



'''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 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     UNetaDModel		,			__snake_case			:     DDPMScheduler		,			__snake_case			:     Any		,			)			->		Union[str, Any]:
								super().__init__()
								UpperCAmelCase :       str 							=		value_function
								UpperCAmelCase :       Optional[Any] 							=		unet
								UpperCAmelCase :       List[str] 							=		scheduler
								UpperCAmelCase :       Optional[int] 							=		env
								UpperCAmelCase :       Tuple 							=		env.get_dataset()
								UpperCAmelCase :       Union[str, Any] 							=		{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase :       List[Any] 							=		self.data[key].mean()
															except:  # noqa: E722
																						pass
								UpperCAmelCase :       int 							=		{}
								for key in self.data.keys():
															try:
																						UpperCAmelCase :       Optional[int] 							=		self.data[key].std()
															except:  # noqa: E722
																						pass
								UpperCAmelCase :       str 							=		env.observation_space.shape[0]
								UpperCAmelCase :       Tuple 							=		env.action_space.shape[0]







	def      A	(						self			:     Dict		,			__snake_case			:     Any		,			__snake_case			:     Tuple		)			->		str:
								return (x_in - self.means[key]) / self.stds[key]







	def      A	(						self			:     List[str]		,			__snake_case			:     List[Any]		,			__snake_case			:     List[Any]		)			->		Dict:
								return x_in * self.stds[key] + self.means[key]







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     Any		)			->		Any:
								if type(__snake_case		) is dict:
															return {k: self.to_torch(__snake_case		) for k, v in x_in.items()}
								elif torch.is_tensor(__snake_case		):
															return x_in.to(self.unet.device		)
								return torch.tensor(__snake_case		,			device=self.unet.device		)







	def      A	(						self			:     int		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     int		)			->		Optional[Any]:
								for key, val in cond.items():
															UpperCAmelCase :       str 							=		val.clone()
								return x_in







	def      A	(						self			:     str		,			__snake_case			:     List[str]		,			__snake_case			:     List[str]		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Union[str, Any] 							=		x.shape[0]
								UpperCAmelCase :       Union[str, Any] 							=		None
								for i in tqdm.tqdm(self.scheduler.timesteps		):
															# create batch of timesteps to pass into model
															UpperCAmelCase :       Optional[Any] 							=		torch.full((batch_size,)		,			__snake_case		,			device=self.unet.device		,			dtype=torch.long		)
															for _ in range(__snake_case		):
																						with torch.enable_grad():
																													x.requires_grad_()

																													# permute to match dimension for pre-trained models
																													UpperCAmelCase :       List[Any] 							=		self.value_function(x.permute(0		,			2		,			1		)		,			__snake_case		).sample
																													UpperCAmelCase :       Optional[int] 							=		torch.autograd.grad([y.sum()]		,			[x]		)[0]

																													UpperCAmelCase :       List[str] 							=		self.scheduler._get_variance(__snake_case		)
																													UpperCAmelCase :       List[str] 							=		torch.exp(0.5 * posterior_variance		)
																													UpperCAmelCase :       str 							=		model_std * grad

																						UpperCAmelCase :       Tuple 							=		0
																						UpperCAmelCase :       Any 							=		x.detach()
																						UpperCAmelCase :       Tuple 							=		x + scale * grad
																						UpperCAmelCase :       Tuple 							=		self.reset_xa(__snake_case		,			__snake_case		,			self.action_dim		)

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

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase :       List[str] 							=		self.scheduler.step(__snake_case		,			__snake_case		,			__snake_case		,			predict_epsilon=__snake_case		)['''prev_sample''']

															# apply conditions to the trajectory (set the initial state)
															UpperCAmelCase :       Dict 							=		self.reset_xa(__snake_case		,			__snake_case		,			self.action_dim		)
															UpperCAmelCase :       str 							=		self.to_torch(__snake_case		)
								return x, y







	def __call__(						self			:     List[str]		,			__snake_case			:     int		,			__snake_case			:     Any=64		,			__snake_case			:     Dict=32		,			__snake_case			:     Union[str, Any]=2		,			__snake_case			:     Any=0.1		)			->		List[Any]:
								# normalize the observations and create  batch dimension
								UpperCAmelCase :       Dict 							=		self.normalize(__snake_case		,			'''observations'''		)
								UpperCAmelCase :       Tuple 							=		obs[None].repeat(__snake_case		,			axis=0		)

								UpperCAmelCase :       str 							=		{0: self.to_torch(__snake_case		)}
								UpperCAmelCase :       Optional[Any] 							=		(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 :       Tuple 							=		randn_tensor(__snake_case		,			device=self.unet.device		)
								UpperCAmelCase :       Optional[Any] 							=		self.reset_xa(__snake_case		,			__snake_case		,			self.action_dim		)
								UpperCAmelCase :       Optional[Any] 							=		self.to_torch(__snake_case		)

								# run the diffusion process
								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		self.run_diffusion(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		)

								# sort output trajectories by value
								UpperCAmelCase :       int 							=		y.argsort(0		,			descending=__snake_case		).squeeze()
								UpperCAmelCase :       str 							=		x[sorted_idx]
								UpperCAmelCase :       Dict 							=		sorted_values[:, :, : self.action_dim]
								UpperCAmelCase :       List[Any] 							=		actions.detach().cpu().numpy()
								UpperCAmelCase :       Tuple 							=		self.de_normalize(__snake_case		,			key='''actions'''		)

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

								UpperCAmelCase :       Optional[Any] 							=		denorm_actions[selected_index, 0]
								return denorm_actions










