




'''simple docstring'''







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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



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 :			str					   = []
     for i in range(__lowercase							):
          snake_case :			List[str]					   = i / num_diffusion_timesteps
          snake_case :			List[Any]					   = (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_     :    List[str]									=				[e.name for e in KarrasDiffusionSchedulers]
     lowerCAmelCase_     :    int									=				2







     @register_to_config
     def __init__(							self       ,__a = 1_000       ,__a = 0.00_085       ,__a = 0.012       ,__a = "linear"       ,__a = None       ,__a = "epsilon"       ,__a = "linspace"       ,__a = 0       ,)			->							Union[str, Any]:
          if trained_betas is not None:
               snake_case :			Union[str, Any]					   = torch.tensor(__a       ,dtype=torch.floataa       )
          elif beta_schedule == "linear":
               snake_case :			str					   = torch.linspace(__a       ,__a       ,__a       ,dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               snake_case :			Tuple					   = (
                   torch.linspace(beta_start**0.5       ,beta_end**0.5       ,__a       ,dtype=torch.floataa       ) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               snake_case :			Optional[int]					   = betas_for_alpha_bar(__a       )
          else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

          snake_case :			int					   = 1.0 - self.betas
          snake_case :			Tuple					   = torch.cumprod(self.alphas       ,dim=0       )

          #  set all values
          self.set_timesteps(__a       ,__a       ,__a       )







     def  snake_case_			(							self       ,__a       ,__a=None       )			->							Optional[Any]:
          if schedule_timesteps is None:
               snake_case :			Any					   = self.timesteps

          snake_case :			str					   = (schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter       ) == 0:
               snake_case :			Dict					   = 1 if len(__a       ) > 1 else 0
          else:
               snake_case :			str					   = timestep.cpu().item() if torch.is_tensor(__a       ) else timestep
               snake_case :			Union[str, Any]					   = self._index_counter[timestep_int]

          return indices[pos].item()







     @property
     def  snake_case_			(							self       )			->							Dict:
          # 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       ,)			->							torch.FloatTensor:
          snake_case :			List[Any]					   = self.index_for_timestep(__a       )

          if self.state_in_first_order:
               snake_case :			Optional[Any]					   = self.sigmas[step_index]
          else:
               snake_case :			Dict					   = self.sigmas_interpol[step_index - 1]

          snake_case :			Any					   = sample / ((sigma**2 + 1) ** 0.5)
          return sample







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

          snake_case :			Any					   = num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
               snake_case :			Optional[int]					   = np.linspace(0       ,num_train_timesteps - 1       ,__a       ,dtype=__a       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               snake_case :			Any					   = 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 :			str					   = (np.arange(0       ,__a       ) * step_ratio).round()[::-1].copy().astype(__a       )
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               snake_case :			Dict					   = 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 :			Optional[Any]					   = (np.arange(__a       ,0       ,-step_ratio       )).round().copy().astype(__a       )
               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 :			List[str]					   = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          snake_case :			List[Any]					   = torch.from_numpy(np.log(__a       )       ).to(__a       )

          snake_case :			List[Any]					   = np.interp(__a       ,np.arange(0       ,len(__a       )       )       ,__a       )
          snake_case :			Optional[Any]					   = np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          snake_case :			Any					   = torch.from_numpy(__a       ).to(device=__a       )

          # compute up and down sigmas
          snake_case :			Optional[int]					   = sigmas.roll(-1       )
          snake_case :			Optional[int]					   = 0.0
          snake_case :			Optional[int]					   = (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          snake_case :			str					   = (sigmas_next**2 - sigmas_up**2) ** 0.5
          snake_case :			List[Any]					   = 0.0

          # compute interpolated sigmas
          snake_case :			Tuple					   = sigmas.log().lerp(sigmas_down.log()       ,0.5       ).exp()
          snake_case :			Optional[Any]					   = 0.0

