import os
import pandas as pd
import numpy as np
import argparse
import datasets
import torch
from collections import defaultdict

from typing import List
from tqdm import tqdm
from transformers.trainer_utils import set_seed


'''
wget https://huggingface.co/datasets/haonan-li/cmmlu/resolve/main/cmmlu_v1_0_1.zip
mkdir data/cmmlu
mv cmmlu_v1_0_1.zip data/cmmlu
cd data/cmmlu; unzip cmmlu_v1_0_1.zip
cd ../../
python evaluate_cmmlu.py -d data/cmmlu/
'''

def load_models_tokenizer(args):
    from transformers import AutoModelForCausalLM, AutoTokenizer
    from transformers.generation import GenerationConfig

    tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
    model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
    return model, tokenizer


def format_example(line, include_answer=True):
    example = '问题：' + line['Question']
    for choice in choices:
        example += f'\n{choice}. {line[f"{choice}"]}'

    if include_answer:
        example += '\n答案：' + line["Answer"] + '\n\n'
    else:
        example += '\n答案：'
    return example


def generate_few_shot_prompt(k, subject, dev_df):
    prompt = ''
    if k == -1:
        k = dev_df.shape[0]
    for i in range(k):
        prompt += format_example(
            dev_df.iloc[i, :],
            include_answer=True,
        )
    return prompt


def get_logits(tokenizer, model, inputs: List[str]):
    input_ids = tokenizer(inputs, padding=False)['input_ids']
    input_ids = torch.tensor(input_ids, device=model.device)
    tokens = {'input_ids': input_ids}

    outputs = model(input_ids)['logits']
    logits = outputs[:, -1, :]
    log_probs = torch.nn.functional.softmax(logits, dim=-1)
    return log_probs, {'tokens': tokens}


@torch.no_grad()
def eval_subject(
        model,
        tokenizer,
        subject_name,
        test_df,
        k=5,
        dev_df=None,
        few_shot=False,
        save_result_dir=None,
        **kwargs
):
    result = []
    score = []

    few_shot_prompt = generate_few_shot_prompt(
        k, subject_name, dev_df) if few_shot else []
    all_probs = {'prob_A': [], 'prob_B': [], 'prob_C': [], 'prob_D': []}
    if args.debug: print(f"few_shot_prompt: {few_shot_prompt}")

    for _, row in tqdm(test_df.iterrows(), total=len(test_df)):
        question = format_example(row, include_answer=False)
        full_prompt = few_shot_prompt + question

        output, input_info = get_logits(tokenizer, model, [full_prompt])
        assert output.shape[0] == 1
        logits = output.flatten()

        softval = torch.nn.functional.softmax(
                torch.tensor(
                    [
                        logits[tokenizer("A")['input_ids']],
                        logits[tokenizer("B")['input_ids']],
                        logits[tokenizer("C")['input_ids']],
                        logits[tokenizer("D")['input_ids']],
                    ]
                ),
                dim=0,
            )
        if softval.dtype in {torch.bfloat16, torch.float16}:
            softval = softval.to(dtype=torch.float32)
        probs = softval.detach().cpu().numpy()

        for i, choice in enumerate(choices):
            all_probs[f'prob_{choice}'].append(probs[i])
        pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]

        if 'Answer' in row:
            correct = 1 if pred == row['Answer'] else 0
            score.append(correct)
            if args.debug: print(f'{question} pred: {pred} ref: {row["Answer"]}')
        result.append(pred)

    if score:
        correct_ratio = 100 * sum(score) / len(score)
        if args.debug: print(subject_name, correct_ratio)
    else:
        correct_ratio = 0
    if save_result_dir:
        test_df['model_output'] = result
        for i, choice in enumerate(choices):
            test_df[f'prob_{choice}'] = (all_probs[f'prob_{choice}'])
        if score:
            test_df["correctness"] = score
        os.makedirs(save_result_dir, exist_ok=True)
        test_df.to_csv(os.path.join(
            save_result_dir, f'{subject_name}_result.csv'), encoding="utf-8", index=False)

    return correct_ratio


def cal_cmmlu(res):
    print('\n\n\n')
    res = {k.split('-')[-1]:float(v) for k,v in res.items()}
    for k, v in TASK_NAME_MAPPING.items():
        avg_acc = np.mean(list(map(lambda x: res[x], v)))
        print(f"{k} acc: {avg_acc:.2f}")
    avg_all_acc = np.mean(list(res.values()))
    print(f"AVERAGE acc: {avg_all_acc:.2f}")


