msr.py

import json
import os
from datetime import datetime, timezone, timedelta
from collections import defaultdict
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.errors import HfHubHTTPError
from dotenv import load_dotenv
import duckdb
import backoff
import requests
import requests.exceptions
import traceback
import re

# Load environment variables
load_dotenv(override=True)

# =============================================================================
# CONFIGURATION
# =============================================================================

# Get script directory for relative paths
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
BASE_DIR = os.path.dirname(SCRIPT_DIR)  # Parent directory

AGENTS_REPO = "SWE-Arena/bot_data"
AGENTS_REPO_LOCAL_PATH = os.path.join(BASE_DIR, "bot_data")  # Local git clone path
DUCKDB_CACHE_FILE = os.path.join(SCRIPT_DIR, "cache.duckdb")
GHARCHIVE_DATA_LOCAL_PATH = os.path.join(BASE_DIR, "gharchive/data")
LEADERBOARD_FILENAME = f"{os.getenv('COMPOSE_PROJECT_NAME')}.json"
LEADERBOARD_REPO = "SWE-Arena/leaderboard_data"
LEADERBOARD_TIME_FRAME_DAYS = 180
LONGSTANDING_GAP_DAYS = 30  # Minimum days for an issue to be considered long-standing

# GitHub organizations and repositories to track for wanted issues
TRACKED_ORGS = [
    "apache",
    "github",
    "huggingface",
]

# Labels that indicate "patch wanted" status
PATCH_WANTED_LABELS = [
    "bug",
    "enhancement",
]

# Git sync configuration (mandatory to get latest bot data)
GIT_SYNC_TIMEOUT = 300  # 5 minutes timeout for git pull

# Streaming batch configuration
BATCH_SIZE_DAYS = 1  # Process 1 day at a time (~24 hourly files)

# Retry configuration
MAX_RETRIES = 5

# =============================================================================
# UTILITY FUNCTIONS
# =============================================================================

def load_jsonl(filename):
    """Load JSONL file and return list of dictionaries."""
    if not os.path.exists(filename):
        return []

    data = []
    with open(filename, 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if line:
                try:
                    data.append(json.loads(line))
                except json.JSONDecodeError as e:
                    print(f"Warning: Skipping invalid JSON line: {e}")
    return data


def save_jsonl(filename, data):
    """Save list of dictionaries to JSONL file."""
    with open(filename, 'w', encoding='utf-8') as f:
        for item in data:
            f.write(json.dumps(item) + '\n')


def normalize_date_format(date_string):
    """Convert date strings or datetime objects to standardized ISO 8601 format with Z suffix."""
    if not date_string or date_string == 'N/A':
        return 'N/A'

    try:
        if isinstance(date_string, datetime):
            return date_string.strftime('%Y-%m-%dT%H:%M:%SZ')

        date_string = re.sub(r'\\s+', ' ', date_string.strip())
        date_string = date_string.replace(' ', 'T')

        if len(date_string) >= 3:
            if date_string[-3:-2] in ('+', '-') and ':' not in date_string[-3:]:
                date_string = date_string + ':00'

        dt = datetime.fromisoformat(date_string.replace('Z', '+00:00'))
        return dt.strftime('%Y-%m-%dT%H:%M:%SZ')
    except Exception as e:
        print(f"Warning: Could not parse date '{date_string}': {e}")
        return date_string


def get_hf_token():
    """Get HuggingFace token from environment variables."""
    token = os.getenv('HF_TOKEN')
    if not token:
        print("Warning: HF_TOKEN not found in environment variables")
    return token


# =============================================================================
# GHARCHIVE DOWNLOAD FUNCTIONS
# =============================================================================

def download_file(url):
    """Download a GHArchive file with a single attempt."""
    filename = url.split("/")[-1]
    filepath = os.path.join(GHARCHIVE_DATA_LOCAL_PATH, filename)

    if os.path.exists(filepath):
        return True

    try:
        response = requests.get(url, timeout=30)
        response.raise_for_status()
        with open(filepath, "wb") as f:
            f.write(response.content)
        return True
    except Exception as e:
        print(f"   ⚠ {filename}: {e}")
        return False


def download_all_gharchive_data():
    """Download all GHArchive data files for the last LEADERBOARD_TIME_FRAME_DAYS."""
    os.makedirs(GHARCHIVE_DATA_LOCAL_PATH, exist_ok=True)
    
    end_date = datetime.now(timezone.utc)
    start_date = end_date - timedelta(days=LEADERBOARD_TIME_FRAME_DAYS)

    urls = []
    current_date = start_date
    while current_date <= end_date:
        date_str = current_date.strftime("%Y-%m-%d")
        for hour in range(24):
            url = f"https://data.gharchive.org/{date_str}-{hour}.json.gz"
            urls.append(url)
        current_date += timedelta(days=1)

    success = True
    for url in urls:
        if not download_file(url):
            success = False

    return success


# =============================================================================
# HUGGINGFACE API WRAPPERS
# =============================================================================

def is_retryable_error(e):
    """Check if exception is retryable (rate limit or timeout error)."""
    if isinstance(e, HfHubHTTPError):
        if e.response.status_code == 429:
            return True

    if isinstance(e, (requests.exceptions.Timeout,
                     requests.exceptions.ReadTimeout,
                     requests.exceptions.ConnectTimeout)):
        return True

    if isinstance(e, Exception):
        error_str = str(e).lower()
        if 'timeout' in error_str or 'timed out' in error_str:
            return True

    return False


@backoff.on_exception(
    backoff.expo,
    (HfHubHTTPError, requests.exceptions.Timeout, requests.exceptions.RequestException, Exception),
    max_tries=MAX_RETRIES,
    base=300,
    max_value=3600,
    giveup=lambda e: not is_retryable_error(e),
    on_backoff=lambda details: print(
        f"   {details['exception']} error. Retrying in {details['wait']/60:.1f} minutes ({details['wait']:.0f}s) - attempt {details['tries']}/5..."
    )
)
def list_repo_files_with_backoff(api, **kwargs):
    """Wrapper for api.list_repo_files() with exponential backoff."""
    return api.list_repo_files(**kwargs)


@backoff.on_exception(
    backoff.expo,
    (HfHubHTTPError, requests.exceptions.Timeout, requests.exceptions.RequestException, Exception),
    max_tries=MAX_RETRIES,
    base=300,
    max_value=3600,
    giveup=lambda e: not is_retryable_error(e),
    on_backoff=lambda details: print(
        f"   {details['exception']} error. Retrying in {details['wait']/60:.1f} minutes ({details['wait']:.0f}s) - attempt {details['tries']}/5..."
    )
)
def hf_hub_download_with_backoff(**kwargs):
    """Wrapper for hf_hub_download() with exponential backoff."""
    return hf_hub_download(**kwargs)


@backoff.on_exception(
    backoff.expo,
    (HfHubHTTPError, requests.exceptions.Timeout, requests.exceptions.RequestException, Exception),
    max_tries=MAX_RETRIES,
    base=300,
    max_value=3600,
    giveup=lambda e: not is_retryable_error(e),
    on_backoff=lambda details: print(
        f"   {details['exception']} error. Retrying in {details['wait']/60:.1f} minutes ({details['wait']:.0f}s) - attempt {details['tries']}/5..."
    )
)
def upload_file_with_backoff(api, **kwargs):
    """Wrapper for api.upload_file() with exponential backoff."""
    return api.upload_file(**kwargs)


@backoff.on_exception(
    backoff.expo,
    (HfHubHTTPError, requests.exceptions.Timeout, requests.exceptions.RequestException, Exception),
    max_tries=MAX_RETRIES,
    base=300,
    max_value=3600,
    giveup=lambda e: not is_retryable_error(e),
    on_backoff=lambda details: print(
        f"   {details['exception']} error. Retrying in {details['wait']/60:.1f} minutes ({details['wait']:.0f}s) - attempt {details['tries']}/5..."
    )
)
def upload_folder_with_backoff(api, **kwargs):
    """Wrapper for api.upload_folder() with exponential backoff."""
    return api.upload_folder(**kwargs)


def get_duckdb_connection():
    """
    Initialize DuckDB connection with OPTIMIZED memory settings.
    Uses persistent database and reduced memory footprint.
    Automatically removes cache file if lock conflict is detected.
    """
    try:
        conn = duckdb.connect(DUCKDB_CACHE_FILE)
    except Exception as e:
        # Check if it's a locking error
        error_msg = str(e)
        if "lock" in error_msg.lower() or "conflicting" in error_msg.lower():
            print(f"   ⚠ Lock conflict detected, removing {DUCKDB_CACHE_FILE}...")
            if os.path.exists(DUCKDB_CACHE_FILE):
                os.remove(DUCKDB_CACHE_FILE)
                print(f"   ✓ Cache file removed, retrying connection...")
            # Retry connection after removing cache
            conn = duckdb.connect(DUCKDB_CACHE_FILE)
        else:
            # Re-raise if it's not a locking error
            raise

    # CORE MEMORY & THREADING SETTINGS
    conn.execute(f"SET threads TO 6;")
    conn.execute(f"SET max_memory = '50GB';")
    conn.execute("SET temp_directory = '/tmp/duckdb_temp';")

    # PERFORMANCE OPTIMIZATIONS
    conn.execute("SET preserve_insertion_order = false;")  # Disable expensive ordering
    conn.execute("SET enable_object_cache = true;")  # Cache repeatedly read files

    return conn


def generate_file_path_patterns(start_date, end_date, data_dir=GHARCHIVE_DATA_LOCAL_PATH):
    """Generate file path patterns for GHArchive data in date range (only existing files)."""
    file_patterns = []
    missing_dates = set()

    current_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
    end_day = end_date.replace(hour=0, minute=0, second=0, microsecond=0)

    while current_date <= end_day:
        date_has_files = False
        for hour in range(24):
            pattern = os.path.join(data_dir, f"{current_date.strftime('%Y-%m-%d')}-{hour}.json.gz")
            if os.path.exists(pattern):
                file_patterns.append(pattern)
                date_has_files = True

        if not date_has_files:
            missing_dates.add(current_date.strftime('%Y-%m-%d'))

        current_date += timedelta(days=1)

    if missing_dates:
        print(f"   ⚠ Skipping {len(missing_dates)} date(s) with no data")

    return file_patterns


# =============================================================================
# STREAMING BATCH PROCESSING - UNIFIED QUERY FOR ALL METADATA
# =============================================================================

def fetch_all_metadata_streaming(conn, identifiers, start_date, end_date):
    """
    QUERY: Fetch both issue and discussion metadata using streaming batch processing:
    - IssuesEvent, IssueCommentEvent (for assistant-assigned issues AND wanted issues)
    - PullRequestEvent (for wanted issue tracking)
    - DiscussionEvent (for discussion tracking)

    Args:
        conn: DuckDB connection instance
        identifiers: List of GitHub usernames/bot identifiers
        start_date: Start datetime (timezone-aware)
        end_date: End datetime (timezone-aware)

    Returns:
        Dictionary with four keys:
        - 'agent_issues': {agent_id: [issue_metadata]} for assistant-assigned issues
        - 'wanted_open': [open_wanted_issues] for long-standing open issues
        - 'wanted_resolved': {agent_id: [resolved_wanted]} for resolved wanted issues
        - 'agent_discussions': {agent_id: [discussion_metadata]} for assistant discussions
    """
    identifier_set = set(identifiers)
    identifier_list = ', '.join([f"'{id}'" for id in identifiers])
    tracked_orgs_list = ', '.join([f"'{org}'" for org in TRACKED_ORGS])

    # Storage for assistant-assigned issues
    agent_issues = defaultdict(list)  # agent_id -> [issue_metadata]
    agent_issue_urls = defaultdict(set)  # agent_id -> set of issue URLs (for deduplication)

    # Storage for wanted issues
    all_issues = {}  # issue_url -> issue_metadata
    issue_to_prs = defaultdict(set)  # issue_url -> set of PR URLs
    pr_creators = {}  # pr_url -> creator login
    pr_merged_at = {}  # pr_url -> merged_at timestamp

    # Storage for discussions
    discussions_by_agent = defaultdict(list)

    # Calculate total batches
    total_days = (end_date - start_date).days
    total_batches = (total_days // BATCH_SIZE_DAYS) + 1

    # Process in batches
    current_date = start_date
    batch_num = 0

    print(f"   Streaming {total_batches} batches of {BATCH_SIZE_DAYS}-day intervals...")

    while current_date <= end_date:
        batch_num += 1
        batch_end = min(current_date + timedelta(days=BATCH_SIZE_DAYS - 1), end_date)

        # Get file patterns for THIS BATCH ONLY
        file_patterns = generate_file_path_patterns(current_date, batch_end)

        if not file_patterns:
            print(f"   Batch {batch_num}/{total_batches}: {current_date.date()} to {batch_end.date()} - NO DATA")
            current_date = batch_end + timedelta(days=1)
            continue

        # Progress indicator
        print(f"   Batch {batch_num}/{total_batches}: {current_date.date()} to {batch_end.date()} ({len(file_patterns)} files)... ", end="", flush=True)

        # Build file patterns SQL for THIS BATCH
        file_patterns_sql = '[' + ', '.join([f"'{fp}'" for fp in file_patterns]) + ']'

        try:
            # UNIFIED QUERY: Optimized for gzip decompression
            unified_query = f"""
            SELECT
                type,
                json_extract_string(repo, '$.name') as repo_name,
                json_extract_string(repo, '$.url') as repo_url,
                -- Issue fields
                json_extract_string(payload, '$.issue.html_url') as issue_url,
                json_extract_string(payload, '$.issue.title') as issue_title,
                json_extract_string(payload, '$.issue.number') as issue_number,
                json_extract_string(payload, '$.issue.created_at') as issue_created_at,
                json_extract_string(payload, '$.issue.closed_at') as issue_closed_at,
                json_extract(payload, '$.issue.labels') as issue_labels,
                json_extract_string(payload, '$.issue.pull_request') as is_pull_request,
                json_extract_string(payload, '$.issue.state_reason') as issue_state_reason,
                -- Actor/assignee fields for assistant assignment
                json_extract_string(payload, '$.issue.user.login') as issue_creator,
                json_extract_string(payload, '$.issue.assignee.login') as issue_assignee,
                json_extract(payload, '$.issue.assignees') as issue_assignees,
                json_extract_string(payload, '$.comment.user.login') as commenter,
                -- PR fields - simplified with COALESCE
                COALESCE(
                    json_extract_string(payload, '$.issue.html_url'),
                    json_extract_string(payload, '$.pull_request.html_url')
                ) as pr_url,
                COALESCE(
                    json_extract_string(payload, '$.issue.user.login'),
                    json_extract_string(payload, '$.pull_request.user.login')
                ) as pr_creator,
                COALESCE(
                    json_extract_string(payload, '$.issue.pull_request.merged_at'),
                    json_extract_string(payload, '$.pull_request.merged_at')
                ) as pr_merged_at,
                COALESCE(
                    json_extract_string(payload, '$.issue.body'),
                    json_extract_string(payload, '$.pull_request.body')
                ) as pr_body,
                -- Discussion fields
                json_extract_string(payload, '$.discussion.html_url') as discussion_url,
                json_extract_string(payload, '$.discussion.user.login') as discussion_creator,
                json_extract_string(payload, '$.discussion.created_at') as discussion_created_at,
                json_extract_string(payload, '$.discussion.answer_chosen_at') as discussion_closed_at,
                json_extract_string(payload, '$.discussion.state_reason') as discussion_state_reason,
                json_extract_string(payload, '$.action') as action
            FROM read_json(
                {file_patterns_sql},
                union_by_name=true,
                filename=true,
                compression='gzip',
                format='newline_delimited',
                ignore_errors=true
            )
            WHERE
                type IN ('IssuesEvent', 'IssueCommentEvent', 'PullRequestEvent', 'DiscussionEvent')
                AND (
                    -- Assistant-assigned issues: assistant is creator, assignee, or commenter
                    (type = 'IssuesEvent' AND (
                        json_extract_string(payload, '$.issue.user.login') IN ({identifier_list})
                        OR json_extract_string(payload, '$.issue.assignee.login') IN ({identifier_list})
                        OR SPLIT_PART(json_extract_string(repo, '$.name'), '/', 1) IN ({tracked_orgs_list})
                    ))
                    -- Issue comments: assistant is commenter OR tracked org
                    OR (type = 'IssueCommentEvent' AND (
                        json_extract_string(payload, '$.comment.user.login') IN ({identifier_list})
                        OR SPLIT_PART(json_extract_string(repo, '$.name'), '/', 1) IN ({tracked_orgs_list})
                    ))
                    -- PRs: assistant is creator OR tracked org (for wanted issue tracking)
                    OR (type = 'PullRequestEvent' AND (
                        json_extract_string(payload, '$.pull_request.user.login') IN ({identifier_list})
                        OR SPLIT_PART(json_extract_string(repo, '$.name'), '/', 1) IN ({tracked_orgs_list})
                    ))
                    -- Discussions: assistant is creator AND tracked org
                    OR (type = 'DiscussionEvent'
                        AND json_extract_string(payload, '$.discussion.user.login') IN ({identifier_list})
                        AND SPLIT_PART(json_extract_string(repo, '$.name'), '/', 1) IN ({tracked_orgs_list})
                    )
                )
            """

            all_results = conn.execute(unified_query).fetchall()

            # Post-process results to separate into different categories
            # Row structure: [type, repo_name, repo_url, issue_url, issue_title, issue_number,
            #                 issue_created_at, issue_closed_at, issue_labels, is_pull_request,
            #                 issue_state_reason, issue_creator, issue_assignee, issue_assignees,
            #                 commenter, pr_url, pr_creator, pr_merged_at, pr_body,
            #                 discussion_url, discussion_creator, discussion_created_at,
            #                 discussion_closed_at, discussion_state_reason, action]

            issue_events = []  # For wanted tracking
            pr_events = []     # For wanted tracking
            discussion_events = []  # For discussion tracking
            agent_issue_events = []  # For assistant-assigned issues

            for row in all_results:
                event_type = row[0]
                is_pr = row[9]  # is_pull_request field

                if event_type in ('IssuesEvent', 'IssueCommentEvent'):
                    if not is_pr:  # It's an issue, not a PR
                        # Check if this is an assistant-assigned issue
                        issue_creator = row[11]
                        issue_assignee = row[12]
                        issue_assignees_json = row[13]
                        commenter = row[14]

                        agent_identifier = None

                        if event_type == 'IssuesEvent':
                            # Check if issue creator, assignee, or any assignees match our identifiers
                            if issue_creator in identifier_set:
                                agent_identifier = issue_creator
                            elif issue_assignee in identifier_set:
                                agent_identifier = issue_assignee
                            else:
                                # Check assignees array
                                try:
                                    if issue_assignees_json:
                                        if isinstance(issue_assignees_json, str):
                                            assignees_data = json.loads(issue_assignees_json)
                                        else:
                                            assignees_data = issue_assignees_json

                                        if isinstance(assignees_data, list):
                                            for assignee_obj in assignees_data:
                                                if isinstance(assignee_obj, dict):
                                                    assignee_login = assignee_obj.get('login')
                                                    if assignee_login in identifier_set:
                                                        agent_identifier = assignee_login
                                                        break
                                except (json.JSONDecodeError, TypeError):
                                    pass

                        elif event_type == 'IssueCommentEvent':
                            # Check if commenter is an assistant
                            if commenter in identifier_set:
                                agent_identifier = commenter

                        # Add to appropriate list
                        if agent_identifier:
                            agent_issue_events.append((row, agent_identifier))

                        # Always add to issue_events for wanted tracking (if from tracked orgs)
                        issue_events.append(row)
                    else:
                        # It's a PR
                        pr_events.append(row)

                elif event_type == 'PullRequestEvent':
                    pr_events.append(row)

                elif event_type == 'DiscussionEvent':
                    discussion_events.append(row)

            # Process assistant-assigned issues
            for row, agent_identifier in agent_issue_events:
                # Row indices: repo_url=2, issue_url=3, issue_created_at=6, issue_closed_at=7, issue_state_reason=10
                repo_url = row[2]
                issue_url = row[3]
                created_at = row[6]
                closed_at = row[7]
                state_reason = row[10]

                if not issue_url or not agent_identifier:
                    continue

                # Build full URL from repo_url if needed
                if repo_url and '/issues/' not in issue_url:
                    issue_number = row[5]
                    full_url = f"{repo_url.replace('api.github.com/repos/', 'github.com/')}/issues/{issue_number}"
                else:
                    full_url = issue_url

                # Only include issues created within timeframe
                if created_at:
                    try:
                        created_dt = datetime.fromisoformat(created_at.replace('Z', '+00:00'))
                        if created_dt < start_date:
                            continue
                    except:
                        continue

                # Deduplicate: only add if we haven't seen this issue for this assistant
                if full_url in agent_issue_urls[agent_identifier]:
                    continue

                agent_issue_urls[agent_identifier].add(full_url)

                issue_metadata = {
                    'url': full_url,
                    'created_at': normalize_date_format(created_at),
                    'closed_at': normalize_date_format(closed_at) if closed_at else None,
                    'state_reason': state_reason,
                }

                agent_issues[agent_identifier].append(issue_metadata)

            # Process issues for wanted tracking
            for row in issue_events:
                # Row indices: repo_name=1, issue_url=3, issue_title=4, issue_number=5,
                #              issue_created_at=6, issue_closed_at=7, issue_labels=8
                repo_name = row[1]
                issue_url = row[3]
                title = row[4]
                issue_number = row[5]
                created_at = row[6]
                closed_at = row[7]
                labels_json = row[8]

                if not issue_url or not repo_name:
                    continue

                # Extract org from repo_name
                parts = repo_name.split('/')
                if len(parts) != 2:
                    continue
                org = parts[0]

                # Filter by tracked orgs
                if org not in TRACKED_ORGS:
                    continue

                # Parse labels
                try:
                    if isinstance(labels_json, str):
                        labels_data = json.loads(labels_json)
                    else:
                        labels_data = labels_json

                    if not isinstance(labels_data, list):
                        label_names = []
                    else:
                        label_names = [label.get('name', '').lower() for label in labels_data if isinstance(label, dict)]

                except (json.JSONDecodeError, TypeError):
                    label_names = []

                # Determine state
                normalized_closed_at = normalize_date_format(closed_at) if closed_at else None
                state = 'closed' if (normalized_closed_at and normalized_closed_at != 'N/A') else 'open'

                # Store issue metadata
                all_issues[issue_url] = {
                    'url': issue_url,
                    'repo': repo_name,
                    'title': title,
                    'number': issue_number,
                    'state': state,
                    'created_at': normalize_date_format(created_at),
                    'closed_at': normalized_closed_at,
                    'labels': label_names
                }

            # Process PRs for wanted tracking
            for row in pr_events:
                # Row indices: pr_url=15, pr_creator=16, pr_merged_at=17, pr_body=18
                pr_url = row[15]
                pr_creator = row[16]
                merged_at = row[17]
                pr_body = row[18]

                if not pr_url or not pr_creator:
                    continue

                pr_creators[pr_url] = pr_creator
                pr_merged_at[pr_url] = merged_at

                # Extract linked issues from PR body
                if pr_body:
                    # Match issue URLs or #number references
                    issue_refs = re.findall(r'(?:https?://github\.com/[\w-]+/[\w-]+/issues/\d+)|(?:#\d+)', pr_body, re.IGNORECASE)

                    for ref in issue_refs:
                        # Convert #number to full URL if needed
                        if ref.startswith('#'):
                            # Extract org/repo from PR URL
                            pr_parts = pr_url.split('/')
                            if len(pr_parts) >= 5:
                                org = pr_parts[-4]
                                repo = pr_parts[-3]
                                issue_num = ref[1:]
                                issue_url = f"https://github.com/{org}/{repo}/issues/{issue_num}"
                                issue_to_prs[issue_url].add(pr_url)
                        else:
                            issue_to_prs[ref].add(pr_url)

            # Process discussions
            for row in discussion_events:
                # Row indices: repo_name=1, discussion_url=19, discussion_creator=20,
                #              discussion_created_at=21, discussion_closed_at=22,
                #              discussion_state_reason=23
                repo_name = row[1]
                discussion_url = row[19]
                discussion_creator = row[20]
                discussion_created_at = row[21]
                discussion_closed_at = row[22]
                discussion_state_reason = row[23]

                if not discussion_url or not repo_name:
                    continue

                # Extract org from repo_name
                parts = repo_name.split('/')
                if len(parts) != 2:
                    continue
                org = parts[0]

                # Filter by tracked orgs
                if org not in TRACKED_ORGS:
                    continue

                # Parse discussion creation date to filter by time window
                created_dt = None
                if discussion_created_at:
                    try:
                        created_dt = datetime.fromisoformat(discussion_created_at.replace('Z', '+00:00'))
                        # Only track discussions created on or after start_date
                        if created_dt < start_date:
                            continue
                    except:
                        continue

                # Group by creator (assistant identifier)
                # Only track discussions from our assistant identifiers
                if discussion_creator not in identifier_set:
                    continue

                # Determine discussion state
                # A discussion is "resolved" if it has an answer chosen OR is marked answered
                is_resolved = False
                if discussion_closed_at:
                    is_resolved = True
                elif discussion_state_reason and 'answered' in discussion_state_reason.lower():
                    is_resolved = True

                # Store discussion metadata
                discussion_meta = {
                    'url': discussion_url,
                    'repo': repo_name,
                    'created_at': normalize_date_format(discussion_created_at),
                    'closed_at': normalize_date_format(discussion_closed_at) if discussion_closed_at else None,
                    'state': 'resolved' if is_resolved else 'open',
                    'state_reason': discussion_state_reason
                }

                # Group by assistant
                if discussion_creator not in discussions_by_agent:
                    discussions_by_agent[discussion_creator] = []
                discussions_by_agent[discussion_creator].append(discussion_meta)

            print(f"✓ {len(agent_issue_events)} assistant issues, {len(issue_events)} wanted issues, {len(pr_events)} PRs, {len(discussion_events)} discussions")

        except Exception as e:
            print(f"\n   ✗ Batch {batch_num} error: {str(e)}")
            traceback.print_exc()

        # Move to next batch
        current_date = batch_end + timedelta(days=1)

    # Post-processing: Filter issues and assign to assistants
    print(f"\n   Post-processing {len(all_issues)} wanted issues...")

    wanted_open = []
    wanted_resolved = defaultdict(list)  # agent_id -> [resolved wanted issues]
    wanted_in_progress = defaultdict(list)  # agent_id -> [in-progress wanted issues (PR not merged)]
    current_time = datetime.now(timezone.utc)

    for issue_url, issue_meta in all_issues.items():
        # Check if issue has linked PRs
        linked_prs = issue_to_prs.get(issue_url, set())
        if not linked_prs:
            continue

        # Find all agents who have PRs for this issue and their merge status
        agents_with_merged_pr = set()
        agents_with_unmerged_pr = set()

        for pr_url in linked_prs:
            pr_creator = pr_creators.get(pr_url)
            if pr_creator not in identifier_set:
                continue

            merged_at = pr_merged_at.get(pr_url)
            if merged_at:  # PR was merged
                agents_with_merged_pr.add(pr_creator)
            else:  # PR not merged (in progress or rejected)
                agents_with_unmerged_pr.add(pr_creator)

        # Skip if no agent has a PR for this issue
        if not agents_with_merged_pr and not agents_with_unmerged_pr:
            continue

        # Check if issue qualifies as a "wanted" issue (has patch label)
        issue_labels = issue_meta.get('labels', [])
        has_patch_label = False
        for issue_label in issue_labels:
            for wanted_label in PATCH_WANTED_LABELS:
                if wanted_label.lower() in issue_label:
                    has_patch_label = True
                    break
            if has_patch_label:
                break

        if not has_patch_label:
            continue

        # Check if long-standing (open 30+ days)
        created_at_str = issue_meta.get('created_at')
        if not created_at_str or created_at_str == 'N/A':
            continue

        try:
            created_dt = datetime.fromisoformat(created_at_str.replace('Z', '+00:00'))
        except:
            continue

        # Process based on issue state
        if issue_meta['state'] == 'open':
            days_open = (current_time - created_dt).days
            if days_open < LONGSTANDING_GAP_DAYS:
                continue

            # Add to global wanted_open list
            wanted_open.append(issue_meta)

            # Track in-progress for agents with unmerged PRs (excluding those who already resolved it)
            for agent_id in agents_with_unmerged_pr - agents_with_merged_pr:
                wanted_in_progress[agent_id].append(issue_meta)

        elif issue_meta['state'] == 'closed':
            closed_at_str = issue_meta.get('closed_at')
            if not closed_at_str or closed_at_str == 'N/A':
                continue

            try:
                closed_dt = datetime.fromisoformat(closed_at_str.replace('Z', '+00:00'))
                days_open = (closed_dt - created_dt).days

