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.

85 lines
3.0 KiB

# Copyright 2022 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 Blip.
"""
from ...image_utils import ImageInput
from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
from ...utils import auto_docstring
class BlipProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_offsets_mapping": False,
"return_token_type_ids": False,
"return_length": False,
"verbose": True,
},
}
@auto_docstring
class BlipProcessor(ProcessorMixin):
def __init__(self, image_processor, tokenizer, **kwargs):
tokenizer.return_token_type_ids = False
super().__init__(image_processor, tokenizer)
@auto_docstring
def __call__(
self,
images: ImageInput | None = None,
text: str | list[str] | TextInput | PreTokenizedInput | None = None,
**kwargs: Unpack[BlipProcessorKwargs],
) -> BatchEncoding:
if images is None and text is None:
raise ValueError("You have to specify either images or text.")
text_encoding = None
# add pixel_values encoding. If we also have text_encoding, update image encoding and return it.
# else, return the text encoding.
output_kwargs = self._merge_kwargs(
BlipProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if text is not None:
text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
if images is not None:
encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
if text_encoding is not None:
encoding_image_processor.update(text_encoding)
return encoding_image_processor
return text_encoding
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
tokenizer_input_names = [name for name in tokenizer_input_names if name != "token_type_ids"]
return tokenizer_input_names + image_processor_input_names
__all__ = ["BlipProcessor"]