Logics-Thinking-8B / processing_logics.py

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# processing_logics.py

from typing import List, Optional, Union, Dict
import requests
import base64
import io
import os
from PIL import Image
import math
import torch
import copy
import numpy as np

from transformers import PreTrainedTokenizer
from transformers.processing_utils import ProcessorMixin
from transformers.image_utils import ImageInput
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.feature_extraction_utils import BatchFeature

from .configuration_logics import LogicsConfig
from .siglip_encoder import SigLipImageProcessor
from transformers import AutoImageProcessor
try:
    from decord import VideoReader, cpu
except ImportError:
    print("Please install pyav to use video processing functions.")

AutoImageProcessor.register(LogicsConfig, SigLipImageProcessor)


IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"



def process_video_with_decord(video_file, data_args):
    vr = VideoReader(video_file, ctx=cpu(0), num_threads=1)
    total_frame_num = len(vr)
    video_time = total_frame_num / vr.get_avg_fps()
    avg_fps = round(vr.get_avg_fps() / data_args.video_fps)
    frame_idx = [i for i in range(0, total_frame_num, avg_fps)]
    frame_time = [i / avg_fps for i in frame_idx]

    if data_args.frames_upbound > 0:
        if len(frame_idx) > data_args.frames_upbound or data_args.force_sample:
            uniform_sampled_frames = np.linspace(0, total_frame_num - 1, data_args.frames_upbound, dtype=int)
            frame_idx = uniform_sampled_frames.tolist()
            frame_time = [i / vr.get_avg_fps() for i in frame_idx]

    try:
        video = vr.get_batch(frame_idx).asnumpy()
    except:
        video = torch.zeros((10, 720, 720, 3)).numpy()
        print(f"load {video_file} error, use empty tensor instead.")
    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
    num_frames_to_sample = num_frames = len(frame_idx)
    # https://github.com/dmlc/decord/issues/208
    vr.seek(0)
    return video
    # return video, video_time, frame_time, num_frames_to_sample
    


def resize_and_pad_image(image, target_resolution):
    """
    Resize and pad an image to a target resolution while maintaining aspect ratio.
    Args:
        image (PIL.Image.Image): The input image.
        target_resolution (tuple): The target resolution (width, height) of the image.
    Returns:
        PIL.Image.Image: The resized and padded image.
    """
    original_width, original_height = image.size
    target_width, target_height = target_resolution

    # Determine which dimension (width or height) to fill
    scale_w = target_width / original_width
    scale_h = target_height / original_height

    if scale_w < scale_h:
        # Width will be filled completely
        new_width = target_width
        new_height = min(math.ceil(original_height * scale_w), target_height)
    else:
        # Height will be filled completely
        new_height = target_height
        new_width = min(math.ceil(original_width * scale_h), target_width)

    # Resize the image
    resized_image = image.resize((new_width, new_height))

    # Create a new image with the target size and paste the resized image onto it
    new_image = Image.new("RGB", (target_width, target_height), (0, 0, 0))
    paste_x = (target_width - new_width) // 2
    paste_y = (target_height - new_height) // 2
    new_image.paste(resized_image, (paste_x, paste_y))

    return new_image



def select_best_resolution(original_size, possible_resolutions):
    """
    Selects the best resolution from a list of possible resolutions based on the original size.
    Args:
        original_size (tuple): The original size of the image in the format (width, height).
        possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
    Returns:
        tuple: The best fit resolution in the format (width, height).
    """
    original_width, original_height = original_size
    best_fit = None
    max_effective_resolution = 0
    min_wasted_resolution = float("inf")

    for width, height in possible_resolutions:
        # Calculate the downscaled size to keep the aspect ratio
        scale = min(width / original_width, height / original_height)
        downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)

        # Calculate effective and wasted resolutions
        effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
        wasted_resolution = (width * height) - effective_resolution

        if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
            max_effective_resolution = effective_resolution
            min_wasted_resolution = wasted_resolution
            best_fit = (width, height)

    return best_fit


def divide_to_patches(image, patch_size):
    """
    Divides an image into patches of a specified size.
    Args:
        image (PIL.Image.Image): The input image.
        patch_size (int): The size of each patch.
    Returns:
        list: A list of PIL.Image.Image objects representing the patches.
    """
    patches = []
    width, height = image.size
    for i in range(0, height, patch_size):
        for j in range(0, width, patch_size):
            box = (j, i, j + patch_size, i + patch_size)
            patch = image.crop(box)
            patches.append(patch)

