Skip tests properly (#31308)

* Skip tests properly

* [test_all]

* Add 'reason' as kwarg for skipTest

* [test_all] Fix up

* [test_all]

tests/models/yoso/test_modeling_yoso.py

@@ -353,6 +353,7 @@ class YosoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         model = YosoModel.from_pretrained(model_name)
         self.assertIsNotNone(model)
 
+    @unittest.skip(reason="This model does not output attentions")
     def test_attention_outputs(self):
         return
 

tests/pipelines/test_pipelines_audio_classification.py

@@ -132,6 +132,6 @@ class AudioClassificationPipelineTests(unittest.TestCase):
         )
 
     @require_tf
-    @unittest.skip("Audio classification is not implemented for TF")
+    @unittest.skip(reason="Audio classification is not implemented for TF")
     def test_small_model_tf(self):
         pass

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -70,9 +70,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         if tokenizer is None:
             # Side effect of no Fast Tokenizer class for these model, so skipping
             # But the slow tokenizer test should still run as they're quite small
-            self.skipTest("No tokenizer available")
-            return
-            # return None, None
+            self.skipTest(reason="No tokenizer available")
 
         speech_recognizer = AutomaticSpeechRecognitionPipeline(
             model=model, tokenizer=tokenizer, feature_extractor=processor
@@ -271,7 +269,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
 
     @require_tf
     def test_small_model_tf(self):
-        self.skipTest("Tensorflow not supported yet.")
+        self.skipTest(reason="Tensorflow not supported yet.")
 
     @require_torch
     def test_torch_small_no_tokenizer_files(self):
@@ -1855,7 +1853,7 @@ def require_ffmpeg(test_case):
         subprocess.check_output(["ffmpeg", "-h"], stderr=subprocess.DEVNULL)
         return test_case
     except Exception:
-        return unittest.skip("test requires ffmpeg")(test_case)
+        return unittest.skip(reason="test requires ffmpeg")(test_case)
 
 
 def bytes_iter(chunk_size, chunks):

tests/pipelines/test_pipelines_common.py

@@ -651,7 +651,7 @@ class PipelineUtilsTest(unittest.TestCase):
         if len(relevant_auto_classes) == 0:
             # task has no default
             logger.debug(f"{task} in {framework} has no default")
-            return
+            self.skipTest(f"{task} in {framework} has no default")
 
