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How to use _get_labels method in autotest

Best Python code snippet using autotest_python

__init__.py

Source:__init__.py

...47        a way to do this automatically, then we could get rid of this, but all attempts to do this as part of fixture48        initialization, etc didn't work because the fixture is replaced for each phase of the test run (setup, run test)49        """50        caplog.handler.setFormatter(self.caplog)51    def _get_labels(self, metric: ExportRecord):52        return dict(filter(lambda label: not label[0].startswith('_'), metric.labels))53    def _find_metric(self,54                     metric_type: Type,55                     name: str,56                     labels: Optional[Dict[str, str]] = None) -> Optional[ExportRecord]:57        labels = labels or {}58        def fail_no_match(msg: str, candidates: Optional[List[ExportRecord]] = None):59            if candidates is None:60                candidates = self.metrics_exporter.get_exported_metrics()61            msg = f"{msg}\n\nMetric:\n\t{name} {labels}\n\nRecorded {metric_type.__name__} metric(s):\n"62            if len(candidates) > 0:63                for m in candidates:64                    msg = f"{msg}\t{m.instrument.name} {self._get_labels(m)}\n"65            else:66                msg = f"{msg}\t(none)"67            return msg68        if not self.collected:69            self.collect()70        candidates = []71        for metric in self.metrics_exporter.get_exported_metrics():72            m: ExportRecord = metric73            if type(m.instrument) != metric_type:74                continue75            candidates.append(m)76            if m.instrument.name == name:77                if self._get_labels(m) == labels:78                    return m  # exact match, return immediately79        pytest.fail(fail_no_match(f"No matching {metric_type.__name__} metric found!", candidates))80    def collect(self):81        self.collected = True82        for controller in self.metrics.meter_provider._controllers:83            if isinstance(controller, PushController):84                controller.tick()85    def get_metrics(self,86                    type_filter: Callable[[Type], bool] = lambda v: True,87                    name_filter: Callable[[str], bool] = lambda v: True,88                    label_filter: Callable[[Dict[str, str]], bool] = lambda v: True,89                    instrumentor_filter: Callable[[str], bool] = lambda v: True) -> List[90        Union[CounterInfo, ValueRecorderInfo]]:91        metrics = []92        for metric in self.metrics_exporter.get_exported_metrics():93            m: ExportRecord = metric94            if not type_filter(type(m.instrument)) or not name_filter(m.instrument.name) or \95                    not label_filter(self._get_labels(m)) or not instrumentor_filter(m.instrument.meter.instrumentation_info.name):96                continue97            if type(m.instrument) == Counter:98                metrics.append(CounterInfo(m.instrument.name, m.aggregator.checkpoint, self._get_labels(m)))99            elif type(m.instrument) == ValueRecorder:100                metrics.append(ValueRecorderInfo(m.instrument.name,101                                                 m.aggregator.checkpoint.min,102                                                 m.aggregator.checkpoint.max,103                                                 m.aggregator.checkpoint.sum,104                                                 m.aggregator.checkpoint.count,105                                                 self._get_labels(m)))106            else:107                # TODO: other metric types?108                pass109        return metrics110    def get_counters(self, name_filter: Callable[[str], bool] = lambda