





'''simple docstring'''







import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 					__a      (lowerCamelCase     ):

							def __init__(							self							:     int       ,     __magic_name__							:     UNetaDModel       ,     __magic_name__							:     UNetaDModel       ,     __magic_name__							:     DDPMScheduler       ,     __magic_name__							:     Union[str, Any]       ,     )					->       Dict:







													"""simple docstring"""
													super().__init__()
													UpperCAmelCase_			:      Optional[int]     						=  value_function
													UpperCAmelCase_			:      Tuple     						=  unet
													UpperCAmelCase_			:      List[Any]     						=  scheduler
													UpperCAmelCase_			:      List[str]     						=  env
													UpperCAmelCase_			:      Any     						=  env.get_dataset()
													UpperCAmelCase_			:      str     						=  {}
													for key in self.data.keys():
																			try:
																									UpperCAmelCase_			:      Optional[int]     						=  self.data[key].mean()
																			except:  # noqa: E722
																									pass
													UpperCAmelCase_			:      Dict     						=  {}
													for key in self.data.keys():
																			try:
																									UpperCAmelCase_			:      Tuple     						=  self.data[key].std()
																			except:  # noqa: E722
																									pass
													UpperCAmelCase_			:      List[str]     						=  env.observation_space.shape[0]
													UpperCAmelCase_			:      Union[str, Any]     						=  env.action_space.shape[0]

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[int]   )					->       Tuple:







													"""simple docstring"""
													return (x_in - self.means[key]) / self.stds[key]

							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Any   )					->       Optional[int]:







													"""simple docstring"""
													return x_in * self.stds[key] + self.means[key]

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     str   )					->       Dict:







													"""simple docstring"""
													if type(__magic_name__   ) is dict:
																			return {k: self.to_torch(__magic_name__   ) for k, v in x_in.items()}
													elif torch.is_tensor(__magic_name__   ):
																			return x_in.to(self.unet.device   )
													return torch.tensor(__magic_name__       ,     device=self.unet.device   )

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     str       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													for key, val in cond.items():
																			UpperCAmelCase_			:      Union[str, Any]     						=  val.clone()
													return x_in

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     str       ,     __magic_name__							:     List[Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  x.shape[0]
													UpperCAmelCase_			:      int     						=  None
													for i in tqdm.tqdm(self.scheduler.timesteps   ):
																			# create batch of timesteps to pass into model
																			UpperCAmelCase_			:      Union[str, Any]     						=  torch.full((batch_size,)       ,     __magic_name__       ,     device=self.unet.device       ,     dtype=torch.long   )
																			for _ in range(__magic_name__   ):
																									with torch.enable_grad():
																															x.requires_grad_()

																															# permute to match dimension for pre-trained models
																															UpperCAmelCase_			:      List[str]     						=  self.value_function(x.permute(0       ,     2       ,     1   )       ,     __magic_name__   ).sample
																															UpperCAmelCase_			:      str     						=  torch.autograd.grad([y.sum()]       ,     [x]   )[0]

																															UpperCAmelCase_			:      Union[str, Any]     						=  self.scheduler._get_variance(__magic_name__   )
																															UpperCAmelCase_			:      str     						=  torch.exp(0.5 * posterior_variance   )
																															UpperCAmelCase_			:      Union[str, Any]     						=  model_std * grad

																									UpperCAmelCase_			:      int     						=  0
																									UpperCAmelCase_			:      List[str]     						=  x.detach()
																									UpperCAmelCase_			:      str     						=  x + scale * grad
																									UpperCAmelCase_			:      int     						=  self.reset_xa(__magic_name__       ,     __magic_name__       ,     self.action_dim   )

																			UpperCAmelCase_			:      Optional[int]     						=  self.unet(x.permute(0       ,     2       ,     1   )       ,     __magic_name__   ).sample.permute(0       ,     2       ,     1   )

																			# TODO: verify deprecation of this kwarg
																			UpperCAmelCase_			:      Dict     						=  self.scheduler.step(__magic_name__       ,     __magic_name__       ,     __magic_name__       ,     predict_epsilon=__magic_name__   )['''prev_sample''']

																			# apply conditions to the trajectory (set the initial state)
																			UpperCAmelCase_			:      Any     						=  self.reset_xa(__magic_name__       ,     __magic_name__       ,     self.action_dim   )
																			UpperCAmelCase_			:      Union[str, Any]     						=  self.to_torch(__magic_name__   )
													return x, y



							def __call__(							self							:     Optional[Any]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     str=64       ,     __magic_name__							:     Optional[Any]=32       ,     __magic_name__							:     List[str]=2       ,     __magic_name__							:     List[str]=0.1   )					->       int:







													"""simple docstring"""
													# normalize the observations and create  batch dimension
													UpperCAmelCase_			:      Union[str, Any]     						=  self.normalize(__magic_name__       ,     '''observations'''   )
													UpperCAmelCase_			:      Dict     						=  obs[None].repeat(__magic_name__       ,     axis=0   )

													UpperCAmelCase_			:      List[str]     						=  {0: self.to_torch(__magic_name__   )}
													UpperCAmelCase_			:      Optional[int]     						=  (batch_size, planning_horizon, self.state_dim + self.action_dim)

													# generate initial noise and apply our conditions (to make the trajectories start at current state)
													UpperCAmelCase_			:      Optional[int]     						=  randn_tensor(__magic_name__       ,     device=self.unet.device   )
													UpperCAmelCase_			:      Union[str, Any]     						=  self.reset_xa(__magic_name__       ,     __magic_name__       ,     self.action_dim   )
													UpperCAmelCase_			:      Tuple     						=  self.to_torch(__magic_name__   )

													# run the diffusion process
													UpperCAmelCase_						,       UpperCAmelCase_			:      Dict     						=  self.run_diffusion(__magic_name__       ,     __magic_name__       ,     __magic_name__       ,     __magic_name__   )

													# sort output trajectories by value
													UpperCAmelCase_			:      List[str]     						=  y.argsort(0       ,     descending=__magic_name__   ).squeeze()
													UpperCAmelCase_			:      Optional[Any]     						=  x[sorted_idx]
													UpperCAmelCase_			:      List[Any]     						=  sorted_values[:, :, : self.action_dim]
													UpperCAmelCase_			:      Optional[Any]     						=  actions.detach().cpu().numpy()
													UpperCAmelCase_			:      Union[str, Any]     						=  self.de_normalize(__magic_name__       ,     key='''actions'''   )

													# select the action with the highest value
													if y is not None:
																			UpperCAmelCase_			:      List[str]     						=  0
													else:
																			# if we didn't run value guiding, select a random action
																			UpperCAmelCase_			:      List[Any]     						=  np.random.randint(0       ,     __magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  denorm_actions[selected_index, 0]
													return denorm_actions












'''simple docstring'''







import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  get_activation('''swish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  get_activation('''silu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.SiLU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  get_activation('''mish'''   )

													self.assertIsInstance(__magic_name__       ,     nn.Mish   )

													self.assertEqual(act(torch.tensor(-2_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  get_activation('''gelu'''   )

													self.assertIsInstance(__magic_name__       ,     nn.GELU   )

													self.assertEqual(act(torch.tensor(-1_00       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertNotEqual(act(torch.tensor(-1       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(0       ,     dtype=torch.floataa   )   ).item()       ,     0   )
													self.assertEqual(act(torch.tensor(20       ,     dtype=torch.floataa   )   ).item()       ,     20   )





