import torch
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
from appPublic.worker import awaitify
from appPublic.jsonConfig import getConfig
from ahserver.serverenv import ServerEnv
from ahserver.webapp import webapp

class Qwen2VLClass:
	def __init__(self, modelname):
		# default: Load the model on the available device(s)
		self.model = Qwen2VLForConditionalGeneration.from_pretrained(
			modelname,
			torch_dtype=torch.bfloat16,
			# attn_implementation="flash_attention_2",
			device_map="auto"
		)
		self.min_pixels = 256 * 28 * 28
		self.max_pixels = 1280 * 28 * 28
		# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
		# model = Qwen2VLForConditionalGeneration.from_pretrained(
		#     "Qwen/Qwen2-VL-7B-Instruct",
		#     torch_dtype=torch.bfloat16,
		#     attn_implementation="flash_attention_2",
		#     device_map="auto",
		# )

		# default processer
		self.processor = AutoProcessor.from_pretrained(modelname,
			min_pixels=self.min_pixels,
			max_pixels=self.max_pixels
		)

		# The default range for the number of visual tokens per image in the model is 4-16384.
		# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
		# min_pixels = 256*28*28
		# max_pixels = 1280*28*28
		# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
		
	def inference(self, prompt, image=None, videofile=None):
		content = [
			{
				"type":"text",
				"text":prompt
			}
		]
		if image:
			if not image.startswith('file:///') \
						and not image.startswith('http://') \
						and not image.startswith('https://'):
				image = f'data:image;base64,{image}'
			content.append({
				"type":"image",
				"image":image
			})
		if videofile:
			if not videofile.startswith('file:///'):
				return 'only local video file support'

			content.append({
				"type":"video",
				"video":videofile
			})
		
		messages = [
			{
				"role": "user",
				"content": content
			}
		]

		# Preparation for inference
		text = self.processor.apply_chat_template(
			messages, tokenize=False, add_generation_prompt=True
		)
		image_inputs, video_inputs = process_vision_info(messages)
		inputs = self.processor(
			text=[text],
			images=image_inputs,
			videos=video_inputs,
			padding=True,
			return_tensors="pt",
		)
		inputs = inputs.to("cuda")

		# Inference: Generation of the output
		generated_ids = self.model.generate(**inputs, max_new_tokens=128)
		generated_ids_trimmed = [
			out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
		]
		output_text = processor.batch_decode(
			generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
		)
		return output_text

def main():
	config = getConfig()
	modelname = config.modelname
	m = Qwen2VLClass(modelname)
	g = ServerEnv()
	g.inference = awaitify(m.inference)


if __name__ == '__main__':
	webapp(main)