import argparse
import yaml
import os
import json
import logging
import datetime # Added for timestamp
from typing import Dict, Any, List, Set, Tuple # Added for type hinting
from datasets import load_dataset, Image as HFImage # Import Image feature type
from tqdm import tqdm
from PIL import Image as PILImage # Import PIL for type hinting

# ANSI escape codes for colors
GREEN = '\033[92m'
RED = '\033[91m'
RESET = '\033[0m'
YELLOW = '\033[93m' # For skipped
CYAN = '\033[96m' # For parse failures
MAGENTA = '\033[95m' # For API failures

# Import local modules
from utils import load_api_key
from llm_interface import get_openrouter_prediction
# Import evaluation functions
from evaluation import calculate_accuracy, calculate_exam_scores, calculate_single_question_score_details

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

def get_available_models(config_path: str) -> List[str]:
    """Loads models from the benchmark configuration YAML file."""
    try:
        with open(config_path, 'r') as f:
            config = yaml.safe_load(f)
        models = config.get("openrouter_models", [])
        if not models:
            logging.warning(f"No models found in {config_path} under 'openrouter_models'.")
        return models
    except FileNotFoundError:
        logging.error(f"Configuration file not found at {config_path} for model retrieval.")
        return []
    except yaml.YAMLError as e:
        logging.error(f"Error parsing configuration file {config_path} for model retrieval: {e}")
        return []
    except Exception as e:
        logging.error(f"Unexpected error retrieving models from {config_path}: {e}")
        return []

def get_available_exam_details(metadata_path: str) -> Tuple[List[str], List[str]]:
    """Reads metadata.jsonl to get unique exam names and years."""
    exam_names: Set[str] = set()
    exam_years: Set[str] = set()
    try:
        with open(metadata_path, 'r') as f:
            for line in f:
                try:
                    data = json.loads(line)
                    if 'exam_name' in data:
                        exam_names.add(data['exam_name'])
                    if 'exam_year' in data:
                        exam_years.add(str(data['exam_year']))
                except json.JSONDecodeError:
                    logging.warning(f"Skipping malformed JSON line in {metadata_path}: {line.strip()}")
        
        sorted_exam_names = sorted(list(exam_names))
        sorted_exam_years = sorted(list(exam_years))
        
        if not sorted_exam_names:
            logging.warning(f"No exam names found in {metadata_path}.")
        if not sorted_exam_years:
            logging.warning(f"No exam years found in {metadata_path}.")
            
        return sorted_exam_names, sorted_exam_years
    except FileNotFoundError:
        logging.error(f"Metadata file not found at {metadata_path}.")
        return [], []
    except Exception as e:
        logging.error(f"Unexpected error reading or parsing {metadata_path}: {e}")
        return [], []

def load_config(config_path: str) -> dict:
    """Loads the benchmark configuration from a YAML file."""
    try:
        with open(config_path, 'r') as f:
            config = yaml.safe_load(f)
        logging.info(f"Configuration loaded from {config_path}")
        return config
    except FileNotFoundError:
        logging.error(f"Configuration file not found at {config_path}")
        raise
    except yaml.YAMLError as e:
        logging.error(f"Error parsing configuration file {config_path}: {e}")
        raise

def append_prediction(result: Dict[str, Any], filepath: str):
    """Appends a single prediction result to a JSONL file."""
    # Create a copy to avoid modifying the original dict that might be used elsewhere
    # and remove evaluation-specific fields before saving to predictions.jsonl
    prediction_data = result.copy()
    prediction_data.pop('marks_awarded', None)
    prediction_data.pop('evaluation_status', None)
    prediction_data.pop('predicted_answer', None) # Remove predicted_answer
    prediction_data.pop('ground_truth', None) # Remove ground_truth
    try:
        with open(filepath, 'a') as f:
            json.dump(prediction_data, f)
            f.write('\n')
    except IOError as e:
        logging.error(f"Failed to append prediction to {filepath}: {e}")
    except Exception as e:
        logging.error(f"Unexpected error appending prediction to {filepath}: {e}")

def append_summary_detail(result_detail: Dict[str, Any], filepath: str):
    """Appends a single question's summary details (evaluation status, marks, predicted, truth) to a JSONL file."""
    try:
        with open(filepath, 'a') as f:
            json.dump(result_detail, f)
            f.write('\n')
    except IOError as e:
        logging.error(f"Failed to append summary detail to {filepath}: {e}")
    except Exception as e:
        logging.error(f"Unexpected error appending summary detail to {filepath}: {e}")


# Removed save_summary function as summary.json is no longer needed.

def generate_markdown_summary(summary: Dict[str, Any], filepath: str):
    """Generates a human-readable Markdown summary from the results dictionary."""
    try:
        md_content = []
        model_name = summary.get("model_name", "N/A")
        exam_name = summary.get("exam_name", "N/A")
        exam_year = summary.get("exam_year", "N/A")
        timestamp = summary.get("timestamp", "N/A")
        total_questions_in_dataset = summary.get("total_questions_in_dataset", 0)
        total_questions_processed_in_run = summary.get("total_questions_processed_in_run", 0)

        filtered_questions_count = 0
        if total_questions_in_dataset > 0 and total_questions_processed_in_run > 0:
             filtered_questions_count = total_questions_in_dataset - total_questions_processed_in_run


        md_content.append(f"# Benchmark Results: {model_name}")
        if exam_name and exam_name not in ["N/A", "All_Exams"]: # Only display if a specific exam was targeted
            md_content.append(f"**Exam Name:** {exam_name}")
        if exam_year and exam_year not in ["N/A", "All_Years"]: # Only display if a specific year was targeted
            md_content.append(f"**Exam Year:** {exam_year}")
        md_content.append(f"**Timestamp:** {timestamp}")
        md_content.append(f"**Total Questions in Dataset:** {total_questions_in_dataset if total_questions_in_dataset > 0 else 'N/A'}")
        if filtered_questions_count > 0:
            md_content.append(f"**Questions Filtered Out:** {filtered_questions_count}")
        md_content.append(f"**Total Questions Processed in this Run:** {total_questions_processed_in_run}")
        # md_content.append(f"**Estimated Total API Cost:** ${total_api_cost:.6f}") # Removed
        md_content.append("\n---\n")

