




"""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      _snake_case			(				lowercase__		):
				if hor == 128:
								_lowerCamelCase     :			Dict    =    ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
								_lowerCamelCase     :			List[Any]    =    (32, 128, 256)
								_lowerCamelCase     :			List[str]    =    ('UpResnetBlock1D', 'UpResnetBlock1D')

				elif hor == 32:
								_lowerCamelCase     :			Optional[Any]    =    ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
								_lowerCamelCase     :			List[Any]    =    (32, 64, 128, 256)
								_lowerCamelCase     :			Any    =    ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
				_lowerCamelCase     :			Dict    =    torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''		)
				_lowerCamelCase     :			Dict    =    model.state_dict()
				_lowerCamelCase     :			List[str]    =    {
				    '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     :			List[str]    =    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()		)}'''		)
				_lowerCamelCase     :			List[Any]    =    dict(zip(model.state_dict().keys()							,    hf_value_function.state_dict().keys()		)		)
				for k, v in mapping.items():
								_lowerCamelCase     :			List[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      _snake_case			(				):
				_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     :			Optional[int]    =    torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'		)
				_lowerCamelCase     :			Union[str, Any]    =    model
				_lowerCamelCase     :			Union[str, 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()		)}'''		)

				_lowerCamelCase     :			Tuple    =    dict(zip(state_dict.keys()							,    hf_value_function.state_dict().keys()		)		)
				for k, v in mapping.items():
								_lowerCamelCase     :			Optional[int]    =    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"""






def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				if len(lowercase__		) == 1:
								return True
				_lowerCamelCase     :			List[Any]    =    series[1] - series[0]
				for index in range(len(lowercase__		) - 1		):
								if series[index + 1] - series[index] != common_diff:
												return False
				return True







def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				_lowerCamelCase     :			Optional[int]    =    0
				for val in series:
								answer += val
				return answer / len(lowercase__		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()