[Test refactor 1/5] Per-folder tests reorganization (#15725)

* Per-folder tests reorganization

Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>

tests/pipelines/test_pipelines_text_classification.py

@@ -0,0 +1,95 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+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
+
+from .test_pipelines_common import ANY, PipelineTestCaseMeta
+
+
+@is_pipeline_test
+class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
+    model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
+    tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
+
+    @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}])
+
+    @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, feature_extractor):
+        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
+        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
+        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())