





"""simple docstring"""




import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCamelCase_	:		int         =   pathlib.Path.cwd().joinpath('project_euler')
lowerCamelCase_	:		str         =   pathlib.Path.cwd().joinpath(
    'scripts', 'project_euler_answers.json'
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
   lowerCamelCase_	:		dict[str, str]         =   json.load(file_handle)





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    importlib.util.spec_from_file_location(file_path.name					,   str(_UpperCAmelCase   )   )
  A_    :			int									=    importlib.util.module_from_spec(_UpperCAmelCase   )  # type: ignore
  spec.loader.exec_module(_UpperCAmelCase   )  # type: ignore
  return module





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Tuple									=    []
  for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
    if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'   ):
      continue
    for file_path in problem_dir_path.iterdir():
      if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')   ):
        continue
      solution_file_paths.append(_UpperCAmelCase   )
  return solution_file_paths





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  with open(os.environ['GITHUB_EVENT_PATH']   ) as file:
    A_    :			str									=    json.load(_UpperCAmelCase   )
  return event["pull_request"]["url"] + "/files"





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Any									=    []
  A_    :			Optional[Any]									=    {
      'Accept': 'application/vnd.github.v3+json',
      'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
  }
  A_    :			Union[str, Any]									=    requests.get(get_files_url()					,   headers=_UpperCAmelCase   ).json()
  for file in files:
    A_    :			Optional[int]									=    pathlib.Path.cwd().joinpath(file['filename']   )
    if (
        filepath.suffix != ".py"
        or filepath.name.startswith(('_', 'test')   )
        or not filepath.name.startswith('sol'   )
    ):
      continue
    solution_file_paths.append(_UpperCAmelCase   )
  return solution_file_paths





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  if os.environ.get('CI'   ) and os.environ.get('GITHUB_EVENT_NAME'   ) == "pull_request":
    # Return only if there are any, otherwise default to all solutions
    if filepaths := added_solution_file_path():
      return filepaths
  return all_solution_file_paths()





@pytest.mark.parametrize(
    'solution_path'					,   collect_solution_file_paths()					,   ids=lambda _UpperCAmelCase   : f"""{path.parent.name}/{path.name}"""					,   )
def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			str									=    solution_path.parent.name[8:].zfill(3   )
  A_    :			str									=    PROBLEM_ANSWERS[problem_number]
  A_    :			List[Any]									=    convert_path_to_module(_UpperCAmelCase   )
  A_    :			Optional[int]									=    str(solution_module.solution()   )  # type: ignore
  A_    :			Any									=    hashlib.shaaaa(answer.encode()   ).hexdigest()
  assert (
      answer == expected
  ), f"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""







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			,       )
							}
