auto_processor.py

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 os
import os.path as osp
import warnings
from collections import defaultdict
from io import BytesIO
from typing import List, Optional, Union

import PIL.Image
import requests
import torch
from transformers import AutoConfig, AutoImageProcessor, AutoModel, AutoProcessor, AutoTokenizer
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging

from .constants import DEFAULT_IMAGE_TOKEN, MEDIA_TOKENS
from .media import Image, Video, extract_media, Sound
from .mm_utils import process_image, process_images
from .tokenizer_utils import tokenize_conversation


def to_rgb(pil_image: PIL.Image.Image) -> PIL.Image.Image:
    """Convert PIL image to RGB format."""
    if pil_image.mode == "RGBA":
        white_background = PIL.Image.new("RGB", pil_image.size, (255, 255, 255))
        white_background.paste(pil_image, mask=pil_image.split()[3])  # Use alpha channel as mask
        return white_background
    else:
        return pil_image.convert("RGB")


def fetch_image(ele: dict[str, str | PIL.Image.Image], size_factor=None) -> PIL.Image.Image:
    """Fetch and load image from various sources (local path, URL, base64, PIL.Image)."""
    if "image" in ele:
        image = ele["image"]
    else:
        image = ele["image_url"]
    image_obj = None
    if isinstance(image, PIL.Image.Image):
        image_obj = image
    elif image.startswith("http://") or image.startswith("https://"):
        response = requests.get(image, stream=True)
        image_obj = PIL.Image.open(BytesIO(response.content))
    elif image.startswith("file://"):
        image_obj = PIL.Image.open(image[7:])
    elif image.startswith("data:image"):
        if "base64," in image:
            _, base64_data = image.split("base64,", 1)
            data = base64.b64decode(base64_data)
            image_obj = PIL.Image.open(BytesIO(data))
    else:
        image_obj = PIL.Image.open(image)
    if image_obj is None:
        raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
    image = to_rgb(image_obj)

    return image


def fetch_image_url_or_fpath(url_or_fpath):
    """Fetch image from URL or local file path, returns local file path."""
    if url_or_fpath.startswith("http") or url_or_fpath.startswith("https"):
        import tempfile

        import requests

        # Download the image to a temporary file
        temp_dir = tempfile.mkdtemp()
        temp_file = os.path.join(temp_dir, os.path.basename(url_or_fpath))

        response = requests.get(url_or_fpath, stream=True)
        response.raise_for_status()

        with open(temp_file, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)

        return temp_file
    elif url_or_fpath.startswith("file://"):
        fpath = url_or_fpath.replace("file://", "")
        assert osp.exists(fpath), f"File {fpath} does not exist"
        return fpath
    elif osp.exists(url_or_fpath):
        assert osp.isfile(url_or_fpath), f"File {url_or_fpath} does not exist"
        return url_or_fpath
    else:
        raise ValueError(f"Unsupported image path: {url_or_fpath}")


def pad_fn(input_ids_list: List[torch.Tensor], padding_value=0, target_len=None, padding_side="left") -> torch.Tensor:
    # tensor shape is (batch_size, seq_len)
    max_len = max([ids.shape[1] for ids in input_ids_list])
    if target_len is not None:
        assert target_len >= max_len, "target_len must be greater than or equal to max_len"
        max_len = target_len

    new_input_ids_list = []
    for i, input_ids in enumerate(input_ids_list):
        pad_tensor = torch.ones_like(input_ids) * padding_value
        curr_len = input_ids.shape[1]
        pad_tensor = pad_tensor[:, : max_len - curr_len]
        if padding_side == "right":
            input_ids = torch.cat((input_ids, pad_tensor), dim=1)
        else:
            input_ids = torch.cat((pad_tensor, input_ids), dim=1)
        new_input_ids_list.append(input_ids)
    return torch.cat(new_input_ids_list, dim=0)


def extract_value_from_conv(chat):
    value = []
    if isinstance(chat["content"], str):
        value.append(chat["content"])
        return value

    # otherwise, it's a list of content
    for content in chat["content"]:
        if content["type"] == "image":
            if "path" in content:
                # VILA style, can be either filepath or http url
                value.append(Image(fetch_image_url_or_fpath(content["path"])))
            elif "image" in content:
                # Qwen style
                value.append(Image(fetch_image_url_or_fpath(content["image"])))
            elif "image_pil" in content:
                # Qwen style
                assert isinstance(content["image_pil"], PIL.Image.Image), f"Type of image_pil must be PIL.Image.Image"
                value.append(content["image_pil"])
            else:
                raise ValueError(f"Type = `image` , but no `path` or `image` in  {chat['content']}")
        elif content["type"] == "video":
            if "video" in content:
                # Qwen style
                value.append(Video(fetch_image_url_or_fpath(content["video"])))
            else:
                raise ValueError(f"Type = `video` , but no `video` in {chat['content']}")
        elif content["type"] == "text":
            value.append(content["text"])
        elif content["type"] == "audio":
            value.append(Sound(fetch_image_url_or_fpath(content["audio"])))
        elif content["type"] == "sound":
            value.append(Sound(fetch_image_url_or_fpath(content["sound"])))
        elif content["type"] == "speech":
            value.append(Sound(fetch_image_url_or_fpath(content["speech"])))
        else:
            raise ValueError(f"Unsupported content type: {content['type']}")
    return value


class VILAProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
    }


class VILAProcessor(ProcessorMixin):
    attributes = []
    valid_kwargs = []

    def __init__(
        self, image_processor=None, tokenizer=None, chat_template=None, config=None, padding_side="left", **kwargs
    ):
        self.image_token = MEDIA_TOKENS["image"]
        self.video_token = MEDIA_TOKENS["video"]
        self.speech_token = MEDIA_TOKENS["speech"]
        self.sound_token = MEDIA_TOKENS["sound"]
        self.config = config
        self.image_processor = image_processor
        self.tokenizer = tokenizer
        self.padding_side = padding_side

        # Use <|endoftext|> token as padding token for Qwen models
        self.pad_token_id = self.tokenizer("<|endoftext|>").input_ids[0]
        self.eos_token_id = self.tokenizer.eos_token_id
        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    @staticmethod
    def extract_vision_info(conversations: list[dict] | list[list[dict]]) -> list[dict]:
        """
        Extract vision information from conversations.
        Reference: qwen_vl_utils
        """
        vision_infos = []
        if isinstance(conversations[0], dict):
            conversations = [conversations]
        for conversation in conversations:
            for message in conversation:
                if isinstance(message["content"], list):
                    for ele in message["content"]:
                        if (
                            "image" in ele
                            or "image_url" in ele
                            or "video" in ele
                            or ele["type"] in ("image", "image_url", "video")
                        ):
                            vision_infos.append(ele)
        return vision_infos

    @staticmethod
    def process_vision_info(
        conversations: list[dict] | list[list[dict]],
        return_video_kwargs: bool = False,
    ) -> tuple[list[PIL.Image.Image] | None, list[torch.Tensor | list[PIL.Image.Image]] | None, Optional[dict]]:
        """
        Process vision information from conversations.
        Reference: qwen_vl_utils
        
        Note: NVILA does not depend on this function, but maintains the same interface.
        """
        vision_infos = extract_vision_info(conversations)
        # Read images or videos
        image_inputs = []
        video_inputs = []
        video_sample_fps_list = []
        for vision_info in vision_infos:
            if "image" in vision_info or "image_url" in vision_info:
                image_inputs.append(fetch_image(vision_info))
            elif "video" in vision_info:
                video_input, video_sample_fps = fetch_video(vision_info, return_video_sample_fps=True)
                video_sample_fps_list.append(video_sample_fps)
                video_inputs.append(video_input)
            else:
                raise ValueError("image, image_url or video should in content.")
        if len(image_inputs) == 0:
            image_inputs = None
        if len(video_inputs) == 0:
            video_inputs = None
        if return_video_kwargs:
            return image_inputs, video_inputs, {"fps": video_sample_fps_list}
        return image_inputs, video_inputs

    @staticmethod
    def move_data_to_device(cls, prompt_inputs):
        def _move_data_to_device(item):
            # wrap function grpo trainer _prepare_input
            kwargs = {"device": cls.args.device}
            if cls.is_deepspeed_enabled and (torch.is_floating_point(item) or torch.is_complex(item)):
                kwargs.update({"dtype": cls.accelerator.state.deepspeed_plugin.hf_ds_config.dtype()})
            return item.to(**kwargs)

        prompt_inputs.input_ids = _move_data_to_device(prompt_inputs.input_ids)
        prompt_inputs.attention_mask = _move_data_to_device(prompt_inputs.attention_mask)
        if "image" in prompt_inputs.media:
            prompt_inputs.media["image"] = [_move_data_to_device(img) for img in prompt_inputs.media["image"]]
        return prompt_inputs

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        padding_side = kwargs.get("padding_side", "left")
        if os.path.isdir(pretrained_model_name_or_path):
            pretrained_model_name_or_path = pretrained_model_name_or_path
        else:
            print(f"pretrained_model_name_or_path {pretrained_model_name_or_path} is not a directory, downloading")
            from huggingface_hub import snapshot_download

            pretrained_model_name_or_path = snapshot_download(pretrained_model_name_or_path)

        image_processor = AutoImageProcessor.from_pretrained(
            osp.join(pretrained_model_name_or_path, "vision_tower"), trust_remote_code=True
        )
        tokenizer = AutoTokenizer.from_pretrained(
            osp.join(pretrained_model_name_or_path, "llm"), trust_remote_code=True
        )
        config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
        
        return cls(image_processor=image_processor, tokenizer=tokenizer, config=config, padding_side=padding_side)

    def __repr__(self):
        return f"VILAProcessor(image_processor=SigLip, tokenizer={self.tokenizer}, config={self.config})"

