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# This file was automatically generated from src/transformers/models/aria/modular_aria.py.
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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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# coding=utf-8
# Copyright 2024 The Rhymes-AI Teams Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils import PreTokenizedInput, TextInput
from ...utils import TensorType
from ..auto import AutoTokenizer
class AriaProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {
"max_image_size": 980,
"split_image": False,
},
"return_tensors": TensorType.PYTORCH,
}
class AriaProcessor(ProcessorMixin):
"""
AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
Args:
image_processor (`AriaImageProcessor`, *optional*):
The AriaImageProcessor to use for image preprocessing.
tokenizer (`PreTrainedTokenizerBase`, *optional*):
An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
chat_template (`str`, *optional*):
A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.
size_conversion (`Dict`, *optional*):
A dictionary indicating size conversions for images.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["chat_template", "size_conversion"]
image_processor_class = "AriaImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor=None,
tokenizer: Union[AutoTokenizer, str] = None,
chat_template: Optional[str] = None,
size_conversion: Optional[Dict[Union[float, int], int]] = None,
):
if size_conversion is None:
size_conversion = {490: 128, 980: 256}
self.size_conversion = {int(k): v for k, v in size_conversion.items()}
if tokenizer is not None and tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.unk_token
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
images: Optional[ImageInput] = None,
audio=None,
videos=None,
**kwargs: Unpack[AriaProcessorKwargs],
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s).
Args:
text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`ImageInput`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are supported.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **pixel_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`.
"""
output_kwargs = self._merge_kwargs(
AriaProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
if images is not None:
image_inputs = self.image_processor(
images,
**output_kwargs["images_kwargs"],
)
# expand the image_token according to the num_crops and tokens per image
tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]]
prompt_strings = []
num_crops = image_inputs.pop("num_crops") * tokens_per_image
for sample in text:
sample = sample.replace(self.tokenizer.image_token, self.tokenizer.image_token * num_crops)
prompt_strings.append(sample)
else:
image_inputs = {}
prompt_strings = text
text_inputs = self.tokenizer(
prompt_strings,
**output_kwargs["text_kwargs"],
)
return BatchFeature(data={**text_inputs, **image_inputs})
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
# Remove `num_crops`, it is popped and used only when processing. Make a copy of list when remocing
# otherwise `self.image_processor.model_input_names` is also modified
image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
__all__ = ["AriaProcessor"]