docs/transformers/tests/test_pipeline_mixin.py

# Copyright 2023 The HuggingFace Inc. 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 copy
import inspect
import json
import os
import random
import re
import unittest
from dataclasses import fields, is_dataclass
from pathlib import Path
from textwrap import dedent
from typing import get_args

from huggingface_hub import (
    AudioClassificationInput,
    AutomaticSpeechRecognitionInput,
    DepthEstimationInput,
    ImageClassificationInput,
    ImageSegmentationInput,
    ImageToTextInput,
    ObjectDetectionInput,
    QuestionAnsweringInput,
    VideoClassificationInput,
    ZeroShotImageClassificationInput,
)

from transformers.models.auto.processing_auto import PROCESSOR_MAPPING_NAMES
from transformers.pipelines import (
    AudioClassificationPipeline,
    AutomaticSpeechRecognitionPipeline,
    DepthEstimationPipeline,
    ImageClassificationPipeline,
    ImageSegmentationPipeline,
    ImageToTextPipeline,
    ObjectDetectionPipeline,
    QuestionAnsweringPipeline,
    VideoClassificationPipeline,
    ZeroShotImageClassificationPipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    require_av,
    require_pytesseract,
    require_timm,
    require_torch,
    require_torch_or_tf,
    require_vision,
)
from transformers.utils import direct_transformers_import, logging

from .pipelines.test_pipelines_audio_classification import AudioClassificationPipelineTests
from .pipelines.test_pipelines_automatic_speech_recognition import AutomaticSpeechRecognitionPipelineTests
from .pipelines.test_pipelines_depth_estimation import DepthEstimationPipelineTests
from .pipelines.test_pipelines_document_question_answering import DocumentQuestionAnsweringPipelineTests
from .pipelines.test_pipelines_feature_extraction import FeatureExtractionPipelineTests
from .pipelines.test_pipelines_fill_mask import FillMaskPipelineTests
from .pipelines.test_pipelines_image_classification import ImageClassificationPipelineTests
from .pipelines.test_pipelines_image_feature_extraction import ImageFeatureExtractionPipelineTests
from .pipelines.test_pipelines_image_segmentation import ImageSegmentationPipelineTests
from .pipelines.test_pipelines_image_text_to_text import ImageTextToTextPipelineTests
from .pipelines.test_pipelines_image_to_image import ImageToImagePipelineTests
from .pipelines.test_pipelines_image_to_text import ImageToTextPipelineTests
from .pipelines.test_pipelines_mask_generation import MaskGenerationPipelineTests
from .pipelines.test_pipelines_object_detection import ObjectDetectionPipelineTests
from .pipelines.test_pipelines_question_answering import QAPipelineTests
from .pipelines.test_pipelines_summarization import SummarizationPipelineTests
from .pipelines.test_pipelines_table_question_answering import TQAPipelineTests
from .pipelines.test_pipelines_text2text_generation import Text2TextGenerationPipelineTests
from .pipelines.test_pipelines_text_classification import TextClassificationPipelineTests
from .pipelines.test_pipelines_text_generation import TextGenerationPipelineTests
from .pipelines.test_pipelines_text_to_audio import TextToAudioPipelineTests
from .pipelines.test_pipelines_token_classification import TokenClassificationPipelineTests
from .pipelines.test_pipelines_translation import TranslationPipelineTests
from .pipelines.test_pipelines_video_classification import VideoClassificationPipelineTests
from .pipelines.test_pipelines_visual_question_answering import VisualQuestionAnsweringPipelineTests
from .pipelines.test_pipelines_zero_shot import ZeroShotClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_audio_classification import ZeroShotAudioClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_image_classification import ZeroShotImageClassificationPipelineTests
from .pipelines.test_pipelines_zero_shot_object_detection import ZeroShotObjectDetectionPipelineTests


pipeline_test_mapping = {
    "audio-classification": {"test": AudioClassificationPipelineTests},
    "automatic-speech-recognition": {"test": AutomaticSpeechRecognitionPipelineTests},
    "depth-estimation": {"test": DepthEstimationPipelineTests},
    "document-question-answering": {"test": DocumentQuestionAnsweringPipelineTests},
    "feature-extraction": {"test": FeatureExtractionPipelineTests},
    "fill-mask": {"test": FillMaskPipelineTests},
    "image-classification": {"test": ImageClassificationPipelineTests},
    "image-feature-extraction": {"test": ImageFeatureExtractionPipelineTests},
    "image-segmentation": {"test": ImageSegmentationPipelineTests},
    "image-text-to-text": {"test": ImageTextToTextPipelineTests},
    "image-to-image": {"test": ImageToImagePipelineTests},
    "image-to-text": {"test": ImageToTextPipelineTests},
    "mask-generation": {"test": MaskGenerationPipelineTests},
    "object-detection": {"test": ObjectDetectionPipelineTests},
    "question-answering": {"test": QAPipelineTests},
    "summarization": {"test": SummarizationPipelineTests},
    "table-question-answering": {"test": TQAPipelineTests},
    "text2text-generation": {"test": Text2TextGenerationPipelineTests},
    "text-classification": {"test": TextClassificationPipelineTests},
    "text-generation": {"test": TextGenerationPipelineTests},
    "text-to-audio": {"test": TextToAudioPipelineTests},
    "token-classification": {"test": TokenClassificationPipelineTests},
    "translation": {"test": TranslationPipelineTests},
    "video-classification": {"test": VideoClassificationPipelineTests},
    "visual-question-answering": {"test": VisualQuestionAnsweringPipelineTests},
    "zero-shot": {"test": ZeroShotClassificationPipelineTests},
    "zero-shot-audio-classification": {"test": ZeroShotAudioClassificationPipelineTests},
    "zero-shot-image-classification": {"test": ZeroShotImageClassificationPipelineTests},
    "zero-shot-object-detection": {"test": ZeroShotObjectDetectionPipelineTests},
}

task_to_pipeline_and_spec_mapping = {
    # Adding a task to this list will cause its pipeline input signature to be checked against the corresponding
    # task spec in the HF Hub
    "audio-classification": (AudioClassificationPipeline, AudioClassificationInput),
    "automatic-speech-recognition": (AutomaticSpeechRecognitionPipeline, AutomaticSpeechRecognitionInput),
    "depth-estimation": (DepthEstimationPipeline, DepthEstimationInput),
    "image-classification": (ImageClassificationPipeline, ImageClassificationInput),
    "image-segmentation": (ImageSegmentationPipeline, ImageSegmentationInput),
    "image-to-text": (ImageToTextPipeline, ImageToTextInput),
    "object-detection": (ObjectDetectionPipeline, ObjectDetectionInput),
    "question-answering": (QuestionAnsweringPipeline, QuestionAnsweringInput),
    "video-classification": (VideoClassificationPipeline, VideoClassificationInput),
    "zero-shot-image-classification": (ZeroShotImageClassificationPipeline, ZeroShotImageClassificationInput),
}

for task, task_info in pipeline_test_mapping.items():
    test = task_info["test"]
    task_info["mapping"] = {
        "pt": getattr(test, "model_mapping", None),
        "tf": getattr(test, "tf_model_mapping", None),
    }


# The default value `hf-internal-testing` is for running the pipeline testing against the tiny models on the Hub.
# For debugging purpose, we can specify a local path which is the `output_path` argument of a previous run of
# `utils/create_dummy_models.py`.
TRANSFORMERS_TINY_MODEL_PATH = os.environ.get("TRANSFORMERS_TINY_MODEL_PATH", "hf-internal-testing")
if TRANSFORMERS_TINY_MODEL_PATH == "hf-internal-testing":
    TINY_MODEL_SUMMARY_FILE_PATH = os.path.join(Path(__file__).parent.parent, "tests/utils/tiny_model_summary.json")
else:
    TINY_MODEL_SUMMARY_FILE_PATH = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, "reports", "tiny_model_summary.json")
with open(TINY_MODEL_SUMMARY_FILE_PATH) as fp:
    tiny_model_summary = json.load(fp)


PATH_TO_TRANSFORMERS = os.path.join(Path(__file__).parent.parent, "src/transformers")


# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
transformers_module = direct_transformers_import(PATH_TO_TRANSFORMERS)

logger = logging.get_logger(__name__)


class PipelineTesterMixin:
    model_tester = None
    pipeline_model_mapping = None
    supported_frameworks = ["pt", "tf"]

    def run_task_tests(self, task, torch_dtype="float32"):
        """Run pipeline tests for a specific `task`

        Args:
            task (`str`):
                A task name. This should be a key in the mapping `pipeline_test_mapping`.
            torch_dtype (`str`, `optional`, defaults to `'float32'`):
                The torch dtype to use for the model. Can be used for FP16/other precision inference.
        """
        if task not in self.pipeline_model_mapping:
            self.skipTest(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')}_{torch_dtype} is skipped: `{task}` is not in "
                f"`self.pipeline_model_mapping` for `{self.__class__.__name__}`."
            )

        model_architectures = self.pipeline_model_mapping[task]
        if not isinstance(model_architectures, tuple):
            model_architectures = (model_architectures,)

        # We are going to run tests for multiple model architectures, some of them might be skipped
        # with this flag we are control if at least one model were tested or all were skipped
        at_least_one_model_is_tested = False

        for model_architecture in model_architectures:
            model_arch_name = model_architecture.__name__
            model_type = model_architecture.config_class.model_type

            # Get the canonical name
            for _prefix in ["Flax", "TF"]:
                if model_arch_name.startswith(_prefix):
                    model_arch_name = model_arch_name[len(_prefix) :]
                    break

            if model_arch_name not in tiny_model_summary:
                continue

            tokenizer_names = tiny_model_summary[model_arch_name]["tokenizer_classes"]

            # Sort image processors and feature extractors from tiny-models json file
            image_processor_names = []
            feature_extractor_names = []

            processor_classes = tiny_model_summary[model_arch_name]["processor_classes"]
            for cls_name in processor_classes:
                if "ImageProcessor" in cls_name:
                    image_processor_names.append(cls_name)
                elif "FeatureExtractor" in cls_name:
                    feature_extractor_names.append(cls_name)

            # Processor classes are not in tiny models JSON file, so extract them from the mapping
            # processors are mapped to instance, e.g. "XxxProcessor"
            processor_names = PROCESSOR_MAPPING_NAMES.get(model_type, None)
            if not isinstance(processor_names, (list, tuple)):
                processor_names = [processor_names]

            commit = None
            if model_arch_name in tiny_model_summary and "sha" in tiny_model_summary[model_arch_name]:
                commit = tiny_model_summary[model_arch_name]["sha"]

            repo_name = f"tiny-random-{model_arch_name}"
            if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
                repo_name = model_arch_name

            self.run_model_pipeline_tests(
                task,
                repo_name,
                model_architecture,
                tokenizer_names=tokenizer_names,
                image_processor_names=image_processor_names,
                feature_extractor_names=feature_extractor_names,
                processor_names=processor_names,
                commit=commit,
                torch_dtype=torch_dtype,
            )
            at_least_one_model_is_tested = True

        if task in task_to_pipeline_and_spec_mapping:
            pipeline, hub_spec = task_to_pipeline_and_spec_mapping[task]
            compare_pipeline_args_to_hub_spec(pipeline, hub_spec)

        if not at_least_one_model_is_tested:
            self.skipTest(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')}_{torch_dtype} is skipped: Could not find any "
                f"model architecture in the tiny models JSON file for `{task}`."
            )

    def run_model_pipeline_tests(
        self,
        task,
        repo_name,
        model_architecture,
        tokenizer_names,
        image_processor_names,
        feature_extractor_names,
        processor_names,
        commit,
        torch_dtype="float32",
    ):
        """Run pipeline tests for a specific `task` with the give model class and tokenizer/processor class names

