processing.py

import pickle
import re
from abc import ABC, abstractmethod
from collections.abc import Callable, ItemsView, Iterable, Mapping, Sequence
from dataclasses import dataclass, field
from functools import lru_cache
from typing import Any, NamedTuple, Optional, Protocol, TypeVar, Union

import numpy as np
import torch
from blake3 import blake3
from PIL.Image import Image
from transformers import BatchFeature, ProcessorMixin

from vllm.inputs import DummyData, InputProcessingContext
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
from vllm.utils import LRUCache, flatten_2d_lists, full_groupby

from .inputs import (MultiModalDataDict, MultiModalFieldConfig,
                     MultiModalFieldItem, MultiModalInputsV2, MultiModalKwargs,
                     PlaceholderRange)
from .parse import MultiModalDataItems, MultiModalDataParser

logger = init_logger(__name__)

_S = TypeVar("_S", str, list[int])
_PromptSeq = Union[str, list[int]]


@dataclass
class PromptReplacement:
    modality: str
    """The modality for which the replacement is made."""

    target: _PromptSeq
    """The text or token sequence to find and replace."""

    replacement: Union[Callable[[int], _PromptSeq],
                       _PromptSeq] = field(repr=False)
    """
    Given the index of the processed item within :attr:`modality`, output the
    replacement text or token sequence.

    For convenience, you can pass in the replacement instead of a function
    if it does not depend on the input.
    """

    def bind(self, tokenizer: AnyTokenizer) -> "_BoundPromptReplacement":
        return _BoundPromptReplacement(
            tokenizer=tokenizer,
            modality=self.modality,
            _target=self.target,
            _replacement=self.replacement,
        )


def _encode(
    tokenizer: AnyTokenizer,
    text: str,
    *,
    add_special_tokens: bool = False,
) -> list[int]:
    """
    Backend-agnostic equivalent of HF's
    :code:`tokenizer.encode(text, add_special_tokens=...)`.
    """
    if isinstance(tokenizer, MistralTokenizer):
        return tokenizer.tokenizer.encode(text,
                                          bos=add_special_tokens,
                                          eos=add_special_tokens)

    return tokenizer.encode(text, add_special_tokens=add_special_tokens)


@lru_cache(maxsize=2048)
def _cached_encode(
    tokenizer: AnyTokenizer,
    text: str,
    *,
    add_special_tokens: bool = False,
) -> list[int]:
    return _encode(tokenizer, text, add_special_tokens=add_special_tokens)


def _decode(
    tokenizer: AnyTokenizer,
    token_ids: list[int],
    *,
    skip_special_tokens: bool = False,
) -> str:
    """
    Backend-agnostic equivalent of HF's
    :code:`tokenizer.decode(token_ids, skip_special_tokens=...)`.
    """
    return tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)


@lru_cache(maxsize=2048)
def _cached_decode(
    tokenizer: AnyTokenizer,
    token_ids: tuple[int, ...],
    *,
    skip_special_tokens: bool = False,
) -> str:
    return _decode(tokenizer,
                   list(token_ids),
                   skip_special_tokens=skip_special_tokens)


class _HasModalityAttr(Protocol):
    modality: str


class _HasModalityProp(Protocol):

    @property
    def modality(self) -> str:
        ...


_M = TypeVar("_M", bound=Union[_HasModalityAttr, _HasModalityProp])


def full_groupby_modality(values: Iterable[_M]) -> ItemsView[str, list[_M]]:
    """Convenience function to apply :func:`full_groupby` based on modality."""
    return full_groupby(values, key=lambda x: x.modality)


@dataclass
class _BoundPromptSequence:
    tokenizer: AnyTokenizer = field(repr=False)

    _text: Optional[str]
    _token_ids: Optional[list[int]]

    def __post_init__(self) -> None:
        if self._text is None and self._token_ids is None:
            raise ValueError("At least one of 'text' and 'token_ids' must be "
                             "specified")

