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167 lines
7.1 KiB
167 lines
7.1 KiB
# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for Janus.
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"""
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import auto_docstring, logging
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logger = logging.get_logger(__name__)
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DEFAULT_SYSTEM_PROMPT = (
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"You are a helpful language and vision assistant. "
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"You are able to understand the visual content that the user provides, "
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"and assist the user with a variety of tasks using natural language.\n\n"
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)
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class JanusTextKwargs(TextKwargs, total=False):
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"""
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generation_mode (`str`, *optional*, defaults to `"text"`):
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The generation mode indicating which modality to generate. Can be one of `"text"` or `"image"`. When set
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to `"text"`, the processor prepares inputs for text generation. When set to `"image"`, it prepares inputs
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for image generation by appending image start tokens to the prompt.
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"""
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generation_mode: str
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class JanusProcessorKwargs(ProcessingKwargs, total=False):
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text_kwargs: JanusTextKwargs
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_defaults = {
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"text_kwargs": {"padding": False, "generation_mode": "text"},
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"common_kwargs": {"return_tensors": "pt"},
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}
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@auto_docstring
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class JanusProcessor(ProcessorMixin):
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def __init__(self, image_processor, tokenizer, chat_template=None, use_default_system_prompt=False, **kwargs):
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r"""
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use_default_system_prompt (`bool`, *optional*, defaults to `False`):
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Use default system prompt for Text Generation.
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"""
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self.num_image_tokens = 576
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self.image_token = tokenizer.image_token
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self.image_start_token = tokenizer.boi_token
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self.image_end_token = tokenizer.eoi_token
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self.use_default_system_prompt = use_default_system_prompt
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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@auto_docstring
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def __call__(
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self,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
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images: ImageInput | None = None,
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**kwargs: Unpack[JanusProcessorKwargs],
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) -> BatchFeature:
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r"""
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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"""
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output_kwargs = self._merge_kwargs(
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JanusProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs
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)
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if text is None and images is None:
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raise ValueError("You must specify either text or images.")
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if text is not None:
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if isinstance(text, str):
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text = [text]
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elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
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raise ValueError("Invalid input text. Please provide a string, or a list of strings")
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generation_mode = output_kwargs["text_kwargs"].pop("generation_mode")
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# Replace the image token with expanded image tokens.
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prompt_strings = []
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one_img_tokens = self.image_start_token + (self.image_token * self.num_image_tokens) + self.image_end_token
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for prompt in text:
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prompt = prompt.replace(self.image_token, one_img_tokens)
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if self.use_default_system_prompt and generation_mode == "text":
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prompt = DEFAULT_SYSTEM_PROMPT + prompt
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if generation_mode == "image":
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prompt += self.image_start_token
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prompt_strings.append(prompt)
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data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
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# Process images if pixel values are provided.
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if images is not None and generation_mode != "image":
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data["pixel_values"] = self.image_processor(images=images, **output_kwargs["images_kwargs"])[
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"pixel_values"
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]
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return BatchFeature(data=data)
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def postprocess(self, images: ImageInput, **kwargs):
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"""
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Forwards all arguments to the image processor's `postprocess` method.
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Refer to the original method's docstring for more details.
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"""
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return self.image_processor.postprocess(images, **kwargs)
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def post_process_multimodal_output(
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self, generated_outputs, skip_special_tokens=True, generation_mode=None, **kwargs
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):
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"""
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Post-process the output of a multimodal model to return the requested modality output.
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If the model cannot generated the requested modality, an error will be raised.
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Args:
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generated_outputs (`torch.Tensor` or `np.ndarray`):
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The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
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or `(sequence_length,)`.
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skip_special_tokens (`bool`, *optional*, defaults to `True`):
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Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
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generation_mode (`str`, *optional*):
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Generation mode indicated which modality to output and can be one of `["text", "image", "audio"]`.
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**kwargs:
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Additional arguments to be passed to the tokenizer's `batch_decode method`.
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Returns:
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`list[Union[str, PIL.Image.Image]]`: The decoded text or generated image.
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"""
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if generation_mode is None or generation_mode == "text":
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return self.post_process_image_text_to_text(
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generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs
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)
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elif generation_mode == "image":
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generated_outputs = list(generated_outputs.float())
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images = self.postprocess(generated_outputs, return_tensors="PIL.Image.Image")
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return images["pixel_values"]
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else:
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raise ValueError(
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f"{self.__class__.__name__} got an unexpected generation_mode={generation_mode}. Supported options are only `text` and `image"
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)
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__all__ = ["JanusProcessor"]
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