    return patches



def process_anyres_image(image, processor, grid_pinpoints):
    """
    Process an image with variable resolutions.
    Args:
        image (PIL.Image.Image): The input image to be processed.
        processor: The image processor object.
        grid_pinpoints (str): A string representation of a list of possible resolutions.
    Returns:
        torch.Tensor: A tensor containing the processed image patches.
    """
    possible_resolutions = grid_pinpoints

    best_resolution = select_best_resolution(image.size, possible_resolutions)
    image_padded = resize_and_pad_image(image, best_resolution)

    patches = divide_to_patches(image_padded, processor.crop_size["height"])
    
    if isinstance(processor.size, dict):
        shortest_edge = processor.size["height"]
    else:
        shortest_edge = min(processor.size)
    image_original_resize = image.resize((shortest_edge, shortest_edge))

    image_patches = [image_original_resize] + patches
    image_patches = [processor.preprocess(image_patch, return_tensors="pt")["pixel_values"][0] for image_patch in image_patches]
    return torch.stack(image_patches, dim=0)


   
def preprocess_qwen(sources, tokenizer: PreTrainedTokenizer,enable_thinking: bool = True,  has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:

    roles = {"human": "user", "gpt": "assistant", "system": "system"}
    
    tokenizer = copy.deepcopy(tokenizer)
    if has_image:
        tokenizer.add_tokens(["<image>"], special_tokens=True)

    image_token_index = tokenizer.convert_tokens_to_ids("<image>")
    

    chat_template = "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
    tokenizer.chat_template = chat_template

    input_ids, targets = [], []
    messages = []

    messages.append({"role" : "system", "content" : system_message})

    for conv in sources:
        try:
            role = conv["role"]
            content = conv["content"]
        except:
            role = conv["from"]
            content = conv["value"]
        role =  roles.get(role, role)

        if role == 'assistant':
            if content is None:
                continue
            else:
                pass
        elif role == 'user':
            pass
        conv = {"role" : role, "content" : content}
        messages.append(conv)

    input_id = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=enable_thinking)
    

    for idx, encode_id in enumerate(input_id):
        if encode_id == image_token_index:
            input_id[idx] = IMAGE_TOKEN_INDEX
    input_ids = torch.tensor([input_id], dtype=torch.long)
    
    return input_ids



def prepare_prompt_ids(question, img_path, tokenizer, enable_thinking):
    if img_path is not None:
        question = DEFAULT_IMAGE_TOKEN * len(img_path) + f"\n{question}"
        input_ids = preprocess_qwen([{'from': 'human', 'value': question},{'from': 'gpt','value': None}], tokenizer, enable_thinking, has_image=True)
    else:
        input_ids = preprocess_qwen([{'from': 'human', 'value': question},{'from': 'gpt','value': None}], tokenizer, enable_thinking, has_image=False)

    return input_ids
    

class LogicsProcessor(ProcessorMixin):

    config_class = LogicsConfig  
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "SigLipImageProcessor" 
    tokenizer_class = "Qwen2Tokenizer"


    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
        self.config= LogicsConfig()  
        self.image_token = "<image>" if not hasattr(tokenizer, "image_token") else tokenizer.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)
        self.text_tokenizer = prepare_prompt_ids

    
    def _load_image(self, image_source: Union[str, Image.Image]) -> Image.Image:
        if isinstance(image_source, Image.Image):
            return image_source

        if not isinstance(image_source, str):
            raise TypeError(f"Unsupported image source type: {type(image_source)}")

        if image_source.startswith("http://") or image_source.startswith("https://"):
            try:
                response = requests.get(image_source)
                response.raise_for_status() 
                image_bytes = response.content
                image = Image.open(io.BytesIO(image_bytes))
            except Exception as e:
                raise IOError(f"Failed to load image from URL: {image_source}") from e
        
        elif image_source.startswith("data:image"):
            try:
                header, encoded_data = image_source.split(',', 1)
                image_bytes = base64.b64decode(encoded_data)
                image = Image.open(io.BytesIO(image_bytes))
            except Exception as e:
                raise IOError(f"Failed to decode Base64 image string.") from e
        

        elif os.path.exists(image_source):
            try:
                image = Image.open(image_source)
            except Exception as e:
                raise IOError(f"Failed to load image from file path: {image_source}") from e
        else:
             raise ValueError(f"Input string is not a valid file path, URL, or Base64-encoded image: {image_source[:100]}...")
             
