317 lines
16 KiB
Python
317 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc. 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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from typing import Optional, Union
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import numpy as np
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from ...image_processing_utils import BatchFeature
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from ...image_utils import ImageInput, concatenate_list, make_flat_list_of_images
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from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...video_utils import VideoInput, make_batched_videos
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class InternVLImagesKwargs(ImagesKwargs, total=False):
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crop_to_patches: Optional[bool]
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min_patches: Optional[int]
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max_patches: Optional[int]
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class InternVLProcessorKwargs(ProcessingKwargs, total=False):
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images_kwargs: InternVLImagesKwargs
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_defaults = {
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"text_kwargs": {
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"padding_side": "left",
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"return_mm_token_type_ids": False,
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},
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"images_kwargs": {
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"crop_to_patches": True,
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},
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"videos_kwargs": {},
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}
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class InternVLProcessor(ProcessorMixin):
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r"""
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Constructs a InternVL processor which wraps a [`AutoImageProcessor`] and
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[`PretrainedTokenizerFast`] tokenizer into a single processor that inherits both the image processor and
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tokenizer functionalities. See the [`~InternVLProcessor.__call__`] and [`~InternVLProcessor.decode`] for more information.
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Args:
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image_processor ([`AutoImageProcessor`], *optional*):
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The image processor is a required input.
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tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*):
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The tokenizer is a required input.
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video_processor ([`AutoVideoProcessor`], *optional*):
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The video processor is a required input.
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image_seq_length (`int`, *optional*, defaults to 256):
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The number of image token to use per image patch. it should be set so that:
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image_seq_length = (config.image_size // config.patch_size) ** 2 * (config.scale_factor**2)
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chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
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in a chat into a tokenizable string.
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"""
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attributes = ["image_processor", "tokenizer", "video_processor"]
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image_processor_class = "AutoImageProcessor"
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video_processor_class = "AutoVideoProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(
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self,
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image_processor=None,
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tokenizer=None,
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video_processor=None,
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image_seq_length: int = 256,
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chat_template=None,
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**kwargs,
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):
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self.image_seq_length = image_seq_length
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self.start_image_token = tokenizer.start_image_token
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self.end_image_token = tokenizer.end_image_token
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self.start_image_token_id = tokenizer.start_image_token_id
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self.end_image_token_id = tokenizer.end_image_token_id
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self.image_token = tokenizer.context_image_token
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self.video_token = tokenizer.video_token
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self.image_token_id = tokenizer.context_image_token_id
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self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id]
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super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs)
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def _insert_media_placeholders(
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self,
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text: list[str],
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image_pixel_values,
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video_pixel_values,
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image_num_patches: list[int],
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video_num_patches: list[int],
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image_num_patches_indices: np.ndarray,
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video_num_patches_indices: np.ndarray,
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video_patch_indices: np.ndarray,
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):
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"""
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Processes interleaved text with <image> and <video> placeholders, replacing them with appropriate
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image and video tokens while keeping track of the patches used.
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"""
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image_index = 0
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video_index = 0
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processed_text = []
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image_video_patches = []
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replace_strings = []
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# Support interleaved image and video in prompts:
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# Processed patches of images and videos are inserted in `image_video_patches` in the order they appear in the prompts
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for prompt in text:
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new_prompt = prompt
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while self.image_token in new_prompt or self.video_token in new_prompt:
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if self.image_token in new_prompt and (
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self.video_token not in new_prompt
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or new_prompt.index(self.image_token) < new_prompt.index(self.video_token)
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):
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# Get the slice of patches corresponding to the current image
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start_index = image_num_patches_indices[image_index - 1] if image_index > 0 else 0
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end_index = image_num_patches_indices[image_index]
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image_video_patches.append(image_pixel_values[start_index:end_index])
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# Replace the corresponding image placeholder with the correct number of image tokens
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new_prompt = new_prompt.replace(self.image_token, "<placeholder>", 1)
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replace_strings.append(
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f"{self.start_image_token}{self.image_token * self.image_seq_length * image_num_patches[image_index]}{self.end_image_token}"
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)
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image_index += 1
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else:
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# Get the slice of patches corresponding to the current video
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# Here we need to account for both the multiple video frames and the potential multiple patches per frame
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# As of now, InternVL only supports one patch per frame, but we keep the code flexible for future updates
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current_patch_index = video_patch_indices[video_index - 1] if video_index > 0 else 0
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end_patch_index = video_patch_indices[video_index]
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start_index = video_num_patches_indices[current_patch_index] if video_index > 0 else 0
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end_index = video_num_patches_indices[end_patch_index - 1]
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image_video_patches.append(video_pixel_values[start_index:end_index])
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# Get the number of patches per frame and replace the video placeholder with the correct number of image tokens
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num_patches = list(video_num_patches[current_patch_index:end_patch_index])
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video_prompt = "\n".join(
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f"Frame{i + 1}: {self.start_image_token}{self.image_token * self.image_seq_length * num_patches[i]}{self.end_image_token}"
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for i in range(len(num_patches))
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)
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replace_strings.append(video_prompt)
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new_prompt = new_prompt.replace(self.video_token, "<placeholder>", 1)
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video_index += 1
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while "<placeholder>" in new_prompt:
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replace_str = replace_strings.pop(0)
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new_prompt = new_prompt.replace("<placeholder>", replace_str, 1)
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processed_text.append(new_prompt)
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return processed_text, image_video_patches, image_index, video_index
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def __call__(
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self,
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images: Optional[ImageInput] = None,
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text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
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audio=None,
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videos: Optional[VideoInput] = None,
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**kwargs: Unpack[InternVLProcessorKwargs],
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) -> BatchFeature:
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"""
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Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] to encode the text if `text`
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is not `None`, otherwise encode default OCR queries which depends on the `format`, `box`, `color`, `multi_page` and
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`crop_to_patches` arguments. To prepare the vision inputs, this method forwards the `images` and `kwrags` arguments to
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GotOcr2ImageProcessor's [`~GotOcr2ImageProcessor.__call__`] if `images` is not `None`.
