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

import numpy as np
import torch
from scipy import integrate

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	42
							_snake_case  		=	None





def 						A_			(				snake_case				: List[Any]			,   snake_case				: List[Any]=0.999			,   snake_case				: Union[str, Any]="cosine"			,   )   ->     Dict:



						'''simple docstring'''

						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	[e.name for e in KarrasDiffusionSchedulers]
							_snake_case  		=	1



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = 1000  ,						SCREAMING_SNAKE_CASE_ = 0.0_0_0_1  ,						SCREAMING_SNAKE_CASE_ = 0.0_2  ,						SCREAMING_SNAKE_CASE_ = "linear"  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = "epsilon"  ,						SCREAMING_SNAKE_CASE_ = "linspace"  ,						SCREAMING_SNAKE_CASE_ = 0  ,						)-> str:





													'''simple docstring'''






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

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

													__UpperCamelCase  	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
													__UpperCamelCase  	=      np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      )

													# setable values
													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      use_karras_sigmas
													self.set_timesteps(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      []
													__UpperCamelCase  	=      False



							@property
							def      A__		(	self      )-> Any:





													'''simple docstring'''






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

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> torch.FloatTensor:





													'''simple docstring'''






													if isinstance(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      ):
																			__UpperCamelCase  	=      timestep.to(self.timesteps.device      )
													__UpperCamelCase  	=      (self.timesteps == timestep).nonzero().item()
													__UpperCamelCase  	=      self.sigmas[step_index]
													__UpperCamelCase  	=      sample / ((sigma**2 + 1) ** 0.5)
													__UpperCamelCase  	=      True
													return sample



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> List[str]:





													'''simple docstring'''






													def lms_derivative(SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      1.0
																			for k in range(SCREAMING_SNAKE_CASE_      ):
																									if current_order == k:
																															continue
																									prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																			return prod

													__UpperCamelCase  	=      integrate.quad(SCREAMING_SNAKE_CASE_  ,						self.sigmas[t]  ,						self.sigmas[t + 1]  ,						epsrel=1E-4      )[0]

													return integrated_coeff



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      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":
																			__UpperCamelCase  	=      np.linspace(0  ,						self.config.num_train_timesteps - 1  ,						SCREAMING_SNAKE_CASE_  ,						dtype=SCREAMING_SNAKE_CASE_      )[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			__UpperCamelCase  	=      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
																			__UpperCamelCase  	=      (np.arange(0  ,						SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			__UpperCamelCase  	=      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
																			__UpperCamelCase  	=      (np.arange(self.config.num_train_timesteps  ,						0  ,						-step_ratio      )).round().copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps -= 1
													else:
																			raise ValueError(
																			    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

													__UpperCamelCase  	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
													__UpperCamelCase  	=      np.log(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.interp(SCREAMING_SNAKE_CASE_  ,						np.arange(0  ,						len(SCREAMING_SNAKE_CASE_      )      )  ,						SCREAMING_SNAKE_CASE_      )

													if self.use_karras_sigmas:
																			__UpperCamelCase  	=      self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for sigma in sigmas]      )

													__UpperCamelCase  	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )

													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )
													if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      []



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      np.log(SCREAMING_SNAKE_CASE_      )

													# get distribution
													__UpperCamelCase  	=      log_sigma - log_sigmas[:, np.newaxis]

													# get sigmas range
													__UpperCamelCase  	=      np.cumsum((dists >= 0)  ,						axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
													__UpperCamelCase  	=      low_idx + 1

													__UpperCamelCase  	=      log_sigmas[low_idx]
													__UpperCamelCase  	=      log_sigmas[high_idx]

													# interpolate sigmas
													__UpperCamelCase  	=      (low - log_sigma) / (low - high)
													__UpperCamelCase  	=      np.clip(SCREAMING_SNAKE_CASE_  ,						0  ,						1      )

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      in_sigmas[-1].item()
													__UpperCamelCase  	=      in_sigmas[0].item()

