processing_emu3.py

# coding=utf-8
# Copyright 2024 The Emu team, BAAI and The HuggingFace Inc. team. All rights reserved.
#
# 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.
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""" Processor class for Emu3. """

import re
from typing import List, Optional, Sequence, Union
from functools import partial

from PIL import Image
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, get_image_size, to_numpy_array
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
from transformers.tokenization_utils_base import TextInput, PreTokenizedInput
from transformers.utils import logging

from .utils_emu3 import Emu3PrefixConstrainedLogitsHelper


logger = logging.get_logger(__name__)


class Emu3Processor(ProcessorMixin):
    r"""
    Constructs an Emu3 processor which wraps an Emu3 image processor and an Emu3 vision vq model and an Emu3 tokenizer into a single processor.

    [`Emu3Processor`] offers all the functionalities of [`Emu3VisionVQModel`] and [`Emu3Tokenizer`]. See the
    [`~Emu3Processor.__call__`], [`~Emu3Processor.decode`], [`~Emu3Processor.vision_encode`], [`~Emu3Processor.vision_decode`]
    for more information.

    Args:
        image_processor ([`Emu3VisionVQImageProcessor`]):
            The image processor is a required input.
        vision_tokenizer ([`Emu3VisionVQModel`]):
            The vision tokenizer is a required input.
        tokenizer ([`Emu3Tokenizer`]):
            The tokenizer is a required input.
        prefix_template(`str`, *optional*):
            The prefix template for image tokens
        visual_template(`Tuple[str, ...]`, *optional*):
            The visual token template for image tokens
    """

    attributes = ["image_processor", "tokenizer"]
    valid_kwargs = ["vision_tokenizer", "prefix_template", "visual_template"]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(
        self,
        image_processor=None,
        vision_tokenizer=None,
        tokenizer=None,
        chat_template="You are a helpful assistant. USER: {image_prompt}{text_prompt}. ASSISTANT:",
        prefix_template="{H}*{W}",
        visual_template=("<|visual token {token_id:0>6d}|>", r"<\|visual token (\d+)\|>"),
        **kwargs,
    ):
        assert vision_tokenizer is not None, "image tokenizer can not be None"

        self.vision_tokenizer = vision_tokenizer
        self.prefix_template = prefix_template
        self.visual_template = visual_template

        super().__init__(image_processor, tokenizer, chat_template=chat_template)
        self.const_helper = self.build_const_helper()

    @torch.no_grad()
    def __call__(
        self,
        text: Optional[TextInput | PreTokenizedInput] = None,
        image: Optional[Image.Image | List[Image.Image]] = None,
        *,
        mode: str = "G",
        ratio: str = "1:1",
        image_area: int = 518400,
        **kwargs,
    ) -> 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 Emu3Tokenizer's [`~Emu3Tokenizer.__call__`] to encode the text.
        To prepare the image(s), this method forwards the `image` argument to
        Emu3VisionVQImageProcessor's [`~Emu3VisionVQImageProcessor.__call__`] and Emu3VisionVQModel's [`~EmuVideoVQModel.encode`]
        if `image` is not `None`. Please refer to the doctsring of the above two methods for more information.

        Args:
            text (`str` or `List[str]`):
                The sequence or a batch of sequence to be encoded. A sequence is a string.
            image (`PIL.Image.Image` or `List[PIL.Image.Image]`, *optional*):
                The image or a batch of images to be prepared. An image is a PIL image.
            mode (`str`, *optional*, in `G` or `U`):
                task mode, `G` for generation and `U` for understanding
            ratio (`str`, *optional*):
                the image width-height ratio for generation
            image_area (`int`, *optional*):
                image area used to calcualte the generated image height and width
            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.
            - **image_size** -- List of image size of input images or generated images.
        """
        assert mode in ('G', 'U'), "mode must be 'G' or 'U'."
        if isinstance(text, str):
            text = [text]

        if not isinstance(text[0], str):
            raise ValueError("`text` must be string or list of string")

        image_inputs = None
        if mode == 'G':
            if image is not None:
                raise ValueError("You have to specify only `text` in generation mode")

            if len(text) > 1:
                raise ValueError("`text` can only be `str` in generation mode")
        else:
            if image is None:
                raise ValueError("Invalid input image. Please provide exactly one PIL.Image.Image per text.")

            if not isinstance(image, Sequence) and not isinstance(image, Image.Image):
                raise ValueError("Invalid input image. Please provide PIL.Image.Image or List[PIL.Image.Image].")

            if isinstance(image, Sequence) and not isinstance(image[0], Image.Image):
                raise ValueError("Invalid input image. Please provide PIL.Image.Image or List[PIL.Image.Image].")

