






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			(							lowerCamelCase__					):





							def __init__(   self		:		Tuple      ,				__lowercase		:		UNetaDModel      ,				__lowercase		:		UNetaDModel      ,				__lowercase		:		DDPMScheduler      ,				__lowercase		:		Tuple      ,				):

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





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Any      ,				__lowercase		:		Optional[int]	):

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





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Any      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											return x_in * self.stds[key] + self.means[key]





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		List[Any]	):

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





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Dict      ,				__lowercase		:		Dict	):

											'''simple docstring'''
											for key, val in cond.items():
															__a       =		val.clone()
											return x_in





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		int      ,				__lowercase		:		List[str]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Dict	):

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

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

																							__a       =		self.scheduler._get_variance(__lowercase	)
																							__a       =		torch.exp(0.5 * posterior_variance	)
																							__a       =		model_std * grad

																			__a       =		0
																			__a       =		x.detach()
																			__a       =		x + scale * grad
																			__a       =		self.reset_xa(__lowercase      ,				__lowercase      ,				self.action_dim	)

															__a       =		self.unet(x.permute(0      ,				2      ,				1	)      ,				__lowercase	).sample.permute(0      ,				2      ,				1	)

															# TODO: verify deprecation of this kwarg
															__a       =		self.scheduler.step(__lowercase      ,				__lowercase      ,				__lowercase      ,				predict_epsilon=__lowercase	)["""prev_sample"""]

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







							def __call__(   self		:		str      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Tuple=64      ,				__lowercase		:		Dict=32      ,				__lowercase		:		Optional[Any]=2      ,				__lowercase		:		Optional[int]=0.1	):

											'''simple docstring'''
											__a       =		self.normalize(__lowercase      ,				"""observations"""	)
											__a       =		obs[None].repeat(__lowercase      ,				axis=0	)

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

											# run the diffusion process
											__a     ,					__a       =		self.run_diffusion(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

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

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

											__a       =		denorm_actions[selected_index, 0]
											return denorm_actions





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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Dict    ='mgp-str'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Dict=[32, 128]      ,				__lowercase		:		Tuple=4      ,				__lowercase		:		Optional[Any]=3      ,				__lowercase		:		str=27      ,				__lowercase		:		Any=38      ,				__lowercase		:		str=50257      ,				__lowercase		:		List[str]=30522      ,				__lowercase		:		Tuple=768      ,				__lowercase		:		str=12      ,				__lowercase		:		Optional[Any]=12      ,				__lowercase		:		str=4.0      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		Union[str, Any]=1E-5      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Dict=0.0      ,				__lowercase		:		Union[str, Any]=0.0      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		List[Any]=0.02      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)

											__a       =		image_size
											__a       =		patch_size
											__a       =		num_channels
											__a       =		max_token_length
											__a       =		num_character_labels
											__a       =		num_bpe_labels
											__a       =		num_wordpiece_labels
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		mlp_ratio
											__a       =		distilled
											__a       =		layer_norm_eps
											__a       =		drop_rate
											__a       =		qkv_bias
											__a       =		attn_drop_rate
											__a       =		drop_path_rate
											__a       =		output_aa_attentions
											__a       =		initializer_range
