




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       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase=0.999	,      __UpperCamelCase="cosine"	,      )    ->  List[Any]:
     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

     a											=   []
     for i in range(__UpperCamelCase):
          a											=   i / num_diffusion_timesteps
          a											=   (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__ (      UpperCamelCase__     ,      UpperCamelCase__   ):
     a	:    Tuple	      =			[e.name for e in KarrasDiffusionSchedulers]
     a	:    int	      =			2







     @register_to_config
     def __init__(			self						,	A = 1000						,	A = 0.0_0_0_8_5						,	A = 0.0_1_2						,	A = "linear"						,	A = None						,	A = "epsilon"						,	A = "linspace"						,	A = 0						,	)						->					Any:



          '''simple docstring'''



          if trained_betas is not None:
               a											=   torch.tensor(A						,	dtype=torch.floataa				)
          elif beta_schedule == "linear":
               a											=   torch.linspace(A						,	A						,	A						,	dtype=torch.floataa				)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               a											=   (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	A						,	dtype=torch.floataa				) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               a											=   betas_for_alpha_bar(A				)
          else:
               raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''				)

          a											=   1.0 - self.betas
          a											=   torch.cumprod(self.alphas						,	dim=0				)

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







     def     lowerCAmelCase_       (			self						,	A						,	A=None				)						->					List[Any]:



          '''simple docstring'''



          if schedule_timesteps is None:
               a											=   self.timesteps

          a											=   (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:
               a											=   1 if len(A				) > 1 else 0
          else:
               a											=   timestep.cpu().item() if torch.is_tensor(A				) else timestep
               a											=   self._index_counter[timestep_int]

          return indices[pos].item()







     @property
     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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







     def     lowerCAmelCase_       (			self						,	A						,	A						,	)						->					torch.FloatTensor:



          '''simple docstring'''



          a											=   self.index_for_timestep(A				)

          if self.state_in_first_order:
               a											=   self.sigmas[step_index]
          else:
               a											=   self.sigmas_interpol[step_index - 1]

          a											=   sample / ((sigma**2 + 1) ** 0.5)
          return sample







     def     lowerCAmelCase_       (			self						,	A						,	A = None						,	A = None						,	)						->					List[Any]:



          '''simple docstring'''



          a											=   num_inference_steps

          a											=   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":
               a											=   np.linspace(0						,	num_train_timesteps - 1						,	A						,	dtype=A				)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               a											=   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
               a											=   (np.arange(0						,	A				) * step_ratio).round()[::-1].copy().astype(A				)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               a											=   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
               a											=   (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\'.'''				)

          a											=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
          a											=   torch.from_numpy(np.log(A				)				).to(A				)

          a											=   np.interp(A						,	np.arange(0						,	len(A				)				)						,	A				)
          a											=   np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
          a											=   torch.from_numpy(A				).to(device=A				)

          # compute up and down sigmas
          a											=   sigmas.roll(-1				)
          a											=   0.0
          a											=   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          a											=   (sigmas_next**2 - sigmas_up**2) ** 0.5
          a											=   0.0

          # compute interpolated sigmas
          a											=   sigmas.log().lerp(sigmas_down.log()						,	0.5				).exp()
          a											=   0.0

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

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

          a											=   self.sigma_to_t(A				).to(A						,	dtype=timesteps.dtype				)
          a											=   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1				).flatten()

          a											=   torch.cat([timesteps[:1], interleaved_timesteps]				)

          a											=   None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          a											=   defaultdict(A				)







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[Any]:



          '''simple docstring'''



          a											=   sigma.log()

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

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

          a											=   self.log_sigmas[low_idx]
          a											=   self.log_sigmas[high_idx]

          # interpolate sigmas
          a											=   (low - log_sigma) / (low - high)
          a											=   w.clamp(0						,	1				)

          # transform interpolation to time range
          a											=   (1 - w) * low_idx + w * high_idx
          a											=   t.view(sigma.shape				)
          return t







     @property
     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          return self.sample is None







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A = None						,	A = True						,	)						->					Union[SchedulerOutput, Tuple]:



          '''simple docstring'''



          a											=   self.index_for_timestep(A				)

          # advance index counter by 1
          a											=   timestep.cpu().item() if torch.is_tensor(A				) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               a											=   self.sigmas[step_index]
               a											=   self.sigmas_interpol[step_index]
               a											=   self.sigmas_up[step_index]
               a											=   self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               a											=   self.sigmas[step_index - 1]
               a											=   self.sigmas_interpol[step_index - 1]
               a											=   self.sigmas_up[step_index - 1]
               a											=   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
          a											=   0
          a											=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          a											=   model_output.device
          a											=   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":
               a											=   sigma_hat if self.state_in_first_order else sigma_interpol
               a											=   sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               a											=   sigma_hat if self.state_in_first_order else sigma_interpol
               a											=   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
               a											=   (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               a											=   sigma_interpol - sigma_hat

               # store for 2nd order step
               a											=   sample
               a											=   dt
               a											=   sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               a											=   (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               a											=   sigma_down - sigma_hat

               a											=   self.sample
               a											=   None

               a											=   sample + derivative * dt
               a											=   prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=A				)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	)						->					torch.FloatTensor:



          '''simple docstring'''



          a											=   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
               a											=   self.timesteps.to(original_samples.device						,	dtype=torch.floataa				)
               a											=   timesteps.to(original_samples.device						,	dtype=torch.floataa				)
          else:
               a											=   self.timesteps.to(original_samples.device				)
               a											=   timesteps.to(original_samples.device				)

          a											=   [self.index_for_timestep(A						,	A				) for t in timesteps]

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

          a											=   original_samples + noise * sigma
          return noisy_samples







     def __len__(			self				)						->					Optional[Any]:



          '''simple docstring'''



          return self.config.num_train_timesteps







from sklearn.metrics import mean_absolute_error

import datasets


lowercase__   :    int 					=      "\\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"

lowercase__   :    str 					=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase__   :    Tuple 					=      "\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     lowerCAmelCase_       (			self				)						->					Dict:



          '''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_absolute_error.html"
              ]						,	)







     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''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						,	A						,	A						,	A=None						,	A="uniform_average"				)						->					List[Any]:



          '''simple docstring'''



          a											=   mean_absolute_error(A						,	A						,	sample_weight=A						,	multioutput=A				)

          return {"mae": mae_score}

