File size: 4,022 Bytes
d6a610e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | from typing import List, Union, Optional
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, ImagesKwargs
class AXVEImagesKwargs(ImagesKwargs):
min_pixels: Optional[int]
max_pixels: Optional[int]
patch_size: Optional[int]
merge_size: Optional[int]
class AXVEProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: AXVEImagesKwargs
_defaults = {
"text_kwargs": {
"padding": False,
}
}
class AXVEProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor=None,
tokenizer=None,
chat_template=None,
**kwargs
):
self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
self.image_token_id = (
tokenizer.image_token_id
if getattr(tokenizer, "image_token_id", None)
else tokenizer.convert_tokens_to_ids(self.image_token)
)
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
**kwargs
) -> BatchFeature:
output_kwargs = self._merge_kwargs(
AXVEProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
image_grid_hw = image_inputs["image_grid_hw"]
else:
image_inputs = {}
image_grid_hw = None
if not isinstance(text, list):
text = [text]
text = text.copy() # below lines change text in-place
if image_grid_hw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.image_token in text[i]:
num_image_tokens = image_grid_hw[index].prod() // merge_length
text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.image_token)
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", None)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
text_inputs.pop('token_type_ids', None)
return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
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
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
__all__ = ["AXVEProcessor"] |