    @property
    def text(self) -> str:
        if self._text is None:
            assert self._token_ids is not None
            self._text = _cached_decode(self.tokenizer, tuple(self._token_ids))

        return self._text

    @property
    def token_ids(self) -> list[int]:
        if self._token_ids is None:
            assert self._text is not None
            self._token_ids = _cached_encode(self.tokenizer, self._text)

        return self._token_ids


@dataclass
class _BoundPromptReplacement:
    tokenizer: AnyTokenizer = field(repr=False)
    modality: str

    _target: _PromptSeq
    _replacement: Union[Callable[[int], _PromptSeq],
                        _PromptSeq] = field(repr=False)

    def __post_init__(self) -> None:
        self._replacement_cache = dict[int, _BoundPromptSequence]()

    @property
    def target(self) -> _BoundPromptSequence:
        target = self._target

        return _BoundPromptSequence(
            tokenizer=self.tokenizer,
            _text=target if isinstance(target, str) else None,
            _token_ids=target if isinstance(target, list) else None,
        )

    def get_replacement(self, item_idx: int) -> _BoundPromptSequence:
        replacement = self._replacement
        if callable(replacement):
            cache_key = item_idx
            if cache_key in self._replacement_cache:
                return self._replacement_cache[cache_key]

            replacement = replacement(item_idx)
        else:
            cache_key = None

        bound_replacement = _BoundPromptSequence(
            tokenizer=self.tokenizer,
            _text=replacement if isinstance(replacement, str) else None,
            _token_ids=replacement if isinstance(replacement, list) else None,
        )

        if cache_key is not None:
            self._replacement_cache[cache_key] = bound_replacement

        return bound_replacement


class _TokenMatch(NamedTuple):
    start_idx: int
    end_idx: int


def iter_token_matches(
    token_ids: list[int],
    match_ids: list[int],
) -> Iterable[_TokenMatch]:
    """
    Yield each occurrence of :code:`match_ids` in :code:`token_ids`.

    Note that empty matches are ignored.
    """
    prompt_len = len(token_ids)
    match_len = len(match_ids)

    if match_len == 0:
        return

    start_idx = 0
    while start_idx < prompt_len - match_len + 1:
        end_idx = start_idx + match_len

        if token_ids[start_idx:end_idx] == match_ids:
            yield _TokenMatch(start_idx=start_idx, end_idx=end_idx)

            # Exclude overlapping matches
            start_idx = end_idx
        else:
            start_idx += 1


@dataclass(repr=False)
class _PromptReplacementMatch(ABC):
    prompt_repl: _BoundPromptReplacement

    @property
    def modality(self) -> str:
        return self.prompt_repl.modality

    @property
    @abstractmethod
    def start_idx(self) -> int:
        raise NotImplementedError

    @property
    @abstractmethod
    def end_idx(self) -> int:
        raise NotImplementedError

    def __repr__(self) -> str:
        return (f"{type(self).__name__}(modality={self.modality!r}, "
                f"start_idx={self.start_idx!r}, end_idx={self.end_idx!r})")


@dataclass(repr=False)
class _PromptReplacementTokenMatch(_PromptReplacementMatch):
    match: _TokenMatch

    @property
    def start_idx(self) -> int:
        return self.match.start_idx

    @property
    def end_idx(self) -> int:
        return self.match.end_idx


@dataclass(repr=False)
class _PromptReplacementTextMatch(_PromptReplacementMatch):
    match: re.Match[str]

    @property
    def start_idx(self) -> int:
        return self.match.start()

    @property
    def end_idx(self) -> int:
        return self.match.end()


class _PlaceholderInfo(NamedTuple):
    modality: str
    start_idx: int
    replacement: list[int]