'''simple docstring'''






from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       Optional[int] 							=		tau * frequency / samplerate
							UpperCAmelCase :       List[Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       Any 							=		(1 - _cos) / 2
							UpperCAmelCase :       List[Any] 							=		1 - _cos

							UpperCAmelCase :       Union[str, Any] 							=		1 + alpha
							UpperCAmelCase :       Any 							=		-2 * _cos
							UpperCAmelCase :       Dict 							=		1 - alpha

							UpperCAmelCase :       Union[str, Any] 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       Any 							=		tau * frequency / samplerate
							UpperCAmelCase :       Tuple 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       int 							=		(1 + _cos) / 2
							UpperCAmelCase :       List[Any] 							=		-1 - _cos

							UpperCAmelCase :       Tuple 							=		1 + alpha
							UpperCAmelCase :       List[str] 							=		-2 * _cos
							UpperCAmelCase :       Optional[Any] 							=		1 - alpha

							UpperCAmelCase :       Tuple 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       Optional[int] 							=		tau * frequency / samplerate
							UpperCAmelCase :       Optional[int] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       Union[str, Any] 							=		_sin / 2
							UpperCAmelCase :       Any 							=		0
							UpperCAmelCase :       int 							=		-ba

							UpperCAmelCase :       Optional[Any] 							=		1 + alpha
							UpperCAmelCase :       List[Any] 							=		-2 * _cos
							UpperCAmelCase :       Optional[Any] 							=		1 - alpha

							UpperCAmelCase :       int 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )  )      ->   IIRFilter:
							UpperCAmelCase :       List[str] 							=		tau * frequency / samplerate
							UpperCAmelCase :       Union[str, Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       str 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		_sin / (2 * q_factor)

							UpperCAmelCase :       List[str] 							=		1 - alpha
							UpperCAmelCase :       Any 							=		-2 * _cos
							UpperCAmelCase :       Optional[int] 							=		1 + alpha

							UpperCAmelCase :       Union[str, Any] 							=		IIRFilter(2  )
							filt.set_coefficients([ba, ba, ba]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )   ,      )      ->   IIRFilter:
							UpperCAmelCase :       Optional[Any] 							=		tau * frequency / samplerate
							UpperCAmelCase :       Union[str, Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		_sin / (2 * q_factor)
							UpperCAmelCase :       str 							=		10 ** (gain_db / 40)

							UpperCAmelCase :       int 							=		1 + alpha * big_a
							UpperCAmelCase :       Union[str, Any] 							=		-2 * _cos
							UpperCAmelCase :       Optional[Any] 							=		1 - alpha * big_a
							UpperCAmelCase :       Union[str, Any] 							=		1 + alpha / big_a
							UpperCAmelCase :       Tuple 							=		-2 * _cos
							UpperCAmelCase :       Any 							=		1 - alpha / big_a

							UpperCAmelCase :       Optional[Any] 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )   ,      )      ->   IIRFilter:
							UpperCAmelCase :       Any 							=		tau * frequency / samplerate
							UpperCAmelCase :       Optional[int] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       str 							=		_sin / (2 * q_factor)
							UpperCAmelCase :       List[str] 							=		10 ** (gain_db / 40)
							UpperCAmelCase :       Optional[int] 							=		(big_a + 1) - (big_a - 1) * _cos
							UpperCAmelCase :       int 							=		(big_a + 1) + (big_a - 1) * _cos
							UpperCAmelCase :       int 							=		(big_a - 1) - (big_a + 1) * _cos
							UpperCAmelCase :       Optional[int] 							=		(big_a - 1) + (big_a + 1) * _cos
							UpperCAmelCase :       str 							=		2 * sqrt(_lowerCAmelCase  ) * alpha

							UpperCAmelCase :       Dict 							=		big_a * (pmc + aaa)
							UpperCAmelCase :       Any 							=		2 * big_a * mpc
							UpperCAmelCase :       Union[str, Any] 							=		big_a * (pmc - aaa)
							UpperCAmelCase :       Optional[int] 							=		ppmc + aaa
							UpperCAmelCase :       Optional[Any] 							=		-2 * pmpc
							UpperCAmelCase :       Optional[Any] 							=		ppmc - aaa

							UpperCAmelCase :       int 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt




def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float = 1 / sqrt(2  )   ,      )      ->   IIRFilter:
							UpperCAmelCase :       int 							=		tau * frequency / samplerate
							UpperCAmelCase :       Union[str, Any] 							=		sin(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		cos(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		_sin / (2 * q_factor)
							UpperCAmelCase :       int 							=		10 ** (gain_db / 40)
							UpperCAmelCase :       List[str] 							=		(big_a + 1) - (big_a - 1) * _cos
							UpperCAmelCase :       Union[str, Any] 							=		(big_a + 1) + (big_a - 1) * _cos
							UpperCAmelCase :       Optional[Any] 							=		(big_a - 1) - (big_a + 1) * _cos
							UpperCAmelCase :       Union[str, Any] 							=		(big_a - 1) + (big_a + 1) * _cos
							UpperCAmelCase :       List[str] 							=		2 * sqrt(_lowerCAmelCase  ) * alpha

							UpperCAmelCase :       Any 							=		big_a * (ppmc + aaa)
							UpperCAmelCase :       str 							=		-2 * big_a * pmpc
							UpperCAmelCase :       List[Any] 							=		big_a * (ppmc - aaa)
							UpperCAmelCase :       Optional[Any] 							=		pmc + aaa
							UpperCAmelCase :       Any 							=		2 * mpc
							UpperCAmelCase :       str 							=		pmc - aaa

							UpperCAmelCase :       Union[str, Any] 							=		IIRFilter(2  )
							filt.set_coefficients([aa, aa, aa]   ,      [ba, ba, ba]  )
							return filt