            image_inputs = self.image_processor(image, return_tensors="pt")["pixel_values"]
            image_inputs = image_inputs.to(self.vision_tokenizer.device, self.vision_tokenizer.dtype)
            image_tokens = self.vision_tokenizer.encode(image_inputs)

            if len(text) != len(image_tokens):
                raise ValueError("number of image must match number of text prompt")

        prompt_list, size_list = [], []
        for idx, text_prompt in enumerate(text):
            prompt = self.tokenizer.bos_token
            if mode == 'U':
                h, w = image_tokens[idx].shape
                imgstr = self.to_imgstr(image_tokens[idx])
                image_prompt = (
                    self.tokenizer.boi_token +
                    self.prefix_template.format(H=h, W=w) +
                    self.tokenizer.img_token + 
                    imgstr +
                    self.tokenizer.eol_token +
                    self.tokenizer.eof_token +
                    self.tokenizer.eoi_token
                )
                prompt += self.chat_template.format(image_prompt=image_prompt, text_prompt=text_prompt)
            else:
                h, w = self.calculate_generate_size(ratio, image_area, self.vision_tokenizer.spatial_scale_factor)
                image_prompt = (
                    self.tokenizer.boi_token +
                    self.prefix_template.format(H=h, W=w) +
                    self.tokenizer.img_token
                )
                prompt += (text_prompt + image_prompt)

            prompt_list.append(prompt)
            size_list.append([h, w])

        text_inputs = self.tokenizer(prompt_list, **kwargs)
        return BatchFeature(data={**text_inputs, "image_size": size_list}, tensor_type=kwargs.get("return_tensors"))

    @torch.no_grad()
    def batch_decode(self, *args, **kwargs):
        docs = self.tokenizer.batch_decode(*args, **kwargs)
        return [self.multimodal_decode(d) for d in docs]

    @torch.no_grad()
    def decode(self, *args, **kwargs):
        doc = self.tokenizer.decode(*args, **kwargs)
        return self.multimodal_decode(doc)

    @torch.no_grad()
    def vision_encode(self, *args, **kwargs):
        return self.vision_tokenizer.encode(*args, **kwargs)

    @torch.no_grad()
    def vision_decode(self, *args, **kwargs):
        return self.vision_tokenizer.decode(*args, **kwargs)

    @torch.no_grad()
    def multimodal_decode(self, doc):
        multimodal_output = []
        pattern = rf'({re.escape(self.tokenizer.boi_token)}.*?{re.escape(self.tokenizer.eoi_token)})'
        chunks = re.split(pattern, doc)
        for c in chunks:
            if len(c) == 0:
                continue

            if self.tokenizer.boi_token in c:
                image = []
                image_rows = re.split(re.escape(self.tokenizer.eol_token), c)
                for r in image_rows:
                    token_ids = re.findall(self.visual_template[1], r)
                    if len(token_ids) > 0:
                        row_token = [int(m) for m in token_ids]
                        image.append(row_token)
                image = torch.tensor(image, dtype=torch.long, device=self.vision_tokenizer.device)
                image = self.vision_tokenizer.decode(image[None]).float()
                image = self.image_processor.postprocess(image)["pixel_values"][0]
                multimodal_output.append(image)
            else:
                multimodal_output.append(c)

        return multimodal_output if len(multimodal_output) > 1 else multimodal_output[0]

    @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 to_imgstr(self, image_tokens):
        image_tokens = image_tokens.cpu().numpy().tolist()
        image_token_str = [
            [
                self.visual_template[0].format(token_id=token_id)
                for token_id in token_row
            ]
            for token_row in image_tokens
        ]
        image_row_str = ["".join(token_row) for token_row in image_token_str]
        imgstr = self.tokenizer.eol_token.join(image_row_str)
        return imgstr

    def calculate_generate_size(self, ratio, image_area, spatial_scale_factor):
        w, h = map(int, ratio.split(":"))
        current_area = h * w
        target_ratio = (image_area / current_area) ** 0.5

        th = int(round(h * target_ratio / spatial_scale_factor))
        tw = int(round(w * target_ratio / spatial_scale_factor))
        return th, tw

    def build_const_helper(self):
        (
            img_token,
            eoi_token,
            eos_token,
            eol_token,
            eof_token,
            pad_token,
            vis_start,
            vis_end,
        ) = self.tokenizer.encode([
            self.tokenizer.img_token,
            self.tokenizer.eoi_token,
            self.tokenizer.eos_token,
            self.tokenizer.eol_token,
            self.tokenizer.eof_token,
            self.tokenizer.pad_token,
            self.visual_template[0].format(token_id=0),
            self.visual_template[0].format(token_id=self.vision_tokenizer.config.codebook_size - 1),
        ])

        const_helper = partial(
            Emu3PrefixConstrainedLogitsHelper,
            img_token=img_token,
            eoi_token=eoi_token,
            eos_token=eos_token,
            eol_token=eol_token,
            eof_token=eof_token,
            pad_token=pad_token,
            visual_tokens=list(range(vis_start, vis_end + 1)),
        )
        return const_helper

    def build_prefix_constrained_fn(self, height, width):
        helper = self.const_helper(height=height, width=width)
        return helper