def run_pipeline_test(self, task, repo_name, model_architecture, tokenizer_name, processor_name, commit):
        """Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class name

        The model will be loaded from a model repository on the Hub.

        Args:
            task (`str`):
                A task name. This should be a key in the mapping `pipeline_test_mapping`.
            repo_name (`str`):
                A model repository id on the Hub.
            model_architecture (`type`):
                A subclass of `PretrainedModel` or `PretrainedModel`.
            tokenizer_name (`str`):
                The name of a subclass of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
            processor_name (`str`):
                The name of a subclass of `BaseImageProcessor` or `FeatureExtractionMixin`.
        """
        repo_id = f"{TRANSFORMERS_TINY_MODEL_PATH}/{repo_name}"
        if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
            model_type = model_architecture.config_class.model_type
            repo_id = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, model_type, repo_name)

        tokenizer = None
        if tokenizer_name is not None:
            tokenizer_class = getattr(transformers_module, tokenizer_name)
            tokenizer = tokenizer_class.from_pretrained(repo_id, revision=commit)

        processor = None
        if processor_name is not None:
            processor_class = getattr(transformers_module, processor_name)
            # If the required packages (like `Pillow` or `torchaudio`) are not installed, this will fail.
            try:
                processor = processor_class.from_pretrained(repo_id, revision=commit)
            except Exception:
                logger.warning(
                    f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not load the "
                    f"processor from `{repo_id}` with `{processor_name}`."
                )
                return

        # TODO: Maybe not upload such problematic tiny models to Hub.
        if tokenizer is None and processor is None:
            logger.warning(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not find or load "
                f"any tokenizer / processor from `{repo_id}`."
            )
            return

        # TODO: We should check if a model file is on the Hub repo. instead.
        try:
            model = model_architecture.from_pretrained(repo_id, revision=commit)
        except Exception:
            logger.warning(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not find or load "
                f"the model from `{repo_id}` with `{model_architecture}`."
            )
            return

        pipeline_test_class_name = pipeline_test_mapping[task]["test"].__name__
        if self.is_pipeline_test_to_skip_more(pipeline_test_class_name, model.config, model, tokenizer, processor):
            logger.warning(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: test is "
                f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
                f"`{tokenizer_name}` | processor `{processor_name}`."
            )
            return

        # validate
        validate_test_components(self, task, model, tokenizer, processor)

        if hasattr(model, "eval"):
            model = model.eval()

        # Get an instance of the corresponding class `XXXPipelineTests` in order to use `get_test_pipeline` and
        # `run_pipeline_test`.
        task_test = pipeline_test_mapping[task]["test"]()

        pipeline, examples = task_test.get_test_pipeline(model, tokenizer, processor)
        if pipeline is None:
            # The test can disable itself, but it should be very marginal
            # Concerns: Wav2Vec2ForCTC without tokenizer test (FastTokenizer don't exist)
            logger.warning(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')} is skipped: Could not get the "
                "pipeline for testing."
            )
            return

        task_test.run_pipeline_test(pipeline, examples)

        def run_batch_test(pipeline, examples):
            # Need to copy because `Conversation` are stateful
            if pipeline.tokenizer is not None and pipeline.tokenizer.pad_token_id is None:
                return  # No batching for this and it's OK

            # 10 examples with batch size 4 means there needs to be a unfinished batch
            # which is important for the unbatcher
            def data(n):
                for _ in range(n):
                    # Need to copy because Conversation object is mutated
                    yield copy.deepcopy(random.choice(examples))

            out = []
            if task == "conversational":
                for item in pipeline(data(10), batch_size=4, max_new_tokens=5):
                    out.append(item)
            else:
                for item in pipeline(data(10), batch_size=4):
                    out.append(item)
            self.assertEqual(len(out), 10)

        run_batch_test(pipeline, examples)
Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class name

        The model will be loaded from a model repository on the Hub.

        Args:
            task (`str`):
                A task name. This should be a key in the mapping `pipeline_test_mapping`.
            repo_name (`str`):
                A model repository id on the Hub.
            model_architecture (`type`):
                A subclass of `PretrainedModel` or `PretrainedModel`.
            tokenizer_name (`str`):
                The name of a subclass of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
            processor_name (`str`):
                The name of a subclass of `BaseImageProcessor` or `FeatureExtractionMixin`.
        
dpkd/transformers/tests/test_pipeline_mixin.py