[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_text2text_generation.py

@@ -0,0 +1,91 @@
+# 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_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    Text2TextGenerationPipeline,
+    pipeline,
+)
+from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
+
+from .test_pipelines_common import ANY, PipelineTestCaseMeta
+
+
+@is_pipeline_test
+class Text2TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
+    model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+    tf_model_mapping = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+
+    def get_test_pipeline(self, model, tokenizer, feature_extractor):
+        generator = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
+        return generator, ["Something to write", "Something else"]
+
+    def run_pipeline_test(self, generator, _):
+        outputs = generator("Something there")
+        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
+        # These are encoder decoder, they don't just append to incoming string
+        self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))
+
+        outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
+        outputs = generator(
+            ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
+        )
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
+        with self.assertRaises(ValueError):
+            generator(4)
+
+    @require_torch
+    def test_small_model_pt(self):
+        generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="pt")
+        # do_sample=False necessary for reproducibility
+        outputs = generator("Something there", do_sample=False)
+        self.assertEqual(outputs, [{"generated_text": ""}])
+
+        num_return_sequences = 3
+        outputs = generator(
+            "Something there",
+            num_return_sequences=num_return_sequences,
+            num_beams=num_return_sequences,
+        )
+        target_outputs = [
+            {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
+            {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
+            {"generated_text": ""},
+        ]
+        self.assertEqual(outputs, target_outputs)
+
+    @require_tf
+    def test_small_model_tf(self):
+        generator = pipeline("text2text-generation", model="patrickvonplaten/t5-tiny-random", framework="tf")
+        # do_sample=False necessary for reproducibility
+        outputs = generator("Something there", do_sample=False)
+        self.assertEqual(outputs, [{"generated_text": ""}])