# Copyright (c) Alibaba, Inc. and its affiliates.
import unittest

from modelscope.models import Model
from modelscope.msdatasets import MsDataset
from modelscope.pipelines import pipeline
from modelscope.pipelines.nlp import TextClassificationPipeline
from modelscope.preprocessors import SequenceClassificationPreprocessor
from modelscope.utils.constant import Tasks
from modelscope.utils.demo_utils import DemoCompatibilityCheck
from modelscope.utils.test_utils import test_level


class SequenceClassificationTest(unittest.TestCase, DemoCompatibilityCheck):
    sentence1 = 'i like this wonderful place'

    def setUp(self) -> None:
        self.model_id = 'damo/bert-base-sst2'
        self.task = Tasks.text_classification

    def predict(self, pipeline_ins: TextClassificationPipeline):
        from easynlp.appzoo import load_dataset

        set = load_dataset('glue', 'sst2')
        data = set['test']['sentence'][:3]

        results = pipeline_ins(data[0])
        print(results)
        results = pipeline_ins(data[1])
        print(results)

        print(data)

    def printDataset(self, dataset: MsDataset):
        for i, r in enumerate(dataset):
            if i > 10:
                break
            print(r)

    # @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    @unittest.skip('nlp model does not support tensor input, skipped')
    def test_run_with_model_from_modelhub(self):
        model = Model.from_pretrained(self.model_id)
        preprocessor = SequenceClassificationPreprocessor(
            model.model_dir, first_sequence='sentence', second_sequence=None)
        pipeline_ins = pipeline(
            task=Tasks.text_classification,
            model=model,
            preprocessor=preprocessor)
        print(f'sentence1: {self.sentence1}\n'
              f'pipeline1:{pipeline_ins(input=self.sentence1)}')

    # @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    @unittest.skip('nlp model does not support tensor input, skipped')
    def test_run_with_model_name(self):
        text_classification = pipeline(
            task=Tasks.text_classification, model=self.model_id)
        result = text_classification(
            MsDataset.load(
                'xcopa',
                subset_name='translation-et',
                namespace='damotest',
                split='test',
                target='premise'))
        self.printDataset(result)

    # @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    @unittest.skip('nlp model does not support tensor input, skipped')
    def test_run_with_default_model(self):
        text_classification = pipeline(task=Tasks.text_classification)
        result = text_classification(
            MsDataset.load(
                'xcopa',
                subset_name='translation-et',
                namespace='damotest',
                split='test',
                target='premise'))
        self.printDataset(result)

    # @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
    @unittest.skip('nlp model does not support tensor input, skipped')
    def test_run_with_modelscope_dataset(self):
        text_classification = pipeline(task=Tasks.text_classification)
        # loaded from modelscope dataset
        dataset = MsDataset.load(
            'xcopa',
            subset_name='translation-et',
            namespace='damotest',
            split='test',
            target='premise')
        result = text_classification(dataset)
        self.printDataset(result)

    @unittest.skip('demo compatibility test is only enabled on a needed-basis')
    def test_demo_compatibility(self):
        self.compatibility_check()


if __name__ == '__main__':
    unittest.main()
