


'''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__  :					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

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)