    def __call__(
        self,
        conversation=None,
        **kwargs: Unpack[VILAProcessorKwargs],
    ) -> BatchFeature:
        """
        The `conv` will be look like
        [
            {
                'from': 'human',
                'value': [
                    <transformers_modules.NVILA-Lite-2B-hf-preview.media.Image object at 0x154e68e4c460>,
                    'What are the common elements in these pictures?'
                ]
            }
        ]
        and `conversation` will be a list of such `conv`s
        """
        if kwargs.get("text", None) is not None:
            conversation = kwargs.get("text")
        assert conversation is not None, "`conversation` or `text` is required"
        padding_side = kwargs.get("padding_side", self.padding_side)

        input_ids_list = []
        attention_mask = []
        media = defaultdict(list)
        media_config = defaultdict(dict)
        for conv in conversation:
            feat = self.__single_call__(conv, **kwargs)
            input_ids_list.append(feat.input_ids)
            attention_mask.append(feat.attention_mask)
            for name in feat.media:
                media[name] += feat.media[name]
            for name in feat.media_config:
                media_config[name].update(feat.media_config[name])

        # pad the input_ids to batchfy
        input_ids = pad_fn(
            input_ids_list,
            padding_value=self.pad_token_id,
            padding_side=padding_side,
        )
        # Ignore the pad token in the attention mask
        attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
        attention_mask[input_ids == self.pad_token_id] = False
        input_texts = self.tokenizer.batch_decode(input_ids)
        bdata = BatchFeature(
            data={
                # "input_texts": input_texts,
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "media": media,
                "media_config": media_config,
            }
        )
        return bdata

    def __single_call__(
        self,
        conversation,
        images: ImageInput = None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        videos = None,
        **kwargs: Unpack[VILAProcessorKwargs],
    ) -> BatchFeature:
        conversation = copy.deepcopy(conversation)
        media = extract_media(conversation, self.config)
        # Process media
        media_config = defaultdict(dict)
        for name in media:
            if name == "image":
                if len(media["image"]) == 1 and self.config.image_aspect_ratio in ["dynamic", "dynamic_s2"]:
                    self.config.image_processor = self.image_processor
                    if self.config.image_aspect_ratio == "dynamic":
                        images = process_image(media["image"][0], self.config, None, enable_dynamic_res=True).half()
                        # Note: This assumes images appear at the first conversation position
                        conversation[0]["value"] = conversation[0]["value"].replace(
                            DEFAULT_IMAGE_TOKEN, f"{DEFAULT_IMAGE_TOKEN}\n" * images.shape[0]
                        )
                    else:
                        if type(self.config.s2_scales) is str:
                            self.config.s2_scales = list(map(int, self.config.s2_scales.split(",")))
                        images, block_sizes = process_image(
                            media["image"][0], self.config, None, enable_dynamic_s2=True
                        )
                        images = images.half()
                        media_config[name]["block_sizes"] = [block_sizes]
                else:
                    images = process_images(media["image"], self.image_processor, self.config).half()
                media[name] = [image for image in images]
            elif name == "video":
                media[name] = [
                    process_images(images, self.image_processor, self.config).half() for images in media[name]
                ]
            elif name == "speech":
                speeches = media["speech"]
                media[name] = [speech for speech in speeches]
            elif name == "sound":
                sounds = media["sound"]
                for sound in sounds:
                    if type(sound) is dict:
                        for k, v in sound.items():
                            sound[k] = v.half()
                media[name] = [sound for sound in sounds]
            elif name == "video_info":
                media[name] = [media["video_info"]]
            elif name == "audio_info":
                media[name] = [media["audio_info"]]
            else:
                raise ValueError(f"Unsupported media type: {name}")

        inputs = tokenize_conversation(
                conversation,
                self.tokenizer,
                mm_use_bos_eos_tokens=self.config.mm_use_bos_eos_tokens,
                unified_audio_encoder=self.config.unified_audio_encoder,
                add_generation_prompt=True,
            )

        input_ids = inputs.unsqueeze(0)

        attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
        return BatchFeature(
            data={
                "input_ids": input_ids,
                "attention_mask": attention_mask,
                "media": media,
                "media_config": media_config,
            }
        )

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    def post_process_image_text_to_text(self, generated_outputs):
        """
        Post-process the output of the model to decode the text.

        Args:
            generated_outputs (`torch.Tensor` or `np.ndarray`):
                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
                or `(sequence_length,)`.

        Returns:
            `List[str]`: The decoded text.
        """
        return self.tokenizer.batch_decode(
            generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
        )

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

    def convert_gpt_conv_to_vila_conv(self, conversation):
        vila_conv = []
        for chat in conversation:
            vila_chat = {"from": "", "value": []}
            if chat["role"] in ("user", "system"):
                # user allows to input image and text
                vila_chat["from"] = "human" if chat["role"] == "user" else "system"
                vila_chat["value"] = extract_value_from_conv(chat)
            elif chat["role"] == "assistant":
                vila_chat["from"] = "gpt"
                vila_chat["value"] = extract_value_from_conv(chat)
            else:
                raise ValueError(f"Unsupported role: {chat['role']} in chat {chat}")
            vila_conv.append(vila_chat)

        return vila_conv

    def apply_chat_template(self, conversation, add_generation_prompt=True, **kwargs):
        return self.convert_gpt_conv_to_vila_conv(conversation)