


'''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 		_snake_case					(     a__	):




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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								return (x_in - self.means[key]) / self.stds[key]




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								return x_in * self.stds[key] + self.means[key]




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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								for key, val in cond.items():
											UpperCAmelCase__  :					Dict        =     val.clone()
								return x_in




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

																	# permute to match dimension for pre-trained models
																	UpperCAmelCase__  :					Tuple        =     self.value_function(x.permute(0							,							2							,							1)							,							_lowerCamelCase).sample
																	UpperCAmelCase__  :					Optional[int]        =     torch.autograd.grad([y.sum()]							,							[x])[0]

																	UpperCAmelCase__  :					Optional[Any]        =     self.scheduler._get_variance(_lowerCamelCase)
																	UpperCAmelCase__  :					str        =     torch.exp(0.5 * posterior_variance)
																	UpperCAmelCase__  :					Union[str, Any]        =     model_std * grad

														UpperCAmelCase__  :					Optional[Any]        =     0
														UpperCAmelCase__  :					Optional[int]        =     x.detach()
														UpperCAmelCase__  :					int        =     x + scale * grad
														UpperCAmelCase__  :					List[str]        =     self.reset_xa(_lowerCamelCase							,							_lowerCamelCase							,							self.action_dim)

											UpperCAmelCase__  :					List[Any]        =     self.unet(x.permute(0							,							2							,							1)							,							_lowerCamelCase).sample.permute(0							,							2							,							1)

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

											# apply conditions to the trajectory (set the initial state)
											UpperCAmelCase__  :					Tuple        =     self.reset_xa(_lowerCamelCase							,							_lowerCamelCase							,							self.action_dim)
											UpperCAmelCase__  :					List[str]        =     self.to_torch(_lowerCamelCase)
								return x, y




					def __call__(      self							,							_lowerCamelCase							,							_lowerCamelCase=64							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=0.1):
								# normalize the observations and create  batch dimension
								UpperCAmelCase__  :					Optional[Any]        =     self.normalize(_lowerCamelCase							,							"""observations""")
								UpperCAmelCase__  :					Any        =     obs[None].repeat(_lowerCamelCase							,							axis=0)

								UpperCAmelCase__  :					str        =     {0: self.to_torch(_lowerCamelCase)}
								UpperCAmelCase__  :					List[str]        =     (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__  :					Optional[int]        =     randn_tensor(_lowerCamelCase							,							device=self.unet.device)
								UpperCAmelCase__  :					Any        =     self.reset_xa(_lowerCamelCase							,							_lowerCamelCase							,							self.action_dim)
								UpperCAmelCase__  :					List[Any]        =     self.to_torch(_lowerCamelCase)

								# run the diffusion process
								UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     self.run_diffusion(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)

								# sort output trajectories by value
								UpperCAmelCase__  :					Optional[int]        =     y.argsort(0							,							descending=_lowerCamelCase).squeeze()
								UpperCAmelCase__  :					Any        =     x[sorted_idx]
								UpperCAmelCase__  :					Optional[int]        =     sorted_values[:, :, : self.action_dim]
								UpperCAmelCase__  :					Optional[int]        =     actions.detach().cpu().numpy()
								UpperCAmelCase__  :					int        =     self.de_normalize(_lowerCamelCase							,							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							,							_lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     denorm_actions[selected_index, 0]
								return denorm_actions



'''simple docstring'''



import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__A	=object()

# For specifying empty leaf dict `{}`
__A	=object()







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     tuple((re.compile(x + """$"""					) for x in qs)					)
			for i in range(len(UpperCamelCase__					) - len(UpperCamelCase__					) + 1					):
						UpperCAmelCase__  :					Optional[Any]        =     [x.match(UpperCamelCase__					) for x, y in zip(UpperCamelCase__ ,      ks[i:]					)]
						if matches and all(UpperCamelCase__					):
									return True
			return False







def     _UpperCamelCase      (		UpperCamelCase__					):
			def replace(UpperCamelCase__ ,      UpperCamelCase__					):
						for rule, replacement in rules:
									if _match(UpperCamelCase__ ,      UpperCamelCase__					):
												return replacement
						return val

			return replace







def     _UpperCamelCase      (		):
			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp""" ,      UpperCamelCase__					)),
			    (("transformer", "wte", "embedding"), P("""mp""" ,      UpperCamelCase__					)),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__ ,      """mp"""					)),
			    (("attention", "out_proj", "kernel"), P("""mp""" ,      UpperCamelCase__					)),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(UpperCamelCase__ ,      """mp"""					)),
			    (("mlp", "c_fc", "bias"), P("""mp"""					)),
			    (("mlp", "c_proj", "kernel"), P("""mp""" ,      UpperCamelCase__					)),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =     _get_partition_rules()
			UpperCAmelCase__  :					Optional[int]        =     _replacement_rules(UpperCamelCase__					)
			UpperCAmelCase__  :					List[Any]        =     {k: _unmatched for k in flatten_dict(UpperCamelCase__					)}
			UpperCAmelCase__  :					Tuple        =     {k: replace(UpperCamelCase__ ,      UpperCamelCase__					) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(UpperCamelCase__					)					)