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245 lines
11 KiB
245 lines
11 KiB
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
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#
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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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import numpy as np
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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 MultiModalData, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import auto_docstring, is_vision_available
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from ...utils.import_utils import requires
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if is_vision_available():
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from .image_processing_emu3 import Emu3ImageProcessorKwargs, smart_resize
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class Emu3TextKwargs(TextKwargs, total=False):
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"""
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return_for_image_generation (`bool`, *optional*, defaults to `False`):
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Whether the processed text is intended for image generation tasks. When `True`, the processor prepares
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inputs for image generation by appending image start tokens and size information to the prompt, and
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images should not be provided. When `False`, the processor prepares inputs for text generation from
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images and text, requiring both inputs to be provided.
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"""
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return_for_image_generation: bool
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class Emu3ProcessorKwargs(ProcessingKwargs, total=False):
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text_kwargs: Emu3TextKwargs
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images_kwargs: Emu3ImageProcessorKwargs
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_defaults = {
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"text_kwargs": {
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"return_for_image_generation": False,
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"return_mm_token_type_ids": False,
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},
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"images_kwargs": {
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"ratio": "1:1",
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"image_area": 518400,
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},
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}
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@auto_docstring
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@requires(backends=("vision",))
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class Emu3Processor(ProcessorMixin):
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def __init__(
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self,
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image_processor,
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tokenizer,
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chat_template=None,
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**kwargs,
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):
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self.image_token = tokenizer.image_token # image_token as placeholder to be replaced by vq-vae tokens
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self.image_token_id = tokenizer.image_token_id
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self.image_start_token = tokenizer.boi_token # "<|image start|>" fixed tokens for start and end of image
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self.image_end_token = tokenizer.eoi_token # "<|image end|>"
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self.fake_token_around_image = tokenizer.image_wrapper_token # "<|image token|>" every image starts with it
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self.eof_token = tokenizer.eof_token # "<|extra_201|>"
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self.bos_token = tokenizer.bos_token
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self.downsample_ratio = 8
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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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 = None,
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**kwargs: Unpack[Emu3ProcessorKwargs],
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) -> BatchFeature:
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r"""
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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"""
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# check if images and text inputs are reversed for BC
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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 TypeError("Invalid input text. Please provide a string, or a list of strings")
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output_kwargs = self._merge_kwargs(
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Emu3ProcessorKwargs,
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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_for_image_generation = output_kwargs["text_kwargs"].pop("return_for_image_generation", False)
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ratio = output_kwargs["images_kwargs"].pop("ratio", None)
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image_area = output_kwargs["images_kwargs"].pop("image_area", None)
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if return_for_image_generation and images is not None:
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raise ValueError("You should not provide `images` when `return_for_image_generation=True`")
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if not return_for_image_generation and text is None and images is None:
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raise ValueError("You must provide either text or images when `return_for_image_generation=False`")
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image_features = {}
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image_start_tokens = f"{self.image_start_token}"
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image_end_tokens = f"{self.eof_token}{self.image_end_token}"
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# generate text from image + text input, so we add placeholders for image tokens
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if not return_for_image_generation and images is not None:
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image_features = self.image_processor(images, **output_kwargs["images_kwargs"])
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image_sizes = iter(image_features.image_sizes)
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prompt_strings = []
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for sample in text:
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while self.image_token in sample:
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image_size = next(image_sizes)
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height, width = image_size
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height = height // self.downsample_ratio
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width = width // self.downsample_ratio
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image_seq_length = height * (width + 1) # +1 for extra row when converting to BPE in modeling code
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image_placeholder = f"{image_start_tokens}{height}*{width}{self.fake_token_around_image}{'<placeholder>' * image_seq_length}{image_end_tokens}"
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sample = sample.replace(self.image_token, image_placeholder, 1)
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sample = f"{self.bos_token}{sample}" # add BOS because GPT tokenizer doesn't add it
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prompt_strings.append(sample)
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text = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]
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# generate image from text input, so we add begin-of-image tokens from where image generation starts
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elif return_for_image_generation:
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height, width = self.calculate_generate_size(ratio, image_area, self.downsample_ratio)
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image_prompt = f"{image_start_tokens}{height}*{width}{self.fake_token_around_image}"
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text = [f"{self.bos_token}{sample}{image_prompt}" for sample in text]
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image_features["image_sizes"] = [[height, width]] * len(text)
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# else just generate from text-only input, and we do no special treatment for text
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
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self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
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if return_mm_token_type_ids:
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array_ids = np.array(text_inputs["input_ids"])
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mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
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mm_token_type_ids[array_ids == self.image_token_id] = 1
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text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
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return BatchFeature(data={**text_inputs, **image_features}, tensor_type=return_tensors)
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def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
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"""
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Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
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Args:
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image_sizes (`list[list[int]]`, *optional*):
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The input sizes formatted as (height, width) per each image.
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Returns:
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`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
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input modalities, along with other useful data.
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"""
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vision_data = {}
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if image_sizes is not None:
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num_image_tokens = []
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for height, width in image_sizes:
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height, width = smart_resize(
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height,
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width,
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self.image_processor.spatial_factor,
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self.image_processor.min_pixels,
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self.image_processor.max_pixels,
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)
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height = height // self.downsample_ratio
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width = width // self.downsample_ratio
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image_seq_length = height * (width + 1) # +1 for extra row when converting to BPE in modeling code
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num_image_tokens.append(image_seq_length)
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num_image_patches = [1] * len(image_sizes)
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vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
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return MultiModalData(**vision_data)
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def calculate_generate_size(self, ratio, image_area, spatial_factor):
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width, height = map(int, ratio.split(":"))
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current_area = width * height
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target_ratio = (image_area / current_area) ** 0.5
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token_height = int(round(height * target_ratio / spatial_factor))
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token_width = int(round(width * target_ratio / spatial_factor))
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return token_height, token_width
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def postprocess(self, images: ImageInput, **kwargs):
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return self.image_processor.postprocess(images, **kwargs)
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def post_process_multimodal_output(
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self, generated_outputs, skip_special_tokens=True, generation_mode=None, **kwargs
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):
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"""
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Post-process the output of a multimodal model to return the requested modality output.
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If the model cannot generated the requested modality, an error will be raised.
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Args:
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generated_outputs (`torch.Tensor` or `np.ndarray`):
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The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
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or `(sequence_length,)`.
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skip_special_tokens (`bool`, *optional*, defaults to `True`):
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Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
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generation_mode (`str`, *optional*):
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Generation mode indicated which modality to output and can be one of `["text", "image", "audio"]`.
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**kwargs:
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Additional arguments to be passed to the tokenizer's `batch_decode method`.
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Returns:
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`list[Union[str, PIL.Image.Image]]`: The decoded text or generated image.
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"""
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if generation_mode is None or generation_mode == "text":
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return self.post_process_image_text_to_text(
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generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs
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)
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elif generation_mode == "image":
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images = self.postprocess(generated_outputs, return_tensors="PIL.Image.Image")
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return images["pixel_values"]
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else:
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raise ValueError(
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f"{self.__class__.__name__} got an unexpected generation_mode={generation_mode}. Supported options are only `text` and `image"
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)
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__all__ = ["Emu3Processor"]
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