294 lines
13 KiB
Python
294 lines
13 KiB
Python
# 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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"""
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Processor class for Pixtral.
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"""
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from typing import Union
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import numpy as np
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput, is_valid_image, load_image
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from ...processing_utils import (
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MultiModalData,
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ProcessingKwargs,
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ProcessorMixin,
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Unpack,
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)
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import is_vision_available, logging
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if is_vision_available():
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from .image_processing_pixtral import get_resize_output_image_size
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logger = logging.get_logger(__name__)
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class PixtralProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {
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"padding": False,
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"return_mm_token_type_ids": False,
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},
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"images_kwargs": {},
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"common_kwargs": {
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"return_tensors": "pt",
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},
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}
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# Copied from transformers.models.idefics2.processing_idefics2.is_url
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def is_url(val) -> bool:
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return isinstance(val, str) and val.startswith("http")
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# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
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def is_image_or_image_url(elem):
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return is_url(elem) or is_valid_image(elem)
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class PixtralProcessor(ProcessorMixin):
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r"""
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Constructs a Pixtral processor which wraps a Pixtral image processor and a Pixtral tokenizer into a single processor.
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[`PixtralProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
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[`~PixtralProcessor.__call__`] and [`~PixtralProcessor.decode`] for more information.
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Args:
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image_processor ([`PixtralImageProcessor`], *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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patch_size (`int`, *optional*, defaults to 16):
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Patch size from the vision tower.
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spatial_merge_size (`int`, *optional*, defaults to 1):
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The downsampling factor for the spatial merge operation.
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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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image_token (`str`, *optional*, defaults to `"[IMG]"`):
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Special token used to denote image location.
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image_break_token (`str`, *optional*, defaults to `"[IMG_BREAK]"`):
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Special token used to denote the end of a line of pixels in an image.
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image_end_token (`str`, *optional*, defaults to `"[IMG_END]"`):
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Special token used to denote the end of an image input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "AutoImageProcessor"
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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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patch_size: int = 16,
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spatial_merge_size: int = 1,
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chat_template=None,
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image_token="[IMG]", # set the default and let users change if they have peculiar special tokens in rare cases
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image_break_token="[IMG_BREAK]",
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image_end_token="[IMG_END]",
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**kwargs,
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):
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self.patch_size = patch_size
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self.spatial_merge_size = spatial_merge_size
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self.image_token = image_token
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self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
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self.image_break_token = image_break_token
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self.image_end_token = image_end_token
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self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
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self.image_break_token_id = tokenizer.convert_tokens_to_ids(self.image_break_token)
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self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
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self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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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[PixtralProcessorKwargs],
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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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
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the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
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CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
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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. 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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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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output_kwargs = self._merge_kwargs(
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PixtralProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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patch_size = self.patch_size * self.spatial_merge_size
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if images is not None:
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if is_image_or_image_url(images):
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images = [images]
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elif isinstance(images, (list, tuple)) and is_image_or_image_url(images[0]):
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pass
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elif (
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isinstance(images, (list, tuple))
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and isinstance(images[0], (list, tuple))
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and is_image_or_image_url(images[0][0])
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):
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images = [image for sublist in images for image in sublist]
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else:
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raise ValueError(
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"Invalid input images. Please provide a single image, a list of images, or a list of lists of images."
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)
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images = [load_image(im) if isinstance(im, str) else im for im in images]
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image_inputs = self.image_processor(images, patch_size=patch_size, **output_kwargs["images_kwargs"])
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else:
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image_inputs = {}
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if isinstance(text, str):
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text = [text]
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elif not isinstance(text, list) and not isinstance(text[0], str):
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raise TypeError("Invalid input text. Please provide a string, or a list of strings")
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# try to expand inputs in processing if we have the necessary parts
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prompt_strings = text
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if image_inputs.get("pixel_values") is not None:
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# Replace the image token with the expanded image token sequence
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image_sizes = iter(image_inputs["image_sizes"])
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prompt_strings = []
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replace_strings = []
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for sample in text:
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while self.image_token in sample:
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height, width = next(image_sizes)
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num_height_tokens = height // patch_size
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num_width_tokens = width // patch_size
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replace_tokens = [
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[self.image_token] * num_width_tokens + [self.image_break_token]
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] * num_height_tokens
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# Flatten list
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replace_tokens = [item for sublist in replace_tokens for item in sublist]
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replace_tokens[-1] = self.image_end_token
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replace_str = "".join(replace_tokens)
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replace_strings.append(replace_str)
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sample = sample.replace(self.image_token, "<placeholder>", 1)
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while "<placeholder>" in sample:
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replace_str = replace_strings.pop(0)
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sample = sample.replace("<placeholder>", replace_str, 1)
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prompt_strings.append(sample)
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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", False)
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text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
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self._check_special_mm_tokens(prompt_strings, 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_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 = PixtralProcessorKwargs._defaults.get("images_kwargs", {})
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images_kwargs.update(kwargs)
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size = images_kwargs.get("size", None) or self.image_processor.size
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patch_size = self.patch_size * self.spatial_merge_size
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num_image_tokens = []
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for height, width in image_sizes:
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resized_height, resized_width = get_resize_output_image_size(
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np.zeros((height, width, 3)),
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size=(size["longest_edge"], size["longest_edge"]),
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patch_size=(patch_size, patch_size),
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)
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num_height_tokens = resized_height // patch_size
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num_width_tokens = resized_width // patch_size
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num_image_tokens.append((num_width_tokens + 1) * num_height_tokens)
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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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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
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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 LlamaTokenizerFast'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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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast'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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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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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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__all__ = ["PixtralProcessor"]
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