



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__:     str										=    logging.get_logger(__name__)

UpperCamelCase__:     str										=    {
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """pegasus"""
	lowerCamelCase__    				= ["""past_key_values"""]
	lowerCamelCase__    				= {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







	def __init__(		self    :  Optional[int]	,    __snake_case    :  Union[str, Any]=50265	,    __snake_case    :  List[str]=1024	,    __snake_case    :  Optional[int]=12	,    __snake_case    :  Any=4096	,    __snake_case    :  Optional[int]=16	,    __snake_case    :  int=12	,    __snake_case    :  List[Any]=4096	,    __snake_case    :  Tuple=16	,    __snake_case    :  Tuple=0.0	,    __snake_case    :  str=0.0	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  Optional[int]="gelu"	,    __snake_case    :  Optional[Any]=1024	,    __snake_case    :  Union[str, Any]=0.1	,    __snake_case    :  Dict=0.0	,    __snake_case    :  Tuple=0.0	,    __snake_case    :  List[str]=0.02	,    __snake_case    :  Optional[Any]=0	,    __snake_case    :  Optional[Any]=False	,    __snake_case    :  List[str]=0	,    __snake_case    :  List[str]=1	,    __snake_case    :  List[Any]=1	,    **__snake_case    :  int	,    )    ->	Dict:
								UpperCAmelCase				: Optional[Any]											=	vocab_size
								UpperCAmelCase				: Union[str, Any]											=	max_position_embeddings
								UpperCAmelCase				: Optional[Any]											=	d_model
								UpperCAmelCase				: Optional[Any]											=	encoder_ffn_dim
								UpperCAmelCase				: Union[str, Any]											=	encoder_layers
								UpperCAmelCase				: Tuple											=	encoder_attention_heads
								UpperCAmelCase				: List[str]											=	decoder_ffn_dim
								UpperCAmelCase				: Tuple											=	decoder_layers
								UpperCAmelCase				: Optional[int]											=	decoder_attention_heads
								UpperCAmelCase				: Any											=	dropout
								UpperCAmelCase				: Optional[int]											=	attention_dropout
								UpperCAmelCase				: List[str]											=	activation_dropout
								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Optional[int]											=	init_std
								UpperCAmelCase				: int											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop
								UpperCAmelCase				: Dict											=	use_cache
								UpperCAmelCase				: str											=	encoder_layers
								UpperCAmelCase				: str											=	scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=__snake_case	,    eos_token_id=__snake_case	,    is_encoder_decoder=__snake_case	,    decoder_start_token_id=__snake_case	,    forced_eos_token_id=__snake_case	,    **__snake_case	,    )







	@property
	def   A			(		self    :  Tuple	)    ->	int:
								return self.encoder_attention_heads







	@property
	def   A			(		self    :  List[Any]	)    ->	int:
								return self.d_model










'''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





