203 lines
9.7 KiB
Python
203 lines
9.7 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/aria/modular_aria.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_aria.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 Rhymes-AI Teams Authors and The 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
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from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils import PreTokenizedInput, TextInput
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from ...utils import TensorType
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from ..auto import AutoTokenizer
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class AriaProcessorKwargs(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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"max_image_size": 980,
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"split_image": False,
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},
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"return_tensors": TensorType.PYTORCH,
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}
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class AriaProcessor(ProcessorMixin):
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"""
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AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
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Args:
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image_processor (`AriaImageProcessor`, *optional*):
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The AriaImageProcessor to use for image preprocessing.
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tokenizer (`PreTrainedTokenizerBase`, *optional*):
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An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
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chat_template (`str`, *optional*):
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A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.
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size_conversion (`Dict`, *optional*):
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A dictionary indicating size conversions for images.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "AriaImageProcessor"
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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: Union[AutoTokenizer, str] = None,
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chat_template: Optional[str] = None,
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size_conversion: Optional[dict[Union[float, int], int]] = None,
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):
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if size_conversion is None:
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size_conversion = {490: 128, 980: 256}
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self.size_conversion = {int(k): v for k, v in size_conversion.items()}
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self.image_token = tokenizer.image_token
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self.image_token_id = tokenizer.image_token_id
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if tokenizer is not None and tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.unk_token
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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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text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
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images: Optional[ImageInput] = None,
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audio=None,
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videos=None,
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**kwargs: Unpack[AriaProcessorKwargs],
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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).
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Args:
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text (`TextInput`, `PreTokenizedInput`, `list[TextInput]`, `list[PreTokenizedInput]`):
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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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images (`ImageInput`):
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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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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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- **pixel_mask** -- Pixel mask 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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AriaProcessorKwargs,
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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 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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if images is not None:
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image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
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# expand the image_token according to the num_crops and tokens per image
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tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]]
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prompt_strings = []
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num_crops = image_inputs.pop("num_crops") * tokens_per_image
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for sample in text:
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sample = sample.replace(self.tokenizer.image_token, self.tokenizer.image_token * num_crops)
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prompt_strings.append(sample)
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else:
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image_inputs = {}
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prompt_strings = text
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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[array_ids == self.image_token_id] = 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 = AriaProcessorKwargs._defaults.get("images_kwargs", {})
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images_kwargs.update(kwargs)
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max_size = images_kwargs.get("max_image_size", None) or self.image_processor.max_image_size
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num_image_patches = [
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self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
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for image_size in image_sizes
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]
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num_image_tokens = [self.size_conversion[max_size] * 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 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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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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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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# Remove `num_crops`, it is popped and used only when processing. Make a copy of list when remocing
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# otherwise `self.image_processor.model_input_names` is also modified
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image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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__all__ = ["AriaProcessor"]
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