													__UpperCamelCase  	=      7.0  # 7.0 is the value used in the paper
													__UpperCamelCase  	=      np.linspace(0  ,						1  ,						self.num_inference_steps      )
													__UpperCamelCase  	=      sigma_min ** (1 / rho)
													__UpperCamelCase  	=      sigma_max ** (1 / rho)
													__UpperCamelCase  	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = 4  ,						SCREAMING_SNAKE_CASE_ = True  ,						)-> Union[LMSDiscreteSchedulerOutput, Tuple]:





													'''simple docstring'''






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

													if isinstance(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      ):
																			__UpperCamelCase  	=      timestep.to(self.timesteps.device      )
													__UpperCamelCase  	=      (self.timesteps == timestep).nonzero().item()
													__UpperCamelCase  	=      self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			__UpperCamelCase  	=      sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			__UpperCamelCase  	=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													elif self.config.prediction_type == "sample":
																			__UpperCamelCase  	=      model_output
													else:
																			raise ValueError(
																			    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

													# 2. Convert to an ODE derivative
													__UpperCamelCase  	=      (sample - pred_original_sample) / sigma
													self.derivatives.append(SCREAMING_SNAKE_CASE_      )
													if len(self.derivatives      ) > order:
																			self.derivatives.pop(0      )

													# 3. Compute linear multistep coefficients
													__UpperCamelCase  	=      min(step_index + 1  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      [self.get_lms_coefficient(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for curr_order in range(SCREAMING_SNAKE_CASE_      )]

													# 4. Compute previous sample based on the derivatives path
													__UpperCamelCase  	=      sample + sum(
													    coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE_  ,						reversed(self.derivatives      )      )      )

													if not return_dict:
																			return (prev_sample,)

													return LMSDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,						pred_original_sample=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      self.sigmas.to(device=original_samples.device  ,						dtype=original_samples.dtype      )
													if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device      )

													__UpperCamelCase  	=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													__UpperCamelCase  	=      original_samples + noise * sigma
													return noisy_samples



							def __len__(	self      )-> Union[str, Any]:





													'''simple docstring'''






													return self.config.num_train_timesteps












from sklearn.metrics import roc_auc_score

import datasets


_a       =    '''
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model\'s predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
'''

_a       =    '''
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:
    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `\'ovr\'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == \'macro\'`, because class imbalance affects the composition of each of the \'rest\' groupings.
    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric("roc_auc")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results[\'roc_auc\'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')
        >>> print(round(results[\'roc_auc\'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results[\'roc_auc\']])
        [0.83, 0.38, 0.94]
'''

_a       =    '''\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@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}
}
'''





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





	"""simple docstring"""






	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return datasets.MetricInfo(
							    description=_DESCRIPTION			,       citation=_CITATION			,       inputs_description=_KWARGS_DESCRIPTION			,       features=datasets.Features(
							        {
							            'prediction_scores': datasets.Sequence(datasets.Value('float'       )       ),
							            'references': datasets.Value('int32'       ),
							        }
							        if self.config_name == 'multiclass'
							        else {
							            'references': datasets.Sequence(datasets.Value('int32'       )       ),
							            'prediction_scores': datasets.Sequence(datasets.Value('float'       )       ),
							        }
							        if self.config_name == 'multilabel'
							        else {
							            'references': datasets.Value('int32'       ),
							            'prediction_scores': datasets.Value('float'       ),
							        }       )			,       reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html']			,       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase="macro"			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase="raise"			,       UpperCAmelCase=None			,       ):
							"""simple docstring"""







							return {
							    "roc_auc": roc_auc_score(
							        UpperCAmelCase			,       UpperCAmelCase			,       average=UpperCAmelCase			,       sample_weight=UpperCAmelCase			,       max_fpr=UpperCAmelCase			,       multi_class=UpperCAmelCase			,       labels=UpperCAmelCase			,       )
							}
