

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__ (__snake_case					):





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





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





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





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





    def 				snake_case_    (				self						,       a						,       a						,       a):
       for key, val in cond.items():
          lowercase__   :       Optional[Any]				=	val.clone()
       return x_in





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

                # permute to match dimension for pre-trained models
                lowercase__   :       List[str]				=	self.value_function(x.permute(0						,       2						,       1)						,       a).sample
                lowercase__   :       Optional[Any]				=	torch.autograd.grad([y.sum()]						,       [x])[0]

                lowercase__   :       Optional[Any]				=	self.scheduler._get_variance(a)
                lowercase__   :       int				=	torch.exp(0.5 * posterior_variance)
                lowercase__   :       List[str]				=	model_std * grad

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

          lowercase__   :       Union[str, Any]				=	self.unet(x.permute(0						,       2						,       1)						,       a).sample.permute(0						,       2						,       1)

          # TODO: verify deprecation of this kwarg
          lowercase__   :       Dict				=	self.scheduler.step(a						,       a						,       a						,       predict_epsilon=a)['prev_sample']

          # apply conditions to the trajectory (set the initial state)
          lowercase__   :       int				=	self.reset_xa(a						,       a						,       self.action_dim)
          lowercase__   :       str				=	self.to_torch(a)
       return x, y





    def __call__(				self						,       a						,       a=64						,       a=32						,       a=2						,       a=0.1):
       # normalize the observations and create  batch dimension
       lowercase__   :       str				=	self.normalize(a						,       'observations')
       lowercase__   :       List[Any]				=	obs[None].repeat(a						,       axis=0)

       lowercase__   :       Any				=	{0: self.to_torch(a)}
       lowercase__   :       Any				=	(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)
       lowercase__   :       int				=	randn_tensor(a						,       device=self.unet.device)
       lowercase__   :       List[Any]				=	self.reset_xa(a						,       a						,       self.action_dim)
       lowercase__   :       Union[str, Any]				=	self.to_torch(a)

       # run the diffusion process
       lowercase__   :       Dict				=	self.run_diffusion(a						,       a						,       a						,       a)

       # sort output trajectories by value
       lowercase__   :       Any				=	y.argsort(0						,       descending=a).squeeze()
       lowercase__   :       int				=	x[sorted_idx]
       lowercase__   :       Union[str, Any]				=	sorted_values[:, :, : self.action_dim]
       lowercase__   :       int				=	actions.detach().cpu().numpy()
       lowercase__   :       int				=	self.de_normalize(a						,       key='actions')

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

       lowercase__   :       Optional[Any]				=	denorm_actions[selected_index, 0]
       return denorm_actions




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_        =			{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''EncoderDecoderModel''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''TFEncoderDecoderModel''']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
       from .configuration_encoder_decoder import EncoderDecoderConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_encoder_decoder import EncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
       import sys

       snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

