




'''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


lowercase			:      Tuple			=					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 				_a   (_a  ):





     '''simple docstring'''



     lowerCAmelCase_     :    List[Any]									=				42
     lowerCAmelCase_     :    Dict									=				None



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase=0.999    ,      __lowercase="cosine"    ,      ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__lowercase ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__lowercase ):
               return math.exp(t * -12.0 )

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

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







class 				_a   (_a,		_a  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[int]									=				[e.name for e in KarrasDiffusionSchedulers]
     lowerCAmelCase_     :    Tuple									=				1







     @register_to_config
     def __init__(							self       ,__a = 1_000       ,__a = 0.0_001       ,__a = 0.02       ,__a = "linear"       ,__a = None       ,__a = "epsilon"       ,__a = "linspace"       ,__a = 0       ,)			->							Any:
          if trained_betas is not None:
               snake_case :			List[Any]					   = torch.tensor(lowerCamelCase__       ,dtype=torch.floataa       )
          elif beta_schedule == "linear":
               snake_case :			List[str]					   = torch.linspace(lowerCamelCase__       ,lowerCamelCase__       ,lowerCamelCase__       ,dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               snake_case :			Any					   = (
                   torch.linspace(beta_start**0.5       ,beta_end**0.5       ,lowerCamelCase__       ,dtype=torch.floataa       ) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               snake_case :			int					   = betas_for_alpha_bar(lowerCamelCase__       )
          else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

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

          snake_case :			Union[str, Any]					   = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          snake_case :			int					   = np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
          snake_case :			Tuple					   = torch.from_numpy(lowerCamelCase__       )

          # setable values
          snake_case :			Optional[int]					   = None
          snake_case :			Tuple					   = np.linspace(0       ,num_train_timesteps - 1       ,lowerCamelCase__       ,dtype=lowerCamelCase__       )[::-1].copy()
          snake_case :			Optional[int]					   = torch.from_numpy(lowerCamelCase__       )
          snake_case :			Union[str, Any]					   = False







     @property
     def  snake_case_			(							self       )			->							List[str]:
          # 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  snake_case_			(							self       ,__a       ,__a       )			->							Dict:
          if isinstance(lowerCamelCase__       ,torch.Tensor       ):
               snake_case :			Dict					   = timestep.to(self.timesteps.device       )
          snake_case :			int					   = (self.timesteps == timestep).nonzero().item()
          snake_case :			Union[str, Any]					   = self.sigmas[step_index]
          snake_case :			str					   = sample / ((sigma**2 + 1) ** 0.5)
          snake_case :			Union[str, Any]					   = True
          return sample







     def  snake_case_			(							self       ,__a       ,__a = None       )			->							List[str]:
          snake_case :			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":
               snake_case :			int					   = np.linspace(0       ,self.config.num_train_timesteps - 1       ,lowerCamelCase__       ,dtype=lowerCamelCase__       )[
                   ::-1
               ].copy()
          elif self.config.timestep_spacing == "leading":
               snake_case :			int					   = 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
               snake_case :			List[str]					   = (np.arange(0       ,lowerCamelCase__       ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__       )
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               snake_case :			Union[str, 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
               snake_case :			Tuple					   = (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\'.'''       )

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







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a = None       ,__a = True       ,)			->							List[Any]:

          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       ):
               snake_case :			Union[str, Any]					   = timestep.to(self.timesteps.device       )

          snake_case :			Union[str, Any]					   = (self.timesteps == timestep).nonzero().item()
          snake_case :			Tuple					   = self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               snake_case :			Tuple					   = sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               snake_case :			Any					   = 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`'''       )

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

          # 2. Convert to an ODE derivative
          snake_case :			Union[str, Any]					   = (sample - pred_original_sample) / sigma

          snake_case :			Union[str, Any]					   = sigma_down - sigma

          snake_case :			List[Any]					   = sample + derivative * dt

          snake_case :			Tuple					   = model_output.device
          snake_case :			List[Any]					   = randn_tensor(model_output.shape       ,dtype=model_output.dtype       ,device=lowerCamelCase__       ,generator=lowerCamelCase__       )

          snake_case :			Tuple					   = prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=lowerCamelCase__       ,pred_original_sample=lowerCamelCase__       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,)			->							str:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          snake_case :			Optional[Any]					   = 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
               snake_case :			Any					   = self.timesteps.to(original_samples.device       ,dtype=torch.floataa       )
               snake_case :			int					   = timesteps.to(original_samples.device       ,dtype=torch.floataa       )
          else:
               snake_case :			Union[str, Any]					   = self.timesteps.to(original_samples.device       )
               snake_case :			Union[str, Any]					   = timesteps.to(original_samples.device       )

          snake_case :			List[Any]					   = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          snake_case :			Union[str, Any]					   = original_samples + noise * sigma
          return noisy_samples







     def __len__(							self       )			->							str:
          return self.config.num_train_timesteps









'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


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

lowercase			:      Optional[Any]			=					"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowercase			:      List[Any]			=					"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""







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





     '''simple docstring'''



     def  snake_case_			(							self       )			->							int:
          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  snake_case_			(							self       )			->							Any:
          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  snake_case_			(							self       ,__a       ,__a       ,__a=None       ,__a="uniform_average"       )			->							str:
          snake_case :			int					   = mean_absolute_error(__a       ,__a       ,sample_weight=__a       ,multioutput=__a       )

          return {"mae": mae_score}



