

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
					import torch


lowerCAmelCase_	     =     logging.get_logger(__name__)





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Optional[int]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    Optional[int]=2_1							,       _UpperCamelCase			:    str=[]							,       _UpperCamelCase			:    List[str]=None							)				->Optional[int]:
									snake_case_       =     model
									snake_case_       =     trace_batch_nums
									snake_case_       =     abort_after_batch_num

									# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
									snake_case_       =     collections.deque([]							,       _UpperCamelCase							)
									snake_case_       =     []
									snake_case_       =     0
									snake_case_       =     0
									snake_case_       =     False
									snake_case_       =     '''                 '''

									self.analyse_model()

									self.register_forward_hook()







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    Dict=None							)				->Optional[Any]:
									if frame is not None:
															self.expand_frame(_UpperCamelCase							)
									self.frames.append('''\n'''.join(self.frame							)							)
									snake_case_       =     []  # start a new frame







			def     snake_case__(					self			:    str							,       _UpperCamelCase			:    Dict							)				->List[Any]:
									self.frame.append(_UpperCamelCase							)







			def     snake_case__(					self			:    Any							)				->Tuple:
									print('''\n'''.join(self.frames							)							)
									snake_case_       =     []







			def     snake_case__(					self			:    Dict							)				->Optional[Any]:
									snake_case_       =     []







			def     snake_case__(					self			:    str							)				->Any:
									print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''							)
									print(f'''Last {len(self.frames							)} forward frames:'''							)
									print(f'''{'abs min':8} {'abs max':8} metadata'''							)
									print('''\n'''.join(self.frames							)							)
									print('''\n\n'''							)
									snake_case_       =     []







			def     snake_case__(					self			:    Tuple							)				->Optional[Any]:
									# extract the fully qualified module names, to be able to report at run time. e.g.:
									# encoder.block.2.layer.0.SelfAttention.o
									#
									# for shared weights only the first shared module name will be registered
									snake_case_       =     {m: name for name, m in self.model.named_modules()}
									# self.longest_module_name = max(len(v) for v in self.module_names.values())







			def     snake_case__(					self			:    Any							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    List[str]							)				->Optional[int]:
									if torch.is_tensor(_UpperCamelCase							):
															self.expand_frame(get_abs_min_max(_UpperCamelCase							,       _UpperCamelCase							)							)
															if detect_overflow(_UpperCamelCase							,       _UpperCamelCase							):
																					snake_case_       =     True
									elif var is None:
															self.expand_frame(f'''{'None':>17} {ctx}'''							)
									else:
															self.expand_frame(f'''{'not a tensor':>17} {ctx}'''							)







			def     snake_case__(					self			:    List[Any]							)				->Optional[int]:
									self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''							)
									self.expand_frame(f'''{'abs min':8} {'abs max':8} metadata'''							)







			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''							)







			def     snake_case__(					self			:    int							,       _UpperCamelCase			:    List[Any]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    Any							)				->Any:
									self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''							)

									# params
									for name, p in module.named_parameters(recurse=_UpperCamelCase							):
															self.analyse_variable(_UpperCamelCase							,       _UpperCamelCase							)

									# inputs
									if isinstance(_UpperCamelCase							,       _UpperCamelCase							):
															for i, x in enumerate(_UpperCamelCase							):
																					self.analyse_variable(_UpperCamelCase							,       f'''input[{i}]'''							)
									else:
															self.analyse_variable(_UpperCamelCase							,       '''input'''							)

