

from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_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'

SCREAMING_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'

SCREAMING_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 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  bool  ,      lowerCAmelCase:  Optional[Dict[int, int]] = None  ,      lowerCAmelCase:  bool = False  ,      )   ->       str:







				if label_map is not None:
								for old_id, new_id in label_map.items():
												_UpperCAmelCase       :		Union[str, Any] 		=    new_id

    # turn into Numpy arrays
				_UpperCAmelCase       :		Optional[int] 		=    np.array(lowerCAmelCase__ )
				_UpperCAmelCase       :		str 		=    np.array(lowerCAmelCase__ )

				if reduce_labels:
								_UpperCAmelCase       :		Optional[int] 		=    255
								_UpperCAmelCase       :		Optional[int] 		=    label - 1
								_UpperCAmelCase       :		Dict 		=    255

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

				_UpperCAmelCase       :		Optional[Any] 		=    pred_label[pred_label == label]

				_UpperCAmelCase       :		Optional[Any] 		=    np.histogram(lowerCAmelCase__  ,      bins=lowerCAmelCase__  ,      range=(0, num_labels - 1) )[0]
				_UpperCAmelCase       :		int 		=    np.histogram(lowerCAmelCase__  ,      bins=lowerCAmelCase__  ,      range=(0, num_labels - 1) )[0]
				_UpperCAmelCase       :		str 		=    np.histogram(lowerCAmelCase__  ,      bins=lowerCAmelCase__  ,      range=(0, num_labels - 1) )[0]

				_UpperCAmelCase       :		str 		=    area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label

def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  bool  ,      lowerCAmelCase:  Optional[Dict[int, int]] = None  ,      lowerCAmelCase:  bool = False  ,      )   ->       List[Any]:
				_UpperCAmelCase       :		Optional[int] 		=    np.zeros((num_labels,)  ,      dtype=np.floataa )
				_UpperCAmelCase       :		List[str] 		=    np.zeros((num_labels,)  ,      dtype=np.floataa )
				_UpperCAmelCase       :		Tuple 		=    np.zeros((num_labels,)  ,      dtype=np.floataa )
				_UpperCAmelCase       :		Optional[int] 		=    np.zeros((num_labels,)  ,      dtype=np.floataa )
				for result, gt_seg_map in zip(lowerCAmelCase__  ,      lowerCAmelCase__ ):
								_UpperCAmelCase       :		Dict 		=    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 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  bool  ,      lowerCAmelCase:  Optional[int] = None  ,      lowerCAmelCase:  Optional[Dict[int, int]] = None  ,      lowerCAmelCase:  bool = False  ,      )   ->       Optional[Any]:
				_UpperCAmelCase       :		int 		=    total_intersect_and_union(
				    lowerCAmelCase__  ,      lowerCAmelCase__  ,      lowerCAmelCase__  ,      lowerCAmelCase__  ,      lowerCAmelCase__  ,      lowerCAmelCase__ )

				# compute metrics
				_UpperCAmelCase       :		List[Any] 		=    {}

				_UpperCAmelCase       :		Any 		=    total_area_intersect.sum() / total_area_label.sum()
				_UpperCAmelCase       :		Tuple 		=    total_area_intersect / total_area_union
				_UpperCAmelCase       :		List[Any] 		=    total_area_intersect / total_area_label

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

				if nan_to_num is not None:
								_UpperCAmelCase       :		Optional[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 				a  (	datasets.Metric				):


						def        _UpperCAmelCase				(       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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_ = None						,					A_ = None						,					A_ = False						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, 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





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE_        =      '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'

class 				a  (	unittest.TestCase							, UpperCAmelCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    load_tool("text-question-answering"	)
										self.tool.setup()
										_UpperCAmelCase       :		str 		=    load_tool("text-question-answering"						,					remote=A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.remote_tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.remote_tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