        return image.convert('RGB')


    
    def __call__(
        self,
        images: ImageInput = None,
        text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
        **kwargs
    ) -> BatchFeature:
        device='cuda'
        if not isinstance(images, list):
            images = [images]
        

        
        if images is not None and all([x.split('.')[-1] not in {"mp4", "avi", "mov", "mkv"} for x in images]):
            # ---------------- image ----------------
            image_inputs = {}
            pixel_values = []
            image_grid_thw = []
            modalities = []
            for image_source in images:
                image = self._load_image(image_source)
                width, height = image.size
                image = process_anyres_image(image, self.image_processor, self.config.image_grid_pinpoints).to(dtype=torch.bfloat16, device=device)
                pixel_values.append(image)
                image_grid_thw.append((width, height))
                modalities.append("image")
            if all(x.shape == pixel_values[0].shape for x in pixel_values):
                pixel_values = torch.stack(pixel_values, dim=0)
            image_inputs["images_inputs"] = pixel_values

            image_inputs["image_sizes"] = image_grid_thw
            image_inputs["modalities"] = modalities


        elif images is not None:
            #---------------- video ----------------
            image_inputs={}
            video = images[0]
            video_frames = process_video_with_decord(video, self.config)
            video_tensor=self.image_processor.preprocess(video_frames, return_tensors="pt")["pixel_values"].to(dtype=torch.bfloat16, device=device)

            image_inputs["images_inputs"]=[video_tensor]
            image_inputs["image_sizes"] = [video_frames[0].size]
            image_inputs["modalities"] = ["video"]

            

        # ---------------- text ----------------
        text_ids = self.text_tokenizer(question=text, img_path=images, tokenizer=self.tokenizer, enable_thinking=self.config.enable_thinking)
        text_inputs = {}
        text_inputs["text_inputs"] = text_ids

        if images is not None:
            return BatchFeature(data={**text_inputs, **image_inputs})
        else:
            return BatchFeature(data={**text_inputs})




    def batch_decode(self, *args, **kwargs):
        return self.tokenizer.batch_decode(*args, **kwargs)

	# processing_logics.py

	from typing import List, Optional, Union, Dict
	import requests
	import base64
	import io
	import os
	from PIL import Image
	import math
	import torch
	import copy
	import numpy as np

	from transformers import PreTrainedTokenizer
	from transformers.processing_utils import ProcessorMixin
	from transformers.image_utils import ImageInput
	from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
	from transformers.feature_extraction_utils import BatchFeature

	from .configuration_logics import LogicsConfig
	from .siglip_encoder import SigLipImageProcessor
	from transformers import AutoImageProcessor
	try:
	from decord import VideoReader, cpu
	except ImportError:
	print("Please install pyav to use video processing functions.")

	AutoImageProcessor.register(LogicsConfig, SigLipImageProcessor)


	IGNORE_INDEX = -100
	IMAGE_TOKEN_INDEX = -200
	DEFAULT_IMAGE_TOKEN = "<image>"
	DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
	DEFAULT_IM_START_TOKEN = "<im_start>"
	DEFAULT_IM_END_TOKEN = "<im_end>"



	def process_video_with_decord(video_file, data_args):
	vr = VideoReader(video_file, ctx=cpu(0), num_threads=1)
	total_frame_num = len(vr)
	video_time = total_frame_num / vr.get_avg_fps()
	avg_fps = round(vr.get_avg_fps() / data_args.video_fps)
	frame_idx = [i for i in range(0, total_frame_num, avg_fps)]
	frame_time = [i / avg_fps for i in frame_idx]

	if data_args.frames_upbound > 0:
	if len(frame_idx) > data_args.frames_upbound or data_args.force_sample:
	uniform_sampled_frames = np.linspace(0, total_frame_num - 1, data_args.frames_upbound, dtype=int)
	frame_idx = uniform_sampled_frames.tolist()
	frame_time = [i / vr.get_avg_fps() for i in frame_idx]

	try:
	video = vr.get_batch(frame_idx).asnumpy()
	except:
	video = torch.zeros((10, 720, 720, 3)).numpy()
	print(f"load {video_file} error, use empty tensor instead.")
	frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
	num_frames_to_sample = num_frames = len(frame_idx)
	# https://github.com/dmlc/decord/issues/208
	vr.seek(0)
	return video
	# return video, video_time, frame_time, num_frames_to_sample



	def resize_and_pad_image(image, target_resolution):
	"""
	Resize and pad an image to a target resolution while maintaining aspect ratio.

	Args:
	image (PIL.Image.Image): The input image.
	target_resolution (tuple): The target resolution (width, height) of the image.

