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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 List, Optional, Union
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
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 = image_token
self.image_start_token = "<racm3:break>" # fixed tokens for start and end, so can hardcode
self.image_end_token = "<eoss>"
super().__init__(image_processor, tokenizer)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: int = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
return_for_text_completion: bool = False,
) -> 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 `kwrags` arguments to
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
Args:
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).
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.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
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")
# 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)
data = self.tokenizer(
prompt_strings,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
max_length=max_length,
)
if images is not None:
pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
data["pixel_values"] = pixel_values
return BatchFeature(data=data, tensor_type=return_tensors)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
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)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
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
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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