


from typing import Dict, Optional

import numpy as np

import datasets


_UpperCAmelCase			:  Dict     ="""
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.
"""

_UpperCAmelCase			:  List[str]     ="""
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.        ])}
"""

_UpperCAmelCase			:  List[str]     ="""\
@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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = None ,   lowerCAmelCase_ = False ,   )-> Any:
				if label_map is not None:
								for old_id, new_id in label_map.items():
												lowerCAmelCase_ :      List[Any]    				=				new_id

    # turn into Numpy arrays
				lowerCAmelCase_ :      Dict    				=				np.array(lowerCAmelCase_ )
				lowerCAmelCase_ :      Tuple    				=				np.array(lowerCAmelCase_ )

				if reduce_labels:
								lowerCAmelCase_ :      Optional[Any]    				=				255
								lowerCAmelCase_ :      Dict    				=				label - 1
								lowerCAmelCase_ :      Any    				=				255

				lowerCAmelCase_ :      Union[str, Any]    				=				label != ignore_index
				lowerCAmelCase_ :      Tuple    				=				np.not_equal(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Tuple    				=				pred_label[mask]
				lowerCAmelCase_ :      Optional[int]    				=				np.array(lowerCAmelCase_ )[mask]

				lowerCAmelCase_ :      Dict    				=				pred_label[pred_label == label]

				lowerCAmelCase_ :      Optional[int]    				=				np.histogram(lowerCAmelCase_ ,   bins=lowerCAmelCase_ ,   range=(0, num_labels - 1) )[0]
				lowerCAmelCase_ :      List[str]    				=				np.histogram(lowerCAmelCase_ ,   bins=lowerCAmelCase_ ,   range=(0, num_labels - 1) )[0]
				lowerCAmelCase_ :      int    				=				np.histogram(lowerCAmelCase_ ,   bins=lowerCAmelCase_ ,   range=(0, num_labels - 1) )[0]

				lowerCAmelCase_ :      Optional[Any]    				=				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 ,   )-> Tuple:
				lowerCAmelCase_ :      int    				=				np.zeros((num_labels,) ,   dtype=np.floataa )
				lowerCAmelCase_ :      str    				=				np.zeros((num_labels,) ,   dtype=np.floataa )
				lowerCAmelCase_ :      Optional[int]    				=				np.zeros((num_labels,) ,   dtype=np.floataa )
				lowerCAmelCase_ :      Dict    				=				np.zeros((num_labels,) ,   dtype=np.floataa )
				for result, gt_seg_map in zip(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[int]    				=				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[int]:
				lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				total_intersect_and_union(
				    lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

				# compute metrics
				lowerCAmelCase_ :      Dict    				=				{}

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

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

				if nan_to_num is not None:
								lowerCAmelCase_ :      int    				=				{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 				snake_case__(							datasets.Metric							):







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    str:
										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   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = False   ,						)					->    Union[str, Any]:
										lowerCAmelCase_ :      List[str]    				=				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 inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_UpperCAmelCase			:  Any     ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_UpperCAmelCase			:  Optional[Any]     =direct_transformers_import(PATH_TO_TRANSFORMERS)

_UpperCAmelCase			:  List[str]     =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_UpperCAmelCase			:  Dict     =re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_UpperCAmelCase			:  Any     ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      Any    				=				None

				# source code of `config_class`
				lowerCAmelCase_ :      Optional[int]    				=				inspect.getsource(lowerCAmelCase_ )
				lowerCAmelCase_ :      str    				=				_re_checkpoint.findall(lowerCAmelCase_ )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith('''/''' ):
												lowerCAmelCase_ :      Optional[Any]    				=				ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								lowerCAmelCase_ :      Tuple    				=				f"""https://huggingface.co/{ckpt_name}"""
								if ckpt_link == ckpt_link_from_name:
												lowerCAmelCase_ :      List[str]    				=				ckpt_name
												break

				return checkpoint



def 			lowerCAmelCase (		)-> Optional[Any]:
				lowerCAmelCase_ :      Tuple    				=				[]

				for config_class in list(CONFIG_MAPPING.values() ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								lowerCAmelCase_ :      int    				=				get_checkpoint_from_config_class(lowerCAmelCase_ )

								lowerCAmelCase_ :      Tuple    				=				config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase_ )

				if len(lowerCAmelCase_ ) > 0:
								lowerCAmelCase_ :      List[Any]    				=				'''\n'''.join(sorted(lowerCAmelCase_ ) )
								raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}""" )


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()