





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     (__UpperCAmelCase					):



      def __init__(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	)   -> Optional[int]:



             '''simple docstring'''

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



      def  __a			(       self  ,	lowercase_  ,	lowercase_       )   -> int:



             '''simple docstring'''

             return (x_in - self.means[key]) / self.stds[key]



      def  __a			(       self  ,	lowercase_  ,	lowercase_       )   -> Tuple:



             '''simple docstring'''

             return x_in * self.stds[key] + self.means[key]



      def  __a			(       self  ,	lowercase_       )   -> Optional[int]:



             '''simple docstring'''

             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  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_       )   -> Optional[Any]:



             '''simple docstring'''

             for key, val in cond.items():
                    _snake_case   :				Any  					=    val.clone()
             return x_in



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       )   -> List[str]:



             '''simple docstring'''

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

                                  _snake_case   :				List[str]  					=    self.scheduler._get_variance(lowerCamelCase_       )
                                  _snake_case   :				Tuple  					=    torch.exp(0.5 * posterior_variance       )
                                  _snake_case   :				Any  					=    model_std * grad

                           _snake_case   :				Any  					=    0
                           _snake_case   :				Optional[int]  					=    x.detach()
                           _snake_case   :				Tuple  					=    x + scale * grad
                           _snake_case   :				Tuple  					=    self.reset_xa(lowerCamelCase_  ,	lowerCamelCase_  ,	self.action_dim       )

                    _snake_case   :				str  					=    self.unet(x.permute(0  ,	2  ,	1       )  ,	lowerCamelCase_       ).sample.permute(0  ,	2  ,	1       )

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

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



      def __call__(       self  ,	lowercase_  ,	lowercase_=64  ,	lowercase_=32  ,	lowercase_=2  ,	lowercase_=0.1       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				Optional[int]  					=    self.normalize(lowerCamelCase_  ,	'''observations'''       )
             _snake_case   :				List[Any]  					=    obs[None].repeat(lowerCamelCase_  ,	axis=0       )

             _snake_case   :				Optional[int]  					=    {0: self.to_torch(lowerCamelCase_       )}
             _snake_case   :				int  					=    (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)
             _snake_case   :				str  					=    randn_tensor(lowerCamelCase_  ,	device=self.unet.device       )
             _snake_case   :				int  					=    self.reset_xa(lowerCamelCase_  ,	lowerCamelCase_  ,	self.action_dim       )
             _snake_case   :				List[str]  					=    self.to_torch(lowerCamelCase_       )

             # run the diffusion process
             _snake_case   :				Optional[Any]  					=    self.run_diffusion(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_       )

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

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

             _snake_case   :				Any  					=    denorm_actions[selected_index, 0]
             return denorm_actions







'''simple docstring'''

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def __UpperCamelCase( _A       :					Any	,      _A       :					List[str]=()	,      _A       :					List[str]=None	,      _A       :					Dict="no"	,      _A       :					List[str]="29500"     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	False
   UpperCAmelCase__				:					List[str] 					=	False
   if any(key.startswith('''KAGGLE'''     ) for key in os.environ.keys()     ):
      UpperCAmelCase__				:					List[str] 					=	True
   elif "IPython" in sys.modules:
      UpperCAmelCase__				:					List[Any] 					=	'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()     )

   try:
      UpperCAmelCase__				:					Tuple 					=	PrecisionType(mixed_precision.lower()     )
   except ValueError:
      raise ValueError(
          F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''     )

   if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,      _A     ) is not None):
      # TPU launch
      import torch_xla.distributed.xla_multiprocessing as xmp

      if len(AcceleratorState._shared_state     ) > 0:
         raise ValueError(
             '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
             '''your training function. Restart your notebook and make sure no cells initializes an '''
             '''`Accelerator`.'''     )
      if num_processes is None:
         UpperCAmelCase__				:					Tuple 					=	8

      UpperCAmelCase__				:					Optional[Any] 					=	PrepareForLaunch(_A	,      distributed_type='''TPU'''     )
      print(F'''Launching a training on {num_processes} TPU cores.'''     )
      xmp.spawn(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )
   elif in_colab:
      # No need for a distributed launch otherwise as it's either CPU or one GPU.
      if torch.cuda.is_available():
         print('''Launching training on one GPU.'''     )
      else:
         print('''Launching training on one CPU.'''     )
      function(*_A     )
   else:
      if num_processes is None:
         raise ValueError(
             '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''     )

      if num_processes > 1:
         # Multi-GPU launch
         from torch.multiprocessing import start_processes
         from torch.multiprocessing.spawn import ProcessRaisedException

         if len(AcceleratorState._shared_state     ) > 0:
            raise ValueError(
                '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
                '''inside your training function. Restart your notebook and make sure no cells initializes an '''
                '''`Accelerator`.'''     )

         if torch.cuda.is_initialized():
            raise ValueError(
                '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
                '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
                '''function.'''     )

         # torch.distributed will expect a few environment variable to be here. We set the ones common to each
         # process here (the other ones will be set be the launcher).
         with patch_environment(
             world_size=_A	,      master_addr='''127.0.01'''	,      master_port=_A	,      mixed_precision=_A     ):
            UpperCAmelCase__				:					str 					=	PrepareForLaunch(_A	,      distributed_type='''MULTI_GPU'''     )
            print(F'''Launching training on {num_processes} GPUs.'''     )
            try:
               start_processes(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )
            except ProcessRaisedException as e:
               if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                  raise RuntimeError(
                      '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
                      '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
                      '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
                      '''which one is problematic.'''     ) from e

      else:
         # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
         if is_mps_available():
            UpperCAmelCase__				:					Union[str, Any] 					=	'''1'''
            print('''Launching training on MPS.'''     )
         elif torch.cuda.is_available():
            print('''Launching training on one GPU.'''     )
         else:
            print('''Launching training on CPU.'''     )
         function(*_A     )







def __UpperCamelCase( _A       :					List[str]	,      _A       :					Optional[Any]=()	,      _A       :					str=2     ):
   '''simple docstring'''
   from torch.multiprocessing import start_processes

   with tempfile.NamedTemporaryFile() as tmp_file:
      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=_A	,      master_addr='''127.0.01'''	,      master_port='''29500'''	,      accelerate_mixed_precision='''no'''	,      accelerate_debug_rdv_file=tmp_file.name	,      accelerate_use_cpu='''yes'''	,      ):
         UpperCAmelCase__				:					Optional[int] 					=	PrepareForLaunch(_A	,      debug=_A     )
         start_processes(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )




