aphrodite-engine/aphrodite/multimodal/utils.py

import base64
from functools import lru_cache
from io import BytesIO
from typing import Any, List, Optional, Tuple, TypeVar, Union

import librosa
import numpy as np
import numpy.typing as npt
import soundfile
from loguru import logger
from PIL import Image

from aphrodite.common.envs import (APHRODITE_AUDIO_FETCH_TIMEOUT,
                                   APHRODITE_IMAGE_FETCH_TIMEOUT)
from aphrodite.connections import global_http_connection
from aphrodite.multimodal.base import MultiModalDataDict
from aphrodite.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer

cached_get_tokenizer = lru_cache(get_tokenizer)


def _load_image_from_bytes(b: bytes):
    image = Image.open(BytesIO(b))
    image.load()
    return image


def _load_image_from_data_url(image_url: str):
    # Only split once and assume the second part is the base64 encoded image
    _, image_base64 = image_url.split(",", 1)
    return load_image_from_base64(image_base64)


def fetch_image(image_url: str, *, image_mode: str = "RGB") -> Image.Image:
    """
    Load a PIL image from a HTTP or base64 data URL.

    By default, the image is converted into RGB format.
    """
    if image_url.startswith('http'):
        image_raw = global_http_connection.get_bytes(
            image_url, timeout=APHRODITE_IMAGE_FETCH_TIMEOUT)
        image = _load_image_from_bytes(image_raw)

    elif image_url.startswith('data:image'):
        image = _load_image_from_data_url(image_url)
    else:
        raise ValueError("Invalid 'image_url': A valid 'image_url' must start "
                         "with either 'data:image' or 'http'.")

    return image.convert(image_mode)


async def async_fetch_image(image_url: str,
                            *,
                            image_mode: str = "RGB") -> Image.Image:
    """
    Asynchronously load a PIL image from a HTTP or base64 data URL.

    By default, the image is converted into RGB format.
    """
    if image_url.startswith('http'):
        image_raw = await global_http_connection.async_get_bytes(
            image_url, timeout=APHRODITE_IMAGE_FETCH_TIMEOUT)
        image = _load_image_from_bytes(image_raw)

    elif image_url.startswith('data:image'):
        image = _load_image_from_data_url(image_url)
    else:
        raise ValueError("Invalid 'image_url': A valid 'image_url' must start "
                         "with either 'data:image' or 'http'.")

    return image.convert(image_mode)


def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
    """
    Load audio from a URL.
    """
    if audio_url.startswith("http"):
        audio_bytes = global_http_connection.get_bytes(
            audio_url, timeout=APHRODITE_AUDIO_FETCH_TIMEOUT)
    elif audio_url.startswith("data:audio"):
        _, audio_base64 = audio_url.split(",", 1)
        audio_bytes = base64.b64decode(audio_base64)
    else:
        raise ValueError("Invalid 'audio_url': A valid 'audio_url' must start "
                         "with either 'data:audio' or 'http'.")

    return librosa.load(BytesIO(audio_bytes), sr=None)


async def async_fetch_audio(
        audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
    """
    Asynchronously fetch audio from a URL.
    """
    if audio_url.startswith("http"):
        audio_bytes = await global_http_connection.async_get_bytes(
            audio_url, timeout=APHRODITE_AUDIO_FETCH_TIMEOUT)
    elif audio_url.startswith("data:audio"):
        _, audio_base64 = audio_url.split(",", 1)
        audio_bytes = base64.b64decode(audio_base64)
    else:
        raise ValueError("Invalid 'audio_url': A valid 'audio_url' must start "
                         "with either 'data:audio' or 'http'.")

    return librosa.load(BytesIO(audio_bytes), sr=None)


def get_and_parse_audio(audio_url: str) -> MultiModalDataDict:
    audio, sr = fetch_audio(audio_url)
    return {"audio": (audio, sr)}


def get_and_parse_image(image_url: str) -> MultiModalDataDict:
    image = fetch_image(image_url)
    return {"image": image}


async def async_get_and_parse_audio(audio_url: str) -> MultiModalDataDict:
    audio, sr = await async_fetch_audio(audio_url)
    return {"audio": (audio, sr)}


async def async_get_and_parse_image(image_url: str) -> MultiModalDataDict:
    image = await async_fetch_image(image_url)
    return {"image": image}


def encode_audio_base64(
    audio: np.ndarray,
    sampling_rate: int,
) -> str:
    """Encode audio as base64."""
    buffered = BytesIO()
    soundfile.write(buffered, audio, sampling_rate, format="WAV")

    return base64.b64encode(buffered.getvalue()).decode('utf-8')


def encode_image_base64(
    image: Image.Image,
    *,
    image_mode: str = "RGB",
    format: str = "JPEG",
) -> str:
    """
    Encode a pillow image to base64 format.

