processing_moondream3.py

# coding=utf-8
# Copyright 2024 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.
"""
Processor class for Moondream3.
"""

from typing import Optional, Union

import numpy as np

from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, is_valid_image
from transformers.processing_utils import (
    MultiModalData,
    ProcessingKwargs,
    ProcessorMixin,
    Unpack,
)
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import is_vision_available, logging


logger = logging.get_logger(__name__)


class Moondream3ProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
            "return_token_type_ids": False
        },
        "common_kwargs": {
            "return_tensors": "pt",
        },
    }

def _rotate_right_array(x, k: int):
    """
    Rotate a 1D or 2D structure k steps to the right along the last axis.
    Supports: list, numpy.ndarray, torch.Tensor.
    Works even if numpy or torch are not installed.
    Raises TypeError for unsupported input types.
    """
    # optional imports
    try:
        import numpy as np
    except ImportError:
        np = None

    try:
        import torch
    except ImportError:
        torch = None

    # torch.Tensor
    if torch is not None and isinstance(x, torch.Tensor):
        if x.size(-1) == 0:
            return x
        return torch.roll(x, shifts=k % x.size(-1), dims=-1)

    # numpy.ndarray
    if np is not None and isinstance(x, np.ndarray):
        if x.shape[-1] == 0:
            return x
        return np.roll(x, k % x.shape[-1], axis=-1)

    # python list (1D or 2D)
    if isinstance(x, list):
        if not x:  # empty list
            return x
        # 2D (batch, seq)
        if isinstance(x[0], list):
            out = []
            for row in x:
                if not row:
                    out.append(row)
                    continue
                shift = k % len(row)
                out.append(row[-shift:] + row[:-shift] if shift else row[:])
            return out
        # 1D
        shift = k % len(x)
        return x[-shift:] + x[:-shift] if shift else x[:]

    # unsupported type
    raise TypeError(
        f"Unsupported type {type(x).__name__} for rotation. "
        f"Expected list, numpy.ndarray, or torch.Tensor. "
        f"(numpy or torch are optional dependencies)"
    )


# Copied from transformers.models.idefics2.processing_idefics2.is_url
def is_url(val) -> bool:
    return isinstance(val, str) and val.startswith("http")


# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
def is_image_or_image_url(elem):
    return is_url(elem) or is_valid_image(elem)


class Moondream3Processor(ProcessorMixin):
    r"""
    Constructs a Moondream3 processor which wraps a Moondream3 image processor and a Moondream3 tokenizer into a single processor.

    [`Moondream3Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
    [`~Moondream3Processor.__call__`] and [`~Moondream3Processor.decode`] for more information.

    Args:
        image_processor ([`Moondream3ImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerFast`], *optional*):
            The tokenizer is a required input.
        patch_size (`int`, *optional*, defaults to 16):
            Patch size from the vision tower.
        spatial_merge_size (`int`, *optional*, defaults to 1):
            The downsampling factor for the spatial merge operation.
        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
            in a chat into a tokenizable string.
        image_token (`str`, *optional*, defaults to `"[IMG]"`):
            Special token used to denote image location.
        image_break_token (`str`, *optional*, defaults to `"[IMG_BREAK]"`):
            Special token used to denote the end of a line of pixels in an image.
        image_end_token (`str`, *optional*, defaults to `"[IMG_END]"`):
            Special token used to denote the end of an image input.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
        chat_template=None,
        image_token_id=0,
        **kwargs,
    ):
        self.image_token_id = image_token_id
        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    def __call__(
        self,
        images: Optional[ImageInput] = None,
        text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
        **kwargs: Unpack[Moondream3ProcessorKwargs],
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
        of the above two methods for more information.

        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            text (`str`, `list[str]`, `list[list[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:

                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.

        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
            `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
        """

        output_kwargs = self._merge_kwargs(
            Moondream3ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        if images is not None:
            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
        else:
            image_inputs = {}

        if isinstance(text, str):
            text = [text]
        elif not isinstance(text, list) and not isinstance(text[0], str):
            raise TypeError("Invalid input text. Please provide a string, or a list of strings")

        # try to expand inputs in processing if we have the necessary parts
        prompt_strings = text

        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
        # if "input_ids" in text_inputs:
        #     # prepend 1 bos_token_id and 729 image_token_id to the text_inputs
        #     for i in range(len(text_inputs["input_ids"])):
        #         prepended_tokens = [self.tokenizer.bos_token_id] + [self.image_token_id] * 729
        #         text_inputs["input_ids"][i] = prepended_tokens + text_inputs["input_ids"][i]
        # if "attention_mask" in text_inputs:
        #     # attend to the 730 prepended tokens
        #     for i in range(len(text_inputs["attention_mask"])):
        #         prepended_mask = [1] * 730
        #         text_inputs["attention_mask"][i] = prepended_mask + text_inputs["attention_mask"][i]

        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)

    # def apply_chat_template(
    #     self,
    #     conversation: Union[list[dict[str, str]], list[list[dict[str, str]]]],
    #     chat_template: Optional[str] = None,
    #     **kwargs,
    # ) -> str:
    #         # Call the original behavior first
    #     out = super().apply_chat_template(
    #         conversation=conversation,
    #         chat_template=chat_template,
    #         **kwargs,
    #     )

    #     # Only post-process when:
    #     # - user requested assistant mask
    #     # - output is a dict (tokenized + return_dict=True path)
    #     if isinstance(out, BatchFeature) and kwargs.get("return_assistant_tokens_mask", False):
    #         if "assistant_masks" in out and out["assistant_masks"] is not None:
    #             out["assistant_masks"] = _rotate_right_array(out["assistant_masks"], 730)

    #     return out

    @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 tokenizer_input_names + image_processor_input_names + ["image_sizes"]


__all__ = ["Moondream3Processor"]