                # Only include if closed within timeframe AND was open 30+ days
                if start_date <= closed_dt <= end_date and days_open >= LONGSTANDING_GAP_DAYS:
                    # Track resolved for agents with merged PRs
                    for agent_id in agents_with_merged_pr:
                        wanted_resolved[agent_id].append(issue_meta)
            except:
                pass

    print(f"   ✓ Found {sum(len(issues) for issues in agent_issues.values())} assistant-assigned issues across {len(agent_issues)} assistants")
    print(f"   ✓ Found {len(wanted_open)} long-standing open wanted issues")
    print(f"   ✓ Found {sum(len(issues) for issues in wanted_resolved.values())} resolved wanted issues across {len(wanted_resolved)} assistants")
    print(f"   ✓ Found {sum(len(issues) for issues in wanted_in_progress.values())} in-progress wanted issues across {len(wanted_in_progress)} assistants")
    print(f"   ✓ Found {sum(len(discussions) for discussions in discussions_by_agent.values())} discussions across {len(discussions_by_agent)} assistants")

    return {
        'agent_issues': dict(agent_issues),
        'wanted_open': wanted_open,
        'wanted_resolved': dict(wanted_resolved),
        'wanted_in_progress': dict(wanted_in_progress),
        'agent_discussions': dict(discussions_by_agent)
    }


def load_agents_from_hf():
    """
    Load all assistant metadata JSON files from local git repository.
    """
    assistants = []

    # Scan local directory for JSON files
    if not os.path.exists(AGENTS_REPO_LOCAL_PATH):
        raise FileNotFoundError(f"Local repository not found at {AGENTS_REPO_LOCAL_PATH}")

    # Walk through the directory to find all JSON files
    files_processed = 0
    print(f"   Loading assistant metadata from {AGENTS_REPO_LOCAL_PATH}...")

    for root, dirs, files in os.walk(AGENTS_REPO_LOCAL_PATH):
        # Skip .git directory
        if '.git' in root:
            continue

        for filename in files:
            if not filename.endswith('.json'):
                continue

            files_processed += 1
            file_path = os.path.join(root, filename)

            try:
                with open(file_path, 'r', encoding='utf-8') as f:
                    agent_data = json.load(f)

                # Only include active assistants
                if agent_data.get('status') != 'active':
                    continue

                # Extract github_identifier from filename
                github_identifier = filename.replace('.json', '')
                agent_data['github_identifier'] = github_identifier

                assistants.append(agent_data)

            except Exception as e:
                print(f"   ⚠ Error loading {filename}: {str(e)}")
                continue

    print(f"   ✓ Loaded {len(assistants)} active assistants (from {files_processed} total files)")
    return assistants


def calculate_issue_stats_from_metadata(metadata_list):
    """Calculate statistics from a list of issue metadata."""
    total_issues = len(metadata_list)
    closed = sum(1 for issue_meta in metadata_list if issue_meta.get('closed_at'))
    resolved = sum(1 for issue_meta in metadata_list
                   if issue_meta.get('state_reason') == 'completed')

    # Resolved rate = resolved / closed (not resolved / total)
    resolved_rate = (resolved / closed * 100) if closed > 0 else 0

    return {
        'total_issues': total_issues,
        'closed_issues': closed,
        'resolved_issues': resolved,
        'resolved_rate': round(resolved_rate, 2),
    }


def calculate_monthly_metrics_by_issues(all_metadata_dict, assistants):
    """Calculate monthly metrics for all assistants for visualization."""
    identifier_to_name = {assistant.get('github_identifier'): assistant.get('name') for assistant in assistants if assistant.get('github_identifier')}

    if not all_metadata_dict:
        return {'assistants': [], 'months': [], 'data': {}}
    
    agent_month_data = defaultdict(lambda: defaultdict(list))

    for agent_identifier, metadata_list in all_metadata_dict.items():
        for issue_meta in metadata_list:
            created_at = issue_meta.get('created_at')

            if not created_at:
                continue

            agent_name = identifier_to_name.get(agent_identifier, agent_identifier)

            try:
                dt = datetime.fromisoformat(created_at.replace('Z', '+00:00'))                                
                month_key = f"{dt.year}-{dt.month:02d}"
                agent_month_data[agent_name][month_key].append(issue_meta)
            except Exception as e:
                print(f"Warning: Could not parse date '{created_at}': {e}")
                continue

    all_months = set()
    for agent_data in agent_month_data.values():
        all_months.update(agent_data.keys())
    months = sorted(list(all_months))

    result_data = {}
    for agent_name, month_dict in agent_month_data.items():
        resolved_rates = []
        total_issues_list = []
        resolved_issues_list = []
        closed_issues_list = []

        for month in months:
            issues_in_month = month_dict.get(month, [])

            resolved_count = sum(1 for issue in issues_in_month if issue.get('state_reason') == 'completed')
            closed_count = sum(1 for issue in issues_in_month if issue.get('closed_at'))
            total_count = len(issues_in_month)

            # Resolved rate = resolved / closed (not resolved / total)
            resolved_rate = (resolved_count / closed_count * 100) if closed_count > 0 else 0

            resolved_rates.append(resolved_rate)
            total_issues_list.append(total_count)
            resolved_issues_list.append(resolved_count)
            closed_issues_list.append(closed_count)

        result_data[agent_name] = {
            'resolved_rates': resolved_rates,
            'total_issues': total_issues_list,
            'resolved_issues': resolved_issues_list,
            'closed_issues': closed_issues_list
        }

    agents_list = sorted(list(agent_month_data.keys()))

    return {
        'assistants': agents_list,
        'months': months,
        'data': result_data
    }


def calculate_discussion_stats_from_metadata(metadata_list):
    """Calculate statistics from a list of discussion metadata."""
    total_discussions = len(metadata_list)
    resolved = sum(1 for discussion_meta in metadata_list if discussion_meta.get('state') == 'resolved')