        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_names (`List[str]`):
                A list of names of a subclasses of `PreTrainedTokenizerFast` or `PreTrainedTokenizer`.
            image_processor_names (`List[str]`):
                A list of names of subclasses of `BaseImageProcessor`.
            feature_extractor_names (`List[str]`):
                A list of names of subclasses of `FeatureExtractionMixin`.
            processor_names (`List[str]`):
                A list of names of subclasses of `ProcessorMixin`.
            commit (`str`):
                The commit hash of the model repository on the Hub.
            torch_dtype (`str`, `optional`, defaults to `'float32'`):
                The torch dtype to use for the model. Can be used for FP16/other precision inference.
        """
        # Get an instance of the corresponding class `XXXPipelineTests` in order to use `get_test_pipeline` and
        # `run_pipeline_test`.
        pipeline_test_class_name = pipeline_test_mapping[task]["test"].__name__

        # If no image processor or feature extractor is found, we still need to test the pipeline with None
        # otherwise for any empty list we might skip all the tests
        tokenizer_names = tokenizer_names or [None]
        image_processor_names = image_processor_names or [None]
        feature_extractor_names = feature_extractor_names or [None]
        processor_names = processor_names or [None]

        test_cases = [
            {
                "tokenizer_name": tokenizer_name,
                "image_processor_name": image_processor_name,
                "feature_extractor_name": feature_extractor_name,
                "processor_name": processor_name,
            }
            for tokenizer_name in tokenizer_names
            for image_processor_name in image_processor_names
            for feature_extractor_name in feature_extractor_names
            for processor_name in processor_names
        ]

        for test_case in test_cases:
            tokenizer_name = test_case["tokenizer_name"]
            image_processor_name = test_case["image_processor_name"]
            feature_extractor_name = test_case["feature_extractor_name"]
            processor_name = test_case["processor_name"]

            do_skip_test_case = self.is_pipeline_test_to_skip(
                pipeline_test_class_name,
                model_architecture.config_class,
                model_architecture,
                tokenizer_name,
                image_processor_name,
                feature_extractor_name,
                processor_name,
            )

            if do_skip_test_case:
                logger.warning(
                    f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')}_{torch_dtype} is skipped: test is "
                    f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
                    f"`{tokenizer_name}` | image processor `{image_processor_name}` | feature extractor {feature_extractor_name}."
                )
                continue

            self.run_pipeline_test(
                task,
                repo_name,
                model_architecture,
                tokenizer_name=tokenizer_name,
                image_processor_name=image_processor_name,
                feature_extractor_name=feature_extractor_name,
                processor_name=processor_name,
                commit=commit,
                torch_dtype=torch_dtype,
            )

    def run_pipeline_test(
        self,
        task,
        repo_name,
        model_architecture,
        tokenizer_name,
        image_processor_name,
        feature_extractor_name,
        processor_name,
        commit,
        torch_dtype="float32",
    ):
        """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`.
            image_processor_name (`str`):
                The name of a subclass of `BaseImageProcessor`.
            feature_extractor_name (`str`):
                The name of a subclass of `FeatureExtractionMixin`.
            processor_name (`str`):
                The name of a subclass of `ProcessorMixin`.
            commit (`str`):
                The commit hash of the model repository on the Hub.
            torch_dtype (`str`, `optional`, defaults to `'float32'`):
                The torch dtype to use for the model. Can be used for FP16/other precision inference.
        """
        repo_id = f"{TRANSFORMERS_TINY_MODEL_PATH}/{repo_name}"
        model_type = model_architecture.config_class.model_type

        if TRANSFORMERS_TINY_MODEL_PATH != "hf-internal-testing":
            repo_id = os.path.join(TRANSFORMERS_TINY_MODEL_PATH, model_type, repo_name)

        # -------------------- Load model --------------------

        # 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('-', '_')}_{torch_dtype} is skipped: Could not find or load "
                f"the model from `{repo_id}` with `{model_architecture}`."
            )
            self.skipTest(f"Could not find or load the model from {repo_id} with {model_architecture}.")

