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# coding=utf-8
# Copyright 2024 Meta Inc. 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.
"""
Processor class for Chameleon.
"""
from typing import Optional, Union
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import (
MultiModalData,
ProcessingKwargs,
ProcessorMixin,
TextKwargs,
Unpack,
)
from ...tokenization_utils_base import PreTokenizedInput, TextInput
class ChameleonTextKwargs(TextKwargs, total=False):
return_for_text_completion: bool
class ChameleonProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: ChameleonTextKwargs
_defaults = {
"text_kwargs": {
"padding": False,
"return_for_text_completion": False,
"return_mm_token_type_ids": False,
},
"common_kwargs": {
"return_tensors": "pt",
},
}
class ChameleonProcessor(ProcessorMixin):
r"""
Constructs a Chameleon processor which wraps a Chameleon image processor and a Chameleon tokenizer into a single
processor.
[`ChameleonProcessor`] offers all the functionalities of [`ChameleonImageProcessor`] and [`LlamaTokenizerFast`].
See the [`~ChameleonProcessor.__call__`] and [`~ChameleonProcessor.decode`] for more information.
Args:
image_processor ([`ChameleonImageProcessor`]):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`]):
The tokenizer is a required input.
image_seq_length (`int`, *optional*, defaults to 1024):
Sequence length of one image embedding.
image_token (`str`, *optional*, defaults to `"<image>"`):
The special token used to indicate image in the text.
"""
attributes = ["image_processor", "tokenizer"]
tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
image_processor_class = "ChameleonImageProcessor"
def __init__(self, image_processor, tokenizer, image_seq_length: int = 1024, image_token: str = "<image>"):
self.image_seq_length = image_seq_length
self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
self.image_start_token = (
tokenizer.boi_token if hasattr(tokenizer, "boi_token") else "<racm3:break>"
) # fixed tokens for start and end, so can hardcode
self.image_end_token = tokenizer.eoi_token if hasattr(tokenizer, "eoi_token") else "<eoss>"
self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
self.image_start_token_id = tokenizer.convert_tokens_to_ids(self.image_start_token)
self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
self.image_ids = [self.image_token_id, self.image_start_token_id, self.image_end_token_id]
super().__init__(image_processor, tokenizer)
def __call__(
self,
images: Optional[ImageInput] = None,
text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
audio=None,
videos=None,
**kwargs: Unpack[ChameleonProcessorKwargs],
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
of the above two methods for more information.
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
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.
text (`str`, `list[str]`, `list[list[str]]`):
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).
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
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`.
"""
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise TypeError("Invalid input text. Please provide a string, or a list of strings")
if text is None and images is None:
raise ValueError("You must provide either text or images")
output_kwargs = self._merge_kwargs(
ChameleonProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
return_for_text_completion = output_kwargs["text_kwargs"].pop("return_for_text_completion", False)
# Replace the image token with the expanded image token sequence
prompt_strings = []
one_img_tokens = self.image_start_token + (self.image_token * self.image_seq_length) + self.image_end_token
for sample in text:
sample = sample.replace(self.image_token, one_img_tokens)
if not return_for_text_completion:
sample += self.tokenizer.sep_token # special Chameleon treatment to add sep for chat mode
prompt_strings.append(sample)
image_inputs = {}
if images is not None:
image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
if return_mm_token_type_ids:
array_ids = np.array(text_inputs["input_ids"])
mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
"""
Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
Args:
image_sizes (`list[list[int]]`, *optional*):
The input sizes formatted as (height, width) per each image.
Returns:
`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
input modalities, along with other useful data.
"""
vision_data = {}
if image_sizes is not None:
# add 2 for BOI and EOI tokens
num_image_tokens = [self.image_seq_length + 2] * len(image_sizes)
num_image_patches = [1] * len(image_sizes)
vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
return MultiModalData(**vision_data)
__all__ = ["ChameleonProcessor"]
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