

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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



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





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





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





					def    _UpperCAmelCase    (				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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						for key, val in cond.items():
							UpperCAmelCase__:	Optional[Any]									= val.clone()
						return x_in





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= x.shape[0]
						UpperCAmelCase__:	Tuple									= None
						for i in tqdm.tqdm(self.scheduler.timesteps						):
							# create batch of timesteps to pass into model
							UpperCAmelCase__:	str									= 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__:	Optional[int]									= self.value_function(x.permute(0				,      2				,      1						)				,      lowerCamelCase__						).sample
									UpperCAmelCase__:	int									= torch.autograd.grad([y.sum()]				,      [x]						)[0]

									UpperCAmelCase__:	Dict									= self.scheduler._get_variance(lowerCamelCase__						)
									UpperCAmelCase__:	Tuple									= torch.exp(0.5 * posterior_variance						)
									UpperCAmelCase__:	str									= model_std * grad

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

							UpperCAmelCase__:	Optional[int]									= self.unet(x.permute(0				,      2				,      1						)				,      lowerCamelCase__						).sample.permute(0				,      2				,      1						)

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

							# apply conditions to the trajectory (set the initial state)
							UpperCAmelCase__:	Optional[Any]									= self.reset_xa(lowerCamelCase__				,      lowerCamelCase__				,      self.action_dim						)
							UpperCAmelCase__:	Union[str, Any]									= self.to_torch(lowerCamelCase__						)
						return x, y





					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__=6_4				,      lowerCamelCase__=3_2				,      lowerCamelCase__=2				,      lowerCamelCase__=0.1						):
						# normalize the observations and create  batch dimension
						UpperCAmelCase__:	Dict									= self.normalize(lowerCamelCase__				,      "observations"						)
						UpperCAmelCase__:	str									= obs[None].repeat(lowerCamelCase__				,      axis=0						)

						UpperCAmelCase__:	Optional[Any]									= {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__:	str									= randn_tensor(lowerCamelCase__				,      device=self.unet.device						)
						UpperCAmelCase__:	Optional[Any]									= self.reset_xa(lowerCamelCase__				,      lowerCamelCase__				,      self.action_dim						)
						UpperCAmelCase__:	Any									= self.to_torch(lowerCamelCase__						)

						# run the diffusion process
						UpperCAmelCase__							,						UpperCAmelCase__:	str									= self.run_diffusion(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)

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

						# select the action with the highest value
						if y is not None:
							UpperCAmelCase__:	Optional[int]									= 0
						else:
							# if we didn't run value guiding, select a random action
							UpperCAmelCase__:	List[str]									= np.random.randint(0				,      lowerCamelCase__						)

						UpperCAmelCase__:	int									= denorm_actions[selected_index, 0]
						return denorm_actions


from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=0						):
						UpperCAmelCase__:	Any									= 1.0 if scale is None else scale
						UpperCAmelCase__:	int									= 0.0 if loc is None else loc

						super().__init__(lowerCamelCase__				,      [AffineTransform(loc=self.loc				,      scale=self.scale				,      event_dim=lowerCamelCase__						)]						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self.base_dist.mean * self.scale + self.loc





					@property
					def    _UpperCAmelCase    (				self						):
						return self.base_dist.variance * self.scale**2





					@property
					def    _UpperCAmelCase    (				self						):
						return self.variance.sqrt()

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						super().__init__(**lowerCamelCase__						)
						UpperCAmelCase__:	Any									= args_dim
						UpperCAmelCase__:	List[str]									= nn.ModuleList([nn.Linear(lowerCamelCase__				,      lowerCamelCase__						) for dim in args_dim.values()]						)
						UpperCAmelCase__:	Any									= domain_map





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= [proj(lowerCamelCase__						) for proj in self.proj]

						return self.domain_map(*lowerCamelCase__						)

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						super().__init__()
						UpperCAmelCase__:	str									= function





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      *lowerCamelCase__						):
						return self.function(lowerCamelCase__				,      *lowerCamelCase__						)

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   42
					__magic_name__        =   42
					__magic_name__        =   42





					def __init__(				self				,      lowerCamelCase__ = 1						):
						UpperCAmelCase__:	str									= dim
						UpperCAmelCase__:	Any									= {k: dim * self.args_dim[k] for k in self.args_dim}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						if self.dim == 1:
							return self.distribution_class(*lowerCamelCase__						)
						else:
							return Independent(self.distribution_class(*lowerCamelCase__						)				,      1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      ):
						UpperCAmelCase__:	Optional[Any]									= self._base_distribution(lowerCamelCase__						)
						if loc is None and scale is None:
							return distr
						else:
							return AffineTransformed(lowerCamelCase__				,      loc=lowerCamelCase__				,      scale=lowerCamelCase__				,      event_dim=self.event_dim						)





					@property
					def    _UpperCAmelCase    (				self						):
						return () if self.dim == 1 else (self.dim,)





					@property
					def    _UpperCAmelCase    (				self						):
						return len(self.event_shape						)





					@property
					def    _UpperCAmelCase    (				self						):
						return 0.0





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						return ParameterProjection(
						    in_features=lowerCamelCase__				,      args_dim=self.args_dim				,      domain_map=LambdaLayer(self.domain_map						)				,      )





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__						):
						raise NotImplementedError()





					@staticmethod
					def    _UpperCAmelCase    (				lowerCamelCase__						):
						return (x + torch.sqrt(torch.square(lowerCamelCase__						) + 4.0						)) / 2.0

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"df": 1, "loc": 1, "scale": 1}
					__magic_name__        =   StudentT





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						).clamp_min(torch.finfo(scale.dtype						).eps						)
						UpperCAmelCase__:	List[Any]									= 2.0 + cls.squareplus(lowerCamelCase__						)
						return df.squeeze(-1						), loc.squeeze(-1						), scale.squeeze(-1						)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"loc": 1, "scale": 1}
					__magic_name__        =   Normal





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= cls.squareplus(lowerCamelCase__						).clamp_min(torch.finfo(scale.dtype						).eps						)
						return loc.squeeze(-1						), scale.squeeze(-1						)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"total_count": 1, "logits": 1}
					__magic_name__        =   NegativeBinomial





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						)
						return total_count.squeeze(-1						), logits.squeeze(-1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__:	str									= distr_args
						if self.dim == 1:
							return self.distribution_class(total_count=lowerCamelCase__				,      logits=lowerCamelCase__						)
						else:
							return Independent(self.distribution_class(total_count=lowerCamelCase__				,      logits=lowerCamelCase__						)				,      1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None						):
						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= distr_args

						if scale is not None:
							# See scaling property of Gamma.
							logits += scale.log()

						return self._base_distribution((total_count, logits)						)