image-match/python/py/Lib/site-packages/transformers/models/janus/processing_janus.py

# Copyright 2025 Deepseek AI and The HuggingFace 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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Janus.
"""

from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import auto_docstring, logging


logger = logging.get_logger(__name__)

DEFAULT_SYSTEM_PROMPT = (
    "You are a helpful language and vision assistant. "
    "You are able to understand the visual content that the user provides, "
    "and assist the user with a variety of tasks using natural language.\n\n"
)


class JanusTextKwargs(TextKwargs, total=False):
    """
    generation_mode (`str`, *optional*, defaults to `"text"`):
        The generation mode indicating which modality to generate. Can be one of `"text"` or `"image"`. When set
        to `"text"`, the processor prepares inputs for text generation. When set to `"image"`, it prepares inputs
        for image generation by appending image start tokens to the prompt.
    """

    generation_mode: str


class JanusProcessorKwargs(ProcessingKwargs, total=False):
    text_kwargs: JanusTextKwargs
    _defaults = {
        "text_kwargs": {"padding": False, "padding_side": "left", "generation_mode": "text"},
        "common_kwargs": {"return_tensors": "pt"},
    }


@auto_docstring
class JanusProcessor(ProcessorMixin):
    def __init__(self, image_processor, tokenizer, chat_template=None, use_default_system_prompt=False, **kwargs):
        r"""
        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
            Use default system prompt for Text Generation.
        """
        self.num_image_tokens = 576
        self.image_token = tokenizer.image_token
        self.image_start_token = tokenizer.boi_token
        self.image_end_token = tokenizer.eoi_token
        self.use_default_system_prompt = use_default_system_prompt

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

    @auto_docstring
    def __call__(
        self,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
        images: ImageInput | None = None,
        **kwargs: Unpack[JanusProcessorKwargs],
    ) -> BatchFeature:
        r"""
        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(
            JanusProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs
        )

        if text is None and images is None:
            raise ValueError("You must specify either text or images.")

        if text is not None:
            if isinstance(text, str):
                text = [text]
            elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
                raise ValueError("Invalid input text. Please provide a string, or a list of strings")

        generation_mode = output_kwargs["text_kwargs"].pop("generation_mode")

        # Replace the image token with expanded image tokens.
        prompt_strings = []
        one_img_tokens = self.image_start_token + (self.image_token * self.num_image_tokens) + self.image_end_token
        for prompt in text:
            prompt = prompt.replace(self.image_token, one_img_tokens)
            if self.use_default_system_prompt and generation_mode == "text":
                prompt = DEFAULT_SYSTEM_PROMPT + prompt
            if generation_mode == "image":
                prompt += self.image_start_token
            prompt_strings.append(prompt)

        data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])

        # Process images if pixel values are provided.
        if images is not None and generation_mode != "image":
            data["pixel_values"] = self.image_processor(images=images, **output_kwargs["images_kwargs"])[
                "pixel_values"
            ]

        return BatchFeature(data=data)

    def postprocess(self, images: ImageInput, **kwargs):
        """
        Forwards all arguments to the image processor's `postprocess` method.
        Refer to the original method's docstring for more details.
        """
        return self.image_processor.postprocess(images, **kwargs)

    def post_process_multimodal_output(
        self, generated_outputs, skip_special_tokens=True, generation_mode=None, **kwargs
    ):
        """
        Post-process the output of a multimodal model to return the requested modality output.
        If the model cannot generated the requested modality, an error will be raised.

        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,)`.
            skip_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
            generation_mode (`str`, *optional*):
                Generation mode indicated which modality to output and can be one of `["text", "image", "audio"]`.
            **kwargs:
                Additional arguments to be passed to the tokenizer's `batch_decode method`.

        Returns:
            `list[Union[str, PIL.Image.Image]]`: The decoded text or generated image.
        """
        if generation_mode is None or generation_mode == "text":
            return self.post_process_image_text_to_text(
                generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs
            )

        elif generation_mode == "image":
            generated_outputs = list(generated_outputs.float())
            images = self.postprocess(generated_outputs, return_tensors="PIL.Image.Image")
            return images["pixel_values"]

        else:
            raise ValueError(
                f"{self.__class__.__name__} got an unexpected generation_mode={generation_mode}. Supported options are only `text` and `image"
            )


__all__ = ["JanusProcessor"]