        # Check if NEET results are present (or any dataset with overall_score and section_breakdown)
        if "overall_score" in summary and "section_breakdown" in summary: # Generic check for score-based summary
            total_processed = summary.get("total_questions_processed", 0)
            
            overall_score = summary.get('overall_score', 'N/A')
            total_possible_score = summary.get('total_possible_score_for_processed_questions', 'N/A')
            correct_full_count = summary.get('overall_correct_full', 'N/A')
            partial_correct_count = summary.get('overall_partial_correct', 'N/A')
            incorrect_choice_count = summary.get('overall_incorrect_choice', 'N/A')
            skipped_count = summary.get('overall_skipped', 'N/A')
            failures_count = summary.get('overall_api_parse_failures', 'N/A')
            unmapped_count = summary.get('unmapped_section_questions', 'N/A')

            md_content.append("## Exam Scoring Results")
            md_content.append(f"**Overall Score:** **{overall_score}** / **{total_possible_score}**")
            md_content.append(f"- **Fully Correct Answers:** {correct_full_count}")
            if partial_correct_count != 'N/A' and partial_correct_count > 0 :
                md_content.append(f"- **Partially Correct Answers:** {partial_correct_count}")
            md_content.append(f"- **Incorrectly Answered (Choice Made):** {incorrect_choice_count}")
            md_content.append(f"- **Skipped Questions:** {skipped_count}")
            md_content.append(f"- **API/Parse Failures:** {failures_count}")
            md_content.append(f"- **Total Questions Processed:** {total_processed}")
            if unmapped_count > 0:
                md_content.append(f"- **Unmapped Section Questions:** {unmapped_count} *(Not included in section breakdown)*")

            md_content.append("\n### Detailed Score Calculation by Question Type")
            question_type_breakdown = summary.get("question_type_breakdown", {})
            if question_type_breakdown:
                sorted_q_types = sorted(question_type_breakdown.keys())
                for q_type in sorted_q_types:
                    stats = question_type_breakdown[q_type]
                    q_type_display = q_type.replace('_', ' ').title()
                    max_score_per_q = stats.get('max_score_per_question', 0)
                    
                    correct_count_q = stats.get('correct_full', 0)
                    partial_count_q = stats.get('partial_correct', 0)
                    incorrect_count_q = stats.get('incorrect_choice', 0)
                    skipped_count_q = stats.get('skipped', 0)
                    api_fail_count_q = stats.get('api_parse_failures', 0)
                    score_q = stats.get('score', 0)

                    calculation_parts = []
                    if correct_count_q > 0:
                        calculation_parts.append(f"{correct_count_q} Correct (+{max_score_per_q})")
                    if partial_count_q > 0:
                        # For partial, we can't easily show the exact score per question without more detail
                        # For now, just indicate partials.
                        calculation_parts.append(f"{partial_count_q} Partial")
                    if incorrect_count_q > 0:
                        # Need to know penalty for incorrect. Assuming -1 for MCQ_SINGLE_CORRECT, -2 for MCQ_MULTIPLE_CORRECT
                        # For INTEGER, penalty is 0. This needs to be more robust if penalties vary.
                        penalty_per_incorrect = 0
                        if q_type == "MCQ_SINGLE_CORRECT": penalty_per_incorrect = -1
                        elif q_type == "MCQ_MULTIPLE_CORRECT": penalty_per_incorrect = -2
                        calculation_parts.append(f"{incorrect_count_q} Incorrect ({penalty_per_incorrect})")
                    if skipped_count_q > 0:
                        calculation_parts.append(f"{skipped_count_q} Skipped (0)")
                    if api_fail_count_q > 0:
                        # Assuming -1 for API/Parse failures for non-integer types, 0 for integer
                        penalty_per_api_fail = -1
                        if q_type == "INTEGER": penalty_per_api_fail = 0
                        calculation_parts.append(f"{api_fail_count_q} API/Parse Fail ({penalty_per_api_fail})")
                    
                    calculation_str = " + ".join(part for part in calculation_parts if part)
                    if not calculation_str:
                        calculation_str = "No questions of this type processed or all had 0 score change."

                    md_content.append(f"**{q_type_display} ({stats.get('count', 0)} questions):** {score_q} marks")
                    md_content.append(f"  *Calculation:* {calculation_str} = {score_q}")
            else:
                md_content.append("No question type breakdown available.")

