






"""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        lowercase__      (							snake_case__       ):


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


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Tuple ):
										return (x_in - self.means[key]) / self.stds[key]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  str ):
										return x_in * self.stds[key] + self.means[key]


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  List[str] ):
										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      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Any       ,	snake_case__					:  str ):
										for key, val in cond.items():
																	lowerCamelCase_	:							Optional[int]									=val.clone()
										return x_in


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							str									=x.shape[0]
										lowerCamelCase_	:							Optional[Any]									=None
										for i in tqdm.tqdm(self.scheduler.timesteps ):
																	# create batch of timesteps to pass into model
																	lowerCamelCase_	:							List[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
																															lowerCamelCase_	:							Optional[int]									=self.value_function(x.permute(0       ,	2       ,	1 )       ,	snake_case__ ).sample
																															lowerCamelCase_	:							List[Any]									=torch.autograd.grad([y.sum()]       ,	[x] )[0]

																															lowerCamelCase_	:							List[Any]									=self.scheduler._get_variance(snake_case__ )
																															lowerCamelCase_	:							int									=torch.exp(0.5 * posterior_variance )
																															lowerCamelCase_	:							List[Any]									=model_std * grad

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

																	lowerCamelCase_	:							Tuple									=self.unet(x.permute(0       ,	2       ,	1 )       ,	snake_case__ ).sample.permute(0       ,	2       ,	1 )

																	# TODO: verify deprecation of this kwarg
																	lowerCamelCase_	:							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)
																	lowerCamelCase_	:							Tuple									=self.reset_xa(snake_case__       ,	snake_case__       ,	self.action_dim )
																	lowerCamelCase_	:							Optional[int]									=self.to_torch(snake_case__ )
										return x, y


			def __call__(				self					:  Tuple       ,	snake_case__					:  Any       ,	snake_case__					:  Optional[int]=64       ,	snake_case__					:  Any=32       ,	snake_case__					:  List[Any]=2       ,	snake_case__					:  Tuple=0.1 ):
										# normalize the observations and create  batch dimension
										lowerCamelCase_	:							Dict									=self.normalize(snake_case__       ,	"observations" )
										lowerCamelCase_	:							int									=obs[None].repeat(snake_case__       ,	axis=0 )

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

										# run the diffusion process
										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=self.run_diffusion(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__ )

										# sort output trajectories by value
										lowerCamelCase_	:							Optional[int]									=y.argsort(0       ,	descending=snake_case__ ).squeeze()
										lowerCamelCase_	:							Union[str, Any]									=x[sorted_idx]
										lowerCamelCase_	:							Dict									=sorted_values[:, :, : self.action_dim]
										lowerCamelCase_	:							str									=actions.detach().cpu().numpy()
										lowerCamelCase_	:							Optional[Any]									=self.de_normalize(snake_case__       ,	key="actions" )

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

										lowerCamelCase_	:							List[str]									=denorm_actions[selected_index, 0]
										return denorm_actions











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__	:    List[Any]			 = {
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[str]			 = [
						    'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LiltForQuestionAnswering',
						    'LiltForSequenceClassification',
						    'LiltForTokenClassification',
						    'LiltModel',
						    'LiltPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						A__	:    Optional[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



