import argparse
import os

import numpy as np
import pandas as pd
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

from scripts.mmlu.categories import categories, subcategories

choices = ["A", "B", "C", "D"]


def format_subject(subject):
    entries = subject.split("_")
    s = ""
    for entry in entries:
        s += " " + entry
    return s


def format_example(df, idx, include_answer=True):
    prompt = df.iloc[idx, 0]
    k = df.shape[1] - 2
    for j in range(k):
        prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
    prompt += "\nAnswer:"
    if include_answer:
        prompt += " {}\n\n".format(df.iloc[idx, k + 1])
    return prompt


def gen_prompt(train_df, subject, k=-1):
    prompt = "The following are multiple choice questions (with answers) about {}.\n\n".format(format_subject(subject))
    if k == -1:
        k = train_df.shape[0]
    for i in range(k):
        prompt += format_example(train_df, i)
    return prompt


@torch.no_grad()
def eval(args, subject, model, tokenizer, dev_df, test_df):
    cors = []
    all_probs = []

    for i in range(test_df.shape[0]):
        # get prompt and make sure it fits
        k = args.ntrain
        prompt_end = format_example(test_df, i, include_answer=False)
        train_prompt = gen_prompt(dev_df, subject, k)
        prompt = train_prompt + prompt_end

        input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

        while input_ids.shape[-1] > 2048:
            k -= 1
            train_prompt = gen_prompt(dev_df, subject, k)
            prompt = train_prompt + prompt_end
            input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

        label = test_df.iloc[i, test_df.shape[1] - 1]

        decoder_input_ids = tokenizer("", return_tensors="pt").input_ids.cuda()
        decoder_input_ids = model._shift_right(decoder_input_ids)
        logits = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids).logits.flatten()

        probs = (
            torch.nn.functional.softmax(
                torch.tensor(
                    [
                        logits[tokenizer("A").input_ids[0]],
                        logits[tokenizer("B").input_ids[0]],
                        logits[tokenizer("C").input_ids[0]],
                        logits[tokenizer("D").input_ids[0]],
                    ]
                ),
                dim=0,
            )
            .detach()
            .cpu()
            .numpy()
        )
        pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]

        cor = pred == label
        cors.append(cor)
        all_probs.append(probs)

    acc = np.mean(cors)
    cors = np.array(cors)

    all_probs = np.array(all_probs)
    print("Average accuracy {:.3f} - {}".format(acc, subject))

    return cors, acc, all_probs


def main(args):
    model = AutoModelForSeq2SeqLM.from_pretrained(args.model)
    tokenizer = AutoTokenizer.from_pretrained(args.model)
    heads_per_gpu = len(model.encoder.block) // args.ngpu
    device_map = {
        gpu: list(
            range(
                0 + (gpu * heads_per_gpu),
                (0 + (gpu * heads_per_gpu)) + heads_per_gpu,
            )
        )
        for gpu in range(args.ngpu)
    }
    model.parallelize(device_map)
    model.eval()
    subjects = sorted(
        [f.split("_test.csv")[0] for f in os.listdir(os.path.join(args.data_dir, "test")) if "_test.csv" in f]
    )

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    if not os.path.exists(os.path.join(args.save_dir, "results_{}".format(args.model))):
        os.makedirs(os.path.join(args.save_dir, "results_{}".format(args.model)))

    all_cors = []
    subcat_cors = {subcat: [] for subcat_lists in subcategories.values() for subcat in subcat_lists}
    cat_cors = {cat: [] for cat in categories}

    for subject in subjects:
        dev_df = pd.read_csv(os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None)[: args.ntrain]
        test_df = pd.read_csv(os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None)

        cors, acc, probs = eval(args, subject, model, tokenizer, dev_df, test_df)
        subcats = subcategories[subject]
        for subcat in subcats:
            subcat_cors[subcat].append(cors)
            for key in categories.keys():
                if subcat in categories[key]:
                    cat_cors[key].append(cors)
        all_cors.append(cors)

        test_df["{}_correct".format(args.model)] = cors
        for j in range(probs.shape[1]):
            choice = choices[j]
            test_df["{}_choice{}_probs".format(args.model, choice)] = probs[:, j]
        test_df.to_csv(
            os.path.join(args.save_dir, "results_{}".format(args.model), "{}.csv".format(subject)),
            index=None,
        )

    for subcat in subcat_cors:
        subcat_acc = np.mean(np.concatenate(subcat_cors[subcat]))
        print("Average accuracy {:.3f} - {}".format(subcat_acc, subcat))

    for cat in cat_cors:
        cat_acc = np.mean(np.concatenate(cat_cors[cat]))
        print("Average accuracy {:.3f} - {}".format(cat_acc, cat))
    weighted_acc = np.mean(np.concatenate(all_cors))
    print("Average accuracy: {:.3f}".format(weighted_acc))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--ntrain", "-k", type=int, default=5)
    parser.add_argument("--ngpu", "-g", type=int, default=1)
    parser.add_argument("--data_dir", "-d", type=str, default="data/mmlu")
    parser.add_argument("--save_dir", "-s", type=str, default="results")
    parser.add_argument(
        "--model",
        "-m",
        type=str,
        default="google/flan-t5-small",
    )
    args = parser.parse_args()
    main(args)