	Returns:
	PIL.Image.Image: The resized and padded image.
	"""
	original_width, original_height = image.size
	target_width, target_height = target_resolution

	# Determine which dimension (width or height) to fill
	scale_w = target_width / original_width
	scale_h = target_height / original_height

	if scale_w < scale_h:
	# Width will be filled completely
	new_width = target_width
	new_height = min(math.ceil(original_height * scale_w), target_height)
	else:
	# Height will be filled completely
	new_height = target_height
	new_width = min(math.ceil(original_width * scale_h), target_width)

	# Resize the image
	resized_image = image.resize((new_width, new_height))

	# Create a new image with the target size and paste the resized image onto it
	new_image = Image.new("RGB", (target_width, target_height), (0, 0, 0))
	paste_x = (target_width - new_width) // 2
	paste_y = (target_height - new_height) // 2
	new_image.paste(resized_image, (paste_x, paste_y))

	return new_image



	def select_best_resolution(original_size, possible_resolutions):
	"""
	Selects the best resolution from a list of possible resolutions based on the original size.

	Args:
	original_size (tuple): The original size of the image in the format (width, height).
	possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].

	Returns:
	tuple: The best fit resolution in the format (width, height).
	"""
	original_width, original_height = original_size
	best_fit = None
	max_effective_resolution = 0
	min_wasted_resolution = float("inf")

	for width, height in possible_resolutions:
	# Calculate the downscaled size to keep the aspect ratio
	scale = min(width / original_width, height / original_height)
	downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)

	# Calculate effective and wasted resolutions
	effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
	wasted_resolution = (width * height) - effective_resolution

	if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
	max_effective_resolution = effective_resolution
	min_wasted_resolution = wasted_resolution
	best_fit = (width, height)

	return best_fit


	def divide_to_patches(image, patch_size):
	"""
	Divides an image into patches of a specified size.

	Args:
	image (PIL.Image.Image): The input image.
	patch_size (int): The size of each patch.

	Returns:
	list: A list of PIL.Image.Image objects representing the patches.
	"""
	patches = []
	width, height = image.size
	for i in range(0, height, patch_size):
	for j in range(0, width, patch_size):
	box = (j, i, j + patch_size, i + patch_size)
	patch = image.crop(box)
	patches.append(patch)

	return patches



	def process_anyres_image(image, processor, grid_pinpoints):
	"""
	Process an image with variable resolutions.

	Args:
	image (PIL.Image.Image): The input image to be processed.
	processor: The image processor object.
	grid_pinpoints (str): A string representation of a list of possible resolutions.

	Returns:
	torch.Tensor: A tensor containing the processed image patches.
	"""
	possible_resolutions = grid_pinpoints

	best_resolution = select_best_resolution(image.size, possible_resolutions)
	image_padded = resize_and_pad_image(image, best_resolution)

	patches = divide_to_patches(image_padded, processor.crop_size["height"])

	if isinstance(processor.size, dict):
	shortest_edge = processor.size["height"]
	else:
	shortest_edge = min(processor.size)
	image_original_resize = image.resize((shortest_edge, shortest_edge))

	image_patches = [image_original_resize] + patches
	image_patches = [processor.preprocess(image_patch, return_tensors="pt")["pixel_values"][0] for image_patch in image_patches]
	return torch.stack(image_patches, dim=0)



	def preprocess_qwen(sources, tokenizer: PreTrainedTokenizer,enable_thinking: bool = True, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.") -> Dict:

	roles = {"human": "user", "gpt": "assistant", "system": "system"}

	tokenizer = copy.deepcopy(tokenizer)
	if has_image:
	tokenizer.add_tokens(["<image>"], special_tokens=True)

	image_token_index = tokenizer.convert_tokens_to_ids("<image>")


	chat_template = "{%- if tools %}\n {{- '<\|im_start\|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool \| tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><\|im_end\|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<\|im_start\|>system\\n' + messages[0].content + '<\|im_end\|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages\|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<\|im_start\|>' + message.role + '\\n' + message.content + '<\|im_end\|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<\|im_start\|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<\|im_start\|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<\|im_start\|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments \| tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<\|im_end\|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<\|im_start\|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<\|im_end\|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<\|im_start\|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
	tokenizer.chat_template = chat_template

	input_ids, targets = [], []
	messages = []

	messages.append({"role" : "system", "content" : system_message})

	for conv in sources:
	try:
	role = conv["role"]
	content = conv["content"]
	except:
	role = conv["from"]
	content = conv["value"]
	role = roles.get(role, role)

	if role == 'assistant':
	if content is None:
	continue
	else:
	pass
	elif role == 'user':
	pass
	conv = {"role" : role, "content" : content}
	messages.append(conv)

