import yaml
from tqdm import tqdm
import torch
from torch import nn
from transformers import AutoTokenizer

from models.peptide_classifiers import *

from utils.parsing import parse_guidance_args
args = parse_guidance_args()

import pdb
import random
import inspect

# MOO hyper-parameters
step_size = 1 / 100
n_samples = 1
length = args.length
target = args.target_protein
motifs = args.motifs # args.motifs
vocab_size = 24
source_distribution = "uniform"
device = 'cuda:0'

tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
target_sequence = tokenizer(target, return_tensors='pt')['input_ids'].to(device)
motifs = parse_motifs(motifs).to(device)
print(motifs)

# Load Models
solver = load_solver('./ckpt/peptide/cnn_epoch200_lr0.0001_embed512_hidden256_loss3.1051.ckpt', vocab_size, device)

bindevaluator = load_bindevaluator('./classifier_ckpt/finetuned_BindEvaluator.ckpt', device)
motif_model = MotifModel(bindevaluator, target_sequence, motifs, penalty=True)

affinity_predictor = load_affinity_predictor('./classifier_ckpt/binding_affinity_unpooled.pt', device)
affinity_model = AffinityModel(affinity_predictor, target_sequence)

score_models = [motif_model, affinity_model]

for i in range(args.n_batches):
    if source_distribution == "uniform":
        x_init = torch.randint(low=4, high=vocab_size, size=(n_samples, length), device=device)
    elif source_distribution == "mask":
        x_init = (torch.zeros(size=(n_samples, length), device=device) + 3).long()
    else:
        raise NotImplementedError

    zeros = torch.zeros((n_samples, 1), dtype=x_init.dtype, device=x_init.device)
    twos = torch.full((n_samples, 1), 2, dtype=x_init.dtype, device=x_init.device)
    x_init = torch.cat([zeros, x_init, twos], dim=1)

    x_1 = solver.multi_guidance_sample(args=args, x_init=x_init, 
                                    step_size=step_size, 
                                    verbose=True, 
                                    time_grid=torch.tensor([0.0, 1.0-1e-3]),
                                    score_models=score_models,
                                    num_objectives=3,
                                    weights=args.weights)

    samples = x_1.tolist()
    samples = [tokenizer.decode(seq).replace(' ', '')[5:-5] for seq in samples]
    print(samples)

    scores = []
    for i, s in enumerate(score_models):
        sig = inspect.signature(s.forward) if hasattr(s, 'forward') else inspect.signature(s)
        if 't' in sig.parameters:
            candidate_scores = s(x_1, 1)
        else:
            candidate_scores = s(x_1)

        if isinstance(candidate_scores, tuple):
            for score in candidate_scores:
                scores.append(score.item())
        else:
            scores.append(candidate_scores.item())
    print(scores)
    
    with open(args.output_file, 'a') as f:
        f.write(samples[0])
        for score in scores:
            f.write(f",{score}")
        f.write('\n')
    # samples = x_1.tolist()
    # sample = [tokenizer.decode(seq).replace(' ', '')[5:-5] for seq in samples][0]
    # with open(f"/vast/home/c/chentong/MOG-DFM/samples/{name}.csv", "a") as f:
    #     f.write(sample + ',' + str(score_list_0[-1]) + ',' + str(score_list_1[-1]) + '\n')