You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
143 lines
6.1 KiB
143 lines
6.1 KiB
# Copyright 2023 The HuggingFace Inc. team.
|
|
#
|
|
# 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 Nougat.
|
|
"""
|
|
|
|
from typing import Optional, Union
|
|
|
|
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput, TruncationStrategy
|
|
|
|
from ...processing_utils import ProcessorMixin
|
|
from ...utils import PaddingStrategy, TensorType, auto_docstring
|
|
|
|
|
|
@auto_docstring
|
|
class NougatProcessor(ProcessorMixin):
|
|
def __init__(self, image_processor, tokenizer):
|
|
super().__init__(image_processor, tokenizer)
|
|
|
|
@auto_docstring
|
|
def __call__(
|
|
self,
|
|
images=None,
|
|
text=None,
|
|
do_crop_margin: bool | None = None,
|
|
do_resize: bool | None = None,
|
|
size: dict[str, int] | None = None,
|
|
resample: "PILImageResampling" = None, # noqa: F821
|
|
do_thumbnail: bool | None = None,
|
|
do_align_long_axis: bool | None = None,
|
|
do_pad: bool | None = None,
|
|
do_rescale: bool | None = None,
|
|
rescale_factor: int | float | None = None,
|
|
do_normalize: bool | None = None,
|
|
image_mean: float | list[float] | None = None,
|
|
image_std: float | list[float] | None = None,
|
|
data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821
|
|
input_data_format: Union[str, "ChannelDimension"] | None = None, # noqa: F821
|
|
text_pair: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
|
|
text_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
|
|
text_pair_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding: bool | str | PaddingStrategy = False,
|
|
truncation: bool | str | TruncationStrategy | None = None,
|
|
max_length: int | None = None,
|
|
stride: int = 0,
|
|
is_split_into_words: bool = False,
|
|
pad_to_multiple_of: int | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
):
|
|
r"""
|
|
do_crop_margin (`bool`, *optional*):
|
|
Whether to automatically crop white margins from document images. When enabled, the processor detects
|
|
and removes white space around the edges of document pages, which is useful for processing scanned
|
|
documents or PDFs with large margins.
|
|
do_thumbnail (`bool`, *optional*):
|
|
Whether to create a thumbnail version of the image. When enabled, a smaller version of the image is
|
|
generated alongside the main processed image, which can be useful for preview or faster processing.
|
|
do_align_long_axis (`bool`, *optional*):
|
|
Whether to automatically align images so that the longer axis is horizontal. When enabled, portrait
|
|
images are rotated to landscape orientation, which is typically better for document processing tasks.
|
|
"""
|
|
if images is None and text is None:
|
|
raise ValueError("You need to specify either an `images` or `text` input to process.")
|
|
|
|
if images is not None:
|
|
inputs = self.image_processor(
|
|
images,
|
|
do_crop_margin=do_crop_margin,
|
|
do_resize=do_resize,
|
|
size=size,
|
|
resample=resample,
|
|
do_thumbnail=do_thumbnail,
|
|
do_align_long_axis=do_align_long_axis,
|
|
do_pad=do_pad,
|
|
do_rescale=do_rescale,
|
|
rescale_factor=rescale_factor,
|
|
do_normalize=do_normalize,
|
|
image_mean=image_mean,
|
|
image_std=image_std,
|
|
return_tensors=return_tensors,
|
|
data_format=data_format,
|
|
input_data_format=input_data_format,
|
|
)
|
|
if text is not None:
|
|
encodings = self.tokenizer(
|
|
text,
|
|
text_pair=text_pair,
|
|
text_target=text_target,
|
|
text_pair_target=text_pair_target,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
is_split_into_words=is_split_into_words,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
)
|
|
|
|
if text is None:
|
|
return inputs
|
|
elif images is None:
|
|
return encodings
|
|
else:
|
|
inputs["labels"] = encodings["input_ids"]
|
|
return inputs
|
|
|
|
def post_process_generation(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.post_process_generation`].
|
|
Please refer to the docstring of this method for more information.
|
|
"""
|
|
return self.tokenizer.post_process_generation(*args, **kwargs)
|
|
|
|
|
|
__all__ = ["NougatProcessor"]
|