

'''simple docstring'''
from typing import Dict, Optional

import numpy as np

import datasets


_snake_case         =  '\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'

_snake_case         =  '\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'

_snake_case         =  '\\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		(     snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case = None		,							snake_case = False		,							)      ->     Optional[int]:
   if label_map is not None:
      for old_id, new_id in label_map.items():
         _lowercase      :				Any      					=       new_id

    # turn into Numpy arrays
   _lowercase      :				List[str]      					=       np.array(snake_case					)
   _lowercase      :				Any      					=       np.array(snake_case					)

   if reduce_labels:
      _lowercase      :				Any      					=       2_55
      _lowercase      :				List[Any]      					=       label - 1
      _lowercase      :				Union[str, Any]      					=       2_55

   _lowercase      :				List[Any]      					=       label != ignore_index
   _lowercase      :				Tuple      					=       np.not_equal(snake_case		,							snake_case					)
   _lowercase      :				List[str]      					=       pred_label[mask]
   _lowercase      :				Any      					=       np.array(snake_case					)[mask]

   _lowercase      :				Optional[int]      					=       pred_label[pred_label == label]

   _lowercase      :				Union[str, Any]      					=       np.histogram(snake_case		,							bins=snake_case		,							range=(0, num_labels - 1)					)[0]
   _lowercase      :				Dict      					=       np.histogram(snake_case		,							bins=snake_case		,							range=(0, num_labels - 1)					)[0]
   _lowercase      :				Dict      					=       np.histogram(snake_case		,							bins=snake_case		,							range=(0, num_labels - 1)					)[0]

   _lowercase      :				List[Any]      					=       area_pred_label + area_label - area_intersect

   return area_intersect, area_union, area_pred_label, area_label







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case = None		,							snake_case = False		,							)      ->     str:
   _lowercase      :				str      					=       np.zeros((num_labels,)		,							dtype=np.floataa					)
   _lowercase      :				List[str]      					=       np.zeros((num_labels,)		,							dtype=np.floataa					)
   _lowercase      :				List[str]      					=       np.zeros((num_labels,)		,							dtype=np.floataa					)
   _lowercase      :				List[str]      					=       np.zeros((num_labels,)		,							dtype=np.floataa					)
   for result, gt_seg_map in zip(snake_case		,							snake_case					):
      _lowercase					,				_lowercase					,				_lowercase					,				_lowercase      :				Tuple      					=       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  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case = None		,							snake_case = None		,							snake_case = False		,							)      ->     List[Any]:
   _lowercase					,				_lowercase					,				_lowercase					,				_lowercase      :				Optional[int]      					=       total_intersect_and_union(
       snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case					)

   # compute metrics
   _lowercase      :				Optional[int]      					=       {}

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

   _lowercase      :				Dict      					=       np.nanmean(snake_case					)
   _lowercase      :				Any      					=       np.nanmean(snake_case					)
   _lowercase      :				List[Any]      					=       all_acc
   _lowercase      :				str      					=       iou
   _lowercase      :				List[str]      					=       acc

   if nan_to_num is not None:
      _lowercase      :				Optional[int]      					=       {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        a__  (							datasets.Metric  ):


   def 						_lowerCamelCase       (			self  ):





      """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      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       mean_iou(
          results=_UpperCamelCase      ,    gt_seg_maps=_UpperCamelCase      ,    num_labels=_UpperCamelCase      ,    ignore_index=_UpperCamelCase      ,    nan_to_num=_UpperCamelCase      ,    label_map=_UpperCamelCase      ,    reduce_labels=_UpperCamelCase      ,    )
      return iou_result









'''simple docstring'''
from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






