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124 lines
5.3 KiB
124 lines
5.3 KiB
# Copyright 2023 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former.
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"""
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from ...image_processing_utils import BatchFeature
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from ...image_utils import ImageInput
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput
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from ...utils import auto_docstring, logging
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logger = logging.get_logger(__name__)
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class InstructBlipProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {
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"add_special_tokens": True,
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"padding": False,
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"stride": 0,
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"return_overflowing_tokens": False,
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"return_special_tokens_mask": False,
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"return_offsets_mapping": False,
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"return_token_type_ids": False,
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"return_length": False,
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"verbose": True,
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},
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}
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@auto_docstring
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class InstructBlipProcessor(ProcessorMixin):
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def __init__(self, image_processor, tokenizer, qformer_tokenizer, num_query_tokens=None, **kwargs):
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r"""
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qformer_tokenizer (`AutoTokenizer`):
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An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
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num_query_tokens (`int`, *optional*):
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"
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Number of tokens used by the Qformer as queries, should be same as in model's config.
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"""
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if not hasattr(tokenizer, "image_token"):
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self.image_token = AddedToken("<image>", normalized=False, special=True)
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tokenizer.add_tokens([self.image_token], special_tokens=True)
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else:
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self.image_token = tokenizer.image_token
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self.num_query_tokens = num_query_tokens
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super().__init__(image_processor, tokenizer, qformer_tokenizer)
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@auto_docstring
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def __call__(
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self,
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images: ImageInput | None = None,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
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**kwargs: Unpack[InstructBlipProcessorKwargs],
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) -> BatchFeature:
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if images is None and text is None:
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raise ValueError("You have to specify at least images or text.")
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output_kwargs = self._merge_kwargs(
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InstructBlipProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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encoding = {}
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if text is not None:
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if isinstance(text, str):
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text = [text]
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elif not isinstance(text, list) and not isinstance(text[0], str):
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raise ValueError("Invalid input text. Please provide a string, or a list of strings")
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qformer_text_encoding = self.qformer_tokenizer(text, **output_kwargs["text_kwargs"])
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encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids")
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encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask")
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# We need this hacky manipulation because BLIP expects image tokens to be at the beginning even before BOS token
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if output_kwargs["text_kwargs"].get("max_length") is not None:
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output_kwargs["text_kwargs"]["max_length"] -= self.num_query_tokens
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text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
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if images is not None:
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# Image tokens should not be padded/truncated or prepended with special BOS token
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image_tokens = self.image_token.content * self.num_query_tokens
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output_kwargs["text_kwargs"]["add_special_tokens"] = False
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output_kwargs["text_kwargs"]["padding"] = False
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output_kwargs["text_kwargs"]["truncation"] = False
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image_text_encoding = self.tokenizer(image_tokens, **output_kwargs["text_kwargs"])
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for k in text_encoding:
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text_encoding[k] = [image_text_encoding[k] + sample for sample in text_encoding[k]]
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encoding.update(text_encoding)
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if images is not None:
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image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
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encoding.update(image_encoding)
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# Cast to desired return tensors type
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encoding = BatchFeature(encoding, tensor_type=return_tensors)
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return encoding
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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qformer_input_names = ["qformer_input_ids", "qformer_attention_mask"]
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return tokenizer_input_names + image_processor_input_names + qformer_input_names
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__all__ = ["InstructBlipProcessor"]
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