#!/usr/bin/env python3
from evaluation_utils import quac_correct_retrieved_instance_idx_list
from evaluation_utils import unanswerable_keyphrases
from arguments import get_args_scores
import json
from metrics import F1Metric
import copy
import re
import pandas as pd
import os
import io
import sys
import argparse

def compute_f1_score(predicted_answers, groundtruth_answer, exp_name="default"):
    """Evaluating F1 Score"""
    print(len(predicted_answers), len(groundtruth_answer))
    if len(predicted_answers) != len(groundtruth_answer):
        groundtruth_answer = groundtruth_answer[:len(predicted_answers)]

    guess_list = []
    for guess in predicted_answers:
        guess = guess.strip()
        if "</s>" in guess:
            guess = guess.replace("</s>", "")
        guess_list.append(guess)

    answer_list = []
    for answer in groundtruth_answer:
        answer_list.append(answer)

    assert len(guess_list) == len(answer_list), \
        "lengths of guess and answer are different!"

    precision, recall, f1 = F1Metric.compute_all_pairs(guess_list, answer_list)
    print('Method: %s; Precision: %.4f; recall: %.4f; f1: %.4f' % (
        exp_name, precision, recall, f1))
    
    return f1


def load_groundtruth_file(data_file):
    """Load ground truth answers from JSON file"""
    with open(data_file, "r") as f:
        examples = json.load(f)

    data = []
    for instance in examples:
        if "answers" in instance:
            answers = instance["answers"]
        elif "answer" in instance:
            if type(instance["answer"]) is str:
                answers = [instance["answer"]]
            elif type(instance["answer"]) is list:
                answers = instance["answer"]
            else:
                answers = [str(instance["answer"])]
        else:
            raise ValueError("need to have answer or answers")
        data.append(answers)

    return data


def load_prediction(data_file):
    """Load predictions from text file"""
    data = []
    with open(data_file, "r") as f:
        for line in f.readlines():
            if "_on" in data_file or "_medium" in data_file or "_high" in data_file:
                data.append(line.strip()[:300])
            else:
                data.append(line.strip())
    return data


def evaluate_f1(ground_truth_file, prediction_file):
    """Evaluate F1 score for general QA datasets"""
    groundtruth_answers = load_groundtruth_file(ground_truth_file)
    
    # Special handling for inscit dataset
    if "inscit" in ground_truth_file:
        groundtruth_answers_update = []
        for answers in groundtruth_answers:
            answers_update = []
            for ans in answers:
                # Remove default answer added to inscit dataset
                if ans != "Sorry. I cannot find the answer based on the context.":
                    answers_update.append(ans)
            assert len(answers_update) > 0
            groundtruth_answers_update.append(copy.deepcopy(answers_update))
        groundtruth_answers = groundtruth_answers_update

    predicted_answers = load_prediction(prediction_file)
    
    # Special handling for quac and doqa datasets (unanswerable questions)
    if "quac" in prediction_file or "doqa" in prediction_file:
        predicted_answers_new = []
        for pred in predicted_answers:
            pred = pred.lower()
            for keyphrase in unanswerable_keyphrases:
                if "उत्तर नहीं" in pred:
                    pred = "क्षमा करें, मैं संदर्भ के आधार पर उत्तर नहीं ढूँढ पा रहा हूँ।"
                    break
            predicted_answers_new.append(pred)
        predicted_answers = predicted_answers_new

    f1_score = compute_f1_score(predicted_answers, groundtruth_answers)
    return f1_score


def evaluate_convfinqa(ground_truth_file, prediction_file):
    """
    Evaluate ConvFinQA dataset with special numeric matching logic.
    Since the model gives a long answer output, while the gold answer for ConvFinQA 
    are either an arithmetic formula or a final executed number.
    We consider the output containing either the executed number or the arithmetic 
    formula as correct.
    """

    def _is_float(string):
        try:
            float(string)
            return True
        except ValueError:
            return False

    with open(ground_truth_file, "r") as f:
        gold_list = json.load(f)
    
    groundtruth_answers = [item['exe_answer'] for item in gold_list]
    groundtruth_answers_formula = [item['answers'][0] for item in gold_list]