         # by default use first class
         auto_model_cls = relevant_auto_classes[0]

tests/pipelines/test_pipelines_conversational.py

+# 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 gc
+import unittest
+
+from transformers import (
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
+    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    BlenderbotSmallForConditionalGeneration,
+    BlenderbotSmallTokenizer,
+    Conversation,
+    ConversationalPipeline,
+    TFAutoModelForCausalLM,
+    pipeline,
+)
+from transformers.testing_utils import (
+    backend_empty_cache,
+    is_pipeline_test,
+    is_torch_available,
+    require_tf,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from .test_pipelines_common import ANY
+
+
+@is_pipeline_test
+class ConversationalPipelineTests(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        # clean-up as much as possible GPU memory occupied by PyTorch
+        gc.collect()
+        if is_torch_available():
+            backend_empty_cache(torch_device)
+
+    model_mapping = dict(
+        list(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
+        if MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+        else [] + list(MODEL_FOR_CAUSAL_LM_MAPPING.items())
+        if MODEL_FOR_CAUSAL_LM_MAPPING
+        else []
+    )
+    tf_model_mapping = dict(
+        list(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
+        if TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+        else [] + list(TF_MODEL_FOR_CAUSAL_LM_MAPPING.items())
+        if TF_MODEL_FOR_CAUSAL_LM_MAPPING
+        else []
+    )
+
+    def get_test_pipeline(self, model, tokenizer, processor):
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+        return conversation_agent, [Conversation("Hi there!")]
+
+    def run_pipeline_test(self, conversation_agent, _):
+        # Simple
+        outputs = conversation_agent(Conversation("Hi there!"), max_new_tokens=5)
+        self.assertEqual(
+            outputs,
+            Conversation([{"role": "user", "content": "Hi there!"}, {"role": "assistant", "content": ANY(str)}]),
+        )
+
+        # Single list
+        outputs = conversation_agent([Conversation("Hi there!")], max_new_tokens=5)
+        self.assertEqual(
+            outputs,
+            Conversation([{"role": "user", "content": "Hi there!"}, {"role": "assistant", "content": ANY(str)}]),
+        )
+
+        # Batch
+        conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
+        conversation_2 = Conversation("What's the last book you have read?")
+        self.assertEqual(len(conversation_1), 1)
+        self.assertEqual(len(conversation_2), 1)
+
+        outputs = conversation_agent([conversation_1, conversation_2], max_new_tokens=5)
+        self.assertEqual(outputs, [conversation_1, conversation_2])
+        self.assertEqual(
+            outputs,
+            [
+                Conversation(
+                    [
+                        {"role": "user", "content": "Going to the movies tonight - any suggestions?"},
+                        {"role": "assistant", "content": ANY(str)},
+                    ],
+                ),
+                Conversation(
+                    [
+                        {"role": "user", "content": "What's the last book you have read?"},
+                        {"role": "assistant", "content": ANY(str)},
+                    ]
+                ),
+            ],
+        )
+
+        # One conversation with history
+        conversation_2.add_message({"role": "user", "content": "Why do you recommend it?"})
+        outputs = conversation_agent(conversation_2, max_new_tokens=5)
+        self.assertEqual(outputs, conversation_2)
+        self.assertEqual(
+            outputs,
+            Conversation(
+                [
+                    {"role": "user", "content": "What's the last book you have read?"},
+                    {"role": "assistant", "content": ANY(str)},
+                    {"role": "user", "content": "Why do you recommend it?"},
+                    {"role": "assistant", "content": ANY(str)},
+                ]
+            ),
+        )
+
+    @require_torch
+    @slow
+    def test_integration_torch_conversation(self):
+        # When
+        conversation_agent = pipeline(task="conversational", device=torch_device)
+        conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
+        conversation_2 = Conversation("What's the last book you have read?")
+        # Then
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
+        self.assertEqual(len(conversation_2.past_user_inputs), 0)
+        # When
+        result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
+        # Then
+        self.assertEqual(result, [conversation_1, conversation_2])
+        self.assertEqual(len(result[0].past_user_inputs), 1)
+        self.assertEqual(len(result[1].past_user_inputs), 1)
+        self.assertEqual(len(result[0].generated_responses), 1)
+        self.assertEqual(len(result[1].generated_responses), 1)
+        self.assertEqual(result[0].past_user_inputs[0], "Going to the movies tonight - any suggestions?")
