2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license these 2 model types require different inputs than those of the usual text models legacy behavior small inputs because barttokenizer tiny has maximum position embeddings 22 forcing to get all results with topknone this is not the legacy format this might be used a text pair but tokenizer pipe interaction makes it hard to understand that it s not using the pair properly https github comhuggingfacetransformersissues17305 we disabled this usage instead as it was outputting wrong outputs this used to be valid for doing text pairs we re keeping it working because of backward compatibility 2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license these 2 model types require different inputs than those of the usual text models legacy behavior small inputs because barttokenizer tiny has maximum position embeddings 22 forcing to get all results with top_k none this is not the legacy format this might be used a text pair but tokenizer pipe interaction makes it hard to understand that it s not using the pair properly https github com huggingface transformers issues 17305 we disabled this usage instead as it was outputting wrong outputs this used to be valid for doing text pairs we re keeping it working because of backward compatibility
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow, torch_device

from .test_pipelines_common import ANY



_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}


@is_pipeline_test
class TextClassificationPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        tf_model_mapping = {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }

    @require_torch
    def test_small_model_pt(self):
        text_classifier = pipeline(
            task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"
        )

        outputs = text_classifier("This is great !")
        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])

        outputs = text_classifier("This is great !", top_k=2)
        self.assertEqual(
            nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]
        )

        outputs = text_classifier(["This is great !", "This is bad"], top_k=2)
        self.assertEqual(
            nested_simplify(outputs),
            [
                [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            ],
        )

        outputs = text_classifier("This is great !", top_k=1)
        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])

        
        outputs = text_classifier("This is great !", return_all_scores=False)
        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])

        outputs = text_classifier("This is great !", return_all_scores=True)
        self.assertEqual(
            nested_simplify(outputs), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]
        )

        outputs = text_classifier(["This is great !", "Something else"], return_all_scores=True)
        self.assertEqual(
            nested_simplify(outputs),
            [
                [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
                [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            ],
        )

        outputs = text_classifier(["This is great !", "Something else"], return_all_scores=False)
        self.assertEqual(
            nested_simplify(outputs),
            [
                {"label": "LABEL_0", "score": 0.504},
                {"label": "LABEL_0", "score": 0.504},
            ],
        )

    @require_torch
    def test_accepts_torch_device(self):
        text_classifier = pipeline(
            task="text-classification",
            model="hf-internal-testing/tiny-random-distilbert",
            framework="pt",
            device=torch_device,
        )

        outputs = text_classifier("This is great !")
        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])

    @require_tf
    def test_small_model_tf(self):
        text_classifier = pipeline(
            task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf"
        )

        outputs = text_classifier("This is great !")
        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])

    @slow
    @require_torch
    def test_pt_bert(self):
        text_classifier = pipeline("text-classification")

        outputs = text_classifier("This is great !")
        self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
        outputs = text_classifier("This is bad !")
        self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
        outputs = text_classifier("Birds are a type of animal")
        self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])

    @slow
    @require_tf
    def test_tf_bert(self):
        text_classifier = pipeline("text-classification", framework="tf")

        outputs = text_classifier("This is great !")
        self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
        outputs = text_classifier("This is bad !")
        self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
        outputs = text_classifier("Birds are a type of animal")
        self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])

    def get_test_pipeline(self, model, tokenizer, processor):
        text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
        return text_classifier, ["HuggingFace is in", "This is another test"]

    def run_pipeline_test(self, text_classifier, _):
        model = text_classifier.model
        
        valid_inputs = "HuggingFace is in"
        outputs = text_classifier(valid_inputs)

        self.assertEqual(nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}])
        self.assertTrue(outputs[0]["label"] in model.config.id2label.values())

        valid_inputs = ["HuggingFace is in ", "Paris is in France"]
        outputs = text_classifier(valid_inputs)
        self.assertEqual(
            nested_simplify(outputs),
            [{"label": ANY(str), "score": ANY(float)}, {"label": ANY(str), "score": ANY(float)}],
        )
        self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
        self.assertTrue(outputs[1]["label"] in model.config.id2label.values())

        
        
        outputs = text_classifier(valid_inputs, top_k=None)
        N = len(model.config.id2label.values())
        self.assertEqual(
            nested_simplify(outputs),
            [[{"label": ANY(str), "score": ANY(float)}] * N, [{"label": ANY(str), "score": ANY(float)}] * N],
        )

        valid_inputs = {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        outputs = text_classifier(valid_inputs)
        self.assertEqual(
            nested_simplify(outputs),
            {"label": ANY(str), "score": ANY(float)},
        )
        self.assertTrue(outputs["label"] in model.config.id2label.values())

        
        
        
        
        invalid_input = [["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(ValueError):
            text_classifier(invalid_input)

        
        
        outputs = text_classifier([[["HuggingFace is in ", "Paris is in France"]]])
        self.assertEqual(
            nested_simplify(outputs),
            [{"label": ANY(str), "score": ANY(float)}],
        )
        self.assertTrue(outputs[0]["label"] in model.config.id2label.values())