# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/deepseek_vl/modular_deepseek_vl.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_deepseek_vl.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved. # # 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. from typing import Optional, Union import torch.nn.functional as F from ...image_processing_utils import BatchFeature from ...image_processing_utils_fast import ( BaseImageProcessorFast, DefaultFastImageProcessorKwargs, group_images_by_shape, reorder_images, ) from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling, SizeDict from ...processing_utils import Unpack from ...utils import ( TensorType, auto_docstring, is_torch_available, ) if is_torch_available(): import torch class DeepseekVLFastImageProcessorKwargs(DefaultFastImageProcessorKwargs): r""" min_size (`int`, *optional*, defaults to 14): The minimum allowed size for the resized image. Ensures that neither the height nor width falls below this value after resizing. """ min_size: int @auto_docstring class DeepseekVLImageProcessorFast(BaseImageProcessorFast): resample = PILImageResampling.BICUBIC image_mean = OPENAI_CLIP_MEAN image_std = OPENAI_CLIP_STD size = {"height": 384, "width": 384} min_size = 14 do_resize = True do_rescale = True do_normalize = True valid_kwargs = DeepseekVLFastImageProcessorKwargs def __init__(self, **kwargs: Unpack[DeepseekVLFastImageProcessorKwargs]): super().__init__(**kwargs) if kwargs.get("image_mean", None) is None: background_color = (127, 127, 127) else: background_color = tuple([int(x * 255) for x in kwargs.get("image_mean")]) self.background_color = tuple(background_color) def resize( self, image: "torch.Tensor", size: SizeDict, min_size: int, interpolation: "F.InterpolationMode" = None, antialias: bool = True, **kwargs, ) -> "torch.Tensor": if size.height is None or size.width is None or size.height != size.width: raise ValueError( f"Output height and width must be the same. Got height={size['height']} and width={size['width']}" ) size = size.height height, width = image.shape[-2:] max_size = max(height, width) delta = size / max_size # Largest side becomes `size` and the other side is scaled according to the aspect ratio. output_size_nonpadded = SizeDict( height=max(int(height * delta), min_size), width=max(int(width * delta), min_size), ) return super().resize(image, size=output_size_nonpadded, interpolation=interpolation, antialias=antialias) def pad_to_square( self, images: "torch.Tensor", background_color: Union[int, tuple[int, int, int]] = 0, ) -> "torch.Tensor": """ Pads an image to a square based on the longest edge. Args: images (`torch.Tensor`): The images to pad. background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): The color to use for the padding. Can be an integer for single channel or a tuple of integers representing for multi-channel images. If passed as integer in mutli-channel mode, it will default to `0` in subsequent channels. Returns: `torch.Tensor`: The padded images. """ height, width = images.shape[-2:] num_channels = images.shape[1] batch_size = images.shape[0] if height == width: return images max_dim = max(height, width) # Ensure background_color is the correct shape if isinstance(background_color, int): background_color = [background_color] elif len(background_color) != num_channels: raise ValueError( f"background_color must have no more than {num_channels} elements to match the number of channels" ) padded_images = torch.zeros( (batch_size, num_channels, max_dim, max_dim), dtype=images.dtype, device=images.device ) for i, color in enumerate(background_color): padded_images[:, i, :, :] = color if width > height: start = (max_dim - height) // 2 padded_images[:, :, start : start + height, :] = images else: start = (max_dim - width) // 2 padded_images[:, :, :, start : start + width] = images return padded_images def _preprocess( self, images: list["torch.Tensor"], do_resize: bool, size: SizeDict, min_size: int, interpolation: Optional["F.InterpolationMode"], do_rescale: bool, rescale_factor: float, do_normalize: bool, image_mean: Optional[Union[float, list[float]]], image_std: Optional[Union[float, list[float]]], disable_grouping: Optional[bool], return_tensors: Optional[Union[str, TensorType]], do_pad: bool = True, **kwargs, ) -> BatchFeature: # Group images by size for batched resizing grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) resized_images_grouped = {} for shape, stacked_images in grouped_images.items(): if do_resize: stacked_images = self.resize( image=stacked_images, size=size, min_size=min_size, interpolation=interpolation ) resized_images_grouped[shape] = stacked_images resized_images = reorder_images(resized_images_grouped, grouped_images_index) # Group images by size for further processing # Needed in case do_resize is False, or resize returns images with different sizes grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) processed_images_grouped = {} for shape, stacked_images in grouped_images.items(): if do_pad: stacked_images = self.pad_to_square(stacked_images, background_color=self.background_color) # Fused rescale and normalize stacked_images = self.rescale_and_normalize( stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std ) processed_images_grouped[shape] = stacked_images processed_images = reorder_images(processed_images_grouped, grouped_images_index) processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) __all__ = ["DeepseekVLImageProcessorFast"]