# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/interns2_preview/modular_interns2_preview.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_interns2_preview.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 The Qwen Team and The HuggingFace Inc. 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. import importlib import os import numpy as np from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput from transformers.processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack from transformers.tokenization_utils_base import PreTokenizedInput, TextInput from transformers.utils import auto_docstring, logging from transformers.video_utils import VideoInput logger = logging.get_logger(__name__) class InternS2PreviewProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "padding": False, "return_token_type_ids": False, "return_mm_token_type_ids": False, }, "videos_kwargs": {"return_metadata": True}, "time_series_kwargs": {}, } @auto_docstring class InternS2PreviewProcessor(ProcessorMixin): def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs): self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_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) ) self.video_token_id = ( tokenizer.video_token_id if getattr(tokenizer, "video_token_id", None) else tokenizer.convert_tokens_to_ids(self.video_token) ) super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template) self.vision_start_token = ( "<|vision_start|>" if not hasattr(tokenizer, "vision_start_token") else tokenizer.vision_start_token ) self.vision_end_token = ( "<|vision_end|>" if not hasattr(tokenizer, "vision_end_token") else tokenizer.vision_end_token ) self.vision_start_token_id = ( tokenizer.vision_start_token_id if getattr(tokenizer, "vision_start_token_id", None) else tokenizer.convert_tokens_to_ids(self.vision_start_token) ) self.vision_end_token_id = ( tokenizer.vision_end_token_id if getattr(tokenizer, "vision_end_token_id", None) else tokenizer.convert_tokens_to_ids(self.vision_end_token) ) self.ts_token = "" if not hasattr(tokenizer, "ts_token") else tokenizer.ts_token self.ts_start_token = "<|ts|>" if not hasattr(tokenizer, "ts_start_token") else tokenizer.ts_start_token self.ts_end_token = "<|/ts|>" if not hasattr(tokenizer, "ts_end_token") else tokenizer.ts_end_token self.ts_start_token_id = ( tokenizer.ts_start_token_id if getattr(tokenizer, "ts_start_token_id", None) else tokenizer.convert_tokens_to_ids(self.ts_start_token) ) self.ts_end_token_id = ( tokenizer.ts_end_token_id if getattr(tokenizer, "ts_end_token_id", None) else tokenizer.convert_tokens_to_ids(self.ts_end_token) ) self.ts_token_id = ( tokenizer.ts_token_id if getattr(tokenizer, "ts_token_id", None) else tokenizer.convert_tokens_to_ids(self.ts_token) ) @auto_docstring def __call__( self, images: ImageInput = None, text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, videos: VideoInput = None, time_series_paths: list[str] = None, time_series_sampling_rates: list[int] = None, **kwargs: Unpack[InternS2PreviewProcessorKwargs], ) -> BatchFeature: r""" Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **ts_values** -- List of time series values to be fed to a model. Returned when `time_series_paths` is not `None`. - **ts_sr** -- List of time series sampling rates to be fed to a model. Returned when `time_series_sampling_rates` is not `None`. - **ts_lens** -- List of time series lengths to be fed to a model. Returned when `time_series_paths` is not `None`. - **num_ts_tokens** -- List of number of time series tokens to be fed to a model. Returned when `time_series_paths` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. """ output_kwargs = self._merge_kwargs( InternS2PreviewProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) image_grid_thw = image_inputs["image_grid_thw"] else: image_inputs = {} image_grid_thw = None if videos is not None: videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) video_grid_thw = videos_inputs["video_grid_thw"] # If user has not requested video metadata, pop it if not kwargs.get("return_metadata"): video_metadata = videos_inputs.pop("video_metadata") else: video_metadata = videos_inputs["video_metadata"] else: videos_inputs = {} video_grid_thw = None if not isinstance(text, list): text = [text] text = text.copy() # below lines change text in-place if time_series_paths is not None: assert time_series_sampling_rates is not None, ( "If time_series_signals is provided, time_series_sampling_rates must also be provided." ) assert len(time_series_paths) == len(time_series_sampling_rates), ( "The number of time series signals must match the number of sampling rates." ) time_series_inputs = self.time_series_processor( ts_paths=time_series_paths, sampling_rates=time_series_sampling_rates ) num_ts_tokens = time_series_inputs.pop("num_ts_tokens") assert len(num_ts_tokens) == len(text), ( "The number of time series signals must match the number of text prompts." ) for i in range(len(text)): if f"{self.ts_start_token}{self.ts_token}{self.ts_end_token}" in text[i]: ts_placeholder = self.ts_start_token + self.ts_token * num_ts_tokens[i] + self.ts_end_token text[i] = text[i].replace( f"{self.ts_start_token}{self.ts_token}{self.ts_end_token}", ts_placeholder, 1 ) elif self.ts_token in text[i]: text[i] = text[i].replace(self.ts_token, self.ts_token * num_ts_tokens[i]) else: time_series_inputs = {} if image_grid_thw is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.image_token in text[i]: num_image_tokens = image_grid_thw[index].prod() // merge_length text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if video_grid_thw is not None: merge_length = self.video_processor.merge_size**2 index = 0 for i in range(len(text)): while self.video_token in text[i]: metadata = video_metadata[index] if metadata.fps is None: logger.warning_once( "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. " "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. " "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results." ) metadata.fps = 24 if metadata.fps is None else metadata.fps # if timestamps are not provided, calculate them curr_timestamp = self._calculate_timestamps( metadata.frames_indices, metadata.fps, self.video_processor.temporal_patch_size, ) video_placeholder = "" frame_seqlen = video_grid_thw[index][1:].prod() // merge_length for frame_idx in