





"""simple docstring"""




from sklearn.metrics import roc_auc_score

import datasets


lowerCamelCase_	:		Union[str, 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'

lowerCamelCase_	:		Optional[int]         =   '\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'

lowerCamelCase_	:		List[str]         =   '\\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 					_UpperCAmelCase    (  datasets.Metric						):



      '''simple docstring'''

      def    lowerCamelCase_     (	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    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_="macro"   , snake_case_=None   , snake_case_=None   , snake_case_="raise"   , snake_case_=None   , ):

        """simple docstring"""

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




import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case=None     ):





					"""simple docstring"""

					_lowerCAmelCase         =       None
					# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
					if str(weight.dtype     ) == "torch.bfloat16":
										# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
										_lowerCAmelCase         =       weight.view(torch.intaa     )
										_lowerCAmelCase         =       """bfloat16"""
					_lowerCAmelCase         =       weight.cpu().numpy()
					_lowerCAmelCase         =       os.path.join(snake_case      ,		F'{weight_name}.dat'     )
					if index is not None:
										if dtype is None:
															_lowerCAmelCase         =       str(array.dtype     )
										_lowerCAmelCase         =       {"""dtype""": dtype, """shape""": list(array.shape     )}
					if array.ndim == 0:
										_lowerCAmelCase         =       array[None]
					_lowerCAmelCase         =       np.memmap(snake_case      ,		dtype=array.dtype      ,		mode="""w+"""      ,		shape=array.shape     )
					_lowerCAmelCase         =       array[:]
					file_array.flush()
					return index


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       tuple(weight_info["""shape"""]     )
					if shape == ():
										# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
										_lowerCAmelCase         =       (1,)

					_lowerCAmelCase         =       weight_info["""dtype"""]
					if dtype == "bfloat16":
										# NumPy does not support bfloat16 so this was saved as a int16
										_lowerCAmelCase         =       """int16"""

					_lowerCAmelCase         =       np.memmap(snake_case      ,		dtype=snake_case      ,		shape=snake_case      ,		mode="""r"""     )

					if len(weight_info["""shape"""]     ) == 0:
										_lowerCAmelCase         =       weight[0]
					_lowerCAmelCase         =       torch.tensor(snake_case     )
					if weight_info["dtype"] == "bfloat16":
										_lowerCAmelCase         =       weight.view(torch.bfloataa     )

					return weight


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					if index is None or len(snake_case     ) == 0:
										# Nothing to save
										return

					_lowerCAmelCase         =       os.path.join(snake_case      ,		"""index.json"""     )
					if os.path.isfile(snake_case     ):
										with open(snake_case      ,		"""r"""      ,		encoding="""utf-8"""     ) as f:
															_lowerCAmelCase         =       json.load(snake_case     )
					else:
										_lowerCAmelCase         =       {}
					current_index.update(snake_case     )

					with open(snake_case      ,		"""w"""      ,		encoding="""utf-8"""     ) as f:
										json.dump(snake_case      ,		snake_case      ,		indent=2     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					os.makedirs(snake_case      ,		exist_ok=snake_case     )
					_lowerCAmelCase         =       {}
					for name, parameter in state_dict.items():
										_lowerCAmelCase         =       offload_weight(snake_case      ,		snake_case      ,		snake_case      ,		index=snake_case     )

					# Update index
					save_offload_index(snake_case      ,		snake_case     )





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       dataset
									_lowerCAmelCase         =       prefix




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									return self.dataset[F'{self.prefix}{key}']




				def __iter__(				self						):



									"""simple docstring"""





									return iter([key for key in self.dataset if key.startswith(self.prefix						)]						)




				def __len__(				self						):



									"""simple docstring"""





									return len(self.dataset						)





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case=None		,							):



									"""simple docstring"""





									if state_dict is None and save_folder is None:
														raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights."""						)

									_lowerCAmelCase         =       {} if state_dict is None else state_dict
									_lowerCAmelCase         =       save_folder
									if index is None and save_folder is not None:
														with open(os.path.join(_snake_case		,							"""index.json"""						)						) as f:
																			_lowerCAmelCase         =       json.load(_snake_case						)
									_lowerCAmelCase         =       {} if index is None else index
									_lowerCAmelCase         =       list(self.state_dict.keys()						)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]						)
									_lowerCAmelCase         =       device




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									if key in self.state_dict:
														return self.state_dict[key]
									_lowerCAmelCase         =       self.index[key]
									if weight_info.get("""safetensors_file"""						) is not None:
														if not is_safetensors_available():
																			raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`."""						)

														from safetensors import safe_open

														_lowerCAmelCase         =       """cpu""" if self.device is None else self.device
														with safe_open(weight_info["""safetensors_file"""]		,							framework="""pt"""		,							device=_snake_case						) as f:
																			_lowerCAmelCase         =       f.get_tensor(weight_info.get("""weight_name"""		,							_snake_case						)						)

														if "dtype" in weight_info:
																			return tensor.to(getattr(_snake_case		,							weight_info["""dtype"""]						)						)
														else:
																			return tensor

									_lowerCAmelCase         =       os.path.join(self.save_folder		,							F'{key}.dat'						)
									return load_offloaded_weight(_snake_case		,							_snake_case						)




				def __iter__(				self						):



									"""simple docstring"""





									return iter(self.all_keys						)




				def __len__(				self						):



									"""simple docstring"""





									return len(self.all_keys						)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       {}
					for module_name in submodule_names:
										# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
										# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
										result.update(
										    {
										        key: param
										        for key, param in state_dict.items()
										        if key == module_name or key.startswith(module_name + """."""     )
										    }     )
					return result