+        self.assertEqual(result[0].generated_responses[0], "The Big Lebowski")
+        self.assertEqual(result[1].past_user_inputs[0], "What's the last book you have read?")
+        self.assertEqual(result[1].generated_responses[0], "The Last Question")
+        # When
+        conversation_2.add_user_input("Why do you recommend it?")
+        result = conversation_agent(conversation_2, do_sample=False, max_length=1000)
+        # Then
+        self.assertEqual(result, conversation_2)
+        self.assertEqual(len(result.past_user_inputs), 2)
+        self.assertEqual(len(result.generated_responses), 2)
+        self.assertEqual(result.past_user_inputs[1], "Why do you recommend it?")
+        self.assertEqual(result.generated_responses[1], "It's a good book.")
+
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_truncated_history(self):
+        # When
+        conversation_agent = pipeline(task="conversational", min_length_for_response=24, device=torch_device)
+        conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
+        # Then
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
+        # When
+        result = conversation_agent(conversation_1, do_sample=False, max_length=36)
+        # Then
+        self.assertEqual(result, conversation_1)
+        self.assertEqual(len(result.past_user_inputs), 1)
+        self.assertEqual(len(result.generated_responses), 1)
+        self.assertEqual(result.past_user_inputs[0], "Going to the movies tonight - any suggestions?")
+        self.assertEqual(result.generated_responses[0], "The Big Lebowski")
+        # When
+        conversation_1.add_user_input("Is it an action movie?")
+        result = conversation_agent(conversation_1, do_sample=False, max_length=36)
+        # Then
+        self.assertEqual(result, conversation_1)
+        self.assertEqual(len(result.past_user_inputs), 2)
+        self.assertEqual(len(result.generated_responses), 2)
+        self.assertEqual(result.past_user_inputs[1], "Is it an action movie?")
+        self.assertEqual(result.generated_responses[1], "It's a comedy.")
+
+    @require_torch
+    def test_small_model_pt(self):
+        tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+        model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+        conversation = Conversation("hello")
+        output = conversation_agent(conversation)
+        self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
+
+    @require_tf
+    def test_small_model_tf(self):
+        tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+        model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+        conversation = Conversation("hello")
+        output = conversation_agent(conversation)
+        self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
+
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_dialogpt_input_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+        model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+
+        conversation_1 = Conversation("hello")
+        inputs = conversation_agent.preprocess(conversation_1)
+        self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
+
+        conversation_2 = Conversation("how are you ?", past_user_inputs=["hello"], generated_responses=["Hi there!"])
+        inputs = conversation_agent.preprocess(conversation_2)
+        self.assertEqual(
+            inputs["input_ids"].tolist(), [[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256]]
+        )
+
+    @unittest.skip(reason="Model is curently gated")
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_llama2_input_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", use_default_system_prompt=True)
+
+        conversation = Conversation(
+            "What is so great about #1?",
+            past_user_inputs=["I am going to Paris, what should I see?"],
+            generated_responses=[
+                """\
+Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:
+
+1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.
+2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.
+3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.
+
+These are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."""
+            ],
+        )
+        inputs = tokenizer._build_conversation_input_ids(conversation)
+        EXPECTED_INPUTS_IDS = [ 1, 518, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 29892, 3390, 1319, 322, 15993, 20255, 29889, 29849, 1234, 408, 1371, 3730, 408, 1950, 29892, 1550, 1641, 9109, 29889, 29871, 3575, 6089, 881, 451, 3160, 738, 10311, 1319, 29892, 443, 621, 936, 29892, 11021, 391, 29892, 7916, 391, 29892, 304, 27375, 29892, 18215, 29892, 470, 27302, 2793, 29889, 3529, 9801, 393, 596, 20890, 526, 5374, 635, 443, 5365, 1463, 322, 6374, 297, 5469, 29889, 13, 13, 3644, 263, 1139, 947, 451, 1207, 738, 4060, 29892, 470, 338, 451, 2114, 1474, 16165, 261, 296, 29892, 5649, 2020, 2012, 310, 22862, 1554, 451, 1959, 29889, 960, 366, 1016, 29915, 29873, 1073, 278, 1234, 304, 263, 1139, 29892, 3113, 1016, 29915, 29873, 6232, 2089, 2472, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 29902, 626, 2675, 304, 3681, 29892, 825, 