image-match/python/py/Lib/site-packages/transformers/models/auto/processing_auto.py

# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""AutoProcessor class."""

import importlib
import json
from collections import OrderedDict
from typing import TYPE_CHECKING

# Build the list of all feature extractors
from ...configuration_utils import PreTrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...processing_utils import ProcessorMixin
from ...tokenization_python import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, PROCESSOR_NAME, VIDEO_PROCESSOR_NAME, cached_file, logging
from ...video_processing_utils import BaseVideoProcessor
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer
from .video_processing_auto import AutoVideoProcessor


logger = logging.get_logger(__name__)
if TYPE_CHECKING:
    # This significantly improves completion suggestion performance when
    # the transformers package is used with Microsoft's Pylance language server.
    PROCESSOR_MAPPING_NAMES: OrderedDict[str, str | None] = OrderedDict()
else:
    PROCESSOR_MAPPING_NAMES = OrderedDict(
        [
            ("aimv2", "CLIPProcessor"),
            ("align", "AlignProcessor"),
            ("altclip", "AltCLIPProcessor"),
            ("aria", "AriaProcessor"),
            ("audioflamingo3", "AudioFlamingo3Processor"),
            ("aya_vision", "AyaVisionProcessor"),
            ("bark", "BarkProcessor"),
            ("blip", "BlipProcessor"),
            ("blip-2", "Blip2Processor"),
            ("bridgetower", "BridgeTowerProcessor"),
            ("chameleon", "ChameleonProcessor"),
            ("chinese_clip", "ChineseCLIPProcessor"),
            ("clap", "ClapProcessor"),
            ("clip", "CLIPProcessor"),
            ("clipseg", "CLIPSegProcessor"),
            ("clvp", "ClvpProcessor"),
            ("cohere2_vision", "Cohere2VisionProcessor"),
            ("cohere_asr", "CohereAsrProcessor"),
            ("colmodernvbert", "ColModernVBertProcessor"),
            ("colpali", "ColPaliProcessor"),
            ("colqwen2", "ColQwen2Processor"),
            ("deepseek_vl", "DeepseekVLProcessor"),
            ("deepseek_vl_hybrid", "DeepseekVLHybridProcessor"),
            ("dia", "DiaProcessor"),
            ("edgetam", "Sam2Processor"),
            ("emu3", "Emu3Processor"),
            ("ernie4_5_vl_moe", "Ernie4_5_VLMoeProcessor"),
            ("evolla", "EvollaProcessor"),
            ("flava", "FlavaProcessor"),
            ("florence2", "Florence2Processor"),
            ("fuyu", "FuyuProcessor"),
            ("gemma3", "Gemma3Processor"),
            ("gemma3n", "Gemma3nProcessor"),
            ("gemma4", "Gemma4Processor"),
            ("git", "GitProcessor"),
            ("glm46v", "Glm46VProcessor"),
            ("glm4v", "Glm4vProcessor"),
            ("glm4v_moe", "Glm4vProcessor"),
            ("glm_image", "Glm4vProcessor"),
            ("glmasr", "GlmAsrProcessor"),
            ("got_ocr2", "GotOcr2Processor"),
            ("granite_speech", "GraniteSpeechProcessor"),
            ("grounding-dino", "GroundingDinoProcessor"),
            ("groupvit", "CLIPProcessor"),
            ("higgs_audio_v2", "HiggsAudioV2Processor"),
            ("hubert", "Wav2Vec2Processor"),
            ("idefics", "IdeficsProcessor"),
            ("idefics2", "Idefics2Processor"),
            ("idefics3", "Idefics3Processor"),
            ("instructblip", "InstructBlipProcessor"),
            ("instructblipvideo", "InstructBlipVideoProcessor"),
            ("internvl", "InternVLProcessor"),
            ("janus", "JanusProcessor"),
            ("kosmos-2", "Kosmos2Processor"),
            ("kosmos-2.5", "Kosmos2_5Processor"),
            ("kyutai_speech_to_text", "KyutaiSpeechToTextProcessor"),
            ("lasr_ctc", "LasrProcessor"),
            ("lasr_encoder", "LasrProcessor"),
            ("layoutlmv2", "LayoutLMv2Processor"),
            ("layoutlmv3", "LayoutLMv3Processor"),
            ("layoutxlm", "LayoutXLMProcessor"),
            ("lfm2_vl", "Lfm2VlProcessor"),
            ("lighton_ocr", "LightOnOcrProcessor"),
            ("llama4", "Llama4Processor"),
            ("llava", "LlavaProcessor"),
            ("llava_next", "LlavaNextProcessor"),
            ("llava_next_video", "LlavaNextVideoProcessor"),
            ("llava_onevision", "LlavaOnevisionProcessor"),
            ("markuplm", "MarkupLMProcessor"),
            ("metaclip_2", "CLIPProcessor"),
            ("mgp-str", "MgpstrProcessor"),
            ("mistral3", "PixtralProcessor"),
            ("mllama", "MllamaProcessor"),
            ("mm-grounding-dino", "GroundingDinoProcessor"),
            ("modernvbert", "Idefics3Processor"),
            ("moonshine", "Wav2Vec2Processor"),
            ("moonshine_streaming", "MoonshineStreamingProcessor"),
            ("musicflamingo", "MusicFlamingoProcessor"),
            ("omdet-turbo", "OmDetTurboProcessor"),
            ("oneformer", "OneFormerProcessor"),
            ("ovis2", "Ovis2Processor"),
            ("owlv2", "Owlv2Processor"),
            ("owlvit", "OwlViTProcessor"),
            ("paddleocr_vl", "PaddleOCRVLProcessor"),
            ("paligemma", "PaliGemmaProcessor"),
            ("perception_lm", "PerceptionLMProcessor"),
            ("phi4_multimodal", "Phi4MultimodalProcessor"),
            ("pi0", "PI0Processor"),
            ("pix2struct", "Pix2StructProcessor"),
            ("pixtral", "PixtralProcessor"),
            ("pop2piano", "Pop2PianoProcessor"),
            ("pp_chart2table", "PPChart2TableProcessor"),
            ("qwen2_5_omni", "Qwen2_5OmniProcessor"),
            ("qwen2_5_vl", "Qwen2_5_VLProcessor"),
            ("qwen2_audio", "Qwen2AudioProcessor"),
            ("qwen2_vl", "Qwen2VLProcessor"),
            ("qwen3_5", "Qwen3VLProcessor"),
            ("qwen3_5_moe", "Qwen3VLProcessor"),
            ("qwen3_omni_moe", "Qwen3OmniMoeProcessor"),
            ("qwen3_vl", "Qwen3VLProcessor"),
            ("qwen3_vl_moe", "Qwen3VLProcessor"),
            ("sam", "SamProcessor"),
            ("sam2", "Sam2Processor"),
            ("sam3", "Sam3Processor"),
            ("sam_hq", "SamHQProcessor"),
            ("seamless_m4t", "SeamlessM4TProcessor"),
            ("sew", "Wav2Vec2Processor"),
            ("sew-d", "Wav2Vec2Processor"),
            ("shieldgemma2", "ShieldGemma2Processor"),
            ("siglip", "SiglipProcessor"),
            ("siglip2", "Siglip2Processor"),
            ("smolvlm", "SmolVLMProcessor"),
            ("speech_to_text", "Speech2TextProcessor"),
            ("speecht5", "SpeechT5Processor"),
            ("t5gemma2", "Gemma3Processor"),
            ("t5gemma2_encoder", "Gemma3Processor"),
            ("trocr", "TrOCRProcessor"),
            ("tvp", "TvpProcessor"),
            ("udop", "UdopProcessor"),
            ("unispeech", "Wav2Vec2Processor"),
            ("unispeech-sat", "Wav2Vec2Processor"),
            ("vibevoice_asr", "VibeVoiceAsrProcessor"),
            ("video_llava", "VideoLlavaProcessor"),
            ("vilt", "ViltProcessor"),
            ("vipllava", "LlavaProcessor"),
            ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
            ("voxtral", "VoxtralProcessor"),
            ("voxtral_realtime", "VoxtralRealtimeProcessor"),
            ("wav2vec2", "Wav2Vec2Processor"),
            ("wav2vec2-bert", "Wav2Vec2Processor"),
            ("wav2vec2-conformer", "Wav2Vec2Processor"),
            ("wavlm", "Wav2Vec2Processor"),
            ("whisper", "WhisperProcessor"),
            ("xclip", "XCLIPProcessor"),
        ]
    )

PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def processor_class_from_name(class_name: str):
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            module_name = model_type_to_module_name(module_name)

            module = importlib.import_module(f".{module_name}", "transformers.models")
            try:
                return getattr(module, class_name)
            except AttributeError:
                continue

    for processor in PROCESSOR_MAPPING._extra_content.values():
        if getattr(processor, "__name__", None) == class_name:
            return processor

    # We did not find the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    main_module = importlib.import_module("transformers")
    if hasattr(main_module, class_name):
        return getattr(main_module, class_name)

    return None


class AutoProcessor:
    r"""
    This is a generic processor class that will be instantiated as one of the processor classes of the library when
    created with the [`AutoProcessor.from_pretrained`] class method.

