



'''simple docstring'''






import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)

class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  str	)    ->	str:
								UpperCAmelCase				: Dict											=	['''a''', '''b''', '''c''']

								# Defaults to last layer if both are None
								UpperCAmelCase							,	UpperCAmelCase				: str											=	get_aligned_output_features_output_indices(__snake_case	,    __snake_case	,    __snake_case	)
								self.assertEqual(__snake_case	,    ['''c''']	)
								self.assertEqual(__snake_case	,    [2]	)

								# Out indices set to match out features
								UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	get_aligned_output_features_output_indices(['''a''', '''c''']	,    __snake_case	,    __snake_case	)
								self.assertEqual(__snake_case	,    ['''a''', '''c''']	)
								self.assertEqual(__snake_case	,    [0, 2]	)

								# Out features set to match out indices
								UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	get_aligned_output_features_output_indices(__snake_case	,    [0, 2]	,    __snake_case	)
								self.assertEqual(__snake_case	,    ['''a''', '''c''']	)
								self.assertEqual(__snake_case	,    [0, 2]	)

								# Out features selected from negative indices
								UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	get_aligned_output_features_output_indices(__snake_case	,    [-3, -1]	,    __snake_case	)
								self.assertEqual(__snake_case	,    ['''a''', '''c''']	)
								self.assertEqual(__snake_case	,    [-3, -1]	)







	def   A			(		self    :  List[str]	)    ->	int:
								# Stage names must be set
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(['''a''', '''b''']	,    (0, 1)	,    __snake_case	)

								# Out features must be a list
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(('''a''', '''b''')	,    (0, 1)	,    ['''a''', '''b''']	)

								# Out features must be a subset of stage names
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(['''a''', '''b''']	,    (0, 1)	,    ['''a''']	)

								# Out indices must be a list or tuple
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(__snake_case	,    0	,    ['''a''', '''b''']	)

								# Out indices must be a subset of stage names
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(__snake_case	,    (0, 1)	,    ['''a''']	)

								# Out features and out indices must be the same length
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(['''a''', '''b''']	,    (0,)	,    ['''a''', '''b''', '''c''']	)

								# Out features should match out indices
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(['''a''', '''b''']	,    (0, 2)	,    ['''a''', '''b''', '''c''']	)

								# Out features and out indices should be in order
								with self.assertRaises(__snake_case	):
															verify_out_features_out_indices(['''b''', '''a''']	,    (0, 1)	,    ['''a''', '''b''']	)

								# Check passes with valid inputs
								verify_out_features_out_indices(['''a''', '''b''', '''d''']	,    (0, 1, -1)	,    ['''a''', '''b''', '''c''', '''d''']	)







	def   A			(		self    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase				: Optional[Any]											=	BackboneMixin()

								UpperCAmelCase				: Optional[int]											=	['''a''', '''b''', '''c''']
								UpperCAmelCase				: Optional[Any]											=	['''a''', '''c''']
								UpperCAmelCase				: Optional[int]											=	[0, 2]

								# Check that the output features and indices are set correctly
								self.assertEqual(backbone.out_features	,    ['''a''', '''c''']	)
								self.assertEqual(backbone.out_indices	,    [0, 2]	)

								# Check out features and indices are updated correctly
								UpperCAmelCase				: Tuple											=	['''a''', '''b''']
								self.assertEqual(backbone.out_features	,    ['''a''', '''b''']	)
								self.assertEqual(backbone.out_indices	,    [0, 1]	)

								UpperCAmelCase				: List[Any]											=	[-3, -1]
								self.assertEqual(backbone.out_features	,    ['''a''', '''c''']	)
								self.assertEqual(backbone.out_indices	,    [-3, -1]	)










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







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







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







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







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								UpperCAmelCase				: Any											=	x.shape[0]
								UpperCAmelCase				: Any											=	None
								for i in tqdm.tqdm(self.scheduler.timesteps	):
															# create batch of timesteps to pass into model
															UpperCAmelCase				: Tuple											=	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				: Any											=	self.value_function(x.permute(0	,    2	,    1	)	,    __snake_case	).sample
																													UpperCAmelCase				: Dict											=	torch.autograd.grad([y.sum()]	,    [x]	)[0]

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

																						UpperCAmelCase				: Dict											=	0
																						UpperCAmelCase				: Optional[int]											=	x.detach()
																						UpperCAmelCase				: List[str]											=	x + scale * grad
																						UpperCAmelCase				: List[str]											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	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				: Any											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
															UpperCAmelCase				: List[str]											=	self.to_torch(__snake_case	)
								return x, y







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

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

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

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

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

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