    @property
    def length(self) -> int:
        return len(self.replacement)

    def to_range(self) -> PlaceholderRange:
        return PlaceholderRange(
            offset=self.start_idx,
            length=self.length,
        )


def find_token_matches(
    prompt: list[int],
    prompt_repls: Sequence[_BoundPromptReplacement],
) -> list[_PromptReplacementTokenMatch]:
    """Return each target of :code:`prompt_repls` found in :code:`prompt`."""
    return [
        _PromptReplacementTokenMatch(prompt_repl, match)
        for prompt_repl in prompt_repls
        for match in iter_token_matches(prompt, prompt_repl.target.token_ids)
    ]


def find_text_matches(
    prompt: str,
    prompt_repls: Sequence[_BoundPromptReplacement],
) -> list[_PromptReplacementTextMatch]:
    """Return each target of :code:`prompt_repls` found in :code:`prompt`."""
    return [
        _PromptReplacementTextMatch(prompt_repl, match)
        for prompt_repl in prompt_repls
        for match in re.finditer(re.escape(prompt_repl.target.text), prompt)
    ]


def _resolve_matches(
    prompt: _PromptSeq,
    matches: Sequence[_PromptReplacementMatch],
) -> list[_PromptReplacementMatch]:
    """
    Resolve :code:`matches` to ensure that there are no overlapping matches,
    and sort them such that earlier matches take priority over later ones.
    """
    seen_matches: list[Optional[_PromptReplacementMatch]] = [None
                                                             ] * len(prompt)

    for match in matches:
        for idx in range(match.start_idx, match.end_idx):
            if seen_matches[idx] is not None:
                raise ValueError("Found overlapping matches "
                                 f"({seen_matches[idx]} and {match}) "
                                 f"at index={idx} of prompt={prompt}")

            seen_matches[idx] = match

    return sorted(matches, key=lambda x: x.start_idx)


def _replace_matches(
    prompt: _S,
    matches: Sequence[_PromptReplacementMatch],
    mm_item_counts: Mapping[str, int],
) -> list[_S]:
    out_seqs = list[_S]()
    prev_end_idx = 0
    next_idx_by_modality = {modality: 0 for modality in mm_item_counts}

    for match in _resolve_matches(prompt, matches):
        modality = match.modality

        item_idx = next_idx_by_modality[modality]
        if item_idx >= mm_item_counts[modality]:
            continue

        start_idx = match.start_idx
        end_idx = match.end_idx

        repl_info = match.prompt_repl
        replacement = repl_info.get_replacement(item_idx)

        if isinstance(prompt, str):
            repl_seq = replacement.text
            out_seqs.append(prompt[prev_end_idx:start_idx] + repl_seq)
        else:
            repl_seq = replacement.token_ids
            out_seqs.append(prompt[prev_end_idx:start_idx] + repl_seq)

        prev_end_idx = end_idx
        next_idx_by_modality[modality] += 1

    out_seqs.append(prompt[prev_end_idx:])

    return out_seqs


def replace_token_matches(
    prompt: list[int],
    matches: Sequence[_PromptReplacementTokenMatch],
    mm_item_counts: Mapping[str, int],
) -> list[int]:
    """Apply :code:`prompt_repls` to :code:`prompt`."""
    if not matches:
        return prompt

    token_id_seqs = _replace_matches(prompt, matches, mm_item_counts)

    return flatten_2d_lists(token_id_seqs)


def replace_text_matches(
    prompt: str,
    matches: Sequence[_PromptReplacementTextMatch],
    mm_item_counts: Mapping[str, int],
) -> str:
    """Apply :code:`prompt_repls` to :code:`prompt`."""
    if not matches:
        return prompt

    texts = _replace_matches(prompt, matches, mm_item_counts)

    return "".join(texts)


def _iter_modality_placeholders(
    prompt: list[int],
    modality: str,
    modality_repls: Sequence[_BoundPromptReplacement],
    modal_item_count: int,
) -> Iterable[_PlaceholderInfo]:
    if modal_item_count == 0:
        return

    prompt_len = len(prompt)
    item_index = 0

    start_idx = 0
    while start_idx < prompt_len:
        found = False

        for repl_info in modality_repls:
            replacement = repl_info.get_replacement(item_index)
            repl_tokens = replacement.token_ids
            repl_len = len(repl_tokens)
            end_idx = start_idx + repl_len

            if repl_len == 0 or end_idx > prompt_len:
                continue

            if prompt[start_idx:end_idx] == repl_tokens:
                yield _PlaceholderInfo(
                    modality=modality,
                    start_idx=start_idx,
                    replacement=repl_tokens,
                )

                item_index += 1
                if item_index >= modal_item_count:
                    return

                # Exclude overlapping matches
                start_idx = end_idx
                found = True
                break

        if not found:
            start_idx += 1


def iter_placeholders(
    prompt_repls: Sequence[_BoundPromptReplacement],
    prompt: list[int],
    mm_item_counts: Mapping[str, int],
) -> Iterable[_PlaceholderInfo]:
    """
    Yield each set of placeholder tokens found in :code:`prompt`.

    Note that empty matches are ignored.
    """
    repls_by_modality = dict(full_groupby_modality(prompt_repls))

    for modality, modal_item_count in mm_item_counts.items():
        if modality in repls_by_modality:
            yield from _iter_modality_placeholders(
                prompt,
                modality,
                repls_by_modality[modality],
                modal_item_count,
            )


@dataclass
class ProcessorInputs:
    """Keyword arguments to :meth:`BaseMultiModalProcessor`."""
    prompt_text: str
    mm_data: MultiModalDataDict
    hf_processor_mm_kwargs: Mapping[str, object] = field(default_factory=dict)


class ProcessingCache:

    def __init__(self, capacity: int) -> None:
        super().__init__()

        # DEBUG: Set to None to disable
        self.debug_cache_hit_ratio_steps: Optional[int] = None

        self._cache = LRUCache[str, Mapping[str,
                                            MultiModalFieldItem]](capacity)

    def _maybe_log_cache_stats(self) -> None:
        steps = self.debug_cache_hit_ratio_steps
        if not steps:
            return

        cache_stats = self._cache.stat()
        if cache_stats.total % steps == 0:
            logger.debug("ProcessingCache: hit_ratio = %.2f",
                         cache_stats.hit_ratio)

    def _serialize_item(self, obj: object) -> bytes:
        # Simple cases
        if isinstance(obj, str):
            return obj.encode("utf-8")
        if isinstance(obj, bytes):
            return obj
        if isinstance(obj, Image):
            return obj.tobytes()

        # Convertible to NumPy arrays
        if isinstance(obj, torch.Tensor):
            obj = obj.numpy()
        if isinstance(obj, (int, float)):
            obj = np.array(obj)
        if isinstance(obj, np.ndarray):
            return obj.tobytes()

        logger.warning(
            "No serialization method found for %s. "
            "Falling back to pickle.", type(obj))

        return pickle.dumps(obj)

    def _item_to_bytes(
        self,
        key: str,
        obj: object,
    ) -> Iterable[tuple[bytes, bytes]]:
        # Recursive cases
        if isinstance(obj, (list, tuple)):
            for i, elem in enumerate(obj):
                yield from self._item_to_bytes(f"{key}.{i}", elem)
        elif isinstance(obj, dict):
            for k, v in obj.items():
                yield from self._item_to_bytes(f"{key}.{k}", v)
        else:
            key_bytes = self._serialize_item(key)
            value_bytes = self._serialize_item(obj)
            yield key_bytes, value_bytes

    def _hash_kwargs(self, **kwargs: object) -> str:
        hasher = blake3()

        for k, v in kwargs.items():
            for k_bytes, v_bytes in self._item_to_bytes(k, v):
                hasher.update(k_bytes)
                hasher.update(v_bytes)

        return hasher.hexdigest()

    def get(
        self,
        model_id: str,
        modality: str,
        input_item: object,
        input_kwargs: Mapping[str, object],
    ) -> Optional[Mapping[str, MultiModalFieldItem]]:
        """
        Get a processed multi-modal item from the cache
        according to its dependencies, including:

        - The model ID
        - The modality of the item
        - The original data item passed to the HF processor
        - The configuration options of the HF processor
        """
        self._maybe_log_cache_stats()

        cache_key = self._hash_kwargs(model_id=model_id,
                                      **{modality: input_item},
                                      **input_kwargs)
        return self._cache.get(cache_key)

    def put(
        self,
        model_id: str,
        modality: str,
        input_item: object,
        input_kwargs: Mapping[str, object],
        output_kwargs: Mapping[str, MultiModalFieldItem],
    ) -> None:
        """
        Put a processed multi-modal item into the cache
        according to its dependencies (see :meth:`get`).
        """
        cache_key = self._hash_kwargs(model_id=model_id,
                                      **{modality: input_item},
                                      **input_kwargs)
        self._cache.put(cache_key, output_kwargs)


class BaseMultiModalProcessor(ABC):
    """
    Abstract base class to process multi-modal inputs to be used in vLLM.
    """

    def __init__(self,
                 ctx: InputProcessingContext,
                 *,
                 cache: Optional[ProcessingCache] = None,
                 enable_sanity_checks: bool = True) -> None:
        super().__init__()

        self.ctx = ctx
        self.cache = cache
        self.enable_sanity_checks = enable_sanity_checks

    def __call__(
        self,
        prompt: str,
        mm_data: MultiModalDataDict,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> MultiModalInputsV2:
        return self.apply(prompt, mm_data, hf_processor_mm_kwargs)

    def _get_data_parser(self) -> MultiModalDataParser:
        """
        Construct a data parser to preprocess multi-modal data items
        before passing them to :meth:`_get_hf_mm_data`.

        You can support additional modalities by creating a subclass
        of :class:`MultiModalDataParser` that has additional subparsers.
        """
        return MultiModalDataParser()

    def _get_hf_processor(self) -> ProcessorMixin:
        """
        Subclasses can add keyword arguments to this method to accept
        additional kwargs from model config or user inputs.
        """
        return self.ctx.get_hf_processor()

    def _get_tokenizer(self) -> AnyTokenizer:
        return self.ctx.tokenizer

    def _to_mm_items(
        self,
        mm_data: MultiModalDataDict,
    ) -> MultiModalDataItems:
        """
        Normalize :class:`MultiModalDataDict` to :class:`MultiModalDataItems`
        before passing them to :meth:`_get_hf_mm_data`.
        """
        parser = self._get_data_parser()
        return parser.parse_mm_data(mm_data)

    @abstractmethod
    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        """Given the HF-processed data, output the metadata of each field."""
        raise NotImplementedError

    @abstractmethod
    def _get_prompt_replacements(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> list[PromptReplacement]:
        """
        Given the original multi-modal items for this modality
        and HF-processed data, output the replacements to perform.

        Note:
            Even when the HF processor already performs replacement for us,
            we still use this replacement information to determine
            the placeholder token positions for each multi-modal item.
        """
        raise NotImplementedError

    def _find_placeholders(
        self,
        all_prompt_repls: Sequence[_BoundPromptReplacement],
        new_token_ids: list[int],
        mm_item_counts: Mapping[str, int],
    ) -> list[_PlaceholderInfo]:
        return list(
            iter_placeholders(all_prompt_repls, new_token_ids, mm_item_counts))

    def _get_hf_mm_data(
        self,
        mm_items: MultiModalDataItems,
    ) -> tuple[dict[str, Any], dict[str, Any]]:
        processor_data = dict[str, Any]()
        passthrough_data = dict[str, Any]()

        for items in mm_items.values():
            processor_data.update(items.get_processor_data())
            passthrough_data.update(items.get_passthrough_data())