          # set sigmas
          snake_case :			Optional[Any]					   = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          snake_case :			Tuple					   = torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )
          snake_case :			Any					   = torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2       ), sigmas_up[-1:]]       )
          snake_case :			Any					   = torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2       ), sigmas_down[-1:]]       )

          if str(__a       ).startswith("""mps"""       ):
               # mps does not support float64
               snake_case :			int					   = torch.from_numpy(__a       ).to(__a       ,dtype=torch.floataa       )
          else:
               snake_case :			int					   = torch.from_numpy(__a       ).to(__a       )

          snake_case :			List[str]					   = self.sigma_to_t(__a       ).to(__a       ,dtype=timesteps.dtype       )
          snake_case :			List[str]					   = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])       ,dim=-1       ).flatten()

          snake_case :			List[Any]					   = torch.cat([timesteps[:1], interleaved_timesteps]       )

          snake_case :			List[str]					   = None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          snake_case :			List[str]					   = defaultdict(__a       )







     def  snake_case_			(							self       ,__a       )			->							List[str]:
          # get log sigma
          snake_case :			str					   = sigma.log()

          # get distribution
          snake_case :			Dict					   = log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          snake_case :			List[Any]					   = dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
          snake_case :			int					   = low_idx + 1

          snake_case :			Tuple					   = self.log_sigmas[low_idx]
          snake_case :			Optional[Any]					   = self.log_sigmas[high_idx]

          # interpolate sigmas
          snake_case :			int					   = (low - log_sigma) / (low - high)
          snake_case :			Dict					   = w.clamp(0       ,1       )

          # transform interpolation to time range
          snake_case :			str					   = (1 - w) * low_idx + w * high_idx
          snake_case :			Optional[Any]					   = t.view(sigma.shape       )
          return t







     @property
     def  snake_case_			(							self       )			->							Optional[Any]:
          return self.sample is None







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a = None       ,__a = True       ,)			->							Union[SchedulerOutput, Tuple]:
          snake_case :			Dict					   = self.index_for_timestep(__a       )

          # advance index counter by 1
          snake_case :			Optional[int]					   = timestep.cpu().item() if torch.is_tensor(__a       ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               snake_case :			str					   = self.sigmas[step_index]
               snake_case :			Dict					   = self.sigmas_interpol[step_index]
               snake_case :			str					   = self.sigmas_up[step_index]
               snake_case :			Any					   = self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               snake_case :			List[str]					   = self.sigmas[step_index - 1]
               snake_case :			Tuple					   = self.sigmas_interpol[step_index - 1]
               snake_case :			Any					   = self.sigmas_up[step_index - 1]
               snake_case :			List[Any]					   = self.sigmas_down[step_index - 1]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          snake_case :			Any					   = 0
          snake_case :			List[str]					   = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          snake_case :			List[str]					   = model_output.device
          snake_case :			Union[str, Any]					   = randn_tensor(model_output.shape       ,dtype=model_output.dtype       ,device=__a       ,generator=__a       )

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               snake_case :			Optional[Any]					   = sigma_hat if self.state_in_first_order else sigma_interpol
               snake_case :			List[Any]					   = sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               snake_case :			Dict					   = sigma_hat if self.state_in_first_order else sigma_interpol
               snake_case :			Any					   = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**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`'''       )

          if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               snake_case :			List[str]					   = (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               snake_case :			List[Any]					   = sigma_interpol - sigma_hat

               # store for 2nd order step
               snake_case :			Tuple					   = sample
               snake_case :			Any					   = dt
               snake_case :			Optional[int]					   = sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               snake_case :			Union[str, Any]					   = (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               snake_case :			List[str]					   = sigma_down - sigma_hat

               snake_case :			Optional[Any]					   = self.sample
               snake_case :			List[str]					   = None

               snake_case :			Union[str, Any]					   = sample + derivative * dt
               snake_case :			int					   = prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=__a       )







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

          snake_case :			Tuple					   = [self.index_for_timestep(__a       ,__a       ) for t in timesteps]

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

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







     def __len__(							self       )			->							List[Any]:
          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}



