
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class       _lowercase			:


       """simple docstring"""




       A__ 						=      None
       A__ 						=      None
       A__ 						=      None





       @classmethod
       def 						lowerCAmelCase					(       cls							:      Union[str, Any]	):


             '''simple docstring'''



             return cls()







@dataclass
class       _lowercase			(							SCREAMING_SNAKE_CASE__):


       """simple docstring"""




       A__ 						=      42
       A__ 						=      42
       A__ 						=      None







class       _lowercase			(							SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__):


       """simple docstring"""




       @property
       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             return True





       @register_to_config
       def __init__(       self							:      str ,				__lowerCamelCase							:      int = 2000 ,				__lowerCamelCase							:      float = 0.1_5 ,				__lowerCamelCase							:      float = 0.0_1 ,				__lowerCamelCase							:      float = 1_3_4_8.0 ,				__lowerCamelCase							:      float = 1E-5 ,				__lowerCamelCase							:      int = 1 ,				):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  ScoreSdeVeSchedulerState.create()
             return self.set_sigmas(
                 __lowerCamelCase ,				self.config.num_train_timesteps ,				self.config.sigma_min ,				self.config.sigma_max ,				self.config.sampling_eps ,				)





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      ScoreSdeVeSchedulerState ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Tuple = () ,				__lowerCamelCase							:      float = None	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  sampling_eps if sampling_eps is not None else self.config.sampling_eps

             lowerCamelCase__		:    List[str]   =  jnp.linspace(1 ,				__lowerCamelCase ,				__lowerCamelCase	)
             return state.replace(timesteps=__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Union[str, Any] ,				__lowerCamelCase							:      ScoreSdeVeSchedulerState ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      float = None ,				__lowerCamelCase							:      float = None ,				__lowerCamelCase							:      float = None ,				):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  sigma_min if sigma_min is not None else self.config.sigma_min
             lowerCamelCase__		:    List[Any]   =  sigma_max if sigma_max is not None else self.config.sigma_max
             lowerCamelCase__		:    Optional[Any]   =  sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if state.timesteps is None:
                   lowerCamelCase__		:    Optional[Any]   =  self.set_timesteps(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  jnp.exp(jnp.linspace(jnp.log(__lowerCamelCase	) ,				jnp.log(__lowerCamelCase	) ,				__lowerCamelCase	)	)
             lowerCamelCase__		:    List[str]   =  jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

             return state.replace(discrete_sigmas=__lowerCamelCase ,				sigmas=__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Optional[int] ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             return jnp.where(timesteps == 0 ,				jnp.zeros_like(__lowerCamelCase	) ,				state.discrete_sigmas[timesteps - 1]	)





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      ScoreSdeVeSchedulerState ,				__lowerCamelCase							:      jnp.ndarray ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      jnp.ndarray ,				__lowerCamelCase							:      random.KeyArray ,				__lowerCamelCase							:      bool = True ,				):


             '''simple docstring'''



             if state.timesteps is None:
                   raise ValueError(
                       "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

             lowerCamelCase__		:    List[str]   =  timestep * jnp.ones(
                 sample.shape[0] ,				)
             lowerCamelCase__		:    str   =  (timestep * (len(state.timesteps	) - 1)).long()

             lowerCamelCase__		:    List[Any]   =  state.discrete_sigmas[timesteps]
             lowerCamelCase__		:    Tuple   =  self.get_adjacent_sigma(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    List[str]   =  jnp.zeros_like(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[int]   =  (sigma**2 - adjacent_sigma**2) ** 0.5

             # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
             # also equation 47 shows the analog from SDE models to ancestral sampling methods
             lowerCamelCase__		:    Union[str, Any]   =  diffusion.flatten()
             lowerCamelCase__		:    Any   =  broadcast_to_shape_from_left(__lowerCamelCase ,				sample.shape	)
             lowerCamelCase__		:    Tuple   =  drift - diffusion**2 * model_output

             #  equation 6: sample noise for the diffusion term of
             lowerCamelCase__		:    Optional[int]   =  random.split(__lowerCamelCase ,				num=1	)
             lowerCamelCase__		:    str   =  random.normal(key=__lowerCamelCase ,				shape=sample.shape	)
             lowerCamelCase__		:    int   =  sample - drift  # subtract because `dt` is a small negative timestep
             # TODO is the variable diffusion the correct scaling term for the noise?
             lowerCamelCase__		:    List[Any]   =  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

             if not return_dict:
                   return (prev_sample, prev_sample_mean, state)

             return FlaxSdeVeOutput(prev_sample=__lowerCamelCase ,				prev_sample_mean=__lowerCamelCase ,				state=__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Any ,				__lowerCamelCase							:      ScoreSdeVeSchedulerState ,				__lowerCamelCase							:      jnp.ndarray ,				__lowerCamelCase							:      jnp.ndarray ,				__lowerCamelCase							:      random.KeyArray ,				__lowerCamelCase							:      bool = True ,				):


             '''simple docstring'''



             if state.timesteps is None:
                   raise ValueError(
                       "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

             # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
             # sample noise for correction
             lowerCamelCase__		:    Union[str, Any]   =  random.split(__lowerCamelCase ,				num=1	)
             lowerCamelCase__		:    Dict   =  random.normal(key=__lowerCamelCase ,				shape=sample.shape	)

             # compute step size from the model_output, the noise, and the snr
             lowerCamelCase__		:    List[Any]   =  jnp.linalg.norm(__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  jnp.linalg.norm(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
             lowerCamelCase__		:    str   =  step_size * jnp.ones(sample.shape[0]	)

             # compute corrected sample: model_output term and noise term
             lowerCamelCase__		:    Optional[int]   =  step_size.flatten()
             lowerCamelCase__		:    Any   =  broadcast_to_shape_from_left(__lowerCamelCase ,				sample.shape	)
             lowerCamelCase__		:    List[Any]   =  sample + step_size * model_output
             lowerCamelCase__		:    Optional[Any]   =  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample, state)

             return FlaxSdeVeOutput(prev_sample=__lowerCamelCase ,				state=__lowerCamelCase	)





       def __len__(       self							:      str	):


             '''simple docstring'''



             return self.config.num_train_timesteps






from sklearn.metrics import roc_auc_score

import datasets


A				:   Optional[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				:   str		= "\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				:   Optional[Any]		= "\\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):


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      List[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 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Optional[int]="macro" ,				__lowerCamelCase							:      Tuple=None ,				__lowerCamelCase							:      Any=None ,				__lowerCamelCase							:      Tuple="raise" ,				__lowerCamelCase							:      Tuple=None ,				):


             '''simple docstring'''



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