        return processor_data, passthrough_data

    def _call_hf_processor(
        self,
        prompt: str,
        # Not to be confused with `mm_data` in `self.apply`.
        # This refers to the data to be passed to HF processor.
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        return self.ctx.call_hf_processor(
            self._get_hf_processor(**mm_kwargs),
            dict(text=prompt, **mm_data),
            mm_kwargs,
        )

    def _apply_hf_processor(
        self,
        prompt_text: str,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> tuple[list[int], MultiModalKwargs]:
        """
        Apply the HF processor on the full prompt text and multi-modal data.
        """
        processor_data, passthrough_data = self._get_hf_mm_data(mm_items)

        processed_data = self._call_hf_processor(
            prompt=prompt_text,
            mm_data=processor_data,
            mm_kwargs=hf_processor_mm_kwargs,
        )
        processed_data.update(passthrough_data)

        prompt_ids, = processed_data.pop("input_ids").tolist()

        mm_kwargs = MultiModalKwargs.from_hf_inputs(
            processed_data,
            self._get_mm_fields_config(processed_data, hf_processor_mm_kwargs),
            enable_sanity_checks=self.enable_sanity_checks,
        )

        return prompt_ids, mm_kwargs

    def _apply_hf_processor_missing(
        self,
        prompt_text: str,
        mm_missing_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
    ):
        """
        Apply the HF processor on the full prompt text, but only on the
        multi-modal data that are missing from the cache.

        Note: We pass prompt text and multi-modal data into the HF processor
        in separate calls to avoid HF prompt replacement being done for
        cached items; instead, we rely on our own prompt replacement logic
        for the full text.
        """
        mm_missing_counts = mm_missing_data_items.get_all_counts()

        prompt_ids, _ = self._apply_hf_processor(
            prompt_text=prompt_text,
            mm_items=MultiModalDataItems({}),
            hf_processor_mm_kwargs={},
        )

        # Some HF processors (e.g. Qwen2-VL) expect corresponding
        # multi-modal tokens to be in the prompt text
        dummy_inputs = self._get_dummy_mm_inputs(mm_missing_counts)

        _, mm_missing_kwargs = self._apply_hf_processor(
            prompt_text=dummy_inputs.prompt_text,
            mm_items=mm_missing_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
        )

        return prompt_ids, mm_missing_kwargs

    def _cached_apply_hf_processor(
        self,
        prompt_text: str,
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> tuple[list[int], MultiModalKwargs]:
        """
        Apply the HF processor on the full prompt text,
        caching the results and reusing cached results.
        """
        cache = self.cache
        model_id = self.ctx.model_config.model

        _, passthrough_data = self._get_hf_mm_data(mm_data_items)
        if cache is None or passthrough_data:
            return self._apply_hf_processor(
                prompt_text=prompt_text,
                mm_items=mm_data_items,
                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            )

        mm_maybe_cached_field_items = {
            modality: [
                cache.get(model_id, modality, item, hf_processor_mm_kwargs)
                for item in items
            ]
            for modality, items in mm_data_items.items()
        }

        mm_missing_idxs = {
            modality: [idx for idx, out in enumerate(fields) if out is None]
            for modality, fields in mm_maybe_cached_field_items.items()
        }
        mm_missing_data = {
            modality: [mm_data_items[modality][idx] for idx in idxs]
            for modality, idxs in mm_missing_idxs.items()
        }
        mm_missing_data_items = self._to_mm_items(mm_missing_data)

        prompt_ids, mm_missing_kwargs = self._apply_hf_processor_missing(
            prompt_text=prompt_text,
            mm_missing_data_items=mm_missing_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
        )

        mm_missing_next_idx = {
            modality: 0
            for modality in mm_missing_data_items
        }

        mm_merged_field_items = dict[str, list[Mapping[str,
                                                       MultiModalFieldItem]]]()
        for modality, modal_items_lst in mm_maybe_cached_field_items.items():
            merged_modal_items_lst = list[Mapping[str, MultiModalFieldItem]]()

