


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							(      UpperCamelCase__      ):

							def __init__(							self:     Union[str, Any]   ,						UpperCamelCase_:     UNetaDModel   ,						UpperCamelCase_:     UNetaDModel   ,						UpperCamelCase_:     DDPMScheduler   ,						UpperCamelCase_:     Optional[int]   ,						)      ->       List[str]:





									"""simple docstring"""







									super().__init__()
									lowercase__						=				value_function
									lowercase__						=				unet
									lowercase__						=				scheduler
									lowercase__						=				env
									lowercase__						=				env.get_dataset()
									lowercase__						=				{}
									for key in self.data.keys():
											try:
													lowercase__						=				self.data[key].mean()
											except:  # noqa: E722
													pass
									lowercase__						=				{}
									for key in self.data.keys():
											try:
													lowercase__						=				self.data[key].std()
											except:  # noqa: E722
													pass
									lowercase__						=				env.observation_space.shape[0]
									lowercase__						=				env.action_space.shape[0]

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





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Dict							)      ->       List[str]:





									"""simple docstring"""







									return x_in * self.stds[key] + self.means[key]

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     int							)      ->       List[str]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Tuple							)      ->       int:





									"""simple docstring"""







									for key, val in cond.items():
											lowercase__						=				val.clone()
									return x_in

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Optional[Any]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				x.shape[0]
									lowercase__						=				None
									for i in tqdm.tqdm(self.scheduler.timesteps							):
											# create batch of timesteps to pass into model
											lowercase__						=				torch.full((batch_size,)   ,						__UpperCAmelCase   ,						device=self.unet.device   ,						dtype=torch.long							)
											for _ in range(__UpperCAmelCase							):
													with torch.enable_grad():
															x.requires_grad_()

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

															lowercase__						=				self.scheduler._get_variance(__UpperCAmelCase							)
															lowercase__						=				torch.exp(0.5 * posterior_variance							)
															lowercase__						=				model_std * grad

													lowercase__						=				0
													lowercase__						=				x.detach()
													lowercase__						=				x + scale * grad
													lowercase__						=				self.reset_xa(__UpperCAmelCase   ,						__UpperCAmelCase   ,						self.action_dim							)

											lowercase__						=				self.unet(x.permute(0   ,						2   ,						1							)   ,						__UpperCAmelCase							).sample.permute(0   ,						2   ,						1							)

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

											# apply conditions to the trajectory (set the initial state)
											lowercase__						=				self.reset_xa(__UpperCAmelCase   ,						__UpperCAmelCase   ,						self.action_dim							)
											lowercase__						=				self.to_torch(__UpperCAmelCase							)
									return x, y


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





									"""simple docstring"""







									lowercase__						=				self.normalize(__UpperCAmelCase   ,						'''observations'''							)
									lowercase__						=				obs[None].repeat(__UpperCAmelCase   ,						axis=0							)

									lowercase__						=				{0: self.to_torch(__UpperCAmelCase							)}
									lowercase__						=				(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)
									lowercase__						=				randn_tensor(__UpperCAmelCase   ,						device=self.unet.device							)
									lowercase__						=				self.reset_xa(__UpperCAmelCase   ,						__UpperCAmelCase   ,						self.action_dim							)
									lowercase__						=				self.to_torch(__UpperCAmelCase							)

									# run the diffusion process
									lowercase__					,		lowercase__						=				self.run_diffusion(__UpperCAmelCase   ,						__UpperCAmelCase   ,						__UpperCAmelCase   ,						__UpperCAmelCase							)

									# sort output trajectories by value
									lowercase__						=				y.argsort(0   ,						descending=__UpperCAmelCase							).squeeze()
									lowercase__						=				x[sorted_idx]
									lowercase__						=				sorted_values[:, :, : self.action_dim]
									lowercase__						=				actions.detach().cpu().numpy()
									lowercase__						=				self.de_normalize(__UpperCAmelCase   ,						key='''actions'''							)

									# select the action with the highest value
									if y is not None:
											lowercase__						=				0
									else:
											# if we didn't run value guiding, select a random action
											lowercase__						=				np.random.randint(0   ,						__UpperCAmelCase							)

									lowercase__						=				denorm_actions[selected_index, 0]
									return denorm_actions





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase	=							{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


