
"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase__      :    int    							= '\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'

UpperCAmelCase__      :    List[str]    							= '\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'

UpperCAmelCase__      :    Union[str, Any]    							= '\\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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = False   ,):
							if label_map is not None:
														for old_id, new_id in label_map.items():
																					SCREAMING_SNAKE_CASE__						:       Dict           =   new_id

    # turn into Numpy arrays
							SCREAMING_SNAKE_CASE__						:       Dict           =   np.array(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   np.array(_snake_case						)

							if reduce_labels:
														SCREAMING_SNAKE_CASE__						:       Dict           =   255
														SCREAMING_SNAKE_CASE__						:       Dict           =   label - 1
														SCREAMING_SNAKE_CASE__						:       int           =   255

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

							SCREAMING_SNAKE_CASE__						:       Tuple           =   pred_label[pred_label == label]

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

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = False   ,):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   np.zeros((num_labels,)   ,dtype=np.floataa						)
							for result, gt_seg_map in zip(_snake_case   ,_snake_case						):
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   intersect_and_union(
														    _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_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_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = None   ,_snake_case = None   ,_snake_case = False   ,):
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   total_intersect_and_union(
							    _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						)

							# compute metrics
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {}

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

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

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

							return metrics



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


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   mean_iou(
											    results=SCREAMING_SNAKE_CASE__   ,       gt_seg_maps=SCREAMING_SNAKE_CASE__   ,       num_labels=SCREAMING_SNAKE_CASE__   ,       ignore_index=SCREAMING_SNAKE_CASE__   ,       nan_to_num=SCREAMING_SNAKE_CASE__   ,       label_map=SCREAMING_SNAKE_CASE__   ,       reduce_labels=SCREAMING_SNAKE_CASE__   ,       )
											return iou_result










"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.is_available():
     raise RuntimeError(
         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"ray"
 lowercase__ 					=					"'ray[tune]'"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







A_						=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  available_backends[0].name
    if len(snake_case__    ) > 1:
      logger.info(
          F"{len(snake_case__    )} hyperparameter search backends available. Using {name} as the default."    )
    return name
  raise RuntimeError(
      """No hyperparameter search backend available.\n"""
      + """\n""".join(
          F" - To install {backend.name} run {backend.pip_install()}"
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )


