






'''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 (     lowerCamelCase			):




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




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							Union[str, Any]   , __lowercase		:							str )		->  Tuple:
       return (x_in - self.means[key]) / self.stds[key]




 def        __magic_name__		( self		:							Dict   , __lowercase		:							Tuple   , __lowercase		:							List[str] )		->  Any:
       return x_in * self.stds[key] + self.means[key]




 def        __magic_name__		( self		:							Dict   , __lowercase		:							Optional[int] )		->  Union[str, Any]:
       if type(__lowercase ) is dict:
             return {k: self.to_torch(__lowercase ) for k, v in x_in.items()}
       elif torch.is_tensor(__lowercase ):
             return x_in.to(self.unet.device )
       return torch.tensor(__lowercase   , device=self.unet.device )




 def        __magic_name__		( self		:							Any   , __lowercase		:							Optional[Any]   , __lowercase		:							Optional[int]   , __lowercase		:							Any )		->  str:
       for key, val in cond.items():
             SCREAMING_SNAKE_CASE__       :			List[Any]													=val.clone()
       return x_in




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

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

                         SCREAMING_SNAKE_CASE__       :			Tuple													=self.scheduler._get_variance(__lowercase )
                         SCREAMING_SNAKE_CASE__       :			Any													=torch.exp(0.5 * posterior_variance )
                         SCREAMING_SNAKE_CASE__       :			int													=model_std * grad

                   SCREAMING_SNAKE_CASE__       :			int													=0
                   SCREAMING_SNAKE_CASE__       :			int													=x.detach()
                   SCREAMING_SNAKE_CASE__       :			Dict													=x + scale * grad
                   SCREAMING_SNAKE_CASE__       :			Dict													=self.reset_xa(__lowercase   , __lowercase   , self.action_dim )

             SCREAMING_SNAKE_CASE__       :			List[Any]													=self.unet(x.permute(0   , 2   , 1 )   , __lowercase ).sample.permute(0   , 2   , 1 )

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

             # apply conditions to the trajectory (set the initial state)
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.reset_xa(__lowercase   , __lowercase   , self.action_dim )
             SCREAMING_SNAKE_CASE__       :			Tuple													=self.to_torch(__lowercase )
       return x, y




 def __call__( self		:							int   , __lowercase		:							List[Any]   , __lowercase		:							Optional[Any]=64   , __lowercase		:							List[Any]=32   , __lowercase		:							int=2   , __lowercase		:							int=0.1 )		->  int:
       # normalize the observations and create  batch dimension
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.normalize(__lowercase   , '''observations''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=obs[None].repeat(__lowercase   , axis=0 )

       SCREAMING_SNAKE_CASE__       :			Any													={0: self.to_torch(__lowercase )}
       SCREAMING_SNAKE_CASE__       :			Dict													=(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__       :			Dict													=randn_tensor(__lowercase   , device=self.unet.device )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.reset_xa(__lowercase   , __lowercase   , self.action_dim )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.to_torch(__lowercase )

       # run the diffusion process
       SCREAMING_SNAKE_CASE__       :			Dict													=self.run_diffusion(__lowercase   , __lowercase   , __lowercase   , __lowercase )

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

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

       SCREAMING_SNAKE_CASE__       :			Tuple													=denorm_actions[selected_index, 0]
       return denorm_actions









'''simple docstring'''
from scipy.stats import pearsonr

import datasets


a_					=	'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


a_					=	'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


a_					=	'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,			_KWARGS_DESCRIPTION			)
class        __SCREAMING_SNAKE_CASE (     datasets.Metric			):




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       return datasets.MetricInfo(
           description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''float''' ),
                   '''references''': datasets.Value('''float''' ),
               } )   , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']   , )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[Any]   , __lowercase		:							List[Any]   , __lowercase		:							str=False )		->  int:
       if return_pvalue:
             SCREAMING_SNAKE_CASE__       :			List[str]													=pearsonr(__lowercase   , __lowercase )
             return {"pearsonr": results[0], "p-value": results[1]}
       else:
             return {"pearsonr": float(pearsonr(__lowercase   , __lowercase )[0] )}