






'''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 	_UpperCAmelCase    (  a		:	str							)			->			Optional[int]:





						"""simple docstring"""
						if hor == 1_2_8:
												lowercase_      :   str        =			('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
												lowercase_      :   str        =			(3_2, 1_2_8, 2_5_6)
												lowercase_      :   Tuple        =			('UpResnetBlock1D', 'UpResnetBlock1D')

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

						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(a						,			a							)






def 	_UpperCAmelCase    (  )			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   str        =			{
						    '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',
						}

						lowercase_      :   Tuple        =			torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'							)
						lowercase_      :   Union[str, Any]        =			model
						lowercase_      :   int        =			UNetaDModel(**a							)
						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(state_dict.keys()						,			hf_value_function.state_dict().keys()							)							)
						for k, v in mapping.items():
												lowercase_      :   int        =			state_dict.pop(a							)

						hf_value_function.load_state_dict(a							)

						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(a						,			a							)


if __name__ == "__main__":
						unet(3_2)
						# unet(128)
						value_function()








'''simple docstring'''

import os
from distutils.util import strtobool






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	int							)			->			Any:





						"""simple docstring"""
						for e in env_keys:
												lowercase_      :   Optional[Any]        =			int(os.environ.get(a						,			-1							)							)
												if val >= 0:
																		return val
						return default






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Dict=False							)			->			Optional[Any]:





						"""simple docstring"""
						lowercase_      :   Optional[int]        =			os.environ.get(a						,			str(a							)							)
						return strtobool(a							) == 1  # As its name indicates `strtobool` actually returns an int...






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Dict="no"							)			->			str:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			os.environ.get(a						,			str(a							)							)
						return value