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Args:
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. Both channels-first and channels-last formats are supported.
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text (`str`, `list[str]`, `list[list[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
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The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
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return_tensors (`str` or [`~utils.TensorType`], *optional*):
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If set, will return tensors of a particular framework. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return NumPy `np.ndarray` objects.
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- `'jax'`: Return JAX `jnp.ndarray` objects.
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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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if text is None:
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raise ValueError("You have to specify text.")
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output_kwargs = self._merge_kwargs(
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InternVLProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if not isinstance(text, (list, tuple)):
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text = [text]
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# Process images and videos separately, as videos don't support crop_to_patches
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image_num_patches = []
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video_num_patches = []
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image_videos_inputs = {}
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image_pixel_values = None
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video_pixel_values = None
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image_num_patches_indices = np.array([0])
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video_patch_indices = np.array([0])
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video_num_patches_indices = np.array([0])
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if images is not None:
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images = make_flat_list_of_images(images)
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image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
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image_num_patches = image_inputs.pop("num_patches")
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image_pixel_values = image_inputs.pop("pixel_values")
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image_num_patches_indices = np.cumsum(image_num_patches)
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if videos is not None:
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videos = make_batched_videos(videos)
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video_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
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video_pixel_values = video_inputs.pop("pixel_values_videos")
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# Obtain per frame information first and then flatten to (BS * T, ...)
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num_frames_per_video = [len(video) for video in video_pixel_values]
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video_num_patches = [1 for frames in num_frames_per_video for _ in range(frames)]
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video_patch_indices = np.cumsum(num_frames_per_video)
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video_num_patches_indices = np.cumsum(video_num_patches)
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video_pixel_values = video_pixel_values.flatten(0, 1)
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if images is not None or videos is not None:
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text, image_video_patches, image_index, video_index = self._insert_media_placeholders(
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text,
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image_pixel_values,
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video_pixel_values,
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image_num_patches,
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video_num_patches,
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image_num_patches_indices,
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video_num_patches_indices,
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video_patch_indices,
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)
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if images is not None and image_index != len(images):
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raise ValueError("Number of image placeholders in the prompt does not match the number of images.")
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if videos is not None and video_index != len(videos):
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raise ValueError("Number of video placeholders in the prompt does not match the number of videos.")
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# Concatenate the interleaved image and video patches (function agnostic to the patches type (list, numpy array, torch tensor))
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image_videos_inputs = {"pixel_values": concatenate_list(image_video_patches)}
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
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self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
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if return_mm_token_type_ids:
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array_ids = np.array(text_inputs["input_ids"])
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mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
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mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
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text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
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return BatchFeature(data={**text_inputs, **image_videos_inputs}, tensor_type=return_tensors)
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def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
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"""
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Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
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Args:
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image_sizes (`list[list[int]]`, *optional*):
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The input sizes formatted as (height, width) per each image.
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Returns:
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`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
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input modalities, along with other useful data.
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"""
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vision_data = {}
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if image_sizes is not None:
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images_kwargs = InternVLProcessorKwargs._defaults.get("images_kwargs", {})
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images_kwargs.update(kwargs)
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num_image_patches = [
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self.image_processor.get_number_of_image_tokens(*image_size, images_kwargs)
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for image_size in image_sizes
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]
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# Add 2 for BOI and EOI tokens
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num_image_tokens = [2 + (self.image_seq_length * num_patches) for num_patches in num_image_patches]
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vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
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return MultiModalData(**vision_data)
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(tokenizer_input_names) + list(image_processor_input_names)
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__all__ = ["InternVLProcessor"]
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