
"""simple docstring"""




from sklearn.metrics import roc_auc_score

import datasets


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

a		:					Dict    	=					'\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to \'macro\'. Options are:\n    - `\'micro\'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `\'samples\'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- 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`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `\'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.\n    - `\'ovo\'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == \'macro\'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average=\'None\'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric("roc_auc")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multiclass")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class=\'ovr\')\n        >>> print(round(results[\'roc_auc\'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric("roc_auc", "multilabel")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results[\'roc_auc\']])\n        [0.83, 0.38, 0.94]\n'

a		:					Dict    	=					'\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={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.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n'







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



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







         """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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE="macro"    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE="raise"    ,							__SCREAMING_SNAKE_CASE=None    ,							)		->    Union[str, Any]:







         """simple docstring"""



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





"""simple docstring"""




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 SchedulerMixin


a		:					List[Any]    	=					logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  torch.FloatTensor







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  List[Any]  				=    1



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 4_0    ,							__SCREAMING_SNAKE_CASE = 0.0_02    ,							__SCREAMING_SNAKE_CASE = 80.0    ,							__SCREAMING_SNAKE_CASE = 0.5    ,							__SCREAMING_SNAKE_CASE = 1.0    ,							__SCREAMING_SNAKE_CASE = 7.0    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Dict:







         """simple docstring"""



         a__    = sigma_max

         a__    = np.linspace(0    ,							1    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         # setable values
         a__    = None
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      )
         a__    = False
         a__    = False



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None      )		->    Union[str, Any]:







         """simple docstring"""



         if schedule_timesteps is None:
             a__    = self.timesteps

         a__    = (schedule_timesteps == timestep).nonzero()
         return indices.item()



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    torch.FloatTensor:







         """simple docstring"""



         if isinstance(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )
         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigmas[step_idx]

         a__    = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         a__    = True
         return sample



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         if not isinstance(__SCREAMING_SNAKE_CASE    ,							np.ndarray      ):
             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.floataa      )

         a__    = 1_0_0_0 * 0.25 * np.log(sigmas + 1e-44      )

         return timesteps



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							)		->    Optional[int]:







         """simple docstring"""



         if num_inference_steps is None and timesteps is None:
             raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'      )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'      )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1    ,							len(__SCREAMING_SNAKE_CASE      )      ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError('`timesteps` must be in descending order.'      )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f'`timesteps` must start before `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps}.'      )

             a__    = np.array(__SCREAMING_SNAKE_CASE    ,							dtype=np.intaa      )
             a__    = True
         else:
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                     f' maximal {self.config.num_train_timesteps} timesteps.'      )

             a__    = num_inference_steps

             a__    = self.config.num_train_timesteps // self.num_inference_steps
             a__    = (np.arange(0    ,							__SCREAMING_SNAKE_CASE      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
             a__    = False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         a__    = self.config.num_train_timesteps
         a__    = timesteps[::-1].copy()
         a__    = ramp / (num_train_timesteps - 1)
         a__    = self._convert_to_karras(__SCREAMING_SNAKE_CASE      )
         a__    = self.sigma_to_t(__SCREAMING_SNAKE_CASE      )

         a__    = np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
         a__    = torch.from_numpy(__SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE      )

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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.config.rho
         a__    = sigma_min ** (1 / rho)
         a__    = sigma_max ** (1 / rho)
         a__    = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



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







         """simple docstring"""



         a__    = self.config.sigma_data

         a__    = sigma_data**2 / (sigma**2 + sigma_data**2)
         a__    = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         a__    = self.config.sigma_min
         a__    = self.config.sigma_data

         a__    = sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         a__    = (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[CMStochasticIterativeSchedulerOutput, Tuple]:







         """simple docstring"""



         if (
             isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.IntTensor      )
             or isinstance(__SCREAMING_SNAKE_CASE    ,							torch.LongTensor      )
         ):
             raise ValueError(
                 (
                     'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                     f' `{self.__class__}.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(__SCREAMING_SNAKE_CASE    ,							torch.Tensor      ):
             a__    = timestep.to(self.timesteps.device      )

         a__    = self.config.sigma_min
         a__    = self.config.sigma_max

         a__    = self.index_for_timestep(__SCREAMING_SNAKE_CASE      )

         # sigma_next corresponds to next_t in original implementation
         a__    = self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             a__    = self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             a__    = self.sigmas[-1]

         # Get scalings for boundary conditions
         a__  ,    a__    = self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE      )

         # 1. Denoise model output using boundary conditions
         a__    = c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             a__    = denoised.clamp(-1    ,							1      )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps      ) > 1:
             a__    = randn_tensor(
                 model_output.shape    ,							dtype=model_output.dtype    ,							device=model_output.device    ,							generator=__SCREAMING_SNAKE_CASE      )
         else:
             a__    = torch.zeros_like(__SCREAMING_SNAKE_CASE      )
         a__    = noise * self.config.s_noise

         a__    = sigma_next.clamp(min=__SCREAMING_SNAKE_CASE    ,							max=__SCREAMING_SNAKE_CASE      )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         a__    = denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    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(__SCREAMING_SNAKE_CASE      ):
             # 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__    = [(schedule_timesteps == t).nonzero().item() 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      )		->    str:







         """simple docstring"""



         return self.config.num_train_timesteps