    This class cannot be instantiated directly using `__init__()` (throws an error).
    """

    def __init__(self):
        raise OSError(
            "AutoProcessor is designed to be instantiated "
            "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
        )

    @classmethod
    @replace_list_option_in_docstrings(PROCESSOR_MAPPING_NAMES)
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        r"""
        Instantiate one of the processor classes of the library from a pretrained model vocabulary.

        The processor class to instantiate is selected based on the `model_type` property of the config object (either
        passed as an argument or loaded from `pretrained_model_name_or_path` if possible):

        List options

        Params:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                This can be either:

                - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
                  huggingface.co.
                - a path to a *directory* containing a processor files saved using the `save_pretrained()` method,
                  e.g., `./my_model_directory/`.
            cache_dir (`str` or `os.PathLike`, *optional*):
                Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force to (re-)download the feature extractor files and override the cached versions
                if they exist.
            proxies (`dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `hf auth login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final feature extractor object. If `True`, then this
                functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
                consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
                `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
            trust_remote_code (`bool`, *optional*, defaults to `False`):
                Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
                should only be set to `True` for repositories you trust and in which you have read the code, as it will
                execute code present on the Hub on your local machine.
            kwargs (`dict[str, Any]`, *optional*):
                The values in kwargs of any keys which are feature extractor attributes will be used to override the
                loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
                controlled by the `return_unused_kwargs` keyword parameter.

        <Tip>

        Passing `token=True` is required when you want to use a private model.

        </Tip>

        Examples:

        ```python
        >>> from transformers import AutoProcessor

        >>> # Download processor from huggingface.co and cache.
        >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")

        >>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
        >>> # processor = AutoProcessor.from_pretrained("./test/saved_model/")
        ```"""
        config = kwargs.pop("config", None)
        trust_remote_code = kwargs.pop("trust_remote_code", None)
        kwargs["_from_auto"] = True

        processor_class = None
        processor_auto_map = None

        # First, let's see if we have a processor or preprocessor config.
        # Filter the kwargs for `cached_file`.
        _hub_valid_kwargs = (
            "cache_dir",
            "force_download",
            "proxies",
            "token",
            "revision",
            "local_files_only",
            "subfolder",
            "repo_type",
            "user_agent",
        )
        cached_file_kwargs = {key: kwargs[key] for key in _hub_valid_kwargs if key in kwargs}
        # We don't want to raise
        cached_file_kwargs.update(
            {
                "_raise_exceptions_for_gated_repo": False,
                "_raise_exceptions_for_missing_entries": False,
                "_raise_exceptions_for_connection_errors": False,
            }
        )

        # Let's start by checking whether the processor class is saved in a processor config
        processor_config_file = cached_file(pretrained_model_name_or_path, PROCESSOR_NAME, **cached_file_kwargs)
        if processor_config_file is not None:
            config_dict, _ = ProcessorMixin.get_processor_dict(pretrained_model_name_or_path, **kwargs)
            processor_class = config_dict.get("processor_class")
            if "AutoProcessor" in config_dict.get("auto_map", {}):
                processor_auto_map = config_dict["auto_map"]["AutoProcessor"]

        if processor_class is None:
            # If not found, let's check whether the processor class is saved in an image processor config
            preprocessor_config_file = cached_file(
                pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, **cached_file_kwargs
            )
            if preprocessor_config_file is not None:
                config_dict, _ = ImageProcessingMixin.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)
                processor_class = config_dict.get("processor_class", None)
                if "AutoProcessor" in config_dict.get("auto_map", {}):
                    processor_auto_map = config_dict["auto_map"]["AutoProcessor"]

            # Saved as video processor
            if preprocessor_config_file is None:
                preprocessor_config_file = cached_file(
                    pretrained_model_name_or_path, VIDEO_PROCESSOR_NAME, **cached_file_kwargs
                )
                if preprocessor_config_file is not None:
                    config_dict, _ = BaseVideoProcessor.get_video_processor_dict(
                        pretrained_model_name_or_path, **kwargs
                    )
                    processor_class = config_dict.get("processor_class", None)
                    if "AutoProcessor" in config_dict.get("auto_map", {}):
                        processor_auto_map = config_dict["auto_map"]["AutoProcessor"]
            # Saved as feature extractor
            if preprocessor_config_file is None:
                preprocessor_config_file = cached_file(
                    pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME, **cached_file_kwargs
                )
                if preprocessor_config_file is not None and processor_class is None:
                    config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(
                        pretrained_model_name_or_path, **kwargs
                    )
                    processor_class = config_dict.get("processor_class", None)
                    if "AutoProcessor" in config_dict.get("auto_map", {}):
                        processor_auto_map = config_dict["auto_map"]["AutoProcessor"]

        if processor_class is None:
            # Next, let's check whether the processor class is saved in a tokenizer
            tokenizer_config_file = cached_file(
                pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, **cached_file_kwargs
            )
            if tokenizer_config_file is not None:
                with open(tokenizer_config_file, encoding="utf-8") as reader:
                    config_dict = json.load(reader)

                processor_class = config_dict.get("processor_class", None)
                if "AutoProcessor" in config_dict.get("auto_map", {}):
                    processor_auto_map = config_dict["auto_map"]["AutoProcessor"]

        if processor_class is None:
            # Last resort: try loading the model config to get processor_class.
            # This handles cases where processor info is only in config.json (not in any
            # preprocessor/tokenizer config files). AutoConfig.from_pretrained may raise
            # ValueError if the model_type is unrecognized or the config is invalid -
            # we catch and ignore this to allow fallback to AutoTokenizer/AutoImageProcessor.
            try:
                if not isinstance(config, PreTrainedConfig):
                    config = AutoConfig.from_pretrained(
                        pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
                    )

                processor_class = getattr(config, "processor_class", None)
                if hasattr(config, "auto_map") and "AutoProcessor" in config.auto_map:
                    processor_auto_map = config.auto_map["AutoProcessor"]
            except ValueError:
                # Config loading failed (unrecognized model_type, invalid config, etc.)
                # Continue to fallback logic below (AutoTokenizer, AutoImageProcessor, etc.)
                pass

        if processor_class is not None:
            processor_class = processor_class_from_name(processor_class)

        has_remote_code = processor_auto_map is not None
        has_local_code = processor_class is not None or type(config) in PROCESSOR_MAPPING
        explicit_local_code = has_local_code and not (
            processor_class or PROCESSOR_MAPPING[type(config)]
        ).__module__.startswith("transformers.")
        if has_remote_code:
            if "--" in processor_auto_map:
                upstream_repo = processor_auto_map.split("--")[0]
            else:
                upstream_repo = None
            trust_remote_code = resolve_trust_remote_code(
                trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code, upstream_repo
            )

        if has_remote_code and trust_remote_code and not explicit_local_code:
            processor_class = get_class_from_dynamic_module(
                processor_auto_map, pretrained_model_name_or_path, **kwargs
            )
            _ = kwargs.pop("code_revision", None)
            processor_class.register_for_auto_class()
            return processor_class.from_pretrained(
                pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
            )
        elif processor_class is not None:
            return processor_class.from_pretrained(
                pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
            )
        # Last try: we use the PROCESSOR_MAPPING.
        elif type(config) in PROCESSOR_MAPPING:
            return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)

        # At this stage, there doesn't seem to be a `Processor` class available for this model.
        # Let's try the commonly available classes
        for klass in (AutoTokenizer, AutoImageProcessor, AutoVideoProcessor, AutoFeatureExtractor):
            try:
                return klass.from_pretrained(
                    pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
                )
            except Exception:
                continue

        raise ValueError(
            f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
            "tokenizer, an image processor, a video processor or a feature extractor for this model. "
            "Make sure the repository contains the files of at least one of those processing classes."
        )

    @staticmethod
    def register(config_class, processor_class, exist_ok=False):
        """
        Register a new processor for this class.

        Args:
            config_class ([`PreTrainedConfig`]):
                The configuration corresponding to the model to register.
            processor_class ([`ProcessorMixin`]): The processor to register.
        """
        PROCESSOR_MAPPING.register(config_class, processor_class, exist_ok=exist_ok)


__all__ = ["PROCESSOR_MAPPING", "AutoProcessor"]