        # -------------------- Load tokenizer --------------------

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

        # -------------------- Load processors --------------------

        processors = {}
        for key, name in zip(
            ["image_processor", "feature_extractor", "processor"],
            [image_processor_name, feature_extractor_name, processor_name],
        ):
            if name is not None:
                try:
                    # Can fail if some extra dependencies are not installed
                    processor_class = getattr(transformers_module, name)
                    processor = processor_class.from_pretrained(repo_id, revision=commit)
                    processors[key] = processor
                except Exception:
                    logger.warning(
                        f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')}_{torch_dtype} is skipped: "
                        f"Could not load the {key} from `{repo_id}` with `{name}`."
                    )
                    self.skipTest(f"Could not load the {key} from {repo_id} with {name}.")

        # ---------------------------------------------------------

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

        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, **processors):
            logger.warning(
                f"{self.__class__.__name__}::test_pipeline_{task.replace('-', '_')}_{torch_dtype} is skipped: test is "
                f"currently known to fail for: model `{model_architecture.__name__}` | tokenizer "
                f"`{tokenizer_name}` | image processor `{image_processor_name}` | feature extractor `{feature_extractor_name}`."
            )
            self.skipTest(
                f"Test is known to fail for: model `{model_architecture.__name__}` | tokenizer `{tokenizer_name}` "
                f"| image processor `{image_processor_name}` | feature extractor `{feature_extractor_name}`."
            )

        # validate
        validate_test_components(model, tokenizer)

        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, **processors, torch_dtype=torch_dtype)
        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('-', '_')}_{torch_dtype} is skipped: Could not get the "
                "pipeline for testing."
            )
            self.skipTest(reason="Could not get the pipeline for testing.")

        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 = []
            for item in pipeline(data(10), batch_size=4):
                out.append(item)
            self.assertEqual(len(out), 10)

        run_batch_test(pipeline, examples)

    @is_pipeline_test
    def test_pipeline_audio_classification(self):
        self.run_task_tests(task="audio-classification")

    @is_pipeline_test
    @require_torch
    def test_pipeline_audio_classification_fp16(self):
        self.run_task_tests(task="audio-classification", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_automatic_speech_recognition(self):
        self.run_task_tests(task="automatic-speech-recognition")

    @is_pipeline_test
    @require_torch
    def test_pipeline_automatic_speech_recognition_fp16(self):
        self.run_task_tests(task="automatic-speech-recognition", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    @require_timm
    @require_torch
    def test_pipeline_depth_estimation(self):
        self.run_task_tests(task="depth-estimation")

    @is_pipeline_test
    @require_vision
    @require_timm
    @require_torch
    def test_pipeline_depth_estimation_fp16(self):
        self.run_task_tests(task="depth-estimation", torch_dtype="float16")

    @is_pipeline_test
    @require_pytesseract
    @require_torch
    @require_vision
    def test_pipeline_document_question_answering(self):
        self.run_task_tests(task="document-question-answering")

    @is_pipeline_test
    @require_pytesseract
    @require_torch
    @require_vision
    def test_pipeline_document_question_answering_fp16(self):
        self.run_task_tests(task="document-question-answering", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_feature_extraction(self):
        self.run_task_tests(task="feature-extraction")

    @is_pipeline_test
    @require_torch
    def test_pipeline_feature_extraction_fp16(self):
        self.run_task_tests(task="feature-extraction", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_fill_mask(self):
        self.run_task_tests(task="fill-mask")

    @is_pipeline_test
    @require_torch
    def test_pipeline_fill_mask_fp16(self):
        self.run_task_tests(task="fill-mask", torch_dtype="float16")

    @is_pipeline_test
    @require_torch_or_tf
    @require_vision
    def test_pipeline_image_classification(self):
        self.run_task_tests(task="image-classification")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_image_classification_fp16(self):
        self.run_task_tests(task="image-classification", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    @require_timm
    @require_torch
    def test_pipeline_image_segmentation(self):
        self.run_task_tests(task="image-segmentation")

