



from typing import Dict, Optional

import numpy as np

import datasets


a_   :List[str]        =      """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

a_   :Tuple        =      """
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
"""

a_   :List[Any]        =      """\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""
def        lowercase_							(A : Any		,					A : List[str]		,					A : Union[str, Any]		,					A : Union[str, Any]		,					A : Dict = None		,					A : Dict = False		,					):
       if label_map is not None:
              for old_id, new_id in label_map.items():
                     snake_case__							:	Union[str, Any]   	=	new_id

    # turn into Numpy arrays
       snake_case__							:	Optional[int]   	=	np.array(SCREAMING_SNAKE_CASE__      )
       snake_case__							:	Optional[int]   	=	np.array(SCREAMING_SNAKE_CASE__      )

       if reduce_labels:
              snake_case__							:	Optional[Any]   	=	2_5_5
              snake_case__							:	int   	=	label - 1
              snake_case__							:	int   	=	2_5_5

       snake_case__							:	Tuple   	=	label != ignore_index
       snake_case__							:	Optional[int]   	=	np.not_equal(SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__      )
       snake_case__							:	int   	=	pred_label[mask]
       snake_case__							:	Dict   	=	np.array(SCREAMING_SNAKE_CASE__      )[mask]

       snake_case__							:	Union[str, Any]   	=	pred_label[pred_label == label]

       snake_case__							:	List[Any]   	=	np.histogram(SCREAMING_SNAKE_CASE__		,					bins=SCREAMING_SNAKE_CASE__		,					range=(0, num_labels - 1)      )[0]
       snake_case__							:	Any   	=	np.histogram(SCREAMING_SNAKE_CASE__		,					bins=SCREAMING_SNAKE_CASE__		,					range=(0, num_labels - 1)      )[0]
       snake_case__							:	Tuple   	=	np.histogram(SCREAMING_SNAKE_CASE__		,					bins=SCREAMING_SNAKE_CASE__		,					range=(0, num_labels - 1)      )[0]

       snake_case__							:	str   	=	area_pred_label + area_label - area_intersect

       return area_intersect, area_union, area_pred_label, area_label
def        lowercase_							(A : Optional[Any]		,					A : Union[str, Any]		,					A : int		,					A : Union[str, Any]		,					A : Optional[int] = None		,					A : Tuple = False		,					):
       snake_case__							:	Dict   	=	np.zeros((num_labels,)		,					dtype=np.floataa      )
       snake_case__							:	Dict   	=	np.zeros((num_labels,)		,					dtype=np.floataa      )
       snake_case__							:	Optional[Any]   	=	np.zeros((num_labels,)		,					dtype=np.floataa      )
       snake_case__							:	Optional[int]   	=	np.zeros((num_labels,)		,					dtype=np.floataa      )
       for result, gt_seg_map in zip(SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__      ):
              snake_case__							:	List[str]   	=	intersect_and_union(
                  SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__      )
              total_area_intersect += area_intersect
              total_area_union += area_union
              total_area_pred_label += area_pred_label
              total_area_label += area_label
       return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def        lowercase_							(A : Tuple		,					A : Optional[int]		,					A : str		,					A : Dict		,					A : List[str] = None		,					A : Dict = None		,					A : str = False		,					):
       snake_case__							:	Tuple   	=	total_intersect_and_union(
           SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__      )

       # compute metrics
       snake_case__							:	str   	=	{}

       snake_case__							:	List[Any]   	=	total_area_intersect.sum() / total_area_label.sum()
       snake_case__							:	List[Any]   	=	total_area_intersect / total_area_union
       snake_case__							:	Union[str, Any]   	=	total_area_intersect / total_area_label

       snake_case__							:	Union[str, Any]   	=	np.nanmean(SCREAMING_SNAKE_CASE__      )
       snake_case__							:	List[str]   	=	np.nanmean(SCREAMING_SNAKE_CASE__      )
       snake_case__							:	Tuple   	=	all_acc
       snake_case__							:	str   	=	iou
       snake_case__							:	int   	=	acc

       if nan_to_num is not None:
              snake_case__							:	Tuple   	=	{metric: np.nan_to_num(SCREAMING_SNAKE_CASE__		,					nan=SCREAMING_SNAKE_CASE__      ) for metric, metric_value in metrics.items()}

       return metrics





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






     """simple docstring"""


     def    lowercase_   (       self  :  List[str]					)   ->int:
            return datasets.MetricInfo(
                description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
                    # 1st Seq - height dim, 2nd - width dim
                    {
                        'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'					)					)					),
                        'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'					)					)					),
                    }					), reference_urls=[
                    'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
                ], )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Any, _snake_case  :  Dict, _snake_case  :  Any, _snake_case  :  List[str] = None, _snake_case  :  List[Any] = None, _snake_case  :  str = False, )   ->Optional[Any]:
            snake_case__							:	Union[str, Any]   	=	mean_iou(
                results=_snake_case, gt_seg_maps=_snake_case, num_labels=_snake_case, ignore_index=_snake_case, nan_to_num=_snake_case, label_map=_snake_case, reduce_labels=_snake_case, )
            return iou_result








from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase  :					str						    =							"""\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

lowercase  :					Dict						    =							"""\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

lowercase  :					int						    =							"""
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      float(spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.config_name not in [
               "sst2",
               "mnli",
               "mnli_mismatched",
               "mnli_matched",
               "cola",
               "stsb",
               "mrpc",
               "qqp",
               "qnli",
               "rte",
               "wnli",
               "hans",
           ]:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""      )
           return datasets.MetricInfo(
               description=_DESCRIPTION		,citation=_CITATION		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
                   {
                       """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""      ),
                       """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""      ),
                   }      )		,codebase_urls=[]		,reference_urls=[]		,format="""numpy"""		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(snake_case		,snake_case      )}
           elif self.config_name == "stsb":
                  return pearson_and_spearman(snake_case		,snake_case      )
           elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(snake_case		,snake_case      )
           elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(snake_case		,snake_case      )}
           else:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""      )