            md_content.append("\n### Section Breakdown")
            md_content.append("| Section       | Score | Fully Correct | Partially Correct | Incorrect Choice | Skipped | API/Parse Failures |")
            md_content.append("|---------------|-------|---------------|-------------------|------------------|---------|--------------------|")
            section_breakdown = summary.get("section_breakdown", {})
            
            sorted_section_names = sorted(section_breakdown.keys())
            if not sorted_section_names and section_breakdown:
                logging.warning("Could not sort section names for Markdown summary; using unsorted.")
                sorted_section_names = list(section_breakdown.keys())

            for section_name in sorted_section_names:
                stats = section_breakdown.get(section_name, {})
                score = stats.get('score', 'N/A')
                s_correct = stats.get('correct', 'N/A')
                s_partial = stats.get('partial_correct', 'N/A')
                s_incorrect = stats.get('incorrect', 'N/A')
                s_skipped = stats.get('skipped', 'N/A')
                s_failures = stats.get('api_parse_failures', 'N/A')
                display_section_name = section_name.replace('_', ' ')
                md_content.append(f"| {display_section_name:<13} | {score:<5} | {s_correct:<13} | {s_partial:<17} | {s_incorrect:<16} | {s_skipped:<7} | {s_failures:<18} |")
            if not sorted_section_names:
                md_content.append("| No section data available | N/A   | N/A           | N/A               | N/A              | N/A     | N/A                |")
        
        # Fallback for simple accuracy (if exam scoring wasn't applicable or failed)
        elif "accuracy_on_parsed" in summary:
            md_content.append("## Simple Accuracy Results (Fallback)")
            md_content.append(f"- **Accuracy (on successfully parsed non-skipped):** {summary.get('accuracy_on_parsed', 'N/A'):.4f}")
            md_content.append(f"- **Total Processed Attempts:** {summary.get('total_processed_attempts', 'N/A')}")
            # Add other relevant simple stats if available
        else:
            md_content.append("## Summary")
            md_content.append("*(No specific Exam Scoring or Accuracy metrics found in summary)*")


        with open(filepath, 'w') as f:
            f.write("\n".join(md_content))
        logging.info(f"Markdown summary saved to {filepath}")

    except IOError as e:
        logging.error(f"Failed to save markdown summary to {filepath}: {e}")
    except Exception as e:
        logging.error(f"Unexpected error generating or saving markdown summary to {filepath}: {e}")


def run_benchmark(
    config: dict, 
    api_key: str, 
    model_to_run: str, # Changed from models_override
    output_dir_override: str | None = None, 
    exam_name_choice: str | None = None, # Changed from exam_name_filter
    exam_year_choice: str | None = None,   # Changed from exam_year_filter
    question_ids_str: str | None = None # New argument
):
    """Runs the benchmark evaluation loop with incremental saving and retries."""

    # Determine models to run - now it's a single model
    models_to_run = [model_to_run] # Benchmark will run for the single specified model
    logging.info(f"Target model for this run: {model_to_run}")

     # Determine base output directory
    base_output_dir = output_dir_override if output_dir_override else config.get("results_base_dir", "results")
    os.makedirs(base_output_dir, exist_ok=True)

     # Load dataset
    dataset_path = config.get("dataset_path", ".") # Default to current dir if not specified
    try:
        # Load the dataset using the loading script from the specified path
        # Ensure the 'image' column is decoded
        # Explicitly specify data_files and data_dir for local loading.
        # data_dir should be the project root ('.') when loading a local script,
        # as the script is copied to a cache and needs to know where the actual data is.
        dataset = load_dataset(dataset_path, split='test', data_files={'test': 'data/metadata.jsonl'}, data_dir=os.getcwd(), trust_remote_code=True, download_mode="force_redownload")
        dataset = dataset.cast_column("image", HFImage(decode=True)) # Ensure images are loaded as PIL
        logging.info(f"Dataset loaded successfully from path: {dataset_path}. Original number of questions: {len(dataset)}")
    except Exception as e:
        logging.error(f"Failed to load dataset from path '{dataset_path}': {e}")
        logging.error("Ensure the path is correct and 'jee_neet_benchmark_dataset.py' exists.")
        return

    # Filter dataset based on choices
    original_dataset_size = len(dataset)
    
    # Filter by exam_name
    if exam_name_choice and exam_name_choice.lower() != "all":
        logging.info(f"Filtering dataset for exam_name: '{exam_name_choice}'")
        dataset = dataset.filter(lambda example: example.get('exam_name') == exam_name_choice)
        logging.info(f"Dataset size after exam_name filter: {len(dataset)} questions.")

    # Filter by exam_year
    if exam_year_choice and exam_year_choice.lower() != "all":
        try:
            filter_year_int = int(exam_year_choice)
            logging.info(f"Filtering dataset for exam_year: {filter_year_int}")
            dataset = dataset.filter(lambda example: example.get('exam_year') == filter_year_int)
            logging.info(f"Dataset size after exam_year filter: {len(dataset)} questions.")
        except ValueError:
            logging.error(f"Invalid exam_year provided: '{exam_year_choice}'. Must be an integer or 'all'. Year filtering skipped.")