range(video_grid_thw[index][0]): curr_time = curr_timestamp[frame_idx] video_placeholder += f"<{curr_time:.1f} seconds>" video_placeholder += ( self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token ) if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]: text[i] = text[i].replace( f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1 ) else: # vllm may input video token directly text[i] = text[i].replace(self.video_token, video_placeholder, 1) index += 1 text[i] = text[i].replace("<|placeholder|>", self.video_token) return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video", "ts"]) if return_mm_token_type_ids: array_ids = np.array(text_inputs["input_ids"]) mm_token_type_ids = np.zeros_like(text_inputs["input_ids"]) mm_token_type_ids[array_ids == self.image_token_id] = 1 text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist() return BatchFeature( data={**text_inputs, **image_inputs, **videos_inputs, **time_series_inputs}, tensor_type=return_tensors ) def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs): """ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. Args: image_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (height, width) per each image. video_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (num_frames, height, width) per each video. Returns: `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided input modalities, along with other useful data. """ vision_data = {} if image_sizes is not None: images_kwargs = InternS2PreviewProcessorKwargs._defaults.get("images_kwargs", {}) images_kwargs.update(kwargs) merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size num_image_patches = [ self.image_processor.get_number_of_image_patches(*image_size, images_kwargs) for image_size in image_sizes ] num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches] vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) if video_sizes is not None: videos_kwargs = InternS2PreviewProcessorKwargs._defaults.get("videos_kwargs", {}) videos_kwargs.update(kwargs) num_video_patches = [ self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs) for video_size in video_sizes ] num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches] vision_data["num_video_tokens"] = num_video_tokens return MultiModalData(**vision_data) def post_process_image_text_to_text( self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs ): """ Post-process the output of the model to decode the text. Args: generated_outputs (`torch.Tensor` or `np.ndarray`): The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` or `(sequence_length,)`. skip_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method. **kwargs: Additional arguments to be passed to the tokenizer's `batch_decode method`. Returns: `list[str]`: The decoded text. """ return self.tokenizer.batch_decode( generated_outputs, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) def _calculate_timestamps(self, indices: list[int] | np.ndarray, video_fps: float, merge_size: int = 2): if not isinstance(indices, list): indices = indices.tolist() if len(indices) % merge_size != 0: indices.extend(indices[-1] for _ in range(merge_size - len(indices) % merge_size)) timestamps = [idx / video_fps for idx in indices] # @JJJYmmm frames are merged by self.merge_size, \ # so we need to average the timestamps between the first/last frame within the temporal patch timestamps = [ (timestamps[i] + timestamps[i + merge_size - 1]) / 2 for i in range(0, len(timestamps), merge_size) ] return timestamps def time_series_preprocessor(self, conversation): if isinstance(conversation, (list, tuple)) and ( isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "content") ): conversations = conversation else: conversations = [conversation] batch_time_series = [] batch_time_series_metadata = [] for conversation in conversations: for message in conversation: if message["role"] != "user": continue time_series_fnames = [ content["data"] for content in message["content"] if content.get("type") == "time_series" and "data" in content ] time_series_rates = [ content.get("sampling_rate", None) for content in message["content"] if content.get("type") == "time_series" ] for path, rate in zip(time_series_fnames, time_series_rates): batch_time_series.append(path) batch_time_series_metadata.append(rate) return { "time_series_paths": batch_time_series or None, "time_series_sampling_rates": batch_time_series_metadata or None, } def time_series_processor( self, ts_paths: list[str], sampling_rates: list[float], do_normalize=True, do_truncate=True, ) -> BatchFeature: pd = importlib.import_module("pandas") sf = importlib.import_module("soundfile") assert len(ts_paths) == len(sampling_rates), "ts_paths and sampling_rates must have the same length" ts_values = [] ts_sr = [] ts_lens = [] for idx, ts_path in enumerate(ts_paths): sr = sampling_rates[idx] ext = os.path.splitext(ts_path)[-1].lower() if ext in [".wav", ".mp3", ".flac"]: ts_input, sr = sf.read(ts_path) # ts_input: np.ndarray, shape [T] or [T, C] elif ext == ".csv": df = pd.read_csv(ts_path, header=None) ts_input = df.values # [T, C] elif ext == ".npy": ts_input = np.load(ts_path) # [T, C] else: raise ValueError(f"Unsupported file format: {ext}") if not isinstance(ts_input, np.ndarray): ts_input = np.array(ts_input, dtype=np.float32) if do_normalize: mean = ts_input.mean(axis=0, keepdims=True) std = ts_input.std(axis=0, keepdims=True) ts_input = (ts_input - mean) / (std + 1e-8) if do_truncate and len(ts_input) > 240000: ts_input = ts_input[:240000] # truncate to 240k to avoid oom if ts_input.ndim == 1: ts_input = ts_input[:, None] # [T,C] ts_len = ts_input.shape[0] if sr is None or sr == 0: # if no sr provided sr = ts_len / 4 ts_values.append(ts_input) ts_sr.append(sr) ts_lens.append(ts_len) ts_lens = np.array(ts_lens) ts_sr = np.array(ts_sr) num_ts_tokens = self._get_num_ts_tokens(sampling_rates=ts_sr, ts_lens=ts_lens) return BatchFeature( data={"ts_values": ts_values, "ts_sr": ts_sr, "ts_lens": ts_lens, "num_ts_tokens": num_ts_tokens} ) def _get_num_ts_tokens(self, sampling_rates, ts_lens): strides = np.floor(160 / ((1 + np.exp(-sampling_rates / 100)) ** 6)) patch_sizes = strides * 2 embed_lengths = (np.ceil((ts_lens - patch_sizes) / strides) + 1).astype(np.int64) num_ts_tokens = [(embed_length // 2 + 1) // 2 for embed_length in embed_lengths] return num_ts_tokens __all__ = ["InternS2PreviewProcessor"]