from CircumSpect.vqa.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN
from CircumSpect.vqa.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from CircumSpect.vqa.conversation_obj import conv_templates_obj, SeparatorStyle_obj
from CircumSpect.vqa.conversation_vqa import conv_templates, SeparatorStyle
from transformers import AutoTokenizer, BitsAndBytesConfig
from CircumSpect.vqa.utils import disable_torch_init
from Perceptrix.streamer import TextStreamer
from CircumSpect.vqa.model import *
from utils import setup_device
from io import BytesIO
from PIL import Image
import requests
import torch
import os

device = setup_device()


def load_image(image_file):
    if image_file.startswith('http') or image_file.startswith('https'):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert('RGB')
    else:
        image = Image.open(image_file).convert('RGB')
    return image


disable_torch_init()

model_name = os.environ.get('VLM_MODEL')

model_path = "models/CRYSTAL-vision" if model_name == None else model_name
model_base = None
conv_mode = None
temperature = 0.2
max_new_tokens = 512

model_name = get_model_name_from_path(model_path)
image_processor = None

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16
)

tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = LlavaMPTForCausalLM.from_pretrained(
    model_path,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.float32 if str(device) == "cpu" else torch.float16,
    quantization_config=bnb_config,
    offload_folder="offloads",
)

mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
    tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
    tokenizer.add_tokens(
        [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))

vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
    vision_tower.load_model()
vision_tower.to(device=device, dtype=torch.float16)
image_processor = vision_tower.image_processor

if hasattr(model.config, "max_sequence_length"):
    context_len = model.config.max_sequence_length
else:
    context_len = 2048


if 'llama-2' in model_name.lower():
    conv_mode = "llava_llama_2"
elif "v1" in model_name.lower():
    conv_mode = "llava_v1"
elif "mpt" in model_name.lower():
    conv_mode = "mpt"
else:
    conv_mode = "llava_v0"

if conv_mode is not None and conv_mode != conv_mode:
    print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, conv_mode, conv_mode))
else:
    conv_mode = conv_mode

conv = conv_templates[conv_mode].copy()
if "mpt" in model_name.lower():
    roles = ('User', 'Assistant')
else:
    roles = conv.roles

streamer = TextStreamer(tokenizer, skip_prompt=True,
        skip_special_tokens=True, save_file="vlm-reply.txt")


def answer_question(question, image_file):
    conv = conv_templates[conv_mode].copy()
    inp = question
    image = load_image(image_file)
    if str(device) == "cpu":
        image_tensor = image_processor.preprocess(image, return_tensors='pt')[
            'pixel_values'].to(device)
    else:
        image_tensor = image_processor.preprocess(image, return_tensors='pt')[
            'pixel_values'].half().to(device)

    print(f"{roles[1]}: ", end="")

    if image is not None:
        # first message
        if model.config.mm_use_im_start_end:
            inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + \
                DEFAULT_IM_END_TOKEN + '\n' + inp
        else:
            inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
        conv.append_message(conv.roles[0], inp)
        image = None
    else:
        # later messages
        conv.append_message(conv.roles[0], inp)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    input_ids = tokenizer_image_token(
        prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(device)

    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(
        keywords, tokenizer, input_ids)

    with open("./database/vlm-reply.txt", 'w') as clear_file:
        clear_file.write("")

    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            do_sample=True,
            temperature=0.2,
            max_new_tokens=1024,
            streamer=streamer,
            use_cache=True,
            stopping_criteria=[stopping_criteria])

    outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
    conv.messages[-1][-1] = outputs
    return outputs


conv_obj = conv_templates_obj[conv_mode].copy()
if "mpt" in model_name.lower():
    roles = ('User', 'Assistant')
else:
    roles = conv_obj.roles


def find_object_description(question, image_file):
    conv_obj = conv_templates_obj[conv_mode].copy()
    inp = question
    image = load_image(image_file)
    if str(device) == "cpu":
        image_tensor = image_processor.preprocess(image, return_tensors='pt')[
            'pixel_values'].to(device)
    else:
        image_tensor = image_processor.preprocess(image, return_tensors='pt')[
            'pixel_values'].half().to(device)

    print(f"{roles[1]}: ", end="")

    if image is not None:
        # first message
        if model.config.mm_use_im_start_end:
            inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + \
                DEFAULT_IM_END_TOKEN + '\n' + inp
        else:
            inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
        conv_obj.append_message(conv_obj.roles[0], inp)
        image = None
    else:
        # later messages
        conv_obj.append_message(conv_obj.roles[0], inp)
    conv_obj.append_message(conv_obj.roles[1], None)
    prompt = conv_obj.get_prompt()
    input_ids = tokenizer_image_token(
        prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(device)

    stop_str = conv_obj.sep if conv_obj.sep_style != SeparatorStyle_obj.TWO else conv_obj.sep2
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(
        keywords, tokenizer, input_ids)

    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            do_sample=True,
            temperature=0.1,
            max_new_tokens=32,
            streamer=streamer,
            use_cache=True,
            stopping_criteria=[stopping_criteria])

    outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
    conv_obj.messages[-1][-1] = outputs
    return outputs


if __name__ == "__main__":
    print("RUNNING TEST\n\tTest Image: https://llava-vl.github.io/static/images/view.jpg\n\tPrompt: Describe this image")
    find_object_description("Describe this image",
                    "https://llava-vl.github.io/static/images/view.jpg")
    print("RUNNING TEST\n\tTest Image: https://llava-vl.github.io/static/images/view.jpg\n\tPrompt: Describe this image")
    find_object_description("Describe this image",
                    "https://llava-vl.github.io/static/images/view.jpg")
    print("RUNNING TEST\n\tTest Image: https://llava-vl.github.io/static/images/view.jpg\n\tPrompt: Describe this image")
    find_object_description("Describe this image",
                    "https://llava-vl.github.io/static/images/view.jpg")
    print("RUNNING TEST\n\tTest Image: https://llava-vl.github.io/static/images/view.jpg\n\tPrompt: Describe this image")
    find_object_description("Describe this image",
                    "https://llava-vl.github.io/static/images/view.jpg")