    # Filter by specific question IDs if provided
    if question_ids_str:
        try:
            target_question_ids = {q_id.strip() for q_id in question_ids_str.split(',') if q_id.strip()}
            if target_question_ids:
                logging.info(f"Filtering dataset for specific question IDs: {target_question_ids}")
                dataset = dataset.filter(lambda example: example.get('question_id') in target_question_ids)
                logging.info(f"Dataset size after question_id filter: {len(dataset)} questions.")
            else:
                logging.warning("Empty or invalid question_ids string provided. No question ID filtering applied.")
        except Exception as e:
            logging.error(f"Error processing question_ids_str '{question_ids_str}': {e}. No question ID filtering applied.")
            
    if len(dataset) < original_dataset_size:
        logging.info(f"Final dataset size after all filters: {len(dataset)} (originally {original_dataset_size}).")
    
    if len(dataset) == 0:
        logging.warning("No questions to process after filtering. Skipping model benchmark.")
        return

     # --- Main Loop: Iterate through models ---
    for model_id in models_to_run:
        # total_questions here should refer to the length of the potentially filtered dataset
        current_total_questions = len(dataset) 
        logging.info(f"--- Starting benchmark for model: {model_id} (Processing {current_total_questions} questions) ---")

        # Create timestamped output directory for this model run
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        safe_model_name = model_id.replace('/', '_') # model_id is already the single model_to_run
        dir_name_parts = [safe_model_name]
        
        # Append exam name and year to directory if they are specific (not "all")
        current_exam_name_for_dir = exam_name_choice if exam_name_choice and exam_name_choice.lower() != "all" else "AllExams"
        current_exam_year_for_dir = exam_year_choice if exam_year_choice and exam_year_choice.lower() != "all" else "AllYears"

        if current_exam_name_for_dir != "AllExams":
            dir_name_parts.append(current_exam_name_for_dir.replace('/', '_'))
        if current_exam_year_for_dir != "AllYears":
             dir_name_parts.append(str(current_exam_year_for_dir)) # Already string or 'all'
        
        dir_name_parts.append(timestamp)
        
        model_output_dir_name = "_".join(filter(None, dir_name_parts)) # Filter out None if any part was None
        model_output_dir = os.path.join(base_output_dir, model_output_dir_name)
        os.makedirs(model_output_dir, exist_ok=True)
        predictions_path = os.path.join(model_output_dir, "predictions.jsonl")
        summary_details_path = os.path.join(model_output_dir, "summary.jsonl") # New file for per-question summary details
        markdown_summary_path = os.path.join(model_output_dir, "summary.md") # Define path for MD summary
        logging.info(f"Results for {model_id} will be saved to: {model_output_dir}")

        model_results = [] # Stores results in memory for final calculation
        failed_questions_data = [] # Stores data needed to retry failed questions

        # Counters for tqdm postfix
        initial_correct_count = 0
        initial_incorrect_count = 0
        initial_skipped_count = 0
        initial_parse_fail_count = 0
        initial_api_fail_count = 0

        # --- Initial Pass: Iterate through questions ---
        pbar_initial = tqdm(dataset, desc=f"Processing {model_id} (Initial Pass)", total=current_total_questions)
        for example in pbar_initial:
            question_id = example["question_id"]
            subject = example["subject"]
            exam_name_from_data = example.get("exam_name", "UNKNOWN_EXAM") # Get exam_name from data
            question_type_from_data = example.get("question_type", "MCQ_SINGLE_CORRECT") # Get question_type
            image: PILImage.Image = example["image"]
            truth = json.loads(example["correct_answer"]) # Parse the JSON string back to a list/list of lists

            result_data = {
                "question_id": question_id,
                "subject": subject,
                "exam_name": exam_name_from_data, # Store for evaluation
                "question_type": question_type_from_data, # Store for evaluation
                "ground_truth": truth,
                "predicted_answer": None,
                "raw_response": None,
                "parse_successful": False,
                "api_call_successful": False,
                "error": None,
                "attempt": 1,
                # "api_cost": None, # Removed
                "previous_raw_response_on_reprompt": None # For task 1
            }

            try:
                # --- Initial API Call ---
                logging.info(f"Attempting API call for question: {question_id} with model: {model_id}")
                # Pass exam_name_from_data and question_type_from_data to get_openrouter_prediction
                parsed_answer, raw_response = get_openrouter_prediction( # No longer expect api_cost
                    model_identifier=model_id,
                    api_key=api_key,
                    image=image,
                    exam_name=exam_name_from_data, # Use exam_name from current data item
                    exam_year=str(example.get("exam_year", "UNKNOWN_YEAR")), # Use exam_year from data
                    question_type=question_type_from_data, # Pass question_type
                    max_tokens=config.get("max_tokens", 100),
                    request_timeout=config.get("request_timeout", 60)
                )
                
                api_success_attempt1 = True # If no exception, API call itself was successful
                parse_success_attempt1 = parsed_answer is not None
                raw_response_attempt1 = raw_response
                # result_data["api_cost"] = api_cost # Removed

                # --- Re-prompt Logic ---
                if api_success_attempt1 and not parse_success_attempt1 and raw_response_attempt1 is not None:
                    logging.warning(f"Question {question_id}: Initial parse failed. Attempting re-prompt.")
                    result_data["previous_raw_response_on_reprompt"] = raw_response_attempt1 # Store previous response
                    try:
                        # Assuming re-prompt might also have a cost
                        parsed_answer_rp, raw_response_rp = get_openrouter_prediction( # No longer expect api_cost
                            model_identifier=model_id,
                            api_key=api_key,
                            previous_raw_response=raw_response_attempt1,
                            question_type=question_type_from_data, # Pass question_type for re-prompt
                            max_tokens=config.get("max_tokens", 100),
                            request_timeout=config.get("request_timeout", 60)
                        )
                        # Process parsed_answer_rp before assignment
                        if isinstance(parsed_answer_rp, list):
                            processed_answer_rp = [str(item) for item in parsed_answer_rp]
                        else:
                            processed_answer_rp = parsed_answer_rp

