


from typing import Dict, Optional

import numpy as np

import datasets


lowerCamelCase											=       '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

lowerCamelCase											=       '\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'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.        ])}\n'

lowerCamelCase											=       '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      bool		,			SCREAMING_SNAKE_CASE__					:      Optional[Dict[int, int]] = None		,			SCREAMING_SNAKE_CASE__					:      bool = False		,			):
       '''simple docstring'''





       if label_map is not None:
              for old_id, new_id in label_map.items():
                     _lowerCamelCase       :		List[str]		=new_id

    # turn into Numpy arrays
       _lowerCamelCase       :		Dict		=np.array(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		int		=np.array(SCREAMING_SNAKE_CASE__     )

       if reduce_labels:
              _lowerCamelCase       :		str		=255
              _lowerCamelCase       :		List[str]		=label - 1
              _lowerCamelCase       :		str		=255

       _lowerCamelCase       :		List[Any]		=label != ignore_index
       _lowerCamelCase       :		int		=np.not_equal(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=pred_label[mask]
       _lowerCamelCase       :		Any		=np.array(SCREAMING_SNAKE_CASE__     )[mask]

       _lowerCamelCase       :		str		=pred_label[pred_label == label]

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

       _lowerCamelCase       :		Union[str, Any]		=area_pred_label + area_label - area_intersect

       return area_intersect, area_union, area_pred_label, area_label




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      bool		,			SCREAMING_SNAKE_CASE__					:      Optional[Dict[int, int]] = None		,			SCREAMING_SNAKE_CASE__					:      bool = False		,			):
       '''simple docstring'''





       _lowerCamelCase       :		int		=np.zeros((num_labels,)		,			dtype=np.floataa     )
       _lowerCamelCase       :		List[str]		=np.zeros((num_labels,)		,			dtype=np.floataa     )
       _lowerCamelCase       :		Union[str, Any]		=np.zeros((num_labels,)		,			dtype=np.floataa     )
       _lowerCamelCase       :		Optional[int]		=np.zeros((num_labels,)		,			dtype=np.floataa     )
       for result, gt_seg_map in zip(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     ):
              _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=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 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      Any		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      bool		,			SCREAMING_SNAKE_CASE__					:      Optional[int] = None		,			SCREAMING_SNAKE_CASE__					:      Optional[Dict[int, int]] = None		,			SCREAMING_SNAKE_CASE__					:      bool = False		,			):
       '''simple docstring'''





       _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		str		=total_intersect_and_union(
           SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       # compute metrics
       _lowerCamelCase       :		Dict		={}

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

       _lowerCamelCase       :		str		=np.nanmean(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[str]		=np.nanmean(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=all_acc
       _lowerCamelCase       :		int		=iou
       _lowerCamelCase       :		List[str]		=acc

       if nan_to_num is not None:
              _lowerCamelCase       :		List[Any]		={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 A  (   datasets.Metric     ):






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						List[str]:
             """simple docstring"""




             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        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Tuple			,   lowercase_						:     Dict			,   lowercase_						:     int			,   lowercase_						:     bool			,   lowercase_						:     Optional[int] = None			,   lowercase_						:     Optional[Dict[int, int]] = None			,   lowercase_						:     bool = False			,   )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=mean_iou(
                 results=lowercase_			,   gt_seg_maps=lowercase_			,   num_labels=lowercase_			,   ignore_index=lowercase_			,   nan_to_num=lowercase_			,   label_map=lowercase_			,   reduce_labels=lowercase_			,   )
             return iou_result






import os

import pytest
from attr import dataclass


lowerCamelCase											=       'us-east-1'  # defaults region




@dataclass
class A  :
      UpperCamelCase__							:  str
      UpperCamelCase__							:  Dict      ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
      UpperCamelCase__							:  int      ={
          'task_name': 'mnli',
          'per_device_train_batch_size': 16,
          'per_device_eval_batch_size': 16,
          'do_train': True,
          'do_eval': True,
          'do_predict': True,
          'output_dir': '/opt/ml/model',
          'overwrite_output_dir': True,
          'max_steps': 500,
          'save_steps': 5500,
      }
      UpperCamelCase__							:  Optional[Any]      ={**hyperparameters, 'max_steps': 1000}






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                        {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                    ]
             else:
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                        {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                    ]






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             return F'''{self.framework}-transfromers-test'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return F'''./tests/sagemaker/scripts/{self.framework}'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
             else:
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope='class'     )
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[Any]		=SageMakerTestEnvironment(framework=request.cls.framework     )