            for idx, modal_items in enumerate(modal_items_lst):
                if modal_items is None:
                    modal_items = mm_missing_kwargs.get_items_by_modality(
                        modality,
                        mm_missing_next_idx[modality],
                    )

                    cache.put(
                        model_id,
                        modality,
                        mm_data_items[modality][idx],
                        hf_processor_mm_kwargs,
                        modal_items,
                    )

                    mm_missing_next_idx[modality] += 1

                merged_modal_items_lst.append(modal_items)

            mm_merged_field_items[modality] = merged_modal_items_lst

        if self.enable_sanity_checks:
            mm_missing_counts = mm_missing_data_items.get_all_counts()
            assert all(
                item_count == mm_missing_counts[modality]
                for modality, item_count in mm_missing_next_idx.items()), dict(
                    mm_missing_next_idx=mm_missing_next_idx,
                    mm_missing_counts=mm_missing_counts)

        mm_kwargs = MultiModalKwargs.from_items_by_modality(
            mm_merged_field_items,
            enable_sanity_checks=self.enable_sanity_checks,
        )

        if self.enable_sanity_checks:
            mm_item_counts = mm_data_items.get_all_counts()

            for modality, item_count in mm_item_counts.items():
                for item_idx in range(item_count):
                    try:
                        mm_kwargs.get_items_by_modality(modality, item_idx)
                    except Exception as e:
                        # Make it easy to set a breakpoint in the debugger
                        raise e

        return prompt_ids, mm_kwargs

    def _bind_prompt_replacements(
        self,
        prompt_repls: list[PromptReplacement],
    ) -> list[_BoundPromptReplacement]:
        tokenizer = self._get_tokenizer()

        return [prompt_repl.bind(tokenizer) for prompt_repl in prompt_repls]

    def _apply_prompt_replacements(
        self,
        token_ids: list[int],
        prompt_repls: Sequence[_BoundPromptReplacement],
        mm_item_counts: Mapping[str, int],
    ) -> tuple[list[int], str, list[_PlaceholderInfo]]:
        tokenizer = self._get_tokenizer()

        token_matches = find_token_matches(token_ids, prompt_repls)
        mm_match_counts = {
            modality: len(matches)
            for modality, matches in full_groupby_modality(token_matches)
        }

        # If the search text does not represent a special token,
        # it may have different token IDs in the prompt, because
        # the tokens may go across the boundaries of the search text.
        # ----
        # e.g. when searching for "foo" in "food", if "food" itself makes
        # up a token, then the token ID of "foo" will not appear at all
        # ----
        # Since it is inefficient to search for all possible tokenizations
        # of the search text in the prompt, we instead perform string
        # replacement on the decoded token IDs, then encode them back.
        if all(
            mm_match_counts.get(modality, 0) >= item_count
            for modality, item_count in mm_item_counts.items()
        ):  # yapf: disable
            token_ids = replace_token_matches(
                token_ids,
                token_matches,
                mm_item_counts,
            )

            text = _decode(tokenizer, token_ids)
            matched_repls = [match.prompt_repl for match in token_matches]
        else:
            text = _decode(tokenizer, token_ids)

            text_matches = find_text_matches(text, prompt_repls)
            text = replace_text_matches(
                text,
                text_matches,
                mm_item_counts,
            )

            token_ids = _encode(tokenizer, text)
            matched_repls = [match.prompt_repl for match in text_matches]

        placeholders = self._find_placeholders(matched_repls, token_ids,
                                               mm_item_counts)

        return token_ids, text, placeholders

    def apply(
        self,
        prompt_text: str,
        mm_data: MultiModalDataDict,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> MultiModalInputsV2:
        """
        Process multi-modal inputs to be used in vLLM.

