip(model.state_dict().keys()   , hf_value_function.state_dict().keys()	)	)
    for k, v in mapping.items():
        _lowerCamelCase     :		Union[str, 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 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						{
        """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""": 65_536,
        """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     :		Union[str, Any] 						=						torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""	)
    _lowerCamelCase     :		str 						=						model
    _lowerCamelCase     :		Any 						=						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     :		Dict 						=						dict(zip(state_dict.keys()   , hf_value_function.state_dict().keys()	)	)
    for k, v in mapping.items():
        _lowerCamelCase     :		List[Any] 						=						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()





import math







def 						A__  (					__A	):







    '''simple docstring'''
    assert isinstance(__A   , __A	) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    _lowerCamelCase     :		List[Any] 						=						range(3   , int(math.sqrt(__A	) + 1	)   , 2	)
    return not any(not number % i for i in odd_numbers	)







def 						A__  (					__A   , __A=1   , **__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Dict 						=						factor * value
    _lowerCamelCase     :		str 						=						value

    while not is_prime(__A	):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1   , **__A	)
    return value