subcategories = {
    "agronomy": ['other'],
    "anatomy": ['biology'],
    "ancient_chinese": ['linguistics','china specific'],
    "arts": ['arts'],
    "astronomy": ['physics'],
    "business_ethics": ['business'],
    "chinese_civil_service_exam": ['politics','china specific'],
    "chinese_driving_rule": ['other','china specific'],
    "chinese_food_culture": ['culture','china specific'],
    "chinese_foreign_policy": ['politics','china specific'],
    "chinese_history":['history','china specific'],
    "chinese_literature": ['literature','china specific'],
    "chinese_teacher_qualification": ['education','china specific'],
    "college_actuarial_science":['math'],
    "college_education":['education'],
    "college_engineering_hydrology": ['engineering'],
    "college_law": ['law'],
    "college_mathematics": ['math'],
    "college_medical_statistics":['statistics'],
    "clinical_knowledge": ['other'],
    "college_medicine": ['other'],
    "computer_science": ['computer science'],
    "computer_security": ['other'],
    "conceptual_physics": ['physics'],
    "construction_project_management": ['other','china specific'],
    "economics": ['economics'],
    "education": ['education'],
    "elementary_chinese":['linguistics','china specific'],
    "elementary_commonsense":['other','china specific'],
    "elementary_information_and_technology": ['other'],
    "electrical_engineering": ['engineering'],
    "elementary_mathematics": ['math'],
    "ethnology": ['culture','china specific'],
    "food_science": ['other'],
    "genetics": ['biology'],
    "global_facts": ['global'],
    "high_school_biology": ['biology'],
    "high_school_chemistry": ['chemistry'],
    "high_school_geography": ['geography'],
    "high_school_mathematics": ['math'],
    "high_school_physics": ['physics'],
    "high_school_politics": ['politics','china specific'],
    "human_sexuality": ['other'],
    "international_law": ['law'],
    "journalism": ['sociology'],
    "jurisprudence": ['law'],
    "legal_and_moral_basis": ['other'],
    "logical": ['philosophy'],
    "machine_learning": ['computer science'],
    "management": ['business'],
    "marketing": ['business'],
    "marxist_theory": ['philosophy'],
    "modern_chinese": ['linguistics','china specific'],
    "nutrition": ['other'],
    "philosophy": ['philosophy'],
    "professional_accounting": ['business'],
    "professional_law": ['law'],
    "professional_medicine": ['other'],
    "professional_psychology": ['psychology'],
    "public_relations": ['politics'],
    "security_study": ['politics'],
    "sociology": ['culture'],
    "sports_science": ['other'],
    "traditional_chinese_medicine": ['other','china specific'],
    "virology": ['biology'],
    "world_history":['history'],
    "world_religions": ['global'],
}

categories = {
    "STEM": ["physics", "chemistry", "biology", "computer science", "math", "engineering", "statistics"],
    "Humanities": ["history", "philosophy", "law", "arts", "literature", "global"],
    "Social Science": ['linguistics',"business", "politics", "culture", "economics", "geography", "psychology", "education", "sociology"],
    "Other":["other"],
    "China specific": ["china specific"],
}

TASK_NAME_MAPPING = defaultdict(list)
for k,v in categories.items():
    for subject, subcat in subcategories.items():
        for c in subcat:
            if c in v:
                TASK_NAME_MAPPING[k].append(subject)


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


def main(args):
    model, tokenizer = load_models_tokenizer(args)

    test_result = {}
    for subject_name in tqdm(subcategories.keys()):
        dev_file_path = os.path.join(args.eval_data_path, 'dev', f'{subject_name}.csv')
        test_file_path = os.path.join(args.eval_data_path, 'test', f'{subject_name}.csv')
        dev_df = pd.read_csv(dev_file_path)
        test_df = pd.read_csv(test_file_path)

        score = eval_subject(model, tokenizer, subject_name, dev_df=dev_df, test_df=test_df, k=5, few_shot=True,
                             save_result_dir=f"outs/cmmlu_eval_result")
        test_result[subject_name] = score
    cal_cmmlu(test_result)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Test HF checkpoint.')
    parser.add_argument('-c', '--checkpoint-path', type=str, help='Checkpoint path', default="Qwen/Qwen-7B")
    parser.add_argument('-s', '--seed', type=int, default=1234, help='Random seed')

    """Provide extra arguments required for tasks."""
    group = parser.add_argument_group(title='Evaluation options')
    group.add_argument('-d', '--eval_data_path', type=str, required=True,
                       help='Path to eval data')
    group.add_argument("--max-seq-len", type=int, default=2048,
                       help='Size of the output generated text.')
    group.add_argument("--debug", action='store_true', default=False,
                       help='Print infos.')

    args = parser.parse_args()
    set_seed(args.seed)

    main(args)