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65880fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """
TinyDocVLMProcessor — standalone processor (does not inherit ProcessorMixin to avoid
strict type-checking issues in transformers<4.45). Provides the same public API.
"""
import os
import json
from PIL import Image
from typing import Dict, Any, Union, Optional, List
import torch
from transformers import AutoTokenizer
from .image_processing import TinyDocImageProcessor
class TinyDocVLMProcessor:
"""
Coordinates TinyDocImageProcessor (image tiling + normalisation) and the
SmolLM2 tokenizer extended with document-special tokens.
Usage:
processor = TinyDocVLMProcessor()
inputs = processor(text=["Extract fields: <image>"], images=[pil_img])
# inputs → {"input_ids", "attention_mask", "pixel_values", "image_token_id"}
"""
# Class-level attrs used by some HF utilities (save_pretrained etc.)
image_processor_class = "TinyDocImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor: Optional[TinyDocImageProcessor] = None,
tokenizer=None,
config=None,
**kwargs,
):
self.image_processor = image_processor or TinyDocImageProcessor()
self.tokenizer = tokenizer or AutoTokenizer.from_pretrained(
"HuggingFaceTB/SmolLM2-135M-Instruct"
)
self.config = config
# Ensure <image> special token exists
self.image_token = "<image>"
if self.image_token not in self.tokenizer.get_vocab():
self.tokenizer.add_special_tokens(
{"additional_special_tokens": [self.image_token]}
)
self.image_token_id = self.tokenizer.convert_tokens_to_ids(self.image_token)
# ------------------------------------------------------------------
# Core __call__
# ------------------------------------------------------------------
def __call__(
self,
text: Union[str, List[str]],
images: Optional[Union[Image.Image, List[Image.Image]]] = None,
padding: bool = True,
truncation: bool = True,
max_length: Optional[int] = None,
return_tensors: str = "pt",
) -> Dict[str, Any]:
"""
Preprocesses text and images into tensors for the model.
Returns:
dict with keys: input_ids, attention_mask, pixel_values (optional),
image_token_id.
"""
# ---- 1. Image processing ------------------------------------------------
pixel_values = None
num_tiles_list: List[int] = []
if images is not None:
if not isinstance(images, list):
images = [images]
processed: List[torch.Tensor] = []
for img in images:
tile_tensor = self.image_processor.preprocess(img) # (T, 3, H, W)
processed.append(tile_tensor)
num_tiles_list.append(tile_tensor.shape[0])
# Pad to max tiles so we can stack into a single tensor
max_tiles = max(num_tiles_list)
padded: List[torch.Tensor] = []
sz = self.image_processor.image_size
for tile_tensor in processed:
T = tile_tensor.shape[0]
if T < max_tiles:
pad = torch.zeros(
(max_tiles - T, 3, sz, sz),
dtype=tile_tensor.dtype,
device=tile_tensor.device,
)
tile_tensor = torch.cat([tile_tensor, pad], dim=0)
padded.append(tile_tensor)
pixel_values = torch.stack(padded, dim=0) # (B, max_tiles, 3, H, W)
# ---- 2. Expand <image> tokens in text ----------------------------------
scale = (
getattr(self.config, "pixel_shuffle_scale", 3) if self.config else 3
)
patch_size = (
getattr(self.config, "patch_size", 16) if self.config else 16
)
sz = self.image_processor.image_size
tokens_per_tile = (sz // patch_size // scale) ** 2
if isinstance(text, list):
expanded: List[str] = []
for idx, t in enumerate(text):
if idx < len(num_tiles_list):
total_vis = num_tiles_list[idx] * tokens_per_tile
else:
total_vis = 0
expanded.append(self._expand_image_tokens(t, total_vis))
processed_text = expanded
else:
total_vis = num_tiles_list[0] * tokens_per_tile if num_tiles_list else 0
processed_text = self._expand_image_tokens(text, total_vis)
# ---- 3. Tokenise -------------------------------------------------------
enc = self.tokenizer(
processed_text,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
)
inputs: Dict[str, Any] = {
"input_ids": enc["input_ids"],
"attention_mask": enc["attention_mask"],
}
if pixel_values is not None:
inputs["pixel_values"] = pixel_values
inputs["image_token_id"] = self.image_token_id
return inputs
# ------------------------------------------------------------------
# Helpers
# ------------------------------------------------------------------
def _expand_image_tokens(self, text: str, total_tokens: int) -> str:
"""Replaces the single '<image>' placeholder with `total_tokens` copies."""
expansion = self.image_token * total_tokens
return text.replace(self.image_token, expansion)
# ------------------------------------------------------------------
# save / from_pretrained stubs (for HF Hub compatibility)
# ------------------------------------------------------------------
def save_pretrained(self, save_directory: str, **kwargs):
os.makedirs(save_directory, exist_ok=True)
self.image_processor.save_pretrained(save_directory)
self.tokenizer.save_pretrained(save_directory)
# Write a minimal processor_config.json
cfg = {
"processor_class": "TinyDocVLMProcessor",
"image_token": self.image_token,
"image_token_id": self.image_token_id,
}
with open(os.path.join(save_directory, "processor_config.json"), "w") as f:
json.dump(cfg, f, indent=2)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
image_processor = TinyDocImageProcessor.from_pretrained(
pretrained_model_name_or_path
)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
return cls(image_processor=image_processor, tokenizer=tokenizer, **kwargs)
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