881, 306, 1074, 29973, 518, 29914, 25580, 29962, 3681, 29892, 278, 7483, 310, 3444, 29892, 338, 2998, 363, 967, 380, 27389, 11258, 29892, 1616, 19133, 29879, 29892, 15839, 2982, 22848, 29892, 322, 6017, 7716, 25005, 29889, 2266, 526, 777, 310, 278, 2246, 19650, 1953, 304, 1074, 297, 3681, 29901, 13, 13, 29896, 29889, 450, 382, 2593, 295, 23615, 29901, 450, 9849, 293, 382, 2593, 295, 23615, 338, 697, 310, 278, 1556, 5936, 13902, 2982, 22848, 297, 278, 3186, 322, 16688, 2078, 271, 400, 5086, 8386, 310, 278, 4272, 29889, 13, 29906, 29889, 450, 4562, 12675, 6838, 29901, 450, 4562, 12675, 338, 697, 310, 278, 3186, 29915, 29879, 10150, 322, 1556, 13834, 19133, 29879, 29892, 27261, 385, 21210, 573, 4333, 310, 1616, 322, 24238, 29879, 29892, 3704, 278, 2598, 29874, 29420, 29889, 13, 29941, 29889, 24337, 29899, 29928, 420, 315, 21471, 29901, 910, 9560, 274, 21471, 338, 697, 310, 278, 1556, 13834, 2982, 22848, 297, 3681, 322, 338, 2998, 363, 967, 22883, 293, 11258, 322, 380, 27389, 380, 7114, 12917, 5417, 29889, 13, 13, 1349, 968, 526, 925, 263, 2846, 310, 278, 1784, 19650, 1953, 393, 3681, 756, 304, 5957, 29889, 2973, 577, 1568, 304, 1074, 322, 437, 29892, 372, 29915, 29879, 694, 4997, 393, 3681, 338, 697, 310, 278, 1556, 5972, 6282, 391, 15422, 800, 297, 278, 3186, 29889, 29871, 2, 1, 518, 25580, 29962, 1724, 338, 577, 2107, 1048, 396, 29896, 29973, 518, 29914, 25580, 29962]  # fmt: skip
+        self.assertEqual(inputs, EXPECTED_INPUTS_IDS)
+
+        model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+        EXPECTED_TEXT = "what topic you want to focus on and create content around it. This will help you stand out from other creators and attract a specific audience.\n\nStep 2: Set Up Your Channel\nCreate your YouTube account and customize your channel with your branding and logo. Make sure your channel name and profile picture are consistent with your niche.\n\nStep 3: Plan Your Content\nDevelop a content strategy that includes the type of content you want to create, how often you will post, and when you will post. Consider creating a content calendar to help you stay organized.\n\nStep 4: Invest in Quality Equipment\nInvest in good quality camera and microphone equipment to ensure your videos look and sound professional. You don't need to break the bank, but investing in good equipment will make a big difference in the quality of your videos.\n\nStep 5: Optimize Your Videos for Search\nUse keywords in your video titles, descriptions, and tags to help people find your videos when they search for topics related to your niche"
+        conversation = Conversation(
+            "<<SYS>>\n Only answer with emojis, and charades\n<</SYS>>\n\nHow can I build a house in 10 steps?"
+        )
+        result = conversation_agent(conversation)
+        self.assertEqual(result.generated_responses[-1], EXPECTED_TEXT)
+
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_blenderbot_400M_input_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+        model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+
+        # test1
+        conversation_1 = Conversation("hello")
+        inputs = conversation_agent.preprocess(conversation_1)
+        self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
+
+        # test2
+        conversation_1 = Conversation(
+            "I like lasagne.",
+            past_user_inputs=["hello"],
+            generated_responses=[
+                " Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
+            ],
+        )
+        inputs = conversation_agent.preprocess(conversation_1)
+        self.assertEqual(
+            inputs["input_ids"].tolist(),
+            [
+                # This should be compared with the same conversation on ParlAI `safe_interactive` demo.
+                [
+                    1710,  # hello
+                    86,
+                    228,  # Double space
+                    228,
+                    946,
+                    304,
+                    398,
+                    6881,
+                    558,
+                    964,
+                    38,
+                    452,
+                    315,
+                    265,
+                    6252,
+                    452,
+                    322,
+                    968,
+                    6884,
+                    3146,
+                    278,
+                    306,
+                    265,
+                    617,
+                    87,
+                    388,
+                    75,
+                    341,
+                    286,
+                    521,
+                    21,
+                    228,  # Double space
+                    228,
+                    281,  # I like lasagne.
+                    398,
+                    6881,
+                    558,
+                    964,
+                    21,
+                    2,  # EOS
+                ],
+            ],
+        )
+
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_blenderbot_400M(self):
+        tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+        model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
+
