



'''simple docstring'''






import torch
from torch import nn

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  Dict	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Tuple	,    __snake_case    :  Dict	,    __snake_case    :  str	,    __snake_case    :  int=1	,    __snake_case    :  str=False	)    ->	str:
								super().__init__()

								UpperCAmelCase				: Optional[Any]											=	n_token
								UpperCAmelCase				: Tuple											=	d_embed
								UpperCAmelCase				: List[Any]											=	d_proj

								UpperCAmelCase				: Optional[int]											=	cutoffs + [n_token]
								UpperCAmelCase				: Any											=	[0] + self.cutoffs
								UpperCAmelCase				: Tuple											=	div_val

								UpperCAmelCase				: List[Any]											=	self.cutoffs[0]
								UpperCAmelCase				: Optional[Any]											=	len(self.cutoffs	) - 1
								UpperCAmelCase				: List[str]											=	self.shortlist_size + self.n_clusters

								if self.n_clusters > 0:
															UpperCAmelCase				: Optional[int]											=	nn.Parameter(torch.zeros(self.n_clusters	,    self.d_embed	)	)
															UpperCAmelCase				: List[str]											=	nn.Parameter(torch.zeros(self.n_clusters	)	)

								UpperCAmelCase				: List[str]											=	nn.ModuleList()
								UpperCAmelCase				: Tuple											=	nn.ParameterList()

								if div_val == 1:
															for i in range(len(self.cutoffs	)	):
																						if d_proj != d_embed:
																													self.out_projs.append(nn.Parameter(torch.FloatTensor(__snake_case	,    __snake_case	)	)	)
																						else:
																													self.out_projs.append(__snake_case	)

															self.out_layers.append(nn.Linear(__snake_case	,    __snake_case	)	)
								else:
															for i in range(len(self.cutoffs	)	):
																						UpperCAmelCase							,	UpperCAmelCase				: int											=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCAmelCase				: Tuple											=	d_embed // (div_val**i)

																						self.out_projs.append(nn.Parameter(torch.FloatTensor(__snake_case	,    __snake_case	)	)	)

																						self.out_layers.append(nn.Linear(__snake_case	,    r_idx - l_idx	)	)

								UpperCAmelCase				: Dict											=	keep_order







	def   A			(		self    :  List[str]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Tuple	,    __snake_case    :  List[Any]	,    __snake_case    :  Union[str, Any]	)    ->	Optional[int]:
								if proj is None:
															UpperCAmelCase				: Optional[int]											=	nn.functional.linear(__snake_case	,    __snake_case	,    bias=__snake_case	)
								else:
															# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
															UpperCAmelCase				: Dict											=	nn.functional.linear(__snake_case	,    proj.t().contiguous()	)
															UpperCAmelCase				: Optional[Any]											=	nn.functional.linear(__snake_case	,    __snake_case	,    bias=__snake_case	)
															# else:
															#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
															#     if bias is not None:
															#         logit = logit + bias

								return logit







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  Optional[Any]=False	)    ->	Optional[int]:

								if labels is not None:
															# Shift so that tokens < n predict n
															UpperCAmelCase				: Tuple											=	hidden[..., :-1, :].contiguous()
															UpperCAmelCase				: List[str]											=	labels[..., 1:].contiguous()
															UpperCAmelCase				: Optional[Any]											=	hidden.view(-1	,    hidden.size(-1	)	)
															UpperCAmelCase				: Optional[int]											=	labels.view(-1	)
															if hidden.size(0	) != labels.size(0	):
																						raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''	)
								else:
															UpperCAmelCase				: int											=	hidden.view(-1	,    hidden.size(-1	)	)

								if self.n_clusters == 0:
															UpperCAmelCase				: Tuple											=	self._compute_logit(__snake_case	,    self.out_layers[0].weight	,    self.out_layers[0].bias	,    self.out_projs[0]	)
															if labels is not None:
																						UpperCAmelCase				: List[str]											=	labels != -100
																						UpperCAmelCase				: str											=	torch.zeros_like(__snake_case	,    dtype=hidden.dtype	,    device=hidden.device	)
																						UpperCAmelCase				: List[str]											=	(
																						    -nn.functional.log_softmax(__snake_case	,    dim=-1	)[mask].gather(1	,    labels[mask].unsqueeze(1	)	).squeeze(1	)
																						)
															else:
																						UpperCAmelCase				: Union[str, Any]											=	nn.functional.log_softmax(__snake_case	,    dim=-1	)
								else:
															# construct weights and biases
															UpperCAmelCase							,	UpperCAmelCase				: int											=	[], []
															for i in range(len(self.cutoffs	)	):
																						if self.div_val == 1:
																													UpperCAmelCase							,	UpperCAmelCase				: Any											=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
																													UpperCAmelCase				: Dict											=	self.out_layers[0].weight[l_idx:r_idx]
																													UpperCAmelCase				: Optional[Any]											=	self.out_layers[0].bias[l_idx:r_idx]
																						else:
																													UpperCAmelCase				: int											=	self.out_layers[i].weight
																													UpperCAmelCase				: str											=	self.out_layers[i].bias

																						if i == 0:
																													UpperCAmelCase				: Optional[int]											=	torch.cat([weight_i, self.cluster_weight]	,    dim=0	)
																													UpperCAmelCase				: Union[str, Any]											=	torch.cat([bias_i, self.cluster_bias]	,    dim=0	)

																						weights.append(__snake_case	)
																						biases.append(__snake_case	)

															UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	weights[0], biases[0], self.out_projs[0]

															UpperCAmelCase				: Union[str, Any]											=	self._compute_logit(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)
															UpperCAmelCase				: Tuple											=	nn.functional.log_softmax(__snake_case	,    dim=1	)

															if labels is None:
																						UpperCAmelCase				: List[Any]											=	hidden.new_empty((head_logit.size(0	), self.n_token)	)
															else:
																						UpperCAmelCase				: Union[str, Any]											=	torch.zeros_like(__snake_case	,    dtype=hidden.dtype	,    device=hidden.device	)

															UpperCAmelCase				: List[Any]											=	0
															UpperCAmelCase				: int											=	[0] + self.cutoffs
															for i in range(len(__snake_case	) - 1	):
																						UpperCAmelCase							,	UpperCAmelCase				: Any											=	cutoff_values[i], cutoff_values[i + 1]

																						if labels is not None:
																													UpperCAmelCase				: Optional[int]											=	(labels >= l_idx) & (labels < r_idx)
																													UpperCAmelCase				: Union[str, Any]											=	mask_i.nonzero().squeeze()

																													if indices_i.numel() == 0:
																																				continue

																													UpperCAmelCase				: Optional[Any]											=	labels.index_select(0	,    __snake_case	) - l_idx
																													UpperCAmelCase				: Optional[int]											=	head_logprob.index_select(0	,    __snake_case	)
																													UpperCAmelCase				: Optional[Any]											=	hidden.index_select(0	,    __snake_case	)
																						else:
																													UpperCAmelCase				: str											=	hidden

																						if i == 0:
																													if labels is not None:
																																				UpperCAmelCase				: int											=	head_logprob_i.gather(1	,    target_i[:, None]	).squeeze(1	)
																													else:
																																				UpperCAmelCase				: List[str]											=	head_logprob[:, : self.cutoffs[0]]
																						else:
																													UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: int											=	weights[i], biases[i], self.out_projs[i]

																													UpperCAmelCase				: Union[str, Any]											=	self._compute_logit(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)
																													UpperCAmelCase				: List[Any]											=	nn.functional.log_softmax(__snake_case	,    dim=1	)
																													UpperCAmelCase				: Dict											=	self.cutoffs[0] + i - 1  # No probability for the head cluster
																													if labels is not None:
																																				UpperCAmelCase				: Tuple											=	head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																																				    1	,    target_i[:, None]	).squeeze(1	)
																													else:
																																				UpperCAmelCase				: str											=	head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																																				UpperCAmelCase				: int											=	logprob_i

																						if labels is not None:
																													if (hasattr(self	,    '''keep_order'''	) and self.keep_order) or keep_order:
																																				out.index_copy_(0	,    __snake_case	,    -logprob_i	)
																													else:
																																				out[offset : offset + logprob_i.size(0	)].copy_(-logprob_i	)
																													offset += logprob_i.size(0	)

								return out







	def   A			(		self    :  str	,    __snake_case    :  int	)    ->	Optional[int]:
								if self.n_clusters == 0:
															UpperCAmelCase				: List[Any]											=	self._compute_logit(__snake_case	,    self.out_layers[0].weight	,    self.out_layers[0].bias	,    self.out_projs[0]	)
															return nn.functional.log_softmax(__snake_case	,    dim=-1	)
								else:
															# construct weights and biases
															UpperCAmelCase							,	UpperCAmelCase				: Any											=	[], []
															for i in range(len(self.cutoffs	)	):
																						if self.div_val == 1:
																													UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
																													UpperCAmelCase				: List[Any]											=	self.out_layers[0].weight[l_idx:r_idx]
																													UpperCAmelCase				: Optional[Any]											=	self.out_layers[0].bias[l_idx:r_idx]
																						else:
																													UpperCAmelCase				: Optional[Any]											=	self.out_layers[i].weight
																													UpperCAmelCase				: Optional[int]											=	self.out_layers[i].bias

																						if i == 0:
																													UpperCAmelCase				: List[Any]											=	torch.cat([weight_i, self.cluster_weight]	,    dim=0	)
																													UpperCAmelCase				: List[str]											=	torch.cat([bias_i, self.cluster_bias]	,    dim=0	)

																						weights.append(__snake_case	)
																						biases.append(__snake_case	)

															UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	weights[0], biases[0], self.out_projs[0]
															UpperCAmelCase				: Union[str, Any]											=	self._compute_logit(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

															UpperCAmelCase				: str											=	hidden.new_empty((head_logit.size(0	), self.n_token)	)
															UpperCAmelCase				: Dict											=	nn.functional.log_softmax(__snake_case	,    dim=1	)

															UpperCAmelCase				: str											=	[0] + self.cutoffs
															for i in range(len(__snake_case	) - 1	):
																						UpperCAmelCase							,	UpperCAmelCase				: str											=	cutoff_values[i], cutoff_values[i + 1]

																						if i == 0:
																													UpperCAmelCase				: Any											=	head_logprob[:, : self.cutoffs[0]]
																						else:
																													UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	weights[i], biases[i], self.out_projs[i]

																													UpperCAmelCase				: Union[str, Any]											=	self._compute_logit(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)
																													UpperCAmelCase				: List[Any]											=	nn.functional.log_softmax(__snake_case	,    dim=1	)

																													UpperCAmelCase				: str											=	head_logprob[:, -i] + tail_logprob_i
																													UpperCAmelCase				: Optional[int]											=	logprob_i

															return out










'''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