        The main steps are:

        1. Apply HF Processor on prompt text and multi-modal data together,
           outputting token IDs and processed tensors.
        2. Find and replace sequences in the token IDs with placeholder tokens.
           The number of placeholder tokens equals the feature size of the
           multi-modal data outputted by the multi-modal encoder.
        3. Extract information about the placeholder tokens from the
           processed token IDs.
        """
        mm_items = self._to_mm_items(mm_data)

        prompt_ids, mm_kwargs = self._cached_apply_hf_processor(
            prompt_text,
            mm_items,
            hf_processor_mm_kwargs,
        )

        unbound_prompt_repls = self._get_prompt_replacements(
            mm_items,
            hf_processor_mm_kwargs,
            mm_kwargs,
        )
        prompt_repls = self._bind_prompt_replacements(unbound_prompt_repls)

        # If HF processor already inserts placeholder tokens,
        # there is no need for us to insert them
        mm_item_counts = mm_items.get_all_counts()
        all_placeholders = self._find_placeholders(prompt_repls, prompt_ids,
                                                   mm_item_counts)

        if all_placeholders:
            tokenizer = self._get_tokenizer()
            prompt_text = _decode(tokenizer, prompt_ids)
        else:
            (
                prompt_ids,
                prompt_text,
                all_placeholders,
            ) = self._apply_prompt_replacements(
                prompt_ids,
                prompt_repls,
                mm_item_counts,
            )

        mm_placeholders = {
            modality: [item.to_range() for item in items]
            for modality, items in full_groupby_modality(all_placeholders)
        }

        return MultiModalInputsV2(
            type="multimodal",
            prompt=prompt_text,
            prompt_token_ids=prompt_ids,
            mm_kwargs=mm_kwargs,
            mm_placeholders=mm_placeholders,
        )

    @abstractmethod
    def _get_dummy_mm_inputs(
        self,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        """
        Build the multi-modal portion of the input which, after processing,
        results in `mm_max_tokens` in :meth:`get_dummy_data`.
        """
        raise NotImplementedError

    def get_dummy_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_max_tokens: Mapping[str, int],
    ) -> DummyData:
        # Avoid circular import
        from vllm.sequence import SequenceData

        processor_inputs = self._get_dummy_mm_inputs(mm_counts)
        mm_inputs = self.apply(
            prompt_text=processor_inputs.prompt_text,
            mm_data=processor_inputs.mm_data,
            hf_processor_mm_kwargs=processor_inputs.hf_processor_mm_kwargs,
        )

        prompt_token_ids = mm_inputs["prompt_token_ids"]
        placeholders_by_modality = mm_inputs["mm_placeholders"]

        total_placeholders_by_modality = {
            modality: sum(item["length"] for item in placeholders)
            for modality, placeholders in placeholders_by_modality.items()
        }
        expected_placeholders_by_modality = {
            modality: mm_max_tokens[modality]
            for modality in placeholders_by_modality
        }
        if total_placeholders_by_modality != expected_placeholders_by_modality:
            raise AssertionError(
                f"The processed dummy data has a total of "
                f"{total_placeholders_by_modality} placeholder tokens, which "
                f"is not the expected {expected_placeholders_by_modality} "
                "tokens.")

        total_len = len(prompt_token_ids)
        if total_len > seq_len:
            logger.warning(
                "The context length (%d) of the model is too short "
                "to hold the multi-modal embeddings in the worst case "
                "(%d tokens in total, out of which %s are reserved for "
                "multi-modal embeddings). This may cause certain multi-modal "
                "inputs to fail during inference, even when the input text is "
                "short. To avoid this, you should increase `max_model_len`, "
                "reduce `max_num_seqs`, and/or reduce `mm_counts`.", seq_len,
                total_len, total_placeholders_by_modality)

        prompt_token_ids.extend([0] * (seq_len - len(prompt_token_ids)))

        return DummyData(
            seq_data=SequenceData.from_seqs(prompt_token_ids),
            multi_modal_data=mm_inputs["mm_kwargs"],
            multi_modal_placeholders=placeholders_by_modality,
        )