


'''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 					a			(				snake_case__  ):
 '''simple docstring'''




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    int:
    return (x_in - self.means[key]) / self.stds[key]




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Any:
    return x_in * self.stds[key] + self.means[key]




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Optional[Any]:
    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_      )      ->    List[Any]:
    for key, val in cond.items():
       _a					:  Optional[Any]						=  val.clone()
    return x_in




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Dict:
    _a					:  int						=  x.shape[0]
    _a					:  List[str]						=  None
    for i in tqdm.tqdm(self.scheduler.timesteps      ):
       # create batch of timesteps to pass into model
       _a					:  List[Any]						=  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
             _a					:  Any						=  self.value_function(x.permute(0		,      2		,      1      )		,      lowerCamelCase_      ).sample
             _a					:  Dict						=  torch.autograd.grad([y.sum()]		,      [x]      )[0]

             _a					:  Dict						=  self.scheduler._get_variance(lowerCamelCase_      )
             _a					:  Optional[int]						=  torch.exp(0.5 * posterior_variance      )
             _a					:  Dict						=  model_std * grad

          _a					:  List[Any]						=  0
          _a					:  Dict						=  x.detach()
          _a					:  str						=  x + scale * grad
          _a					:  List[Any]						=  self.reset_xa(lowerCamelCase_		,      lowerCamelCase_		,      self.action_dim      )

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

       # TODO: verify deprecation of this kwarg
       _a					:  int						=  self.scheduler.step(lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      predict_epsilon=lowerCamelCase_      )['prev_sample']

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




 def __call__(    self		,      lowerCamelCase_		,      lowerCamelCase_=6_4		,      lowerCamelCase_=3_2		,      lowerCamelCase_=2		,      lowerCamelCase_=0.1      )      ->    Any:
    # normalize the observations and create  batch dimension
    _a					:  Tuple						=  self.normalize(lowerCamelCase_		,      'observations'      )
    _a					:  Optional[int]						=  obs[None].repeat(lowerCamelCase_		,      axis=0      )

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

    # run the diffusion process
    _a		,						_a					:  List[str]						=  self.run_diffusion(lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )

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

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

    _a					:  List[Any]						=  denorm_actions[selected_index, 0]
    return denorm_actions






'''simple docstring'''




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		0
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: float							  =		3.0







class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Dict:
    # If no defaults are changed, `to_kwargs` returns an empty dict.
    self.assertDictEqual(MockClass().to_kwargs()		,      {}      )
    self.assertDictEqual(MockClass(a=2      ).to_kwargs()		,      {'a': 2}      )
    self.assertDictEqual(MockClass(a=2		,      b=lowerCamelCase_      ).to_kwargs()		,      {'a': 2, 'b': True}      )
    self.assertDictEqual(MockClass(a=2		,      c=2.25      ).to_kwargs()		,      {'a': 2, 'c': 2.25}      )




 @require_cuda
 def   __UpperCamelCase   (    self      )      ->    int:
    # If no defaults are changed, `to_kwargs` returns an empty dict.
    _a					:  List[str]						=  GradScalerKwargs(init_scale=1_0_2_4		,      growth_factor=2      )
    AcceleratorState._reset_state()
    _a					:  Optional[int]						=  Accelerator(mixed_precision='fp16'		,      kwargs_handlers=[scaler_handler]      )
    print(accelerator.use_fpaa      )
    _a					:  Tuple						=  accelerator.scaler

    # Check the kwargs have been applied
    self.assertEqual(scaler._init_scale		,      1024.0      )
    self.assertEqual(scaler._growth_factor		,      2.0      )

    # Check the other values are at the default
    self.assertEqual(scaler._backoff_factor		,      0.5      )
    self.assertEqual(scaler._growth_interval		,      2_0_0_0      )
    self.assertEqual(scaler._enabled		,      lowerCamelCase_      )




 @require_multi_gpu
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  List[str]						=  ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
    execute_subprocess_async(lowerCamelCase_		,      env=os.environ.copy()      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Tuple          =		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 UpperCAmelCase_      :		str          =		Accelerator(kwargs_handlers=[ddp_scaler])
 UpperCAmelCase_      :		List[Any]          =		torch.nn.Linear(100, 200)
 UpperCAmelCase_      :		Tuple          =		accelerator.prepare(model)

 # Check the values changed in kwargs
 UpperCAmelCase_      :		List[str]          =		""
 UpperCAmelCase_      :		Any          =		model.bucket_bytes_cap // (1024 * 1024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)