    @is_pipeline_test
    @require_vision
    @require_timm
    @require_torch
    def test_pipeline_image_segmentation_fp16(self):
        self.run_task_tests(task="image-segmentation", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_image_text_to_text(self):
        self.run_task_tests(task="image-text-to-text")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_image_text_to_text_fp16(self):
        self.run_task_tests(task="image-text-to-text", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    def test_pipeline_image_to_text(self):
        self.run_task_tests(task="image-to-text")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_image_to_text_fp16(self):
        self.run_task_tests(task="image-to-text", torch_dtype="float16")

    @is_pipeline_test
    @require_timm
    @require_vision
    @require_torch
    def test_pipeline_image_feature_extraction(self):
        self.run_task_tests(task="image-feature-extraction")

    @is_pipeline_test
    @require_timm
    @require_vision
    @require_torch
    def test_pipeline_image_feature_extraction_fp16(self):
        self.run_task_tests(task="image-feature-extraction", torch_dtype="float16")

    @unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_mask_generation(self):
        self.run_task_tests(task="mask-generation")

    @unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_mask_generation_fp16(self):
        self.run_task_tests(task="mask-generation", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    @require_timm
    @require_torch
    def test_pipeline_object_detection(self):
        self.run_task_tests(task="object-detection")

    @is_pipeline_test
    @require_vision
    @require_timm
    @require_torch
    def test_pipeline_object_detection_fp16(self):
        self.run_task_tests(task="object-detection", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_question_answering(self):
        self.run_task_tests(task="question-answering")

    @is_pipeline_test
    @require_torch
    def test_pipeline_question_answering_fp16(self):
        self.run_task_tests(task="question-answering", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_summarization(self):
        self.run_task_tests(task="summarization")

    @is_pipeline_test
    @require_torch
    def test_pipeline_summarization_fp16(self):
        self.run_task_tests(task="summarization", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_table_question_answering(self):
        self.run_task_tests(task="table-question-answering")

    @is_pipeline_test
    @require_torch
    def test_pipeline_table_question_answering_fp16(self):
        self.run_task_tests(task="table-question-answering", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_text2text_generation(self):
        self.run_task_tests(task="text2text-generation")

    @is_pipeline_test
    @require_torch
    def test_pipeline_text2text_generation_fp16(self):
        self.run_task_tests(task="text2text-generation", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_text_classification(self):
        self.run_task_tests(task="text-classification")

    @is_pipeline_test
    @require_torch
    def test_pipeline_text_classification_fp16(self):
        self.run_task_tests(task="text-classification", torch_dtype="float16")

    @is_pipeline_test
    @require_torch_or_tf
    def test_pipeline_text_generation(self):
        self.run_task_tests(task="text-generation")

    @is_pipeline_test
    @require_torch
    def test_pipeline_text_generation_fp16(self):
        self.run_task_tests(task="text-generation", torch_dtype="float16")

    @is_pipeline_test
    @require_torch
    def test_pipeline_text_to_audio(self):
        self.run_task_tests(task="text-to-audio")

    @is_pipeline_test
    @require_torch
    def test_pipeline_text_to_audio_fp16(self):
        self.run_task_tests(task="text-to-audio", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_token_classification(self):
        self.run_task_tests(task="token-classification")

    @is_pipeline_test
    @require_torch
    def test_pipeline_token_classification_fp16(self):
        self.run_task_tests(task="token-classification", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_translation(self):
        self.run_task_tests(task="translation")

    @is_pipeline_test
    @require_torch
    def test_pipeline_translation_fp16(self):
        self.run_task_tests(task="translation", torch_dtype="float16")

    @is_pipeline_test
    @require_torch_or_tf
    @require_vision
    @require_av
    def test_pipeline_video_classification(self):
        self.run_task_tests(task="video-classification")

    @is_pipeline_test
    @require_vision
    @require_torch
    @require_av
    def test_pipeline_video_classification_fp16(self):
        self.run_task_tests(task="video-classification", torch_dtype="float16")

    @is_pipeline_test
    @require_torch
    @require_vision
    def test_pipeline_visual_question_answering(self):
        self.run_task_tests(task="visual-question-answering")

