441 lines
20 KiB
Python
441 lines
20 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/colpali/modular_colpali.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_colpali.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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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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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput, is_valid_image, make_flat_list_of_images
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from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput
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from ...utils import is_torch_available
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if is_torch_available():
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import torch
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class ColPaliProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {
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"padding": "longest",
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},
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"images_kwargs": {
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"data_format": "channels_first",
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"do_convert_rgb": True,
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},
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"common_kwargs": {"return_tensors": "pt"},
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}
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IMAGE_TOKEN = "<image>"
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EXTRA_TOKENS = [f"<loc{i:0>4}>" for i in range(1024)] + [f"<seg{i:0>3}>" for i in range(128)]
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def build_string_from_input(prompt, bos_token, image_seq_len, image_token, num_images):
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"""
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Builds a string from the input prompt and image tokens.
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For example, for the call:
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build_string_from_input(
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prompt="Prefix str"
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bos_token="<s>",
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image_seq_len=3,
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image_token="<im>",
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)
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The output will be:
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"<im><im><im><s>Initial str"
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Args:
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prompt (`list[Union[str, ImageInput]]`): The input prompt.
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bos_token (`str`): The beginning of sentence token.
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image_seq_len (`int`): The length of the image sequence.
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image_token (`str`): The image token.
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num_images (`int`): Number of images in the prompt.
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"""
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return f"{image_token * image_seq_len * num_images}{bos_token}{prompt}\n"
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class ColPaliProcessor(ProcessorMixin):
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r"""
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Constructs a ColPali processor which wraps a PaliGemmaProcessor and special methods to process images and queries, as
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well as to compute the late-interaction retrieval score.
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[`ColPaliProcessor`] offers all the functionalities of [`PaliGemmaProcessor`]. See the [`~PaliGemmaProcessor.__call__`]
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for more information.
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Args:
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image_processor ([`SiglipImageProcessor`], *optional*):
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The image processor is a required input.
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tokenizer ([`LlamaTokenizerFast`], *optional*):
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The tokenizer is a required input.
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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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visual_prompt_prefix (`str`, *optional*, defaults to `"Describe the image."`):
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A string that gets tokenized and prepended to the image tokens.
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query_prefix (`str`, *optional*, defaults to `"Question: "`):
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A prefix to be used for the query.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = ("SiglipImageProcessor", "SiglipImageProcessorFast")
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tokenizer_class = ("GemmaTokenizer", "GemmaTokenizerFast")
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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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chat_template=None,
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visual_prompt_prefix: str = "Describe the image.",
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query_prefix: str = "Question: ",
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):
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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if image_processor is None:
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raise ValueError("You need to specify an `image_processor`.")
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if tokenizer is None:
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raise ValueError("You need to specify a `tokenizer`.")
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if not hasattr(image_processor, "image_seq_length"):
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raise ValueError("Image processor is missing an `image_seq_length` attribute.")
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self.image_seq_length = image_processor.image_seq_length
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if not hasattr(tokenizer, "image_token"):
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image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True)
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tokens_to_add = {"additional_special_tokens": [image_token]}
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tokenizer.add_special_tokens(tokens_to_add)
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self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
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self.image_token = IMAGE_TOKEN
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else:
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self.image_token_id = tokenizer.image_token_id
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self.image_token = tokenizer.image_token
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tokenizer.add_tokens(EXTRA_TOKENS)
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tokenizer.add_bos_token = False
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tokenizer.add_eos_token = False
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self.visual_prompt_prefix = visual_prompt_prefix
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self.query_prefix = query_prefix
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def __call__(
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self,
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images: ImageInput = None,
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text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
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audio=None,
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videos=None,
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**kwargs: Unpack[ColPaliProcessorKwargs],
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) -> BatchFeature:
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"""
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Main method to prepare for the model either (1) one or several texts, either (2) one or several image(s). This method is a custom
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wrapper around the PaliGemmaProcessor's [`~PaliGemmaProcessor.__call__`] method adapted for the ColPali model. It cannot process
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both text and images at the same time.
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When preparing the text(s), this method forwards the `text` and `kwargs` arguments to LlamaTokenizerFast's
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[`~LlamaTokenizerFast.__call__`].
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When preparing the image(s), this method forwards the `images` and `kwargs` arguments to SiglipImageProcessor's
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[`~SiglipImageProcessor.__call__`].
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Please refer to the docstring of the above two methods for more information.
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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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
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number of channels, H and W are image height and width.
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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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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.
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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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ColPaliProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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suffix = output_kwargs["text_kwargs"].pop("suffix", None)
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return_token_type_ids = True if suffix is not None else False
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if text is None and images is None:
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raise ValueError("Either text or images must be provided")
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if text is not None and images is not None:
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raise ValueError("Only one of text or images can be processed at a time")
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if images is not None:
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if is_valid_image(images):
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images = [images]
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elif isinstance(images, list) and is_valid_image(images[0]):
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pass
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elif not (isinstance(images, list) and isinstance(images[0], list) and is_valid_image(images[0][0])):
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raise ValueError("images must be an image, list of images or list of list of images")
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texts_doc = [self.visual_prompt_prefix] * len(images)
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images = [image.convert("RGB") for image in images]
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input_strings = [
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build_string_from_input(
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prompt=prompt,
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bos_token=self.tokenizer.bos_token,
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image_seq_len=self.image_seq_length,
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image_token=IMAGE_TOKEN,
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num_images=len(image_list) if isinstance(image_list, list) else 1,
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)
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for prompt, image_list in zip(texts_doc, images)
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]
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images = make_flat_list_of_images(images)
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pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
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# max_length has to account for the image tokens
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if output_kwargs["text_kwargs"].get("max_length", None) is not None:
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output_kwargs["text_kwargs"]["max_length"] += self.image_seq_length
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inputs = self.tokenizer(
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input_strings,
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return_token_type_ids=False,
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**output_kwargs["text_kwargs"],
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)
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return_data = {**inputs, "pixel_values": pixel_values}
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if return_token_type_ids:
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labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
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return_data.update({"labels": labels})
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return BatchFeature(data=return_data)
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elif 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) and isinstance(text[0], str)):
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raise ValueError("Text must be a string or a list of strings")
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if suffix is None:
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suffix = self.query_augmentation_token * 10
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texts_query: list[str] = []
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for query in text:
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query = self.tokenizer.bos_token + self.query_prefix + query + suffix + "\n"
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texts_query.append(query)
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output_kwargs["text_kwargs"]["max_length"] = output_kwargs["text_kwargs"].get("max_length", 50)
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batch_query = self.tokenizer(
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texts_query,
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return_token_type_ids=False,
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**output_kwargs["text_kwargs"],
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)
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return batch_query
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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[str]], *optional*):
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The input sizes formatted as (height, width) per each image.