                        result_data.update({
                            "predicted_answer": processed_answer_rp,
                            "raw_response": raw_response_rp,
                            "parse_successful": processed_answer_rp is not None,
                            "api_call_successful": True,
                            "attempt": 2
                            # Assuming api_cost_rp would be added to existing api_cost or handled separately
                        })
                        # if api_cost_rp is not None: # Add re-prompt cost if available # Removed
                        #     result_data["api_cost"] = (result_data.get("api_cost") or 0.0) + api_cost_rp # Removed
                        logging.info(f"Question {question_id}: Re-prompt {'succeeded' if result_data['parse_successful'] else 'failed to parse'}.")
                    except Exception as e_rp:
                        logging.error(f"Re-prompt API call failed for question {question_id}: {e_rp}")
                        result_data.update({
                            "predicted_answer": None, 
                            "raw_response": raw_response_attempt1, 
                            "parse_successful": False,
                            "api_call_successful": True, 
                            "error": f"Initial parse failed. Re-prompt API call failed: {str(e_rp)}",
                            "attempt": 1 
                        })
                else:
                    current_error = result_data.get("error") 
                    api_actually_successful = api_success_attempt1
                    if api_success_attempt1 and raw_response_attempt1 is None and parsed_answer is None:
                        current_error = "Initial API call returned empty content. Re-prompt skipped."
                    
                    # Process parsed_answer before assignment
                    if isinstance(parsed_answer, list):
                        processed_initial_answer = [str(item) for item in parsed_answer]
                    else:
                        processed_initial_answer = parsed_answer

                    result_data.update({
                        "predicted_answer": processed_initial_answer,
                        "raw_response": raw_response_attempt1,
                        "parse_successful": parse_success_attempt1,
                        "api_call_successful": api_actually_successful,
                        "error": current_error,
                        "attempt": 1 
                    })
                
                # Calculate score details for the current result_data
                score_details = calculate_single_question_score_details(result_data)
                result_data['marks_awarded'] = score_details.get('marks_awarded')
                result_data['evaluation_status'] = score_details.get('evaluation_status')

                # Append evaluation details to summary.jsonl
                summary_detail_data = {
                    "question_id": question_id,
                    "marks_awarded": result_data['marks_awarded'],
                    "evaluation_status": result_data['evaluation_status'],
                    "predicted_answer": result_data['predicted_answer'], # Add predicted_answer
                    "ground_truth": result_data['ground_truth'], # Add ground_truth
                    "attempt": result_data['attempt']
                }
                append_summary_detail(summary_detail_data, summary_details_path)

                model_results.append(result_data)
                append_prediction(result_data, predictions_path) # append_prediction now handles removing evaluation fields

                final_parsed_answer = result_data["predicted_answer"]
                log_message_prefix = f"Question {question_id}:"
                log_message_suffix = f"(Attempt {result_data['attempt']})"

                if not result_data["api_call_successful"]:
                    initial_api_fail_count += 1
                    logging.info(f"{MAGENTA}{log_message_prefix} API Call Failed {log_message_suffix}{RESET}")
                elif not result_data["parse_successful"]:
                    initial_parse_fail_count += 1
                    logging.info(f"{CYAN}{log_message_prefix} Failed to parse answer {log_message_suffix}{RESET}")
                elif final_parsed_answer == "SKIP":
                    initial_skipped_count += 1
                    logging.info(f"{YELLOW}{log_message_prefix} Skipped {log_message_suffix}{RESET}")
                else: # This 'else' means API call was successful, parse was successful, and predicted_answer was not "SKIP" (by model)
                    marks_awarded = result_data.get('marks_awarded', 0) 
                    evaluation_status_value = result_data.get('evaluation_status')
                    
                    # Prepare evaluation_status for checks and logging
                    is_considered_correct = False
                    log_display_status = "N/A" # What to show in the log for status
                    status_check_string = "" # Uppercase string for "CORRECT" and "SKIPPED" checks

                    if evaluation_status_value is True:
                        is_considered_correct = True
                        log_display_status = "True (Boolean)"
                        status_check_string = "CORRECT_TRUE_BOOLEAN" # Contains "CORRECT"
                    elif isinstance(evaluation_status_value, str):
                        log_display_status = evaluation_status_value # Log original string
                        status_check_string = evaluation_status_value.strip().upper()
                        if "CORRECT" in status_check_string:
                            is_considered_correct = True
                    elif evaluation_status_value is None:
                        log_display_status = "None"
                        status_check_string = "NONE_STATUS"
                    else: # Other types
                        log_display_status = str(evaluation_status_value)
                        status_check_string = str(evaluation_status_value).strip().upper()

                    # Define known "skipped by evaluation" statuses (uppercase)
                    known_eval_skip_statuses = ["SKIPPED_BY_EVAL", "SKIPPED"]

                    if is_considered_correct:
                        initial_correct_count +=1
                        logging.info(f"{GREEN}{log_message_prefix} Correct (log) - Marks: {marks_awarded}, Status: {log_display_status} {log_message_suffix}{RESET}")
                    elif status_check_string in known_eval_skip_statuses:
                        initial_skipped_count += 1 
                        logging.info(f"{YELLOW}{log_message_prefix} Skipped by Eval - Marks: {marks_awarded}, Status: {log_display_status} {log_message_suffix}{RESET}")
                    else: # All other statuses are treated as incorrect
                        initial_incorrect_count += 1
                        logging.info(f"{RED}{log_message_prefix} Incorrect (log) - Marks: {marks_awarded}, Status: {log_display_status} {log_message_suffix}{RESET}")
                
                pbar_initial.set_postfix_str(f"✓:{initial_correct_count} ✗:{initial_incorrect_count} S:{initial_skipped_count} P!:{initial_parse_fail_count} A!:{initial_api_fail_count}")

            except Exception as e:
                initial_api_fail_count +=1 # Assume API failure if exception at this level
                pbar_initial.set_postfix_str(f"✓:{initial_correct_count} ✗:{initial_incorrect_count} S:{initial_skipped_count} P!:{initial_parse_fail_count} A!:{initial_api_fail_count}")
                logging.error(f"Initial API call failed for question {question_id} (Attempt 1): {e}")
                result_data["error"] = str(e)
                result_data["api_call_successful"] = False
                failed_questions_data.append(example) # Store original example for retry pass

        pbar_initial.close() # Ensure tqdm finishes cleanly

        # --- Retry Pass for questions with initial API failures ---
        if failed_questions_data:
            logging.info(f"--- Retrying {len(failed_questions_data)} questions with initial API failures for model: {model_id} ---")
            
            retry_correct_count = 0
            retry_incorrect_count = 0
            retry_skipped_count = 0
            retry_parse_fail_count = 0
            retry_api_fail_count = 0
            
            pbar_retry = tqdm(failed_questions_data, desc=f"Processing {model_id} (API Retry Pass)", total=len(failed_questions_data))
            for example_retry in pbar_retry:
                question_id_retry = example_retry["question_id"]
                subject_retry = example_retry["subject"]
                exam_name_retry = example_retry.get("exam_name", "UNKNOWN_EXAM")
                question_type_retry = example_retry.get("question_type", "MCQ_SINGLE_CORRECT")
                image_retry: PILImage.Image = example_retry["image"]
                truth_retry = example_retry["correct_answer"]

                result_data_retry = {
                    "question_id": question_id_retry,
                    "subject": subject_retry,
                    "exam_name": exam_name_retry,
                    "question_type": question_type_retry,
                    "ground_truth": truth_retry,
                    "predicted_answer": None,
                    "raw_response": None,
                    "parse_successful": False,
                    "api_call_successful": False,
                    "error": "Initial API call failed.", # Pre-fill error
                    "attempt": 2,
                    # "api_cost": None, # Removed
                    "previous_raw_response_on_reprompt_after_api_retry": None # For task 1
                }

                try:
                    logging.info(f"Attempting API call for question: {question_id_retry} (API Retry Pass) with model: {model_id}")
                    parsed_answer_retry, raw_response_retry = get_openrouter_prediction( # No longer expect api_cost
                        model_identifier=model_id,
                        api_key=api_key,
                        image=image_retry,
                        exam_name=exam_name_retry,
                        exam_year=str(example_retry.get("exam_year", "UNKNOWN_YEAR")),
                        question_type=question_type_retry,
                        max_tokens=config.get("max_tokens", 100),
                        request_timeout=config.get("request_timeout", 60)
                    )
                    api_success_attempt2 = True
                    parse_success_attempt2 = parsed_answer_retry is not None
                    raw_response_attempt2 = raw_response_retry
                    # result_data_retry["api_cost"] = api_cost_retry # Removed

                    if api_success_attempt2 and not parse_success_attempt2 and raw_response_attempt2 is not None:
                        logging.warning(f"Question {question_id_retry}: API Retry succeeded, but parse failed. Attempting re-prompt.")
                        result_data_retry["previous_raw_response_on_reprompt_after_api_retry"] = raw_response_attempt2 # Store previous response
                        try:
                            # Assuming re-prompt might also have a cost
                            parsed_answer_rp2, raw_response_rp2 = get_openrouter_prediction( # No longer expect api_cost
                                model_identifier=model_id,
                                api_key=api_key,
                                previous_raw_response=raw_response_attempt2,
                                question_type=question_type_retry,
                                max_tokens=config.get("max_tokens", 100),
                                request_timeout=config.get("request_timeout", 60)
                            )
                            # Process parsed_answer_rp2 before assignment
                            if isinstance(parsed_answer_rp2, list):
                                processed_answer_rp2 = [str(item) for item in parsed_answer_rp2]
                            else:
                                processed_answer_rp2 = parsed_answer_rp2

                            result_data_retry.update({
                                "predicted_answer": processed_answer_rp2, "raw_response": raw_response_rp2,
                                "parse_successful": processed_answer_rp2 is not None, "api_call_successful": True,
                                "error": None if processed_answer_rp2 is not None else "Re-prompt after API retry failed to parse.",
                                "attempt": 3
                            })
                            # if api_cost_rp2 is not None: # Add re-prompt cost if available # Removed
                            #     result_data_retry["api_cost"] = (result_data_retry.get("api_cost") or 0.0) + api_cost_rp2 # Removed
                            logging.info(f"Question {question_id_retry}: API Retry + Re-prompt {'succeeded' if result_data_retry['parse_successful'] else 'failed to parse'}.")
                        except Exception as e_rp2:
                            logging.error(f"Re-prompt API call failed for question {question_id_retry} after API retry: {e_rp2}")
                            result_data_retry.update({
                                "error": f"API retry ok, parse failed. Re-prompt API call failed: {str(e_rp2)}",
                                "attempt": 2 # Stay at attempt 2 as re-prompt itself failed
                            })
                    else:
                        current_error_retry = result_data_retry.get("error")
                        if api_success_attempt2 and raw_response_attempt2 is None and parsed_answer_retry is None:
                            current_error_retry = "API retry call returned empty content. Re-prompt skipped."
                        