    # Last turn question_list
    question_list = [item['messages'][-1]['content'] for item in gold_list]
    predicted_answers = load_prediction(prediction_file)

    print(len(predicted_answers), len(groundtruth_answers))
    if len(predicted_answers) != len(groundtruth_answers):
        groundtruth_answers = groundtruth_answers[:len(predicted_answers)]

    count_exact_match = 0
    for question, pred, gold, gold_formula in zip(question_list, predicted_answers, 
                                                   groundtruth_answers, groundtruth_answers_formula):

        original_pred = pred
        # Convert 1,000,000 into 1000000
        original_pred = original_pred.replace(",", "")

        # Convert $10 million + $20 million into 10 + 20
        original_pred = original_pred.replace("$", "").replace("million", "").replace(
            "billion", "").replace("मिलियन", "").replace("बिलियन ", "")

        # Convert 10 (2017) + 20 (2018) into 10 + 20
        pattern = r'\((\b\w+\b)\)'
        original_pred = re.sub(pattern, '', original_pred)

        # Make sure each token only has one space in between
        original_pred = " ".join(original_pred.split())
        
        if str(gold) in original_pred:
            count_exact_match += 1
        elif str(gold_formula) in original_pred:
            count_exact_match += 1
        elif _is_float(gold) and (str(round(float(gold), 3)) in original_pred or 
                                   str(round(float(gold), 2)) in original_pred):
            count_exact_match += 1
        elif "percent" in question and (str(float(gold)*100) in original_pred or 
                                        str(round(float(gold)*100, 1)) in original_pred or 
                                        str(round(float(gold)*100, 2)) in original_pred):
            count_exact_match += 1
        elif str(gold).endswith(".0") and str(int(gold)) in original_pred:
            # Gold is an integer like 80.0 then convert it into 80
            count_exact_match += 1
        elif "decrease" in original_pred and _is_float(gold) and gold < 0 and (
                str(-1 * gold) in original_pred):
            # For the case where model generates something like a decrease of 10 million, 
            # while gold is -10
            count_exact_match += 1

    accuracy = count_exact_match / len(predicted_answers)
    print("accuracy of exact match: %.4f" % accuracy)
    return accuracy


def separate_cannot_answer(ground_truth_file, prediction_file):
    """Separate answerable and unanswerable questions"""
    # Load ground truth
    with open(ground_truth_file, "r") as f:
        groundtruth_answers = json.load(f)
    # Load prediction
    predicted_answers = load_prediction(prediction_file)
    print(len(predicted_answers), len(groundtruth_answers))
    if len(predicted_answers) != len(groundtruth_answers):
        groundtruth_answers = groundtruth_answers[:len(predicted_answers)]

    if "quac" in prediction_file:
        """
        For answerable cases, we want to make sure the retrieved context list contains the gold chunk.
        For QuAC dataset, we use top-5 retrieved contexts as inputs, quac_correct_retrieved_instance_idx_list 
        is the index list where the top-5 retrieved context contains the gold answer
        """
        answerable_instance_idx_list = quac_correct_retrieved_instance_idx_list
    else:
        answerable_instance_idx_list = None

    predicted_answers_new = []
    for pred in predicted_answers:
        pred = pred.lower()
        for keyphrase in unanswerable_keyphrases:
            if keyphrase in pred:
                pred = "Sorry. I cannot find the answer based on the context."
                break
        predicted_answers_new.append(pred)
    predicted_answers = predicted_answers_new

    cannot_answer_idx_list = []
    answerable_idx_list = []
    if answerable_instance_idx_list:
        count_idx = 0
    for idx, item in enumerate(groundtruth_answers):
        if 'answers' in item:
            answer = item["answers"][0]
        else:
            answer = item['answer']
        noanswer_response = "Sorry. I cannot find the answer based on the context."