v: True,111                     label_filter: Callable[[Dict[str, str]], bool] = lambda v: True) -> List[CounterInfo]:112        return self.get_metrics(type_filter=lambda t: t == Counter, name_filter=name_filter, label_filter=label_filter)113    def get_finished_spans(self, name_filter: Callable[[str], bool] = lambda v: True,114                           attribute_filter: Callable[[Optional[Mapping[str, AttributeValue]]], bool] = lambda v: True,115                           label_filter: Callable[[Dict[str, str]], bool] = lambda v: True) -> List[Span]:116        spans = []117        for span in self.span_exporter.get_finished_spans():118            if not name_filter(f"{span.qname}") or not attribute_filter(119                    span.attributes) or not label_filter(span.labels):120                continue121            spans.append(span)122        return spans123    def get_value_recorders(self, name_filter: Callable[[str], bool] = lambda v: True,124                            label_filter: Callable[[Dict[str, str]], bool] = lambda v: True) -> List[ValueRecorderInfo]:125        return self.get_metrics(type_filter=lambda t: t == ValueRecorder, name_filter=name_filter,126                                label_filter=label_filter)127    def get_counter(self, name: str, labels: Optional[Dict[str, str]] = None) -> Optional[CounterInfo]:128        m = self._find_metric(Counter, name, labels)129        if m:130            return CounterInfo(m.instrument.name, m.aggregator.checkpoint, self._get_labels(m))131        else:132            return None133    def get_up_down_counter(self, name: str, labels: Optional[Dict[str, str]] = None) -> Optional[CounterInfo]:134        m = self._find_metric(UpDownCounter, name, labels)135        if m:136            return CounterInfo(m.instrument.name, m.aggregator.checkpoint, self._get_labels(m))137        else:138            return None139    def get_value_recorder(self, name: str, labels: Optional[Dict[str, str]] = None) -> Optional[ValueRecorderInfo]:140        m = self._find_metric(ValueRecorder, name, labels)141        if m:142            return ValueRecorderInfo(m.instrument.name,143                                     m.aggregator.checkpoint.min,144                                     m.aggregator.checkpoint.max,145                                     m.aggregator.checkpoint.sum,146                                     m.aggregator.checkpoint.count,147                                     self._get_labels(m))148        else:149            return None150    def get_gauge(self, name: str, labels: Optional[Dict[str, str]] = None) -> Optional[GaugeInfo]:151        m = self._find_metric(ValueObserver, name, labels)152        if m:153            return GaugeInfo(f"{m.instrument.name}.{name}",154                             m.aggregator.checkpoint.min,155                             m.aggregator.checkpoint.max,156                             m.aggregator.checkpoint.sum,157                             m.aggregator.checkpoint.last,158                             m.aggregator.checkpoint.count,159                             self._get_labels(m))160        else:161            return None162class JsonLogCaptureFormatter(JsonLogFormatter):163    def __init__(self):164        super(JsonLogCaptureFormatter, self).__init__()165        self.records = []166    def add_fields(self, log_record, record, message_dict):167        super(JsonLogCaptureFormatter, self).add_fields(log_record, record, message_dict)168        self.records.append(log_record)169    def find_records(self, f: Callable[[LogRecord], bool]) -> Iterator[LogRecord]:170        return filter(f, self.records)171    def get_record(self, f: Callable[[LogRecord], bool]) -> LogRecord:172        matching = list(filter(f, self.records))173        if len(matching) == 0:...