									# outputs
									if isinstance(_UpperCamelCase							,       _UpperCamelCase							):
															for i, x in enumerate(_UpperCamelCase							):
																					# possibly a tuple of tuples
																					if isinstance(_UpperCamelCase							,       _UpperCamelCase							):
																											for j, y in enumerate(_UpperCamelCase							):
																																	self.analyse_variable(_UpperCamelCase							,       f'''output[{i}][{j}]'''							)
																					else:
																											self.analyse_variable(_UpperCamelCase							,       f'''output[{i}]'''							)
									else:
															self.analyse_variable(_UpperCamelCase							,       '''output'''							)

									self.save_frame()







			def     snake_case__(					self			:    Optional[Any]							)				->Tuple:
									self.model.apply(self._register_forward_hook							)







			def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    str							)				->Tuple:
									module.register_forward_hook(self.forward_hook							)







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    Any							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    List[str]							)				->Union[str, Any]:
									# - input is a tuple of packed inputs (could be non-Tensors)
									# - output could be a Tensor or a tuple of Tensors and non-Tensors

									snake_case_       =     False

									snake_case_       =     True if self.batch_number in self.trace_batch_nums else False
									if trace_mode:
															self.reset_saved_frames()

									if self.total_calls == 0:
															self.batch_start_frame()
									self.total_calls += 1

									# count batch numbers - the very first forward hook of the batch will be called when the
									# batch completes - i.e. it gets called very last - we know this batch has finished
									if module == self.model:
															self.batch_number += 1
															snake_case_       =     True

									self.create_frame(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							)

									# if last_frame_of_batch:
									#     self.batch_end_frame()

									if trace_mode:
															self.trace_frames()

									if last_frame_of_batch:
															self.batch_start_frame()

									if self.detected_overflow and not trace_mode:
															self.dump_saved_frames()

															# now we can abort, as it's pointless to continue running
															raise ValueError(
															    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
															    '''Please scroll up above this traceback to see the activation values prior to this event.'''							)

									# abort after certain batch if requested to do so
									if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
															raise ValueError(
															    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
															    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     var.abs()
						return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     False
						if torch.isnan(SCREAMING_SNAKE_CASE__	).any().item():
												snake_case_       =     True
												print(F'''{ctx} has nans'''	)
						if torch.isinf(SCREAMING_SNAKE_CASE__	).any().item():
												snake_case_       =     True
												print(F'''{ctx} has infs'''	)

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												snake_case_       =     var[torch.ge(var.abs()  ,		100	)]
												if naaa.numel() > 0:
																		print(F'''{ctx}:  n100={naaa.numel()}'''	)
												snake_case_       =     var[torch.ge(var.abs()  ,		1000	)]
												if naaaa.numel() > 0:
																		print(F'''{ctx}: n1000={naaaa.numel()}'''	)
												snake_case_       =     var[torch.ge(var.abs()  ,		10000	)]
												if naaaaa.numel() > 0:
																		print(F'''{ctx}: n10000={naaaaa.numel()}'''	)

						if 0:
												print(F'''min={var.min():9.2e} max={var.max():9.2e}'''	)

						if 0:
												print(F'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''	)

						return detected





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Any						      =    "underflow_overflow"
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    "tpu_metrics_debug"


from sklearn.metrics import roc_auc_score

import datasets


lowerCAmelCase_	     =     '''
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.
'''

lowerCAmelCase_	     =     '''
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]
'''

lowerCAmelCase_	     =     '''\
@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      snake_case_       (		datasets.Metric ):







			'''simple docstring'''

			def     snake_case__(					self			:    List[str]							)				->Tuple:
									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     snake_case__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    Optional[int]							,       _UpperCamelCase			:    int="macro"							,       _UpperCamelCase			:    Any=None							,       _UpperCamelCase			:    str=None							,       _UpperCamelCase			:    List[str]="raise"							,       _UpperCamelCase			:    Union[str, Any]=None							,       )				->int:
									return {
									    "roc_auc": roc_auc_score(
									        _UpperCamelCase							,       _UpperCamelCase							,       average=_UpperCamelCase							,       sample_weight=_UpperCamelCase							,       max_fpr=_UpperCamelCase							,       multi_class=_UpperCamelCase							,       labels=_UpperCamelCase							,       )
									}