from __future__ import annotations

import concurrent.futures
import os
import pathlib
import shlex
import shutil
import subprocess
import sys

import cv2
import torch
from label_prettify import label_prettify

repo_dir = pathlib.Path(__file__).parent
submodule_dir = repo_dir / 'prismer'
sys.path.insert(0, submodule_dir.as_posix())

from dataset import create_dataset, create_loader
from dataset.utils import pre_question
from model.prismer_caption import PrismerCaption
from model.prismer_vqa import PrismerVQA
from model.modules.utils import interpolate_pos_embed


def download_models() -> None:
    if not pathlib.Path('prismer/experts/expert_weights/').exists():
        subprocess.run(shlex.split('python download_checkpoints.py --download_experts=True'), cwd='prismer')

    model_names = [
        'vqa_prismer_base',
        'vqa_prismer_large',
        'pretrain_prismer_base',
        'pretrain_prismer_large',
    ]
    for model_name in model_names:
        if pathlib.Path(f'prismer/logging/{model_name}').exists():
            continue
        subprocess.run(shlex.split(f'python download_checkpoints.py --download_models={model_name}'), cwd='prismer')


def build_deformable_conv() -> None:
    subprocess.run(shlex.split('sh make.sh'), cwd='prismer/experts/segmentation/mask2former/modeling/pixel_decoder/ops')


def run_expert(expert_name: str):
    env = os.environ.copy()
    if 'PYTHONPATH' in env:
        env['PYTHONPATH'] = f'{submodule_dir.as_posix()}:{env["PYTHONPATH"]}'
    else:
        env['PYTHONPATH'] = submodule_dir.as_posix()
    subprocess.run(shlex.split(f'python experts/generate_{expert_name}.py'),
                   cwd='prismer',
                   env=env,
                   check=True)


def run_experts(image_path: str) -> tuple[str | None, ...]:
    helper_dir = submodule_dir / 'helpers'
    shutil.rmtree(helper_dir, ignore_errors=True)
    image_dir = helper_dir / 'images'
    image_dir.mkdir(parents=True, exist_ok=True)
    out_path = image_dir / 'image.jpg'
    cv2.imwrite(out_path.as_posix(), cv2.imread(image_path))

    expert_names = ['edge', 'normal', 'objdet', 'ocrdet', 'segmentation']
    run_expert('depth')
    with concurrent.futures.ProcessPoolExecutor() as executor:
        executor.map(run_expert, expert_names)

    keys = ['depth', 'edge', 'normal', 'seg_coco', 'obj_detection', 'ocr_detection']
    results = [pathlib.Path('prismer/helpers/labels') / key / 'helpers/images/image.png' for key in keys]
    return tuple(path.as_posix() for path in results)


class Model:
    def __init__(self):
        self.config = None
        self.model = None
        self.tokenizer = None
        self.model_name = ''
        self.exp_name = ''
        self.mode = ''

    def set_model(self, exp_name: str, mode: str) -> None:
        if exp_name == self.exp_name and mode == self.mode:
            return

        # load checkpoints
        model_name = exp_name.lower().replace('-', '_')
        if mode == 'caption':
            config = {
                'dataset': 'demo',
                'data_path': 'prismer/helpers',
                'label_path': 'prismer/helpers/labels',
                'experts': ['depth', 'normal', 'seg_coco', 'edge', 'obj_detection', 'ocr_detection'],
                'image_resolution': 480,
                'prismer_model': model_name,
                'freeze': 'freeze_vision',
                'prefix': '',
            }
            model = PrismerCaption(config)
            state_dict = torch.load(f'prismer/logging/pretrain_{model_name}/pytorch_model.bin', map_location='cuda:0')
            state_dict['expert_encoder.positional_embedding'] = interpolate_pos_embed(state_dict['expert_encoder.positional_embedding'],
                                                                                      len(model.expert_encoder.positional_embedding))
        elif mode == 'vqa':
            config = {
                'dataset': 'demo',
                'data_path': 'prismer/helpers',
                'label_path': 'prismer/helpers/labels',
                'experts': ['depth', 'normal', 'seg_coco', 'edge', 'obj_detection', 'ocr_detection'],
                'image_resolution': 480,
                'prismer_model': model_name,
                'freeze': 'freeze_vision',
                'prefix': '',
            }

            model = PrismerVQA(config)
            state_dict = torch.load(f'prismer/logging/vqa_{model_name}/pytorch_model.bin', map_location='cuda:0')
            state_dict['expert_encoder.positional_embedding'] = interpolate_pos_embed(state_dict['expert_encoder.positional_embedding'],
                                                                                      len(model.expert_encoder.positional_embedding))

        model.load_state_dict(state_dict)
        model.eval()

        self.config = config
        self.model = model
        self.tokenizer = model.tokenizer
        self.exp_name = exp_name
        self.mode = mode

    @torch.inference_mode()
    def run_caption_model(self, exp_name: str) -> str:
        self.set_model(exp_name, 'caption')
        _, test_dataset = create_dataset('caption', self.config)
        test_loader = create_loader(test_dataset, batch_size=1, num_workers=4, train=False)
        experts, _ = next(iter(test_loader))
        captions = self.model(experts, train=False, prefix=self.config['prefix'])
        captions = self.tokenizer(captions, max_length=30, padding='max_length', return_tensors='pt').input_ids
        caption = captions.to(experts['rgb'].device)[0]
        caption = self.tokenizer.decode(caption, skip_special_tokens=True)
        caption = caption.capitalize() + '.'
        return caption

    def run_caption(self, image_path: str, model_name: str) -> tuple[str | None, ...]:
        out_paths = run_experts(image_path)
        caption = self.run_caption_model(model_name)
        label_prettify(image_path, out_paths)
        return caption, *out_paths

    @torch.inference_mode()
    def run_vqa_model(self, exp_name: str, question: str) -> str:
        self.set_model(exp_name, 'vqa')
        _, test_dataset = create_dataset('caption', self.config)
        test_loader = create_loader(test_dataset, batch_size=1, num_workers=4, train=False)
        experts, _ = next(iter(test_loader))
        question = pre_question(question)
        answer = self.model(experts, [question], train=False, inference='generate')
        answer = self.tokenizer(answer, max_length=30, padding='max_length', return_tensors='pt').input_ids
        answer = answer.to(experts['rgb'].device)[0]
        answer = self.tokenizer.decode(answer, skip_special_tokens=True)
        answer = answer.capitalize() + '.'
        return answer

    def run_vqa(self, image_path: str, model_name: str, question: str) -> tuple[str | None, ...]:
        out_paths = run_experts(image_path)
        answer = self.run_vqa_model(model_name, question)
        label_prettify(image_path, out_paths)
        return answer, *out_paths