import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

from .base import BaseModel
from ..smp import *
from ..utils import DATASET_TYPE


class MiniCPM_V(BaseModel):

    INSTALL_REQ = False
    INTERLEAVE = False

    def __init__(self, model_path='openbmb/MiniCPM-V', **kwargs):
        assert model_path is not None
        self.model_path = model_path
        print(f'load from {self.model_path}')
        self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True)
        self.model = self.model.to(dtype=torch.bfloat16)
        self.model.eval().cuda()
        self.kwargs = kwargs
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
        torch.cuda.empty_cache()
        self.num_beams = 1 if self.model_path == 'openbmb/MiniCPM-V' else 3

    def use_custom_prompt(self, dataset):
        assert dataset is not None
        if listinstr(['MMMU'], dataset):
            return True
        return False

    def build_prompt(self, line, dataset=None):
        assert dataset is None or isinstance(dataset, str)
        assert self.use_custom_prompt(dataset)
        tgt_path = self.dump_image(line, dataset)

        question = line['question']
        options = {
            cand: line[cand]
            for cand in string.ascii_uppercase
            if cand in line and not pd.isna(line[cand])
        }
        options_prompt = 'Options:\n'
        for key, item in options.items():
            options_prompt += f'{key}. {item}\n'
        hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None
        prompt = ''
        if hint is not None:
            prompt += f'Hint: {hint}\n'
        prompt += f'{question}\n'
        if len(options):
            prompt += options_prompt
            prompt = 'Study the image carefully and pick the option associated with the correct answer. \
                Focus solely on selecting the option and avoid including any other content.\n' + prompt
        message = [dict(type='text', value=prompt)]
        message.extend([dict(type='image', value=p) for p in tgt_path])

        return message

    def generate_inner(self, message, dataset=None):
        prompt, image_path = self.message_to_promptimg(message)
        image = Image.open(image_path).convert('RGB')
        msgs = [{'role': 'user', 'content': prompt}]
        if DATASET_TYPE(dataset) == 'multi-choice':
            max_new_tokens = 20
        elif DATASET_TYPE(dataset) == 'Y/N':
            max_new_tokens = 100
        else:
            max_new_tokens = 1024

        default_kwargs = dict(
            max_new_tokens=max_new_tokens,
            sampling=False,
            num_beams=self.num_beams
        )
        default_kwargs.update(self.kwargs)
        res, _, _ = self.model.chat(
            image=image,
            msgs=msgs,
            context=None,
            tokenizer=self.tokenizer,
            **default_kwargs
        )
        return res