

"""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     a__    (    __SCREAMING_SNAKE_CASE )	->	List[Any]:
		if hor == 1_2_8:
				__lowerCAmelCase:       Dict          =			("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
				__lowerCAmelCase:       str          =			(3_2, 1_2_8, 2_5_6)
				__lowerCAmelCase:       Dict          =			("UpResnetBlock1D", "UpResnetBlock1D")

		elif hor == 3_2:
				__lowerCAmelCase:       Union[str, Any]          =			("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
				__lowerCAmelCase:       Tuple          =			(3_2, 6_4, 1_2_8, 2_5_6)
				__lowerCAmelCase:       Optional[Any]          =			("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
		__lowerCAmelCase:       Optional[Any]          =			torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch" )
		__lowerCAmelCase:       Any          =			model.state_dict()
		__lowerCAmelCase:       int          =			{
		    "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:       List[Any]          =			UNetaDModel(**__SCREAMING_SNAKE_CASE )
		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:       Tuple          =			dict(zip(model.state_dict().keys()					, hf_value_function.state_dict().keys() ) )
		for k, v in mapping.items():
				__lowerCAmelCase:       Tuple          =			state_dict.pop(__SCREAMING_SNAKE_CASE )
		hf_value_function.load_state_dict(__SCREAMING_SNAKE_CASE )

		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(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )






def     a__    (    )	->	Optional[Any]:
		__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:       List[str]          =			torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
		__lowerCAmelCase:       int          =			model
		__lowerCAmelCase:       int          =			UNetaDModel(**__SCREAMING_SNAKE_CASE )
		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:       str          =			state_dict.pop(__SCREAMING_SNAKE_CASE )

		hf_value_function.load_state_dict(__SCREAMING_SNAKE_CASE )

		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(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )


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





"""simple docstring"""


import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, Path]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  True
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  None
				SCREAMING_SNAKE_CASE_   :      int         =  1
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, bool]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None

				def 							lowercase_     (	self							:				str)->   "DownloadConfig":



						'''simple docstring'''


						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__) for k, v in self.__dict__.items()})