+        conversation_1 = Conversation("hello")
+        result = conversation_agent(
+            conversation_1,
+        )
+        self.assertEqual(
+            result.generated_responses[0],
+            # ParlAI implementation output, we have a different one, but it's our
+            # second best, you can check by using num_return_sequences=10
+            # " Hello! How are you? I'm just getting ready to go to work, how about you?",
+            " Hello! How are you doing today? I just got back from a walk with my dog.",
+        )
+
+        conversation_1 = Conversation("Lasagne   hello")
+        result = conversation_agent(conversation_1, encoder_no_repeat_ngram_size=3)
+        self.assertEqual(
+            result.generated_responses[0],
+            " Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie.",
+        )
+
+        conversation_1 = Conversation(
+            "Lasagne   hello   Lasagne is my favorite Italian dish. Do you like lasagne?   I like lasagne."
+        )
+        result = conversation_agent(
+            conversation_1,
+            encoder_no_repeat_ngram_size=3,
+        )
+        self.assertEqual(
+            result.generated_responses[0],
+            " Me too. I like how it can be topped with vegetables, meats, and condiments.",
+        )
+
+    @require_torch
+    @slow
+    def test_integration_torch_conversation_encoder_decoder(self):
+        # When
+        tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
+        model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot_small-90M")
+        conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer, device=torch_device)
+
+        conversation_1 = Conversation("My name is Sarah and I live in London")
+        conversation_2 = Conversation("Going to the movies tonight, What movie would you recommend? ")
+        # Then
+        self.assertEqual(len(conversation_1.past_user_inputs), 0)
+        self.assertEqual(len(conversation_2.past_user_inputs), 0)
+        # When
+        result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
+        # Then
+        self.assertEqual(result, [conversation_1, conversation_2])
+        self.assertEqual(len(result[0].past_user_inputs), 1)
+        self.assertEqual(len(result[1].past_user_inputs), 1)
+        self.assertEqual(len(result[0].generated_responses), 1)
+        self.assertEqual(len(result[1].generated_responses), 1)
+        self.assertEqual(result[0].past_user_inputs[0], "My name is Sarah and I live in London")
+        self.assertEqual(
+            result[0].generated_responses[0],
+            "hi sarah, i live in london as well. do you have any plans for the weekend?",
+        )
+        self.assertEqual(
+            result[1].past_user_inputs[0], "Going to the movies tonight, What movie would you recommend? "
+        )
+        self.assertEqual(
+            result[1].generated_responses[0], "i don't know... i'm not really sure. what movie are you going to see?"
+        )
+        # When
+        conversation_1.add_user_input("Not yet, what about you?")
+        conversation_2.add_user_input("What's your name?")
+        result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
+        # Then
+        self.assertEqual(result, [conversation_1, conversation_2])
+        self.assertEqual(len(result[0].past_user_inputs), 2)
+        self.assertEqual(len(result[1].past_user_inputs), 2)
+        self.assertEqual(len(result[0].generated_responses), 2)
+        self.assertEqual(len(result[1].generated_responses), 2)
+        self.assertEqual(result[0].past_user_inputs[1], "Not yet, what about you?")
+        self.assertEqual(result[0].generated_responses[1], "i don't have any plans yet. i'm not sure what to do yet.")
+        self.assertEqual(result[1].past_user_inputs[1], "What's your name?")
+        self.assertEqual(result[1].generated_responses[1], "i don't have a name, but i'm going to see a horror movie.")
+
+    @require_torch
+    @slow
+    def test_from_pipeline_conversation(self):
+        model_id = "facebook/blenderbot_small-90M"
+
+        # from model id
+        conversation_agent_from_model_id = pipeline("conversational", model=model_id, tokenizer=model_id)
+
+        # from model object
+        model = BlenderbotSmallForConditionalGeneration.from_pretrained(model_id)
+        tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_id)
+        conversation_agent_from_model = pipeline("conversational", model=model, tokenizer=tokenizer)
+
+        conversation = Conversation("My name is Sarah and I live in London")
+        conversation_copy = Conversation("My name is Sarah and I live in London")
+
+        result_model_id = conversation_agent_from_model_id([conversation])
+        result_model = conversation_agent_from_model([conversation_copy])
+
+        # check for equality
+        self.assertEqual(
+            result_model_id.generated_responses[0],
+            "hi sarah, i live in london as well. do you have any plans for the weekend?",
+        )
+        self.assertEqual(
+            result_model_id.generated_responses[0],
+            result_model.generated_responses[0],
+        )

tests/pipelines/test_pipelines_depth_estimation.py

@@ -95,7 +95,7 @@ class DepthEstimationPipelineTests(unittest.TestCase):
         )
 
     @require_tf
-    @unittest.skip("Depth estimation is not implemented in TF")
+    @unittest.skip(reason="Depth estimation is not implemented in TF")
     def test_small_model_tf(self):
         pass
 
@@ -115,4 +115,4 @@ class DepthEstimationPipelineTests(unittest.TestCase):
     @require_torch
     def test_small_model_pt(self):
         # This is highly irregular to have no small tests.
-        self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT")
+        self.skipTest(reason="There is not hf-internal-testing tiny model for either GLPN nor DPT")

tests/pipelines/test_pipelines_document_question_answering.py

@@ -366,6 +366,6 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
         self.assertEqual(nested_simplify(outputs, decimals=4), [{"answer": "us-001"}])
 
     @require_tf
-    @unittest.skip("Document question answering not implemented in TF")
+    @unittest.skip(reason="Document question answering not implemented in TF")
     def test_small_model_tf(self):
         pass

tests/pipelines/test_pipelines_feature_extraction.py

@@ -176,15 +176,15 @@ class FeatureExtractionPipelineTests(unittest.TestCase):
 
     def get_test_pipeline(self, model, tokenizer, processor):
         if tokenizer is None:
-            self.skipTest("No tokenizer")
-            return
+            self.skipTest(reason="No tokenizer")
         elif (
             type(model.config) in FEATURE_EXTRACTOR_MAPPING
             or isinstance(model.config, LxmertConfig)
             or type(model.config) in IMAGE_PROCESSOR_MAPPING
         ):
-            self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
-            return
+            self.skipTest(
+                reason="This is a bimodal model, we need to find a more consistent way to switch on those models."
+            )
         elif model.config.is_encoder_decoder:
             self.skipTest(
                 """encoder_decoder models are trickier for this pipeline.
@@ -193,8 +193,6 @@ class FeatureExtractionPipelineTests(unittest.TestCase):
                 For now ignore those.
                 """
             )
-
-            return
         feature_extractor = FeatureExtractionPipeline(model=model, tokenizer=tokenizer, feature_extractor=processor)
         return feature_extractor, ["This is a test", "This is another test"]
 

tests/pipelines/test_pipelines_fill_mask.py

@@ -253,7 +253,7 @@ class FillMaskPipelineTests(unittest.TestCase):
 
     def get_test_pipeline(self, model, tokenizer, processor):
         if tokenizer is None or tokenizer.mask_token_id is None:
-            self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)")
+            self.skipTest(reason="The provided tokenizer has no mask token, (probably reformer or wav2vec2)")
 
         fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
         examples = [

tests/pipelines/test_pipelines_image_feature_extraction.py

@@ -159,10 +159,12 @@ class ImageFeatureExtractionPipelineTests(unittest.TestCase):
 
     def get_test_pipeline(self, model, tokenizer, processor):
         if processor is None:
-            self.skipTest("No image processor")
+            self.skipTest(reason="No image processor")
 
         elif type(model.config) in TOKENIZER_MAPPING:
-            self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
+            self.skipTest(
+                reason="This is a bimodal model, we need to find a more consistent way to switch on those models."
+            )
 
         elif model.config.is_encoder_decoder:
             self.skipTest(

tests/pipelines/test_pipelines_image_segmentation.py

@@ -169,7 +169,7 @@ class ImageSegmentationPipelineTests(unittest.TestCase):
         )
 
     @require_tf
-    @unittest.skip("Image segmentation not implemented in TF")
+    @unittest.skip(reason="Image segmentation not implemented in TF")
     def test_small_model_tf(self):
         pass
 

tests/pipelines/test_pipelines_mask_generation.py

@@ -74,12 +74,12 @@ class MaskGenerationPipelineTests(unittest.TestCase):
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]
 
-    # TODO: Implement me @Arthur
+    @unittest.skip(reason="TODO @Arthur: Implement me")
     def run_pipeline_test(self, mask_generator, examples):
         pass
 
