

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def       __snake_case						( __UpperCamelCase	: ndarray    ):



      """simple docstring"""

      return np.dot(__UpperCamelCase				,__UpperCamelCase    )

class   _a					:




      """simple docstring"""

      def __init__(   self							:				Dict						,			*,
          UpperCAmelCase							:				float = np.inf						,			UpperCAmelCase							:				str = "linear"						,			UpperCAmelCase							:				float = 0.0						,			):
            A_						  =				regularization
            A_						  =				gamma
            if kernel == "linear":
                  A_						  =				self.__linear
            elif kernel == "rbf":
                  if self.gamma == 0:
                        raise ValueError("rbf kernel requires gamma"	)
                  if not isinstance(self.gamma						,			(float, int)	):
                        raise ValueError("gamma must be float or int"	)
                  if not self.gamma > 0:
                        raise ValueError("gamma must be > 0"	)
                  A_						  =				self.__rbf
                  # in the future, there could be a default value like in sklearn
                  # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                  # previously it was 1/(n_features)
            else:
                  A_						  =				f'''Unknown kernel: {kernel}'''
                  raise ValueError(UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				ndarray						,			UpperCAmelCase							:				ndarray	):
            return np.dot(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]						,			UpperCAmelCase							:				ndarray						,			UpperCAmelCase							:				ndarray	):
            return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)





      def 		__A	(   self							:				int						,			UpperCAmelCase							:				list[ndarray]						,			UpperCAmelCase							:				ndarray	):
            A_						  =				observations
            A_						  =				classes

            # using Wolfe's Dual to calculate w.
            # Primal problem: minimize 1/2*norm_squared(w)
            #   constraint: yn(w . xn + b) >= 1
            #
            # With l a vector
            # Dual problem: maximize sum_n(ln) -
            #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
            #   constraint: self.C >= ln >= 0
            #           and sum_n(ln*yn) = 0
            # Then we get w using w = sum_n(ln*yn*xn)
            # At the end we can get b ~= mean(yn - w . xn)
            #
            # Since we use kernels, we only need l_star to calculate b
            # and to classify observations

            ((A_)  ,     )						  =				np.shape(UpperCAmelCase	)

            def to_minimize(UpperCAmelCase							:				ndarray	) -> float:
                  A_						  =				0
                  ((A_)  ,     )						  =				np.shape(UpperCAmelCase	)
                  for i in range(UpperCAmelCase	):
                        for j in range(UpperCAmelCase	):
                              s += (
                                  candidate[i]
                                  * candidate[j]
                                  * classes[i]
                                  * classes[j]
                                  * self.kernel(observations[i]						,			observations[j]	)
                              )
                  return 1 / 2 * s - sum(UpperCAmelCase	)

            A_						  =				LinearConstraint(UpperCAmelCase						,			0						,			0	)
            A_						  =				Bounds(0						,			self.regularization	)

            A_						  =				minimize(
                UpperCAmelCase						,			np.ones(UpperCAmelCase	)						,			bounds=UpperCAmelCase						,			constraints=[ly_contraint]	).x
            A_						  =				l_star

            # calculating mean offset of separation plane to points
            A_						  =				0
            for i in range(UpperCAmelCase	):
                  for j in range(UpperCAmelCase	):
                        s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                            observations[i]						,			observations[j]	)
            A_						  =				s / n







      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				ndarray	):
            A_						  =				sum(
                self.optimum[n]
                * self.classes[n]
                * self.kernel(self.observations[n]						,			UpperCAmelCase	)
                for n in range(len(self.classes	)	)	)
            return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()




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    (							__UpperCamelCase       )    ->   Union[str, Any]:
			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': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			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(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			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(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			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': 65_536,
			    '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(**__UpperCamelCase       )
			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(__UpperCamelCase       )

			hf_value_function.load_state_dict(__UpperCamelCase       )

			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(__UpperCamelCase			,						__UpperCamelCase       )


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