        if answer == noanswer_response:
            cannot_answer_idx_list.append(idx)
            continue
        
        if answerable_instance_idx_list:
            if count_idx in answerable_instance_idx_list:
                answerable_idx_list.append(idx)
            count_idx += 1
        else:
            answerable_idx_list.append(idx)

    print("number of cannot answer cases: %d (out of %d)" % (len(cannot_answer_idx_list), len(groundtruth_answers)))
    print("number of answerable cases: %d (out of %d)" % (len(answerable_idx_list), len(groundtruth_answers)))

    return predicted_answers, cannot_answer_idx_list, answerable_idx_list


def get_cannot_answer_and_answerable_acc(predicted_answers, cannot_answer_idx_list, answerable_idx_list):
    """Calculate accuracy for answerable and unanswerable questions"""
    # Cannot answer
    noanswer_count = 0
    for idx in cannot_answer_idx_list:
        prediction = predicted_answers[idx]
        prediction = prediction.lower()
        if "sorry" in prediction and "cannot find the answer" in prediction:
            noanswer_count += 1
    cannot_answer_acc = noanswer_count / len(cannot_answer_idx_list) if len(cannot_answer_idx_list) > 0 else 0.0
    print("accuracy of cannot answer cases: %.4f" % cannot_answer_acc)

    # Answerable
    answerable_count = 0
    for idx in answerable_idx_list:
        prediction = predicted_answers[idx]
        prediction = prediction.lower()
        if "sorry" in prediction and "cannot find the answer" in prediction:
            continue
        answerable_count += 1
    answerable_acc = answerable_count / len(answerable_idx_list) if len(answerable_idx_list) > 0 else 0.0
    print("accuracy of answerable cases: %.4f" % answerable_acc)


def evaluate_cannot_answer_acc(ground_truth_file, prediction_file):
    """Evaluate accuracy for answerable and unanswerable questions"""
    predicted_answers, cannot_answer_idx_list, answerable_idx_list = \
                                separate_cannot_answer(ground_truth_file, prediction_file)

    get_cannot_answer_and_answerable_acc(predicted_answers, cannot_answer_idx_list, answerable_idx_list)

def get_dataset_config(args):
    """
    Returns configuration for all datasets using paths from args
    
    Args:
        args: Arguments object with dataset path configurations
        
    Returns:
        dict: Dataset configuration mapping
    """
    return {
        'doc2dial': {
            'file_suffix': 'doc2dial',
            'ground_truth_path': args.doc2dial_path,
            'eval_function': evaluate_f1
        },
        'quac': {
            'file_suffix': 'quac',
            'ground_truth_path': args.quac_path,
            'eval_function': evaluate_f1
        },
        'qrecc': {
            'file_suffix': 'qrecc',
            'ground_truth_path': args.qrecc_path,
            'eval_function': evaluate_f1
        },
        'inscit': {
            'file_suffix': 'inscit',
            'ground_truth_path': args.inscit_path,
            'eval_function': evaluate_f1
        },
        'hybridial': {
            'file_suffix': 'hybridial',
            'ground_truth_path': args.hybridial_path,
            'eval_function': evaluate_f1
        },
        'doqa_cooking': {
            'file_suffix': 'doqa_cooking',
            'ground_truth_path': args.doqa_cooking_path,
            'eval_function': evaluate_f1
        },
        'doqa_travel': {
            'file_suffix': 'doqa_travel',
            'ground_truth_path': args.doqa_travel_path,
            'eval_function': evaluate_f1
        },
        'doqa_movies': {
            'file_suffix': 'doqa_movies',
            'ground_truth_path': args.doqa_movies_path,
            'eval_function': evaluate_f1
        },
        'convfinqa': {
            'file_suffix': 'convfinqa',
            'ground_truth_path': args.convfinqa_path,
            'eval_function': evaluate_convfinqa
        }
    }

def evaluate_single_dataset(dataset_name, prediction_file, ground_truth_file, eval_function):
    """
    Evaluate a single dataset and return the score
    
    Args:
        dataset_name: Name of the dataset
        prediction_file: Path to prediction file
        ground_truth_file: Path to ground truth file
        eval_function: Function to use for evaluation
        
    Returns:
        float: Evaluation score
    """
    print("-" * 80)
    print(f"Evaluating {dataset_name}")
    print(f"Prediction file: {prediction_file}")
    print(f"Ground truth file: {ground_truth_file}")
    
    if not os.path.exists(prediction_file):
        print(f"Warning: Prediction file not found: {prediction_file}")
        return None
    
    if not os.path.exists(ground_truth_file):
        print(f"Warning: Ground truth file not found: {ground_truth_file}")
        return None
    
    try:
        # Capture stdout to extract score
        buffer = io.StringIO()
        sys.stdout = buffer
        score_value = eval_function(ground_truth_file, prediction_file)
        sys.stdout = sys.__stdout__
        