     @require_tf
-    @unittest.skip("Image segmentation not implemented in TF")
+    @unittest.skip(reason="Image segmentation not implemented in TF")
     def test_small_model_tf(self):
         pass
 

tests/pipelines/test_pipelines_object_detection.py

@@ -103,7 +103,7 @@ class ObjectDetectionPipelineTests(unittest.TestCase):
                 )
 
     @require_tf
-    @unittest.skip("Object detection not implemented in TF")
+    @unittest.skip(reason="Object detection not implemented in TF")
     def test_small_model_tf(self):
         pass
 

tests/pipelines/test_pipelines_text_generation.py

@@ -404,7 +404,7 @@ class TextGenerationPipelineTests(unittest.TestCase):
             # TF generation does not support max_new_tokens, and it's impossible
             # to control long generation with only max_length without
             # fancy calculation, dismissing tests for now.
-            return
+            self.skipTest(reason="TF generation does not support max_new_tokens")
         # We don't care about infinite range models.
         # They already work.
         # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.

tests/pipelines/test_pipelines_video_classification.py

@@ -94,5 +94,6 @@ class VideoClassificationPipelineTests(unittest.TestCase):
         )
 
     @require_tf
+    @unittest.skip
     def test_small_model_tf(self):
         pass

tests/pipelines/test_pipelines_visual_question_answering.py

@@ -236,6 +236,6 @@ class VisualQuestionAnsweringPipelineTests(unittest.TestCase):
         )
 
     @require_tf
-    @unittest.skip("Visual question answering not implemented in TF")
+    @unittest.skip(reason="Visual question answering not implemented in TF")
     def test_small_model_tf(self):
         pass

tests/pipelines/test_pipelines_zero_shot_audio_classification.py

@@ -40,7 +40,7 @@ class ZeroShotAudioClassificationPipelineTests(unittest.TestCase):
             [{"score": 0.501, "label": "Sound of a dog"}, {"score": 0.499, "label": "Sound of vaccum cleaner"}],
         )
 
-    @unittest.skip("No models are available in TF")
+    @unittest.skip(reason="No models are available in TF")
     def test_small_model_tf(self):
         pass
 
@@ -89,6 +89,6 @@ class ZeroShotAudioClassificationPipelineTests(unittest.TestCase):
             * 5,
         )
 
-    @unittest.skip("No models are available in TF")
+    @unittest.skip(reason="No models are available in TF")
     def test_large_model_tf(self):
         pass

tests/pipelines/test_pipelines_zero_shot_image_classification.py

@@ -49,7 +49,7 @@ class ZeroShotImageClassificationPipelineTests(unittest.TestCase):
     #     if tokenizer is None:
     #         # Side effect of no Fast Tokenizer class for these model, so skipping
     #         # But the slow tokenizer test should still run as they're quite small
-    #         self.skipTest("No tokenizer available")
+    #         self.skipTest(reason="No tokenizer available")
     #         return
     #         # return None, None
 

tests/pipelines/test_pipelines_zero_shot_object_detection.py

@@ -74,7 +74,7 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase):
         )
 
     @require_tf
-    @unittest.skip("Zero Shot Object Detection not implemented in TF")
+    @unittest.skip(reason="Zero Shot Object Detection not implemented in TF")
     def test_small_model_tf(self):
         pass
 
@@ -185,7 +185,7 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase):
         )
 
     @require_tf
-    @unittest.skip("Zero Shot Object Detection not implemented in TF")
+    @unittest.skip(reason="Zero Shot Object Detection not implemented in TF")
     def test_large_model_tf(self):
         pass
 

tests/quantization/autoawq/test_awq.py

@@ -450,7 +450,7 @@ class AwqFusedTest(unittest.TestCase):
         outputs = model.generate(**inputs, max_new_tokens=12)
         self.assertEqual(tokenizer.decode(outputs[0], skip_special_tokens=True), self.EXPECTED_GENERATION_CUSTOM_MODEL)
 
-    @unittest.skip("Not enough GPU memory on CI runners")
+    @unittest.skip(reason="Not enough GPU memory on CI runners")
     @require_torch_multi_gpu
     def test_generation_mixtral_fused(self):
         """