





"""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 					__lowerCamelCase       (							a_					:		Any			,       a_					:		str=()			,       a_					:		Dict=None			,       a_					:		Union[str, Any]="no"			,       a_					:		Optional[int]="29500"							)      ->				Optional[Any]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  False
      __SCREAMING_SNAKE_CASE       :Tuple		    =  False
      if any(key.startswith('''KAGGLE'''							) for key in os.environ.keys()							):
            __SCREAMING_SNAKE_CASE       :int		    =  True
      elif "IPython" in sys.modules:
            __SCREAMING_SNAKE_CASE       :Dict		    =  '''google.colab''' in str(sys.modules['''IPython'''].get_ipython()							)

      try:
            __SCREAMING_SNAKE_CASE       :Dict		    =  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:
                  __SCREAMING_SNAKE_CASE       :Dict		    =  8

            __SCREAMING_SNAKE_CASE       :Union[str, 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_							):
                        __SCREAMING_SNAKE_CASE       :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():
                        __SCREAMING_SNAKE_CASE       :Dict		    =  '''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 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Union[str, Any]=()			,       a_					:		List[Any]=2							)      ->				List[Any]:
      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'''			,       ):
                  __SCREAMING_SNAKE_CASE       :Any		    =  PrepareForLaunch(a_			,       debug=a_							)
                  start_processes(a_			,       args=a_			,       nprocs=a_			,       start_method='''fork'''							)






"""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  _SCREAMING_SNAKE_CASE(       A  ):




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





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             for key, val in cond.items():
                   __SCREAMING_SNAKE_CASE       :Dict		    =  val.clone()
             return x_in




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






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

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

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

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

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

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

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




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.1							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.normalize(SCREAMING_SNAKE_CASE__		,'''observations'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  obs[None].repeat(SCREAMING_SNAKE_CASE__		,axis=0							)

             __SCREAMING_SNAKE_CASE       :str		    =  {0: self.to_torch(SCREAMING_SNAKE_CASE__							)}
             __SCREAMING_SNAKE_CASE       :Optional[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)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  randn_tensor(SCREAMING_SNAKE_CASE__		,device=self.unet.device							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
             __SCREAMING_SNAKE_CASE       :Any		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)

             # run the diffusion process
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  self.run_diffusion(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

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

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