    @is_pipeline_test
    @require_torch
    @require_vision
    def test_pipeline_visual_question_answering_fp16(self):
        self.run_task_tests(task="visual-question-answering", torch_dtype="float16")

    @is_pipeline_test
    def test_pipeline_zero_shot(self):
        self.run_task_tests(task="zero-shot")

    @is_pipeline_test
    @require_torch
    def test_pipeline_zero_shot_fp16(self):
        self.run_task_tests(task="zero-shot", torch_dtype="float16")

    @is_pipeline_test
    @require_torch
    def test_pipeline_zero_shot_audio_classification(self):
        self.run_task_tests(task="zero-shot-audio-classification")

    @is_pipeline_test
    @require_torch
    def test_pipeline_zero_shot_audio_classification_fp16(self):
        self.run_task_tests(task="zero-shot-audio-classification", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    def test_pipeline_zero_shot_image_classification(self):
        self.run_task_tests(task="zero-shot-image-classification")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_zero_shot_image_classification_fp16(self):
        self.run_task_tests(task="zero-shot-image-classification", torch_dtype="float16")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_zero_shot_object_detection(self):
        self.run_task_tests(task="zero-shot-object-detection")

    @is_pipeline_test
    @require_vision
    @require_torch
    def test_pipeline_zero_shot_object_detection_fp16(self):
        self.run_task_tests(task="zero-shot-object-detection", torch_dtype="float16")

    # This contains the test cases to be skipped without model architecture being involved.
    def is_pipeline_test_to_skip(
        self,
        pipeline_test_case_name,
        config_class,
        model_architecture,
        tokenizer_name,
        image_processor_name,
        feature_extractor_name,
        processor_name,
    ):
        """Skip some tests based on the classes or their names without the instantiated objects.

        This is to avoid calling `from_pretrained` (so reducing the runtime) if we already know the tests will fail.
        """
        # No fix is required for this case.
        if (
            pipeline_test_case_name == "DocumentQuestionAnsweringPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast")
        ):
            # `DocumentQuestionAnsweringPipelineTests` requires a fast tokenizer.
            return True

        return False

    def is_pipeline_test_to_skip_more(
        self,
        pipeline_test_case_name,
        config,
        model,
        tokenizer,
        image_processor=None,
        feature_extractor=None,
        processor=None,
    ):  # noqa
        """Skip some more tests based on the information from the instantiated objects."""
        # No fix is required for this case.
        if (
            pipeline_test_case_name == "QAPipelineTests"
            and tokenizer is not None
            and getattr(tokenizer, "pad_token", None) is None
            and not tokenizer.__class__.__name__.endswith("Fast")
        ):
            # `QAPipelineTests` doesn't work with a slow tokenizer that has no pad token.
            return True

        return False


def validate_test_components(model, tokenizer):
    # TODO: Move this to tiny model creation script
    # head-specific (within a model type) necessary changes to the config
    # 1. for `BlenderbotForCausalLM`
    if model.__class__.__name__ == "BlenderbotForCausalLM":
        model.config.encoder_no_repeat_ngram_size = 0

    # TODO: Change the tiny model creation script: don't create models with problematic tokenizers
    # Avoid `IndexError` in embedding layers
    CONFIG_WITHOUT_VOCAB_SIZE = ["CanineConfig"]
    if tokenizer is not None:
        # Removing `decoder=True` in `get_text_config` can lead to conflicting values e.g. in MusicGen
        config_vocab_size = getattr(model.config.get_text_config(decoder=True), "vocab_size", None)
        # For CLIP-like models
        if config_vocab_size is None:
            if hasattr(model.config, "text_encoder"):
                config_vocab_size = getattr(model.config.text_config, "vocab_size", None)
        if config_vocab_size is None and model.config.__class__.__name__ not in CONFIG_WITHOUT_VOCAB_SIZE:
            raise ValueError(
                "Could not determine `vocab_size` from model configuration while `tokenizer` is not `None`."
            )