    By default, the image is converted into RGB format before being encoded.
    """
    buffered = BytesIO()
    image = image.convert(image_mode)
    image.save(buffered, format)
    return base64.b64encode(buffered.getvalue()).decode('utf-8')


def load_image_from_base64(image: Union[bytes, str]) -> Image.Image:
    """Load image from base64 format."""
    return _load_image_from_bytes(base64.b64decode(image))


def rescale_image_size(image: Image.Image,
                       size_factor: float,
                       transpose: int = -1) -> Image.Image:
    """Rescale the dimensions of an image by a constant factor."""
    new_width = int(image.width * size_factor)
    new_height = int(image.height * size_factor)
    image = image.resize((new_width, new_height))
    if transpose >= 0:
        image = image.transpose(Image.Transpose(transpose))
    return image


def try_import_video_packages() -> Any:
    try:
        import cv2
    except ImportError:
        raise ImportError(
            "Please install opencv-python for video support."
            ) from None
    return cv2


def resize_video(frames: npt.NDArray, size: Tuple[int, int]) -> npt.NDArray:
    cv2 = try_import_video_packages()
    num_frames, _, _, channels = frames.shape
    new_height, new_width = size
    resized_frames = np.empty((num_frames, new_height, new_width, channels),
                              dtype=frames.dtype)
    for i, frame in enumerate(frames):
        resized_frame = cv2.resize(frame, (new_width, new_height))
        resized_frames[i] = resized_frame
    return resized_frames

def rescale_video_size(frames: npt.NDArray, size_factor: float) -> npt.NDArray:
    _, height, width, _ = frames.shape
    new_height = int(height * size_factor)
    new_width = int(width * size_factor)
    return resize_video(frames, (new_height, new_width))

def sample_frames_from_video(frames: npt.NDArray,
                             num_frames: int) -> npt.NDArray:
    total_frames = frames.shape[0]
    if num_frames == -1:
        return frames
    else:
        frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
        sampled_frames = frames[frame_indices, ...]
        return sampled_frames


# Utilities for input processors
_T = TypeVar("_T", str, int)


def repeat_and_pad_token(
    token: _T,
    *,
    repeat_count: int = 1,
    pad_token_left: Optional[_T] = None,
    pad_token_right: Optional[_T] = None,
) -> List[_T]:
    replacement = [token] * repeat_count
    if pad_token_left is not None:
        replacement = [pad_token_left] + replacement
    if pad_token_right is not None:
        replacement = replacement + [pad_token_right]

    return replacement


def repeat_and_pad_placeholder_tokens(
    tokenizer: AnyTokenizer,
    prompt: Optional[str],
    prompt_token_ids: List[int],
    *,
    placeholder_token_id: int,
    repeat_count: Union[int, List[int]],
    pad_token_left: Optional[int] = None,
    pad_token_right: Optional[int] = None,
) -> Tuple[Optional[str], List[int]]:
    if isinstance(repeat_count, int):
        repeat_count = [repeat_count]
    if prompt is None:
        new_prompt = None
    else:
        placeholder_token_str = tokenizer.decode(placeholder_token_id)
        pad_token_str_left = (None if pad_token_left is None else
                              tokenizer.decode(pad_token_left))
        pad_token_str_right = (None if pad_token_right is None else
                               tokenizer.decode(pad_token_right))

        placeholder_token_count = prompt.count(placeholder_token_str)
        # This is an arbitrary number to distinguish between the two cases
        if placeholder_token_count > 16:
            logger.warning(
                "Please follow the prompt format that is "
                "documented on HuggingFace which does not involve "
                f"repeating {placeholder_token_str} tokens.")
        if placeholder_token_count < len(repeat_count):
            logger.warning(
                "The number of multi-modal placeholder tokens in the prompt "
                "is less than the number of multi-modal inputs. Extra "
                "placeholder tokens will be treated as plain text")
            repeat_count = repeat_count[:placeholder_token_count]
        prompt_parts = prompt.split(placeholder_token_str,
                                    maxsplit=len(repeat_count))
        new_prompt = ""
        for i, repeat_count_item in enumerate(repeat_count):
            replacement_str = "".join(
                repeat_and_pad_token(
                    placeholder_token_str,
                    repeat_count=repeat_count_item,
                    pad_token_left=pad_token_str_left,
                    pad_token_right=pad_token_str_right,
                ))
            # The image tokens are removed to be consistent with HuggingFace
            new_prompt += prompt_parts[i] + replacement_str
        new_prompt += prompt_parts[-1]

    new_token_ids: List[int] = []
    placeholder_token_idx = 0
    for i, token in enumerate(prompt_token_ids):
        if token == placeholder_token_id:
            replacement_ids = repeat_and_pad_token(
                placeholder_token_id,
                repeat_count=repeat_count[placeholder_token_idx],
                pad_token_left=pad_token_left,
                pad_token_right=pad_token_right,
            )
            new_token_ids.extend(replacement_ids)
            placeholder_token_idx += 1

            # No need to further scan the list since we replaced all tokens
            if placeholder_token_idx >= len(repeat_count):
                new_token_ids.extend(prompt_token_ids[i + 1:])
                break
        else:
            new_token_ids.append(token)

    return new_prompt, new_token_ids