    # Resolved rate = resolved / total * 100
    resolved_rate = (resolved / total_discussions * 100) if total_discussions > 0 else 0

    return {
        'total_discussions': total_discussions,
        'resolved_discussions': resolved,
        'discussion_resolved_rate': round(resolved_rate, 2),
    }


def calculate_monthly_metrics_by_discussions(all_discussions_dict, assistants):
    """Calculate monthly metrics for discussions for all assistants for visualization."""
    identifier_to_name = {assistant.get('github_identifier'): assistant.get('name') for assistant in assistants if assistant.get('github_identifier')}

    if not all_discussions_dict:
        return {'assistants': [], 'months': [], 'data': {}}

    agent_month_data = defaultdict(lambda: defaultdict(list))

    for agent_identifier, metadata_list in all_discussions_dict.items():
        for discussion_meta in metadata_list:
            created_at = discussion_meta.get('created_at')

            if not created_at:
                continue

            agent_name = identifier_to_name.get(agent_identifier, agent_identifier)

            try:
                dt = datetime.fromisoformat(created_at.replace('Z', '+00:00'))
                month_key = f"{dt.year}-{dt.month:02d}"
                agent_month_data[agent_name][month_key].append(discussion_meta)
            except Exception as e:
                print(f"Warning: Could not parse discussion date '{created_at}': {e}")
                continue

    all_months = set()
    for agent_data in agent_month_data.values():
        all_months.update(agent_data.keys())
    months = sorted(list(all_months))

    result_data = {}
    for agent_name, month_dict in agent_month_data.items():
        resolved_rates = []
        total_discussions_list = []
        resolved_discussions_list = []

        for month in months:
            discussions_in_month = month_dict.get(month, [])

            resolved_count = sum(1 for discussion in discussions_in_month if discussion.get('state') == 'resolved')
            total_count = len(discussions_in_month)

            # Resolved rate = resolved / total * 100
            resolved_rate = (resolved_count / total_count * 100) if total_count > 0 else 0

            resolved_rates.append(resolved_rate)
            total_discussions_list.append(total_count)
            resolved_discussions_list.append(resolved_count)

        result_data[agent_name] = {
            'resolved_rates': resolved_rates,
            'total_discussions': total_discussions_list,
            'resolved_discussions': resolved_discussions_list
        }

    agents_list = sorted(list(agent_month_data.keys()))

    return {
        'assistants': agents_list,
        'months': months,
        'data': result_data
    }


def calculate_monthly_metrics_by_wanted_issues(wanted_resolved_dict, wanted_in_progress_dict, assistants):
    """Calculate monthly metrics for wanted issues for all assistants for visualization.
    """
    identifier_to_name = {assistant.get('github_identifier'): assistant.get('name') for assistant in assistants if assistant.get('github_identifier')}

    if not wanted_resolved_dict and not wanted_in_progress_dict:
        return {'assistants': [], 'months': [], 'data': {}}

    # Calculate the valid date range for filtering
    current_time = datetime.now(timezone.utc)
    start_date = current_time - timedelta(days=LEADERBOARD_TIME_FRAME_DAYS)
    start_month_key = f"{start_date.year}-{start_date.month:02d}"
    current_month_key = f"{current_time.year}-{current_time.month:02d}"

    agent_month_data = defaultdict(lambda: defaultdict(lambda: {'resolved': 0, 'in_progress': 0}))

    # Process resolved wanted issues - group by closed_at date
    for agent_identifier, metadata_list in (wanted_resolved_dict or {}).items():
        for issue_meta in metadata_list:
            closed_at = issue_meta.get('closed_at')

            if not closed_at or closed_at == 'N/A':
                continue

            agent_name = identifier_to_name.get(agent_identifier, agent_identifier)

            try:
                dt = datetime.fromisoformat(closed_at.replace('Z', '+00:00'))

                # Skip if closed before the timeframe window
                if dt < start_date:
                    continue

                month_key = f"{dt.year}-{dt.month:02d}"
                agent_month_data[agent_name][month_key]['resolved'] += 1
            except Exception as e:
                print(f"Warning: Could not parse wanted issue date '{closed_at}': {e}")
                continue

    # Process in-progress wanted issues - count in current month (ongoing work)
    for agent_identifier, metadata_list in (wanted_in_progress_dict or {}).items():
        agent_name = identifier_to_name.get(agent_identifier, agent_identifier)
        # All in-progress issues count towards the current month
        agent_month_data[agent_name][current_month_key]['in_progress'] += len(metadata_list)

    all_months = set()
    for agent_data in agent_month_data.values():
        all_months.update(agent_data.keys())

    # Filter to only include months within the timeframe
    months = sorted([m for m in all_months if m >= start_month_key])

    result_data = {}
    for agent_name, month_dict in agent_month_data.items():
        resolved_rates = []
        total_wanted_list = []
        resolved_wanted_list = []

        for month in months:
            month_data = month_dict.get(month, {'resolved': 0, 'in_progress': 0})
            resolved_count = month_data['resolved']
            in_progress_count = month_data['in_progress']
            total_count = resolved_count + in_progress_count

            # Resolved rate = resolved / total * 100
            resolved_rate = (resolved_count / total_count * 100) if total_count > 0 else 0

            resolved_rates.append(resolved_rate)
            total_wanted_list.append(total_count)
            resolved_wanted_list.append(resolved_count)

        result_data[agent_name] = {
            'resolved_rates': resolved_rates,
            'total_wanted': total_wanted_list,
            'resolved_wanted': resolved_wanted_list
        }

    agents_list = sorted(list(agent_month_data.keys()))

    return {
        'assistants': agents_list,
        'months': months,
        'data': result_data
    }


def construct_leaderboard_from_metadata(all_metadata_dict, assistants, wanted_resolved_dict=None, wanted_in_progress_dict=None, discussions_dict=None):
    """Construct leaderboard from in-memory issue metadata and discussion metadata.

    Args:
        all_metadata_dict: Dictionary mapping assistant ID to list of issue metadata (assistant-assigned issues)
        assistants: List of assistant metadata
        wanted_resolved_dict: Optional dictionary mapping assistant ID to list of resolved wanted issues
        wanted_in_progress_dict: Optional dictionary mapping assistant ID to list of in-progress wanted issues
        discussions_dict: Optional dictionary mapping assistant ID to list of discussion metadata
    """
    if not assistants:
        print("Error: No assistants found")
        return {}

    if wanted_resolved_dict is None:
        wanted_resolved_dict = {}

    if wanted_in_progress_dict is None:
        wanted_in_progress_dict = {}

    if discussions_dict is None:
        discussions_dict = {}

    cache_dict = {}

    for assistant in assistants:
        identifier = assistant.get('github_identifier')
        agent_name = assistant.get('name', 'Unknown')

        bot_data = all_metadata_dict.get(identifier, [])
        stats = calculate_issue_stats_from_metadata(bot_data)