	input_id = tokenizer.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=enable_thinking)


	for idx, encode_id in enumerate(input_id):
	if encode_id == image_token_index:
	input_id[idx] = IMAGE_TOKEN_INDEX
	input_ids = torch.tensor([input_id], dtype=torch.long)

	return input_ids



	def prepare_prompt_ids(question, img_path, tokenizer, enable_thinking):
	if img_path is not None:
	question = DEFAULT_IMAGE_TOKEN * len(img_path) + f"\n{question}"
	input_ids = preprocess_qwen([{'from': 'human', 'value': question},{'from': 'gpt','value': None}], tokenizer, enable_thinking, has_image=True)
	else:
	input_ids = preprocess_qwen([{'from': 'human', 'value': question},{'from': 'gpt','value': None}], tokenizer, enable_thinking, has_image=False)

	return input_ids


	class LogicsProcessor(ProcessorMixin):

	config_class = LogicsConfig
	attributes = ["image_processor", "tokenizer"]
	image_processor_class = "SigLipImageProcessor"
	tokenizer_class = "Qwen2Tokenizer"


	def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
	self.config= LogicsConfig()
	self.image_token = "<image>" if not hasattr(tokenizer, "image_token") else tokenizer.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)
	self.text_tokenizer = prepare_prompt_ids


	def _load_image(self, image_source: Union[str, Image.Image]) -> Image.Image:
	if isinstance(image_source, Image.Image):
	return image_source

	if not isinstance(image_source, str):
	raise TypeError(f"Unsupported image source type: {type(image_source)}")

	if image_source.startswith("http://") or image_source.startswith("https://"):
	try:
	response = requests.get(image_source)
	response.raise_for_status()
	image_bytes = response.content
	image = Image.open(io.BytesIO(image_bytes))
	except Exception as e:
	raise IOError(f"Failed to load image from URL: {image_source}") from e

	elif image_source.startswith("data:image"):
	try:
	header, encoded_data = image_source.split(',', 1)
	image_bytes = base64.b64decode(encoded_data)
	image = Image.open(io.BytesIO(image_bytes))
	except Exception as e:
	raise IOError(f"Failed to decode Base64 image string.") from e


	elif os.path.exists(image_source):
	try:
	image = Image.open(image_source)
	except Exception as e:
	raise IOError(f"Failed to load image from file path: {image_source}") from e
	else:
	raise ValueError(f"Input string is not a valid file path, URL, or Base64-encoded image: {image_source[:100]}...")

	return image.convert('RGB')



	def __call__(
	self,
	images: ImageInput = None,
	text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
	**kwargs
	) -> BatchFeature:
	device='cuda'
	if not isinstance(images, list):
	images = [images]



	if images is not None and all([x.split('.')[-1] not in {"mp4", "avi", "mov", "mkv"} for x in images]):
	# ---------------- image ----------------
	image_inputs = {}
	pixel_values = []
	image_grid_thw = []
	modalities = []
	for image_source in images:
	image = self._load_image(image_source)
	width, height = image.size
	image = process_anyres_image(image, self.image_processor, self.config.image_grid_pinpoints).to(dtype=torch.bfloat16, device=device)
	pixel_values.append(image)
	image_grid_thw.append((width, height))
	modalities.append("image")
	if all(x.shape == pixel_values[0].shape for x in pixel_values):
	pixel_values = torch.stack(pixel_values, dim=0)
	image_inputs["images_inputs"] = pixel_values

	image_inputs["image_sizes"] = image_grid_thw
	image_inputs["modalities"] = modalities


	elif images is not None:
	#---------------- video ----------------
	image_inputs={}
	video = images[0]
	video_frames = process_video_with_decord(video, self.config)
	video_tensor=self.image_processor.preprocess(video_frames, return_tensors="pt")["pixel_values"].to(dtype=torch.bfloat16, device=device)

	image_inputs["images_inputs"]=[video_tensor]
	image_inputs["image_sizes"] = [video_frames[0].size]
	image_inputs["modalities"] = ["video"]



	# ---------------- text ----------------
	text_ids = self.text_tokenizer(question=text, img_path=images, tokenizer=self.tokenizer, enable_thinking=self.config.enable_thinking)
	text_inputs = {}
	text_inputs["text_inputs"] = text_ids

	if images is not None:
	return BatchFeature(data={text_inputs, image_inputs})
	else:
	return BatchFeature(data={**text_inputs})




	def batch_decode(self, args, *kwargs):
	return self.tokenizer.batch_decode(args, *kwargs)