""" Processor class for Granite4 Vision. """ from fractions import Fraction from transformers.feature_extraction_utils import BatchFeature from transformers.image_processing_utils import select_best_resolution from transformers.image_utils import ImageInput, get_image_size, to_numpy_array from transformers.processing_utils import ProcessorMixin from transformers.tokenization_utils_base import PreTokenizedInput, TextInput from ..base import install_auto_processor_patch, load_chat_template, to_mlx class Granite4VisionProcessor(ProcessorMixin): attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, image_processor=None, tokenizer=None, patch_size=None, vision_feature_select_strategy=None, chat_template=None, image_token="", num_additional_image_tokens=0, downsample_rate=None, **kwargs, ): self.patch_size = patch_size self.num_additional_image_tokens = num_additional_image_tokens self.vision_feature_select_strategy = vision_feature_select_strategy self.downsample_rate = downsample_rate self.image_token = ( tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token ) self.image_token_id = ( tokenizer.image_token_id if getattr(tokenizer, "image_token_id", None) else tokenizer.convert_tokens_to_ids(self.image_token) ) super().__init__(image_processor, tokenizer, chat_template=chat_template) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): import json from pathlib import Path from transformers import AutoImageProcessor, AutoTokenizer kwargs.pop("use_fast", None) tokenizer = AutoTokenizer.from_pretrained( pretrained_model_name_or_path, **kwargs ) load_chat_template(tokenizer, pretrained_model_name_or_path) try: image_processor = AutoImageProcessor.from_pretrained( pretrained_model_name_or_path, use_fast=False, **kwargs ) except ValueError: image_processor = AutoImageProcessor.from_pretrained( pretrained_model_name_or_path, **kwargs ) proc_kwargs = {} proc_cfg_path = Path(pretrained_model_name_or_path) / "processor_config.json" if proc_cfg_path.exists(): with open(proc_cfg_path) as f: proc_cfg = json.load(f) for k in ( "patch_size", "vision_feature_select_strategy", "image_token", "num_additional_image_tokens", "downsample_rate", ): if k in proc_cfg: proc_kwargs[k] = proc_cfg[k] # Also try to read downsample_rate from config.json config_path = Path(pretrained_model_name_or_path) / "config.json" if config_path.exists() and "downsample_rate" not in proc_kwargs: with open(config_path) as f: config = json.load(f) if "downsample_rate" in config: proc_kwargs["downsample_rate"] = config["downsample_rate"] return cls( image_processor=image_processor, tokenizer=tokenizer, **proc_kwargs, ) def __call__( self, images: ImageInput = None, text: ( TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] ) = None, **kwargs, ) -> BatchFeature: if images is None and text is None: raise ValueError("You have to specify at least images or text.") kwargs.pop("return_tensors", None) kwargs.pop("padding", None) do_pad = kwargs.pop("do_pad", True) if images is not None: image_inputs = self.image_processor(images, do_pad=do_pad, **kwargs) else: image_inputs = {} if isinstance(text, str): text = [text] elif not isinstance(text, list) and not isinstance(text[0], str): raise TypeError( "Invalid input text. Please provide a string, or a list of strings" ) prompt_strings = text if image_inputs: image_sizes = iter(image_inputs["image_sizes"]) height, width = get_image_size( to_numpy_array(image_inputs["pixel_values"][0][0]) ) prompt_strings = [] for sample in text: while self.image_token in sample: image_size = next(image_sizes) if not isinstance(image_size, (list, tuple)): image_size = image_size.tolist() orig_height, orig_width = image_size num_image_tokens = self._get_number_of_features( orig_height, orig_width, height, width ) if self.vision_feature_select_strategy == "default": num_image_tokens -= 1 sample = sample.replace( self.image_token, "" * num_image_tokens, 1, ) prompt_strings.append(sample) prompt_strings = [ sample.replace("", self.image_token) for sample in prompt_strings ] text_inputs = self.tokenizer(prompt_strings, **kwargs) return BatchFeature(data=to_mlx({**text_inputs, **image_inputs})) def _get_number_of_features( self, orig_height: int, orig_width: int, height: int, width: int ) -> int: image_grid_pinpoints = self.image_processor.image_grid_pinpoints height_best_resolution, width_best_resolution = select_best_resolution( [orig_height, orig_width], image_grid_pinpoints ) scale_height, scale_width = ( height_best_resolution // height, width_best_resolution // width, ) patches_height = height // self.patch_size patches_width = width // self.patch_size # Apply downsample rate if self.downsample_rate is not None: ds_rate = Fraction(self.downsample_rate) patches_height = int(patches_height * ds_rate) patches_width = int(patches_width * ds_rate) unpadded_features, newline_features = self._get_unpadded_features( orig_height, orig_width, patches_height, patches_width, scale_height, scale_width, ) base_features = ( patches_height * patches_width + self.num_additional_image_tokens ) num_image_tokens = unpadded_features + newline_features + base_features return num_image_tokens def _get_unpadded_features( self, height, width, patches_height, patches_width, scale_height, scale_width ): current_height = patches_height * scale_height current_width = patches_width * scale_width original_aspect_ratio = width / height current_aspect_ratio = current_width / current_height if original_aspect_ratio > current_aspect_ratio: new_height = int(round(height * (current_width / width), 7)) padding = (current_height - new_height) // 2 current_height -= padding * 2 else: new_width = int(round(width * (current_height / height), 7)) padding = (current_width - new_width) // 2 current_width -= padding * 2 unpadded_features = current_height * current_width newline_features = current_height return (unpadded_features, newline_features) def batch_decode(self, *args, **kwargs): return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): return self.tokenizer.decode(*args, **kwargs) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) __all__ = ["Granite4VisionProcessor"] install_auto_processor_patch("granite4_vision", Granite4VisionProcessor)