



"""simple docstring"""







from typing import Dict, Optional

import numpy as np

import datasets


A    =			'\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'

A    =			'\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'

A    =			'\\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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = False				,      ) ->							List[str]:
							if label_map is not None:
														for old_id, new_id in label_map.items():
																					A         =					new_id

    # turn into Numpy arrays
							A         =					np.array(lowerCamelCase__			)
							A         =					np.array(lowerCamelCase__			)

							if reduce_labels:
														A         =					255
														A         =					label - 1
														A         =					255

							A         =					label != ignore_index
							A         =					np.not_equal(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					pred_label[mask]
							A         =					np.array(lowerCamelCase__			)[mask]

							A         =					pred_label[pred_label == label]

							A         =					np.histogram(lowerCamelCase__				,      bins=lowerCamelCase__				,      range=(0, num_labels - 1)			)[0]
							A         =					np.histogram(lowerCamelCase__				,      bins=lowerCamelCase__				,      range=(0, num_labels - 1)			)[0]
							A         =					np.histogram(lowerCamelCase__				,      bins=lowerCamelCase__				,      range=(0, num_labels - 1)			)[0]

							A         =					area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = False				,      ) ->							Optional[int]:
							A         =					np.zeros((num_labels,)				,      dtype=np.floataa			)
							A         =					np.zeros((num_labels,)				,      dtype=np.floataa			)
							A         =					np.zeros((num_labels,)				,      dtype=np.floataa			)
							A         =					np.zeros((num_labels,)				,      dtype=np.floataa			)
							for result, gt_seg_map in zip(lowerCamelCase__				,      lowerCamelCase__			):
														A      ,  A      ,  A      ,  A         =					intersect_and_union(
														    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
														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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = False				,      ) ->							Optional[Any]:
							A      ,  A      ,  A      ,  A         =					total_intersect_and_union(
							    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)

							# compute metrics
							A         =					{}

							A         =					total_area_intersect.sum() / total_area_label.sum()
							A         =					total_area_intersect / total_area_union
							A         =					total_area_intersect / total_area_label

							A         =					np.nanmean(lowerCamelCase__			)
							A         =					np.nanmean(lowerCamelCase__			)
							A         =					all_acc
							A         =					iou
							A         =					acc

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

							return metrics






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



		def 					A_		( self  :    Tuple				)			->						Tuple:






									'''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 					A_		( self  :    str      ,							snake_case  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    int      ,							snake_case  :    bool      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    Optional[Dict[int, int]] = None      ,							snake_case  :    bool = False      ,							)			->						Any:






									'''simple docstring'''

									A         =					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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



