





from typing import Dict, Optional

import numpy as np

import datasets


_A					:				Optional[int]				=			"""
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.
"""

_A					:				Dict				=			"""
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.        ])}
"""

_A					:				Optional[int]				=			"""\
@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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = False ,							)				->						int:
    if label_map is not None:
        for old_id, new_id in label_map.items():
            SCREAMING_SNAKE_CASE__											=			new_id

    # turn into Numpy arrays
    SCREAMING_SNAKE_CASE__											=			np.array(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			np.array(lowerCAmelCase_	)

    if reduce_labels:
        SCREAMING_SNAKE_CASE__											=			2_5_5
        SCREAMING_SNAKE_CASE__											=			label - 1
        SCREAMING_SNAKE_CASE__											=			2_5_5

    SCREAMING_SNAKE_CASE__											=			label != ignore_index
    SCREAMING_SNAKE_CASE__											=			np.not_equal(lowerCAmelCase_ ,							lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			pred_label[mask]
    SCREAMING_SNAKE_CASE__											=			np.array(lowerCAmelCase_	)[mask]

    SCREAMING_SNAKE_CASE__											=			pred_label[pred_label == label]

    SCREAMING_SNAKE_CASE__											=			np.histogram(lowerCAmelCase_ ,							bins=lowerCAmelCase_ ,							range=(0, num_labels - 1)	)[0]
    SCREAMING_SNAKE_CASE__											=			np.histogram(lowerCAmelCase_ ,							bins=lowerCAmelCase_ ,							range=(0, num_labels - 1)	)[0]
    SCREAMING_SNAKE_CASE__											=			np.histogram(lowerCAmelCase_ ,							bins=lowerCAmelCase_ ,							range=(0, num_labels - 1)	)[0]

    SCREAMING_SNAKE_CASE__											=			area_pred_label + area_label - area_intersect

    return area_intersect, area_union, area_pred_label, area_label


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = False ,							)				->						Any:
    SCREAMING_SNAKE_CASE__											=			np.zeros((num_labels,) ,							dtype=np.floataa	)
    SCREAMING_SNAKE_CASE__											=			np.zeros((num_labels,) ,							dtype=np.floataa	)
    SCREAMING_SNAKE_CASE__											=			np.zeros((num_labels,) ,							dtype=np.floataa	)
    SCREAMING_SNAKE_CASE__											=			np.zeros((num_labels,) ,							dtype=np.floataa	)
    for result, gt_seg_map in zip(lowerCAmelCase_ ,							lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = None ,							lowerCAmelCase_ = False ,							)				->						List[Any]:
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			total_intersect_and_union(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

    # compute metrics
    SCREAMING_SNAKE_CASE__											=			{}

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

    SCREAMING_SNAKE_CASE__											=			np.nanmean(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			np.nanmean(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			all_acc
    SCREAMING_SNAKE_CASE__											=			iou
    SCREAMING_SNAKE_CASE__											=			acc

    if nan_to_num is not None:
        SCREAMING_SNAKE_CASE__											=			{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     ):





        '''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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_ = None   ,				A_ = None   ,				A_ = False   ,				):





        '''simple docstring'''

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







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