                        # Process parsed_answer_retry before assignment
                        if isinstance(parsed_answer_retry, list):
                            processed_retry_answer = [str(item) for item in parsed_answer_retry]
                        else:
                            processed_retry_answer = parsed_answer_retry

                        result_data_retry.update({
                            "predicted_answer": processed_retry_answer, "raw_response": raw_response_attempt2,
                            "parse_successful": parse_success_attempt2, "api_call_successful": api_success_attempt2,
                            "error": None if parse_success_attempt2 else current_error_retry, # Clear initial error if parse now ok
                            "attempt": 2
                        })
                except Exception as e_retry_api:
                    logging.error(f"API call failed permanently for question {question_id_retry} (Attempt 2 API Retry): {e_retry_api}")
                    result_data_retry["error"] = f"Initial API fail. Retry API call also failed: {str(e_retry_api)}"
                    result_data_retry["api_call_successful"] = False
                
                # Calculate score details for the current result_data_retry
                score_details_retry = calculate_single_question_score_details(result_data_retry)
                result_data_retry['marks_awarded'] = score_details_retry.get('marks_awarded')
                result_data_retry['evaluation_status'] = score_details_retry.get('evaluation_status')

                # Append evaluation details to summary.jsonl for retry pass
                summary_detail_data_retry = {
                    "question_id": question_id_retry,
                    "marks_awarded": result_data_retry['marks_awarded'],
                    "evaluation_status": result_data_retry['evaluation_status'],
                    "predicted_answer": result_data_retry['predicted_answer'], # Add predicted_answer
                    "ground_truth": result_data_retry['ground_truth'], # Add ground_truth
                    "attempt": result_data_retry['attempt']
                }
                append_summary_detail(summary_detail_data_retry, summary_details_path)

                model_results.append(result_data_retry)
                append_prediction(result_data_retry, predictions_path) # append_prediction now handles removing evaluation fields

                # Logging for retry pass
                log_message_prefix_retry = f"Question {question_id_retry} (Retry):"
                log_message_suffix_retry = f"(Attempt {result_data_retry['attempt']})"
                final_parsed_answer_retry = result_data_retry["predicted_answer"]

                if not result_data_retry["api_call_successful"]:
                    retry_api_fail_count += 1
                    logging.info(f"{MAGENTA}{log_message_prefix_retry} API Call Failed {log_message_suffix_retry}{RESET}")
                elif not result_data_retry["parse_successful"]:
                    retry_parse_fail_count += 1
                    logging.info(f"{CYAN}{log_message_prefix_retry} Failed to parse answer {log_message_suffix_retry}{RESET}")
                elif final_parsed_answer_retry == "SKIP":
                    retry_skipped_count += 1
                    logging.info(f"{YELLOW}{log_message_prefix_retry} Skipped {log_message_suffix_retry}{RESET}")
                else: # This 'else' means API call was successful, parse was successful, and predicted_answer was not "SKIP" (by model)
                    marks_awarded_retry = result_data_retry.get('marks_awarded', 0)
                    evaluation_status_value_retry = result_data_retry.get('evaluation_status')

                    is_considered_correct_retry = False
                    log_display_status_retry = "N/A"
                    status_check_string_retry = ""

                    if evaluation_status_value_retry is True:
                        is_considered_correct_retry = True
                        log_display_status_retry = "True (Boolean)"
                        status_check_string_retry = "CORRECT_TRUE_BOOLEAN"
                    elif isinstance(evaluation_status_value_retry, str):
                        log_display_status_retry = evaluation_status_value_retry
                        status_check_string_retry = evaluation_status_value_retry.strip().upper()
                        if "CORRECT" in status_check_string_retry:
                            is_considered_correct_retry = True
                    elif evaluation_status_value_retry is None:
                        log_display_status_retry = "None"
                        status_check_string_retry = "NONE_STATUS"
                    else: # Other types
                        log_display_status_retry = str(evaluation_status_value_retry)
                        status_check_string_retry = str(evaluation_status_value_retry).strip().upper()

                    known_eval_skip_statuses_retry = ["SKIPPED_BY_EVAL", "SKIPPED"]
                        
                    if is_considered_correct_retry:
                        retry_correct_count +=1
                        logging.info(f"{GREEN}{log_message_prefix_retry} Correct (log) - Marks: {marks_awarded_retry}, Status: {log_display_status_retry} {log_message_suffix_retry}{RESET}")
                    elif status_check_string_retry in known_eval_skip_statuses_retry:
                        retry_skipped_count += 1
                        logging.info(f"{YELLOW}{log_message_prefix_retry} Skipped by Eval - Marks: {marks_awarded_retry}, Status: {log_display_status_retry} {log_message_suffix_retry}{RESET}")
                    else:
                        retry_incorrect_count += 1
                        logging.info(f"{RED}{log_message_prefix_retry} Incorrect (log) - Marks: {marks_awarded_retry}, Status: {log_display_status_retry} {log_message_suffix_retry}{RESET}")
                
                pbar_retry.set_postfix_str(f"✓:{retry_correct_count} ✗:{retry_incorrect_count} S:{retry_skipped_count} P!:{retry_parse_fail_count} A!:{retry_api_fail_count}")
            pbar_retry.close() # Ensure tqdm finishes cleanly

        # --- Final Evaluation for the current model ---
        logging.info(f"--- Calculating final results for model: {model_id} ---")
        
        # Always use calculate_exam_scores now
        evaluation_summary = calculate_exam_scores(model_results) # model_results modified in-place
        
