


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						:     Union[str, Any]			,   lowercase_						:     UNetaDModel			,   lowercase_						:     UNetaDModel			,   lowercase_						:     DDPMScheduler			,   lowercase_						:     Dict			,   )					->						List[str]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     List[Any]			,   lowercase_						:     int )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     str )					->						Tuple:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     Optional[int] )					->						Union[str, Any]:
             """simple docstring"""




             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        lowerCamelCase       (					self						:     int			,   lowercase_						:     Optional[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     str )					->						List[Any]:
             """simple docstring"""




             for key, val in cond.items():
                    _lowerCamelCase       :		List[str]		=val.clone()
             return x_in






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Optional[int]			,   lowercase_						:     Any			,   lowercase_						:     Dict			,   lowercase_						:     Optional[Any] )					->						Tuple:
             """simple docstring"""




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

                                  _lowerCamelCase       :		str		=self.scheduler._get_variance(lowercase_ )
                                  _lowerCamelCase       :		str		=torch.exp(0.5 * posterior_variance )
                                  _lowerCamelCase       :		List[str]		=model_std * grad

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

                    _lowerCamelCase       :		List[str]		=self.unet(x.permute(0			,   2			,   1 )			,   lowercase_ ).sample.permute(0			,   2			,   1 )

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

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






      def __call__(					self						:     Dict			,   lowercase_						:     Any			,   lowercase_						:     Union[str, Any]=64			,   lowercase_						:     Union[str, Any]=32			,   lowercase_						:     Any=2			,   lowercase_						:     Tuple=0.1 )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.normalize(lowercase_			,   'observations' )
             _lowerCamelCase       :		List[str]		=obs[None].repeat(lowercase_			,   axis=0 )

             _lowerCamelCase       :		List[str]		={0: self.to_torch(lowercase_ )}
             _lowerCamelCase       :		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)
             _lowerCamelCase       :		Dict		=randn_tensor(lowercase_			,   device=self.unet.device )
             _lowerCamelCase       :		int		=self.reset_xa(lowercase_			,   lowercase_			,   self.action_dim )
             _lowerCamelCase       :		Dict		=self.to_torch(lowercase_ )

             # run the diffusion process
             _lowerCamelCase	,     _lowerCamelCase       :		Any		=self.run_diffusion(lowercase_			,   lowercase_			,   lowercase_			,   lowercase_ )

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

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

             _lowerCamelCase       :		List[str]		=denorm_actions[selected_index, 0]
             return denorm_actions






from sklearn.metrics import mean_squared_error

import datasets


lowerCamelCase											=       '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCamelCase											=       '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCamelCase											=       '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'




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






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             return datasets.MetricInfo(
                 description=_DESCRIPTION			,   citation=_CITATION			,   inputs_description=_KWARGS_DESCRIPTION			,   features=datasets.Features(self._get_feature_types() )			,   reference_urls=[
                     'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
                 ]			,   )






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             if self.config_name == "multilist":
                    return {
                        "predictions": datasets.Sequence(datasets.Value('float' ) ),
                        "references": datasets.Sequence(datasets.Value('float' ) ),
                    }
             else:
                    return {
                        "predictions": datasets.Value('float' ),
                        "references": datasets.Value('float' ),
                    }






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=None			,   lowercase_						:     Union[str, Any]="uniform_average"			,   lowercase_						:     Tuple=True )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=mean_squared_error(
                 lowercase_			,   lowercase_			,   sample_weight=lowercase_			,   multioutput=lowercase_			,   squared=lowercase_ )

             return {"mse": mse}


