



"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


_UpperCamelCase:				str    							=  '\\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'

_UpperCamelCase:				int    							=  '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


_UpperCamelCase:				List[str]    							=  '\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\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'


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


   def        lowercase				(							self      :							List[str]			)    ->       str:
         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_absolute_error.html'
             ],     )


   def        lowercase				(							self      :							Tuple			)    ->       Optional[int]:
         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        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							Any=None,     lowerCAmelCase      :							Optional[int]="uniform_average"			)    ->       Union[str, Any]:
         lowercase  :      Any					=   mean_absolute_error(lowerCAmelCase,     lowerCAmelCase,     sample_weight=lowerCAmelCase,     multioutput=lowerCAmelCase			)

         return {"mae": mae_score}





"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCamelCase:				str    							=  logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				None




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0.9_9_9	,      _UpperCAmelCase="cosine"	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.exp(t * -1_2.0				)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      lowercase  :      Any					=   []
      for i in range(_UpperCAmelCase				):
            lowercase  :      List[str]					=   i / num_diffusion_timesteps
            lowercase  :      str					=   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase				) / alpha_bar_fn(_UpperCAmelCase				)	,      _UpperCAmelCase				)				)
      return torch.tensor(_UpperCAmelCase	,      dtype=torch.floataa				)





class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				[e.name for e in KarrasDiffusionSchedulers]
   _lowerCamelCase												=				1


   @register_to_config
   def __init__(							self      :							str,     lowerCAmelCase      :							int = 1000,     lowerCAmelCase      :							float = 0.0001,     lowerCAmelCase      :							float = 0.02,     lowerCAmelCase      :							str = "linear",     lowerCAmelCase      :							Optional[Union[np.ndarray, List[float]]] = None,     lowerCAmelCase      :							str = "epsilon",     lowerCAmelCase      :							str = "linspace",     lowerCAmelCase      :							int = 0,     )    ->       List[Any]:
         if trained_betas is not None:
               lowercase  :      Union[str, Any]					=   torch.tensor(lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "linear":
               lowercase  :      Union[str, Any]					=   torch.linspace(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowercase  :      str					=   (
                   torch.linspace(beta_start**0.5,     beta_end**0.5,     lowerCAmelCase,     dtype=torch.floataa			) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowercase  :      List[Any]					=   betas_for_alpha_bar(lowerCAmelCase			)
         else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

         lowercase  :      Any					=   1.0 - self.betas
         lowercase  :      Optional[int]					=   torch.cumprod(self.alphas,     dim=0			)

         lowercase  :      Optional[Any]					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      Optional[int]					=   np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
         lowercase  :      int					=   torch.from_numpy(lowerCAmelCase			)

         # setable values
         lowercase  :      List[str]					=   None
         lowercase  :      Tuple					=   np.linspace(0,     num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[::-1].copy()
         lowercase  :      Dict					=   torch.from_numpy(lowerCAmelCase			)
         lowercase  :      str					=   False


   @property
   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor]			)    ->       torch.FloatTensor:
         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      Any					=   timestep.to(self.timesteps.device			)
         lowercase  :      List[Any]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Union[str, Any]					=   self.sigmas[step_index]
         lowercase  :      Dict					=   sample / ((sigma**2 + 1) ** 0.5)
         lowercase  :      Union[str, Any]					=   True
         return sample


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Union[str, torch.device] = None			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
               lowercase  :      int					=   np.linspace(0,     self.config.num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase  :      List[str]					=   self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Any					=   (np.arange(0,     lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase			)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase  :      Optional[Any]					=   self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Optional[int]					=   (np.arange(self.config.num_train_timesteps,     0,     -step_ratio			)).round().copy().astype(lowerCAmelCase			)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

         lowercase  :      int					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      List[str]					=   np.interp(lowerCAmelCase,     np.arange(0,     len(lowerCAmelCase			)			),     lowerCAmelCase			)
         lowercase  :      Any					=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
         lowercase  :      Optional[Any]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)
         if str(lowerCAmelCase			).startswith('mps'			):
               # mps does not support float64
               lowercase  :      List[Any]					=   torch.from_numpy(lowerCAmelCase			).to(lowerCAmelCase,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Optional[torch.Generator] = None,     lowerCAmelCase      :							bool = True,     )    ->       Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(lowerCAmelCase,     lowerCAmelCase			)
             or isinstance(lowerCAmelCase,     torch.IntTensor			)
             or isinstance(lowerCAmelCase,     torch.LongTensor			)
         ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                       ' one of the `scheduler.timesteps` as a timestep.'
                   ),     )

         if not self.is_scale_input_called:
               logger.warning(
                   'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                   'See `StableDiffusionPipeline` for a usage example.'			)

         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   timestep.to(self.timesteps.device			)

         lowercase  :      List[str]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Optional[int]					=   self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               lowercase  :      Optional[Any]					=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowercase  :      int					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample'			)
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

         lowercase  :      List[Any]					=   self.sigmas[step_index]
         lowercase  :      str					=   self.sigmas[step_index + 1]
         lowercase  :      Optional[int]					=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase  :      Optional[int]					=   (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase  :      Tuple					=   (sample - pred_original_sample) / sigma

         lowercase  :      str					=   sigma_down - sigma

         lowercase  :      int					=   sample + derivative * dt

         lowercase  :      Dict					=   model_output.device
         lowercase  :      List[Any]					=   randn_tensor(model_output.shape,     dtype=model_output.dtype,     device=lowerCAmelCase,     generator=lowerCAmelCase			)

         lowercase  :      List[str]					=   prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=lowerCAmelCase,     pred_original_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     )    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase  :      Tuple					=   self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype			)
         if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase			):
               # mps does not support float64
               lowercase  :      Dict					=   self.timesteps.to(original_samples.device,     dtype=torch.floataa			)
               lowercase  :      Union[str, Any]					=   timesteps.to(original_samples.device,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   self.timesteps.to(original_samples.device			)
               lowercase  :      Tuple					=   timesteps.to(original_samples.device			)

         lowercase  :      Union[str, Any]					=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase  :      Union[str, Any]					=   sigmas[step_indices].flatten()
         while len(sigma.shape			) < len(original_samples.shape			):
               lowercase  :      Dict					=   sigma.unsqueeze(-1			)

         lowercase  :      Optional[Any]					=   original_samples + noise * sigma
         return noisy_samples






   def __len__(							self      :							int			)    ->       List[str]:
         return self.config.num_train_timesteps
