auto_processor.py

import copy
import os
import os.path as osp
import warnings
from collections import defaultdict
from typing import List, Union

import torch
from transformers import AutoConfig, AutoImageProcessor, AutoModel, AutoProcessor, AutoTokenizer
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, VideoInput
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
from .mm_utils import process_image, process_images
from .tokenizer_utils import tokenize_conversation

def fetch_image_url_or_fpath(url_or_fpath):
    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} is not a file"
        return url_or_fpath
    else:
        raise ValueError(f"Unsupported image path: {url_or_fpath}")
    

def __pad_fn(input_ids_list, padding_value=0, target_len=None, padding_side="left"):
    # 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)


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




class VILAProcessor(ProcessorMixin):
    # attributes = ["image_processor", "tokenizer"]
    attributes = []
    # valid_kwargs = ["chat_template"]
    valid_kwargs = []
    # image_processor_class = "VILAImageProcessor"
    # tokenizer_class = ("VILATokenizer", "VILATokenizerFast")

    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, config=None, **kwargs):
        # self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
        # self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
        self.image_token = MEDIA_TOKENS["image"]
        self.video_token = MEDIA_TOKENS["video"]
        self.config = config
        self.image_processor = image_processor
        self.tokenizer = tokenizer

        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        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)

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

    def __call__(
        self,
        conversation,
        images: ImageInput = None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        videos: VideoInput = None,
        **kwargs: Unpack[VILAProcessorKwargs],
    ) -> BatchFeature:
        if images is not None:
            warnings.warn("images is not supported in __call__")

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

        return BatchFeature(
            data={
                # "input_ids": torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=self.pad_token_id),
                "input_ids": __pad_fn(
                    input_ids,
                    padding_value=self.tokenizer.pad_token_id,
                    padding_side="left",
                ),
                "media": media,
                "media_config": media_config,
            }
        )

    def __single_call__(
        self,
        conversation,
        images: ImageInput = None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        videos: VideoInput = None,
        **kwargs: Unpack[VILAProcessorKwargs],
    ) -> BatchFeature:
        # TODO: should be merged with llava_arch.py/generate_content()
        # TODO (extract and preprocess should be done together, as the preprocess of image and video can be different, i.e. when dynamic res is used)
        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()
                        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.vision_tower.image_processor, self.config).half()
                media[name] = [image for image in images]
            elif name == "video":
                media[name] = [
                    process_images(images, self.vision_tower.image_processor, self.config).half()
                    for images in media[name]
                ]
            else:
                raise ValueError(f"Unsupported media type: {name}")
        input_ids = tokenize_conversation(conversation, self.tokenizer, add_generation_prompt=True).cuda().unsqueeze(0)
        # Set up the generation config
        return BatchFeature(data={"input_ids": input_ids, "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))

    #     inputs = processor(conversation=llavaconv, padding=True, return_tensors="pt")
    def apply_chat_template(self, conversation, add_generation_prompt=True, **kwargs):
        vila_conv = []
        for chat in conversation:
            vila_chat = {"from": "", "value": []}
            if chat["role"] == "user":
                # user allows to input image and text
                vila_chat["from"] = "human"
                for content in chat["content"]:
                    if content["type"] == "image":
                        if "path" in content:
                            # VILA style
                            vila_chat["value"].append(Image(fetch_image_url_or_fpath(content["path"])))
                        elif "image" in content:
                            # Qwen style
                            vila_chat["value"].append(Image(fetch_image_url_or_fpath(content["image"])))
                        else:
                            raise ValueError(f"Unsupported content type `image`: {content}, `image` and `path` are required")
                    elif content["type"] == "text":
                        vila_chat["value"].append(content["text"])
                    # NOTE(ligeng): video supports are needed here
                    else:
                        raise ValueError(f"Unsupported content type: {content['type']}")
            elif chat["role"] == "assistant":
                vila_chat["from"] = "gpt"
                for content in chat["content"]:
                    assert content["type"] == "text", f"Unsupported content type: {content['type']}"
                    vila_chat["value"].append(content["text"])
            vila_conv.append(vila_chat)

        return vila_conv


if __name__ == "__main__":
    # gpt style: user, assistant
    # vila style: human, gpt
    gpt_conv = [
        {
            "role": "user",
            "content": [
                {"type": "image", "path": "demo_images/demo_img_1.png"},
                {"type": "text", "text": "Describe this image."},
            ],
        }
    ]

    llavaconv = [
        {
            "from": "human",
            "value": [
                PIL.Image.open("demo_images/demo_img_1.png"),
                "Describe this image.",
            ],
        }
    ]

    processor = AutoProcessor.from_pretrained(output_dir, trust_remote_code=True)
    inputs = processor.apply_chat_template(conversation=gpt_conv, padding=True, return_tensors="pt")
    # model = llava.load("Efficient-Large-Model/qwen25_2B_3x3-sft").cuda()
    # print(model)
    model_path = "NVILA-Lite-2B-hf-preview"
    model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map="auto")
    # res = model.generate_content(["how are you today?"])
    # print(model.config)
    # print(model.tokenizer)
    # print(res)
    # exit(0)

    processor = VILAProcessor(
        config=model.config,
        image_processor=model.vision_tower.image_processor,
        tokenizer=model.tokenizer,
    )

    # TODO: add padding, return_tensors,
    inputs = processor(conversation=llavaconv, padding=True, return_tensors="pt")
    print(inputs.keys(), inputs.input_ids.shape, [_.shape for _ in inputs.image])
    print("vila conv pass")

    inputs = processor.apply_chat_template(conversation=gpt_conv, padding=True, return_tensors="pt")
    print(inputs.keys(), inputs.input_ids.shape, [_.shape for _ in inputs.image])
    print("gpt conv pass")

    output_ids = model.generate(
        input_ids=inputs.input_ids,
        media={
            "image": inputs.image,
        },
        media_config={"image": {}},
        generation_config=model.generation_config,
        max_new_tokens=100,
    )
    print(output_ids)