        # Use the actual choices for the summary, defaulting to "All" if not specified or "all"
        summary_exam_name_display = exam_name_choice if exam_name_choice and exam_name_choice.lower() != "all" else "All_Exams"
        summary_exam_year_display = exam_year_choice if exam_year_choice and exam_year_choice.lower() != "all" else "All_Years"

        summary = {
            "model_name": model_id, # This is model_to_run
            "exam_name": summary_exam_name_display,
            "exam_year": summary_exam_year_display,
            "question_ids_filter": question_ids_str if question_ids_str else "None", # Add question ID filter info
            "timestamp": timestamp,
            "total_questions_in_dataset": original_dataset_size, # Total before any filtering
            "total_questions_processed_in_run": len(dataset), # Total after filtering for this run
            **evaluation_summary 
        }
        logging.info(f"Overall Score: {summary.get('overall_score')}")
        logging.info(f"Full Correct: {summary.get('overall_correct_full')}, Partial Correct: {summary.get('overall_partial_correct')}, Incorrect Choice: {summary.get('overall_incorrect_choice')}, Skipped: {summary.get('overall_skipped')}, API/Parse Failures: {summary.get('overall_api_parse_failures')}")
        
        logging.info(f"--- Results Summary for model: {model_id} ---")
        logging.info(json.dumps(summary, indent=2, sort_keys=True))
        logging.info("-------------------------------------")

        # The model_results list was modified in-place by calculate_exam_scores
        # to include evaluation_status and marks_awarded.
        # predictions.jsonl is now written incrementally without evaluation details.
        # No need to overwrite predictions.jsonl here.


        # Save final summary (Markdown) for the current model
        # The summary.json file is no longer generated as per user request.
        generate_markdown_summary(summary, markdown_summary_path) # Call the new function

    logging.info("Benchmark run completed.")


if __name__ == "__main__":
    # Get available choices for arguments
    # Assuming benchmark_config.yaml is in a 'configs' directory relative to script or a fixed path
    # Assuming metadata.jsonl is in a 'data' directory relative to script or a fixed path
    default_config_path = "configs/benchmark_config.yaml"
    default_metadata_path = "data/metadata.jsonl"

    available_models = get_available_models(default_config_path)
    available_exam_names, available_exam_years = get_available_exam_details(default_metadata_path)

    # Add "all" option for exams and years
    exam_name_choices = ["all"] + available_exam_names
    exam_year_choices = ["all"] + available_exam_years

    parser = argparse.ArgumentParser(description="Run JEE/NEET LLM Benchmark.")
    parser.add_argument(
        "--config",
        type=str,
        default=default_config_path,
        help=f"Path to the benchmark configuration YAML file (default: {default_config_path})."
    )
    parser.add_argument(
        "--model", # Changed from --models
        type=str,
        required=True if available_models else False, # Required if models are available
        choices=available_models if available_models else None,
        help="Select the model to run." + (f" Available: {', '.join(available_models)}." if available_models else " (No models found in config)")
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        help="Override the base output directory specified in the config file."
    )
    parser.add_argument(
        "--exam_name",
        type=str,
        default="all",
        choices=exam_name_choices if exam_name_choices else ["all"],
        help="Select the exam name to run, or 'all' for all exams." + (f" Available: {', '.join(available_exam_names)}." if available_exam_names else "")
    )
    parser.add_argument(
        "--exam_year",
        type=str, 
        default="all",
        choices=exam_year_choices if exam_year_choices else ["all"],
        help="Select the exam year to run, or 'all' for all years." + (f" Available: {', '.join(available_exam_years)}." if available_exam_years else "")
    )
    parser.add_argument(
        "--question_ids",
        type=str,
        default=None,
        help="Optional: Comma-separated list of specific question IDs to run (e.g., ID1,ID2,ID3)."
    )
    args = parser.parse_args()

    # Dynamically update config path if user provides a different one
    if args.config != default_config_path:
        logging.info(f"User provided config path: {args.config}. Re-fetching models if necessary.")
        # If models were not found with default, or if user specified a different config, try to load models from it.
        if not available_models or args.model not in available_models: 
            user_config_models = get_available_models(args.config)
            if args.model not in user_config_models:
                logging.error(f"Selected model '{args.model}' not found in the specified config '{args.config}'. Exiting.")
                exit(1) # Or handle more gracefully
            # Potentially update choices if parser allowed any string due to no initial models
            # This is complex with argparse after parsing. For now, we rely on the initial check or error out.

    try:
        # Load API key first - fail fast if not set
        api_key = load_api_key()
        # Load configuration using the (potentially user-overridden) config path
        config = load_config(args.config)
        
        # Ensure the selected model is valid if it was dynamically loaded
        if args.model not in config.get("openrouter_models", []):
             # This check is important if args.model was accepted because available_models was initially empty
            if args.model not in get_available_models(args.config): # Double check with the final config
                logging.error(f"The model '{args.model}' specified is not listed in the config file '{args.config}'. Please check the model name or the config file.")
                exit(1)


        # Run the benchmark
        run_benchmark(
            config=config, 
            api_key=api_key, 
            model_to_run=args.model, 
            output_dir_override=args.output_dir,
            exam_name_choice=args.exam_name,
            exam_year_choice=args.exam_year,
            question_ids_str=args.question_ids
        )
    except (ValueError, FileNotFoundError, yaml.YAMLError) as e:
        logging.error(f"Setup failed: {e}")
    except Exception as e:
        logging.error(f"An unexpected error occurred during benchmark execution: {e}", exc_info=True)