        # Add wanted issues stats
        resolved_wanted = len(wanted_resolved_dict.get(identifier, []))
        in_progress_wanted = len(wanted_in_progress_dict.get(identifier, []))
        total_wanted = resolved_wanted + in_progress_wanted
        wanted_resolved_rate = (resolved_wanted / total_wanted * 100) if total_wanted > 0 else 0.0

        # Add discussion stats
        discussion_metadata = discussions_dict.get(identifier, [])
        discussion_stats = calculate_discussion_stats_from_metadata(discussion_metadata)

        cache_dict[identifier] = {
            'name': agent_name,
            'website': assistant.get('website', 'N/A'),
            'github_identifier': identifier,
            **stats,
            'total_wanted_issues': total_wanted,
            'resolved_wanted_issues': resolved_wanted,
            'wanted_issue_resolved_rate': round(wanted_resolved_rate, 2),
            **discussion_stats
        }

    return cache_dict


def save_leaderboard_data_to_hf(leaderboard_dict, issue_monthly_metrics, wanted_issues=None, discussion_monthly_metrics=None, wanted_issue_monthly_metrics=None):
    """Save leaderboard data, monthly metrics, wanted issues, and discussion metrics to HuggingFace dataset."""
    try:
        token = get_hf_token()
        if not token:
            raise Exception("No HuggingFace token found")

        api = HfApi(token=token)

        if wanted_issues is None:
            wanted_issues = []

        combined_data = {
            'metadata': {
                'last_updated': datetime.now(timezone.utc).isoformat(),
                'leaderboard_time_frame_days': LEADERBOARD_TIME_FRAME_DAYS,
                'longstanding_gap_days': LONGSTANDING_GAP_DAYS,
                'tracked_orgs': TRACKED_ORGS,
                'patch_wanted_labels': PATCH_WANTED_LABELS
            },
            'leaderboard': leaderboard_dict,
            'issue_monthly_metrics': issue_monthly_metrics,
            'wanted_issues': wanted_issues,
            'wanted_issue_monthly_metrics': wanted_issue_monthly_metrics,
            'discussion_monthly_metrics': discussion_monthly_metrics
        }

        with open(LEADERBOARD_FILENAME, 'w') as f:
            json.dump(combined_data, f, indent=2)

        try:
            upload_file_with_backoff(
                api=api,
                path_or_fileobj=LEADERBOARD_FILENAME,
                path_in_repo=LEADERBOARD_FILENAME,
                repo_id=LEADERBOARD_REPO,
                repo_type="dataset"
            )
            return True
        finally:
            if os.path.exists(LEADERBOARD_FILENAME):
                os.remove(LEADERBOARD_FILENAME)

    except Exception as e:
        print(f"Error saving leaderboard data: {str(e)}")
        traceback.print_exc()
        return False


# =============================================================================
# MINING FUNCTION
# =============================================================================

def mine_all_agents():
    """
    Mine issue metadata for all assistants using STREAMING batch processing.
    Downloads GHArchive data, then uses BATCH-based DuckDB queries.
    """
    print(f"\n[1/4] Downloading GHArchive data...")

    if not download_all_gharchive_data():
        print("Warning: Download had errors, continuing with available data...")

    print(f"\n[2/4] Loading assistant metadata...")

    assistants = load_agents_from_hf()
    if not assistants:
        print("Error: No assistants found")
        return

    identifiers = [assistant['github_identifier'] for assistant in assistants if assistant.get('github_identifier')]
    if not identifiers:
        print("Error: No valid assistant identifiers found")
        return

    print(f"\n[3/4] Mining issue metadata ({len(identifiers)} assistants, {LEADERBOARD_TIME_FRAME_DAYS} days)...")

    try:
        conn = get_duckdb_connection()
    except Exception as e:
        print(f"Failed to initialize DuckDB connection: {str(e)}")
        return

    current_time = datetime.now(timezone.utc)
    end_date = current_time.replace(hour=0, minute=0, second=0, microsecond=0)
    start_date = end_date - timedelta(days=LEADERBOARD_TIME_FRAME_DAYS)

    try:
        # USE UNIFIED STREAMING FUNCTION FOR ISSUES, WANTED, AND DISCUSSIONS
        results = fetch_all_metadata_streaming(
            conn, identifiers, start_date, end_date
        )

        agent_issues = results['agent_issues']
        wanted_open = results['wanted_open']
        wanted_resolved = results['wanted_resolved']
        wanted_in_progress = results['wanted_in_progress']
        agent_discussions = results['agent_discussions']
    except Exception as e:
        print(f"Error during DuckDB fetch: {str(e)}")
        traceback.print_exc()
        return
    finally:
        conn.close()

    print(f"\n[4/4] Saving leaderboard...")

    try:
        leaderboard_dict = construct_leaderboard_from_metadata(
            agent_issues, assistants, wanted_resolved, wanted_in_progress, agent_discussions
        )
        issue_monthly_metrics = calculate_monthly_metrics_by_issues(agent_issues, assistants)
        discussion_monthly_metrics = calculate_monthly_metrics_by_discussions(
            agent_discussions, assistants
        )
        wanted_issue_monthly_metrics = calculate_monthly_metrics_by_wanted_issues(
            wanted_resolved, wanted_in_progress, assistants
        )
        save_leaderboard_data_to_hf(
            leaderboard_dict, issue_monthly_metrics, wanted_open, discussion_monthly_metrics, wanted_issue_monthly_metrics
        )
    except Exception as e:
        print(f"Error saving leaderboard: {str(e)}")
        traceback.print_exc()
    finally:
        # Clean up DuckDB cache file to save storage
        if os.path.exists(DUCKDB_CACHE_FILE):
            try:
                os.remove(DUCKDB_CACHE_FILE)
                print(f"   ✓ Cache file removed: {DUCKDB_CACHE_FILE}")
            except Exception as e:
                print(f"   ⚠ Failed to remove cache file: {str(e)}")

# =============================================================================
# ENTRY POINT
# =============================================================================

if __name__ == "__main__":
    mine_all_agents()