





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_       (    __UpperCAmelCase:     List[str] ) ->      Optional[int]:
    if hor == 128:
        UpperCamelCase__				:							Any			  = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
        UpperCamelCase__				:							str			  = (32, 128, 256)
        UpperCamelCase__				:							int			  = ('UpResnetBlock1D', 'UpResnetBlock1D')

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

    UpperCamelCase__				:							str			  = torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch''' )
    UpperCamelCase__				:							List[Any]			  = model
    UpperCamelCase__				:							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() )}" )

    UpperCamelCase__				:							Tuple			  = dict(zip(state_dict.keys()						,  hf_value_function.state_dict().keys() ) )
    for k, v in mapping.items():
        UpperCamelCase__				:							Union[str, Any]			  = 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()








import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict			)   ->							tuple:




		'''simple docstring'''
		return (data["data"], data["target"])


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




		'''simple docstring'''
		lowercase__					:      List[str]      =		XGBRegressor(verbosity=0   ,    random_state=42			)
		xgb.fit(_lowercase   ,    _lowercase			)
		# Predict target for test data
		lowercase__					:      Union[str, Any]      =		xgb.predict(_lowercase			)
		lowercase__					:      Tuple      =		predictions.reshape(len(_lowercase			)   ,    1			)
		return predictions


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


