# coding=utf-8 # Copyright 2025 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. from typing import List, Optional, Union from transformers.processing_utils import ( ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack, ) from transformers.tokenization_utils_base import PreTokenizedInput, TextInput from ...image_processing_utils import BatchFeature from ...image_utils import ( ImageInput, make_flat_list_of_images, ) class Llama4ImagesKwargs(ImagesKwargs, total=False): max_patches: Optional[int] resize_to_max_canvas: Optional[bool] class Llama4ProcessorKwargs(ProcessingKwargs, total=False): images_kwargs: Llama4ImagesKwargs _defaults = { "text_kwargs": { "padding_side": "left", }, } chat_template = "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %} \n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- else %}\n {#- FIXME: The processor requires an array, always. #}\n {%- set system_message = messages[0]['content'][0]['text']|trim %}\n {%- endif %}\n {%- set messages = messages[1:] %}\n {%- set user_supplied_system_message = true %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set user_supplied_system_message = false %}\n{%- endif %}\n\n{#- System message if the user supplied one #}\n{%- if user_supplied_system_message %}\n {{- \"<|header_start|>system<|header_end|>\n\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\n\" }}\n {%- endif %}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|header_start|>user<|header_end|>\n\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\n\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|header_start|>' + message['role'] + '<|header_end|>\n\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- elif 'tool_calls' in message and message.tool_calls|length > 0 %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' -}}\n {{- '<|python_start|>' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|python_end|>' }}\n {%- for tool_call in message.tool_calls %}\n {{- '{\"name\": \"' + tool_call.function.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.function.arguments | tojson }}\n {{- \"}\" }}\n {%- endfor %}\n {{- \"<|eot|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|header_start|>ipython<|header_end|>\n\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' }}\n{%- endif %}\n" class Llama4Processor(ProcessorMixin): r""" Constructs a Llama4 processor which wraps a [`AutoImageProcessor`] and [`PretrainedTokenizerFast`] tokenizer into a single processor that inherits both the image processor and tokenizer functionalities. See the [`~Llama4Processor.__call__`] and [`~Llama4Processor.decode`] for more information. Args: image_processor ([`AutoImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*): The tokenizer is a required input. patch_size (`int`, *optional*, defaults to 28): The size of image patches for tokenization. img_size (`int`, *optional*, defaults to 364): The size of the image to be tokenized. This should correspond to the size given to the image processor. image_token (`str`, *optional*, defaults to `"<|image|>"`): The token to be used to represent an image in the text. downsample_factor (`int`, *optional*, defaults to 1): The factor by which to scale the patch size. start_of_img_token (`str`, *optional*, defaults to `"<|START_OF_IMG|>"`): The token to be used to represent the start of an image in the text. end_of_img_token (`str`, *optional*, defaults to `"<|END_OF_IMG|>"`): The token to be used to represent the end of an image in the text. img_patch_token (`str`, *optional*, defaults to `"<|IMG_PATCH|>"`): The token to be used to represent an image patch in the text. img_line_break_token (`str`, *optional*, defaults to `"<|IMG_LINE_BREAK|>"`): The token to be used to represent a line break in the text. tile_token (`str`, *optional*, defaults to `"TILE"`): The token to be used to represent an image patch in the text. tile_global_token (`str`, *optional*, defaults to `"TILE_GLOBAL"`): The token to be used to represent the cover image in the text. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = [ "chat_template", "image_token", "patch_size", "img_size", "downsample_factor", "start_of_img_token", "end_of_img_token", "img_patch_token", "img_line_break_token", "tile_token", "tile_global_token", ] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, image_processor=None, tokenizer=None, patch_size: int = 14, pixel_shuffle_ratio: float = 0.5, fake_image_token="<|image|>", image_token="<|image|>", start_of_image_token="<|image_start|>", end_of_image_token="<|image_end|>", patch_token="<|patch|>", tile_x_separator_token="<|tile_x_separator|>", tile_y_separator_token="<|tile_y_separator|>", chat_template=chat_template, **kwargs, ): super().__init__(image_processor, tokenizer, chat_template=chat_template) self.downsample_ratio = int(round(1.0 / (pixel_shuffle_ratio**2))) self.patch_size = patch_size self.fake_image_token = fake_image_token self.image_token = image_token self.start_of_img_token = start_of_image_token self.end_of_img_token = end_of_image_token self.img_patch_token = patch_token self.tile_token = tile_x_separator_token self.tile_global_token = tile_y_separator_token def _prompt_split_image(self, aspect_ratio, num_patches_per_chunk): """ Create a structured string representation of image tokens Args: num_patches: Number of patches in the image Returns: String with appropriate image tokens """ img_string = "<|image_start|>" ratio_h, ratio_w = aspect_ratio if ratio_h * ratio_w > 1: for yy in range(ratio_h): for xx in range(ratio_w): img_string += "<|patch|>" * num_patches_per_chunk if xx < ratio_w - 1: img_string += "<|tile_x_separator|>" img_string += "<|tile_y_separator|>" img_string += "<|image|>" img_string += "<|patch|>" * num_patches_per_chunk img_string += "<|image_end|>" return img_string def __call__( self, images: Optional[ImageInput] = None, text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, audio=None, videos=None, **kwargs: Unpack[Llama4ProcessorKwargs], ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] to encode the text. To prepare the vision inputs, this method forwards the `images` and `kwargs` arguments to Llama4ImageProcessor's [`~Llama4ImageProcessor.__call__`] if `images` is not `None`. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. 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`. - **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`. """ if text is None: raise ValueError("You have to specify text.") output_kwargs = self._merge_kwargs( Llama4ProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if not isinstance(text, (list, tuple)): text = [text] # Process images image_inputs = {} if images is not None: images = make_flat_list_of_images(images) image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) image_height, image_width = image_inputs["pixel_values"][0].shape[-2:] num_patches_per_chunk = int( (image_height // self.patch_size) * (image_width // self.patch_size) // self.downsample_ratio ) aspect_ratios = image_inputs.pop("aspect_ratios") total_placeholders = sum(prompt.count(self.fake_image_token) for prompt in text) if total_placeholders != len(images): raise ValueError( f"Found {total_placeholders} placeholders across the batch, " f"but have {len(images)} flattened images." ) image_index = 0 processed_text = [] for prompt in text: placeholder_count = prompt.count(self.fake_image_token) if placeholder_count == 0: # do nothing if there is no image processed_text.append(prompt) continue prompt_splits = prompt.split(self.fake_image_token) new_prompt = [] for local_image_index, split_part in enumerate(prompt_splits): new_prompt.append(split_part) if local_image_index < placeholder_count: tokens_for_this_image = self._prompt_split_image( aspect_ratios[image_index], num_patches_per_chunk ) image_index += 1 new_prompt.append(tokens_for_this_image) processed_text.append("".join(new_prompt)) if image_index != len(images): raise ValueError("Number of image placeholders in the prompt does not match the number of images.") text = processed_text text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) return BatchFeature(data={**text_inputs, **image_inputs}) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ 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(tokenizer_input_names) + list(image_processor_input_names) __all__ = ["Llama4Processor"]