import os
import logging
import json
from pathlib import Path

import yaml
import torch

from policy import Policy


logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO"))
log = logging.getLogger(__name__)


def load_model_args(args):
    checkpoint = Path(args.checkpoint + '.ckpt')
    assert checkpoint.is_file(), f"no checkpoint file: {args.checkpoint}"
    args_path = Path(args.checkpoint + '.json')
    if args_path.is_file():
        with open(args_path) as f:
            hparams = json.load(f)
    else:
        args_path = Path(args.checkpoint + '.yaml')
        with open(args_path) as f:
            hparams = yaml.safe_load(f)
    for key in ['init_model', 'clip_model_type', 'use_caption', 'use_style_reward', 'use_transformer_mapper',
                'prefix_length', 'clipcap_num_layers', 'use_ptuning_v2']:
        if key in hparams:
            setattr(args, key, hparams[key])
    args.loaded_init_model = True
    return args


def load_model(args, device, finetune=False):
    log.info('loading model')
    policy = Policy(model_name=args.init_model, temperature=1.0, device=device,
                    clipcap_path='None', fix_gpt=True,
                    label_path=args.label_path,
                    prefix_length=args.prefix_length,
                    clipcap_num_layers=args.clipcap_num_layers,
                    use_transformer_mapper=args.use_transformer_mapper,
                    model_weight='None', use_label_prefix=args.use_label_prefix)
    ckpt = args.checkpoint + '.ckpt'
    state = torch.load(ckpt)
    policy_key = 'policy_model'
    if policy_key in state:
        policy.model.load_state_dict(state[policy_key])
    else:
        weights = state['state_dict']
        key = 'policy.model.'
        if not any(k for k in weights.keys() if k.startswith(key)):
            key = 'model.model.'
        weights = {k[len(key):]: v for k, v in weights.items() if k.startswith(key)}
        # weights = {k: v for k, v in weights.items() if k.startswith('clip_project.')}
        policy.model.load_state_dict(weights, strict=False)
    model = policy

    model = model.to(device)
    return model