test_client_google.py

Source:test_client_google.py

1import base642import responses3from io import BytesIO4from unittest import mock5from decouple import config6from PIL import Image7from engine.client_google_vision import (find_keywords,8                                         get_identified_labels,9                                         crop_image,10                                         vision_api)11from engine.tests.fixtures import vision_api_non_target, vision_api_target12def test_find_target_keywords():13    not_target = find_keywords(vision_api_non_target['responses'][0])14    target = find_keywords(vision_api_target['responses'][0])15    assert not_target is False16    assert target is True17@responses.activate18def test_get_google_vision_api_response():19    api_version = config('VISION_API_VERSION')20    token = config("VISION_TOKEN")21    with open('engine/tests/test_images/vision_test_image.jpg', 'rb') as image:22        image_content = image.read()23        base64_image = base64.b64encode(image_content).decode()24    responses.add(25            responses.POST,26            "https://vision.googleapis.com/{api_version}/images:annotate?"27            "key={token}".format(api_version=api_version, token=token),28            json=vision_api_non_target,29            status=20030            )31    labels = get_identified_labels(base64_image)32    assert labels == vision_api_non_target['responses'][0]33def test_crop_image():34    with open('engine/tests/test_images/vision_test_image.jpg', 'rb') as image:35        img = base64.b64encode(image.read()).decode()36        cropped_img = crop_image(img, prop=(0.5, 1.0))37        cropped_img_pil = Image.open(BytesIO(base64.b64decode(cropped_img)))38        expected_height = 42739        expected_width = 128040        assert cropped_img_pil.height == expected_height41        assert cropped_img_pil.width == expected_width42@mock.patch('engine.client_google_vision.BytesIO', return_value='img_obj')43@mock.patch('engine.client_google_vision.get_identified_labels')44@mock.patch('engine.client_google_vision.crop_image',45            return_value='cropped')46@mock.patch('engine.client_google_vision._prepare_image',47            return_value='prepared')48@mock.patch('engine.client_google_vision.requests.get')49def test_vision_api_pipeline(_get, _prepare_image, _crop_image, _get_labels,50                             _bytes_io):51    _get_labels.return_value = {52            'labelAnnotations': [{'description': 'shoe'}]53    }54    content_mock = mock.MagicMock()55    content_mock.content = 'image_content'56    _get.return_value = content_mock57    is_target, b64_img = vision_api('image_url')58    _get.assert_called_once_with('image_url')59    _prepare_image.assert_called_once_with(content_mock)60    _crop_image.assert_called_once_with('prepared', (0, 1.0))61    _get_labels.assert_called_once_with('cropped')62    _bytes_io.assert_called_once_with('image_content')63    assert is_target is True64    assert b64_img == 'img_obj'65@mock.patch('engine.client_google_vision.BytesIO', return_value='img_obj')66@mock.patch('engine.client_google_vision.get_identified_labels')67@mock.patch('engine.client_google_vision.crop_image',68            return_value='cropped')69@mock.patch('engine.client_google_vision._prepare_image',70            return_value='prepared')71@mock.patch('engine.client_google_vision.requests.get')72def test_vision_api_pipeline_false_response(_get, _prepare_image, _crop_image,73                                            _get_labels, _bytes_io):74    response_mock = mock.MagicMock()75    response_mock.content = 'response content'76    _get.return_value = response_mock77    _get_labels.return_value = {78            'labelAnnotations': [{'description': 'not_target'}]79    }80    is_target, img_obj = vision_api('image_url')81    crop_image_calls = [82            mock.call('prepared', (0.0, 1.0)),83            mock.call('prepared', (0.5, 1.0)),84            mock.call('prepared', (0.75, 1.0)),85            mock.call('prepared', (0.5, 0.75))86    ]87    get_labels_calls = [mock.call('cropped') for i in range(3)]88    _get.assert_called_once_with('image_url')89    _prepare_image.assert_called_once_with(response_mock)90    _bytes_io.assert_called_once_with('response content')91    _crop_image.assert_has_calls(crop_image_calls)92    _get_labels.assert_has_calls(get_labels_calls)93    assert is_target is False94    assert img_obj is 'img_obj'95@mock.patch('engine.client_google_vision.BytesIO', return_value='img_obj')96@mock.patch('engine.client_google_vision.crop_image',97            return_value='cropped')98@mock.patch('engine.client_google_vision.get_identified_labels')99@mock.patch('engine.client_google_vision._prepare_image',100            return_value='prepared')101@mock.patch('engine.client_google_vision.requests.get')102def test_vision_api_pipeline_with_half_img(_get, _prepare_image, _get_labels,103                                           _crop_image, _bytes_io):104    response_mock = mock.MagicMock()105    response_mock.content = 'response content'106    _get.return_value = response_mock107    _get_labels.side_effect = [108            {'labelAnnotations': [{'description': 'not shoe'}]},109            {'labelAnnotations': [{'description': 'shoe'}]}110    ]111    is_target, img_obj = vision_api('image_url')112    get_labels_calls = [mock.call('prepared'), mock.call('cropped')]113    crop_image_calls = [mock.call('prepared', (0.0, 1.0)),114                        mock.call('prepared', (0.5, 1.0))]115    _get.assert_called_once_with('image_url')116    _bytes_io.assert_called_once_with('response content')117    _prepare_image.assert_called_once_with(response_mock)118    _get_labels.call_args_list == get_labels_calls119    _crop_image.assert_has_calls(crop_image_calls)120    assert is_target is True...

samplers.py

Source:samplers.py

...4import pandas as pd5import torch6import torch.utils.data7import torchvision8def _get_labels(dataset):9    if isinstance(dataset, torchvision.datasets.MNIST):10        return dataset.train_labels.tolist()11    elif isinstance(dataset, torchvision.datasets.ImageFolder):12        return [x[1] for x in dataset.imgs]13    elif isinstance(dataset, torchvision.datasets.DatasetFolder):14        return dataset.samples[:][1]15    elif isinstance(dataset, torch.utils.data.Subset):16        return dataset.dataset.imgs[:][1]  # type: ignore17    elif isinstance(dataset, torch.utils.data.Dataset):18        return dataset.get_labels()  # type: ignore19    else:20        raise NotImplementedError21class ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):22    """...

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