def get_arg_names_from_hub_spec(hub_spec, first_level=True):
    # This util is used in pipeline tests, to verify that a pipeline's documented arguments
    # match the Hub specification for that task
    arg_names = []
    for field in fields(hub_spec):
        # Recurse into nested fields, but max one level
        if is_dataclass(field.type):
            arg_names.extend([field.name for field in fields(field.type)])
            continue
        # Next, catch nested fields that are part of a Union[], which is usually caused by Optional[]
        for param_type in get_args(field.type):
            if is_dataclass(param_type):
                # Again, recurse into nested fields, but max one level
                arg_names.extend([field.name for field in fields(param_type)])
                break
        else:
            # Finally, this line triggers if it's not a nested field
            arg_names.append(field.name)
    return arg_names


def parse_args_from_docstring_by_indentation(docstring):
    # This util is used in pipeline tests, to extract the argument names from a google-format docstring
    # to compare them against the Hub specification for that task. It uses indentation levels as a primary
    # source of truth, so these have to be correct!
    docstring = dedent(docstring)
    lines_by_indent = [
        (len(line) - len(line.lstrip()), line.strip()) for line in docstring.split("\n") if line.strip()
    ]
    args_lineno = None
    args_indent = None
    args_end = None
    for lineno, (indent, line) in enumerate(lines_by_indent):
        if line == "Args:":
            args_lineno = lineno
            args_indent = indent
            continue
        elif args_lineno is not None and indent == args_indent:
            args_end = lineno
            break
    if args_lineno is None:
        raise ValueError("No args block to parse!")
    elif args_end is None:
        args_block = lines_by_indent[args_lineno + 1 :]
    else:
        args_block = lines_by_indent[args_lineno + 1 : args_end]
    outer_indent_level = min(line[0] for line in args_block)
    outer_lines = [line for line in args_block if line[0] == outer_indent_level]
    arg_names = [re.match(r"(\w+)\W", line[1]).group(1) for line in outer_lines]
    return arg_names


def compare_pipeline_args_to_hub_spec(pipeline_class, hub_spec):
    """
    Compares the docstring of a pipeline class to the fields of the matching Hub input signature class to ensure that
    they match. This guarantees that Transformers pipelines can be used in inference without needing to manually
    refactor or rename inputs.
    """
    ALLOWED_TRANSFORMERS_ONLY_ARGS = ["timeout"]

    docstring = inspect.getdoc(pipeline_class.__call__).strip()
    docstring_args = set(parse_args_from_docstring_by_indentation(docstring))
    hub_args = set(get_arg_names_from_hub_spec(hub_spec))

    # Special casing: We allow the name of this arg to differ
    hub_generate_args = [
        hub_arg for hub_arg in hub_args if hub_arg.startswith("generate") or hub_arg.startswith("generation")
    ]
    docstring_generate_args = [
        docstring_arg
        for docstring_arg in docstring_args
        if docstring_arg.startswith("generate") or docstring_arg.startswith("generation")
    ]
    if (
        len(hub_generate_args) == 1
        and len(docstring_generate_args) == 1
        and hub_generate_args != docstring_generate_args
    ):
        hub_args.remove(hub_generate_args[0])
        docstring_args.remove(docstring_generate_args[0])

    # Special casing 2: We permit some transformers-only arguments that don't affect pipeline output
    for arg in ALLOWED_TRANSFORMERS_ONLY_ARGS:
        if arg in docstring_args and arg not in hub_args:
            docstring_args.remove(arg)

    if hub_args != docstring_args:
        error = [f"{pipeline_class.__name__} differs from JS spec {hub_spec.__name__}"]
        matching_args = hub_args & docstring_args
        huggingface_hub_only = hub_args - docstring_args
        transformers_only = docstring_args - hub_args
        if matching_args:
            error.append(f"Matching args: {matching_args}")
        if huggingface_hub_only:
            error.append(f"Huggingface Hub only: {huggingface_hub_only}")
        if transformers_only:
            error.append(f"Transformers only: {transformers_only}")
        raise ValueError("\n".join(error))