        # If the function already returns the score, use it
        if score_value is not None:
            return float(score_value)
        
        # Otherwise, parse from output
        output = buffer.getvalue()
        if "f1:" in output:
            score = output.split("f1:")[-1].strip()
        elif "accuracy of exact match:" in output:
            score = output.split("accuracy of exact match:")[-1].strip()
        else:
            print(f"Warning: Could not parse score from output: {output}")
            return None
            
        return float(score)
    except Exception as e:
        print(f"Error evaluating {dataset_name}: {e}")
        sys.stdout = sys.__stdout__
        return None


def evaluate_single_model(model_name, results_dir, data_path, datasets, args):
    """
    Evaluate a single model across all specified datasets
    
    Args:
        model_name: Name of the model
        results_dir: Directory containing model results
        data_path: Path to ground truth data
        datasets: List of dataset names to evaluate
        args: Arguments object with configuration
        
    Returns:
        dict: Dictionary mapping dataset names to scores
    """
    print(f"\n{'='*80}")
    print(f"Evaluating Model: {model_name}")
    print(f"{'='*80}\n")
    
    output_dir = os.path.join(results_dir, model_name)
    dataset_config = get_dataset_config(args)
    scores = {'model': model_name}
    
    for dataset_name in datasets:
        if dataset_name not in dataset_config:
            print(f"Warning: Unknown dataset {dataset_name}, skipping...")
            continue
        
        config = dataset_config[dataset_name]
        prediction_file = os.path.join(output_dir, f"{config['file_suffix']}.txt")
        ground_truth_file = os.path.join(data_path, config['ground_truth_path'])
        
        score = evaluate_single_dataset(
            dataset_name,
            prediction_file,
            ground_truth_file,
            config['eval_function']
        )
        
        scores[dataset_name] = score
    
    return scores


def evaluate_all_models(results_dir, data_path, datasets, args, output_csv=None):
    """
    Evaluate all models in the results directory
    
    Args:
        results_dir: Directory containing model results (subdirectories for each model)
        data_path: Path to ground truth data directory
        datasets: List of dataset names to evaluate
        args: Arguments object with configuration
        output_csv: Path to output CSV file (default: <results_dir>/scores.csv)
        
    Returns:
        pd.DataFrame: DataFrame containing all evaluation scores
    """    
    # Get all model subdirectories
    model_names = [d for d in os.listdir(results_dir) 
                   if os.path.isdir(os.path.join(results_dir, d))]
    
    if not model_names:
        print(f"Warning: No model directories found in {results_dir}")
        return pd.DataFrame()
    
    print(f"\nFound {len(model_names)} model(s): {model_names}\n")
    
    # Initialize DataFrame
    columns = ['model'] + datasets
    df = pd.DataFrame(columns=columns)
    
    # Evaluate each model
    all_scores = []
    for model_name in model_names:
        scores = evaluate_single_model(model_name, results_dir, data_path, datasets, args)
        all_scores.append(scores)
    
    # Convert to DataFrame
    df = pd.DataFrame(all_scores)
    
    # Calculate average across datasets (excluding model name column)
    numeric_cols = [col for col in df.columns if col != 'model']
    df['average'] = df[numeric_cols].mean(axis=1, skipna=True)
    
    # Save to CSV
    if output_csv is None:
        output_csv = os.path.join(results_dir, 'scores.csv')
    
    df.to_csv(output_csv, index=False)
    print(f"\nScores saved to: {output_csv}")
    print("\nFinal Results:")
    print(df.to_string(index=False))
    
    return df

def main():
    """Main function to run evaluation pipeline"""
    args = get_args_scores()
    
    # Evaluate all models
    df = evaluate_all_models(
        results_dir=args.results_dir,
        data_path=args.data_path,
        datasets=args.datasets,
        args=args,
        output_csv=args.output_csv
    )
    
    return df

if __name__ == "__main__":
    main()