

from sklearn.metrics import recall_score

import datasets


lowercase_			=  "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


lowercase_			=  "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


lowercase_			=  "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"


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







    """simple docstring"""





    def 						snake_case__					(				self			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': datasets.Sequence(datasets.Value('int32'		)		),
                    'references': datasets.Sequence(datasets.Value('int32'		)		),
                }
                if self.config_name == 'multilabel'
                else {
                    'predictions': datasets.Value('int32'		),
                    'references': datasets.Value('int32'		),
                }		),reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'],)


    def 						snake_case__					(				self			:	Any,lowercase_			:	str,lowercase_			:	Optional[int],lowercase_			:	Optional[int]=None,lowercase_			:	int=1,lowercase_			:	Optional[int]="binary",lowercase_			:	List[Any]=None,lowercase_			:	str="warn",)->   Optional[int]:





        '''simple docstring'''







        A__   				=       recall_score(
            lowercase_,lowercase_,labels=lowercase_,pos_label=lowercase_,average=lowercase_,sample_weight=lowercase_,zero_division=lowercase_,)
        return {"recall": float(lowercase_		) if score.size == 1 else score}




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

