

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_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			if hor == 128:
						__a        =('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						__a        =(32, 128, 256)
						__a        =('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						__a        =('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						__a        =(32, 64, 128, 256)
						__a        =('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			__a        =torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'	)
			__a        =model.state_dict()
			__a        ={
			    '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',
			}
			__a        =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()	)}'	)
			__a        =dict(zip(model.state_dict().keys()       ,     hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						__a        =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        UpperCamelCase_(							):




			"""simple docstring"""






			__a        ={
			    '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',
			}

			__a        =torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'	)
			__a        =model
			__a        =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()	)}'	)

			__a        =dict(zip(state_dict.keys()       ,     hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						__a        =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"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()