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Returns:
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dict[str, list[int]]: A dictionary mapping each modality ("image", "video", "audio")
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to a list containing the number of placeholder tokens required. If the model doesn't accept
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a certain modality or no input sizes are provided, the dict value is set to an empty list.
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"""
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vision_data = {}
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if image_sizes is not None:
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num_image_tokens = [self.image_seq_length] * len(image_sizes)
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num_image_patches = [1] * len(image_sizes)
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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 GemmaTokenizerFast'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 GemmaTokenizerFast'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(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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@property
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def query_augmentation_token(self) -> str:
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"""
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Return the query augmentation token.
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Query augmentation buffers are used as reasoning buffers during inference.
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"""
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return self.tokenizer.pad_token
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def process_images(
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self,
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images: ImageInput = None,
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**kwargs: Unpack[ColPaliProcessorKwargs],
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) -> BatchFeature:
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"""
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Prepare for the model one or several image(s). This method is a wrapper around the `__call__` method of the ColPaliProcessor's
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[`ColPaliProcessor.__call__`].
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This method forwards the `images` and `kwargs` arguments to the image processor.
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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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
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number of channels, H and W are image height and width.
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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.
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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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return self.__call__(images=images, **kwargs)
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def process_queries(
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self,
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text: Union[TextInput, list[TextInput]],
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**kwargs: Unpack[ColPaliProcessorKwargs],
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) -> BatchFeature:
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"""
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Prepare for the model one or several texts. This method is a wrapper around the `__call__` method of the ColPaliProcessor's
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[`ColPaliProcessor.__call__`].
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This method forwards the `text` and `kwargs` arguments to the tokenizer.
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Args:
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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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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.
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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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"""
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return self.__call__(text=text, **kwargs)
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def score_retrieval(
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self,
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query_embeddings: Union["torch.Tensor", list["torch.Tensor"]],
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passage_embeddings: Union["torch.Tensor", list["torch.Tensor"]],
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batch_size: int = 128,
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output_dtype: Optional["torch.dtype"] = None,
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output_device: Union["torch.device", str] = "cpu",
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) -> "torch.Tensor":
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"""
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Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector
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query embeddings (`qs`) and passage embeddings (`ps`). For ColPali, a passage is the
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image of a document page.
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Because the embedding tensors are multi-vector and can thus have different shapes, they
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should be fed as:
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(1) a list of tensors, where the i-th tensor is of shape (sequence_length_i, embedding_dim)
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(2) a single tensor of shape (n_passages, max_sequence_length, embedding_dim) -> usually
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obtained by padding the list of tensors.
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Args:
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query_embeddings (`Union[torch.Tensor, list[torch.Tensor]`): Query embeddings.
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passage_embeddings (`Union[torch.Tensor, list[torch.Tensor]`): Passage embeddings.
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batch_size (`int`, *optional*, defaults to 128): Batch size for computing scores.
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output_dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): The dtype of the output tensor.
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If `None`, the dtype of the input embeddings is used.
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output_device (`torch.device` or `str`, *optional*, defaults to "cpu"): The device of the output tensor.
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Returns:
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`torch.Tensor`: A tensor of shape `(n_queries, n_passages)` containing the scores. The score
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tensor is saved on the "cpu" device.
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"""
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if len(query_embeddings) == 0:
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raise ValueError("No queries provided")
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if len(passage_embeddings) == 0:
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raise ValueError("No passages provided")
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if query_embeddings[0].device != passage_embeddings[0].device:
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raise ValueError("Queries and passages must be on the same device")
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if query_embeddings[0].dtype != passage_embeddings[0].dtype:
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raise ValueError("Queries and passages must have the same dtype")
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if output_dtype is None:
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output_dtype = query_embeddings[0].dtype
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scores: list[torch.Tensor] = []
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for i in range(0, len(query_embeddings), batch_size):
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batch_scores: list[torch.Tensor] = []
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batch_queries = torch.nn.utils.rnn.pad_sequence(
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query_embeddings[i : i + batch_size], batch_first=True, padding_value=0
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)
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for j in range(0, len(passage_embeddings), batch_size):
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batch_passages = torch.nn.utils.rnn.pad_sequence(
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passage_embeddings[j : j + batch_size], batch_first=True, padding_value=0
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)
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batch_scores.append(
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torch.einsum("bnd,csd->bcns", batch_queries, batch_passages).max(dim=3)[0].sum(dim=2)
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|
)
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scores.append(torch.cat(batch_scores, dim=1).to(output_dtype).to(output_device))
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|
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|
return torch.cat(scores, dim=0)
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|
|
|
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|
__all__ = ["ColPaliProcessor"]
|