import sys
import requests
import pandas as pd
import json
import re
import argparse
import urllib.parse
from bech32 import bech32_decode

# Column names in the input CSV
PLACEHOLDER_COLUMN = 'Placeholder'
LACONIC_ADDRESS_COLUMN = 'Laconic Address'
TOTAL_LPS_ALLOCATION_COLUMN = 'Total LPS Allocation'
LOCK_MONTHS_COLUMN = 'Lock (months)'
VEST_MONTHS_COLUMN = 'Vest (months)'

# Required columns in the input CSV
REQUIRED_COLUMNS = [
    PLACEHOLDER_COLUMN,
    LACONIC_ADDRESS_COLUMN,
    TOTAL_LPS_ALLOCATION_COLUMN,
    LOCK_MONTHS_COLUMN,
    VEST_MONTHS_COLUMN
]

def to_number(val):
    """
    Convert a value to a number, handling empty values and invalid inputs.
    Returns None for empty or invalid values.
    """
    if pd.isna(val) or str(val).strip() == '':
        return None
    try:
        return float(val)
    except (ValueError, TypeError):
        return None

def get_csv_download_url(google_sheet_url):
    """
    Convert a full Google Sheets URL to a CSV export URL using the `gid` in the query string.
    """
    # Extract the sheet ID
    match = re.search(r'/d/([a-zA-Z0-9-_]+)', google_sheet_url)
    if not match:
        raise ValueError('Invalid Google Sheets URL')
    sheet_id = match.group(1)

    # Extract gid from query params
    gid_match = re.search(r'[?&]gid=([0-9]+)', google_sheet_url)
    if not gid_match:
        raise ValueError('Missing gid in Google Sheets URL')
    gid = gid_match.group(1)

    # Build export URL
    return f'https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=csv&gid={gid}'

def download_csv(url, output_path):
    """
    Download the CSV file from the given URL.
    """
    response = requests.get(url)
    if response.status_code != 200:
        raise Exception(f'Failed to download file: {response.status_code}')
    with open(output_path, 'wb') as f:
        f.write(response.content)

def convert_csv_to_json(csv_path, json_path):
    """
    Read the CSV file, extract columns, and save as JSON.
    """
    df = pd.read_csv(csv_path)
    for col in REQUIRED_COLUMNS:
        if col not in df.columns:
            raise Exception(f'Missing required column: {col}')

    result = {}
    for _, row in df.iterrows():
        placeholder = str(row[PLACEHOLDER_COLUMN]) if not pd.isna(row[PLACEHOLDER_COLUMN]) else ''
        laconic_address = str(row[LACONIC_ADDRESS_COLUMN]) if not pd.isna(row[LACONIC_ADDRESS_COLUMN]) else ''

        # Use laconic_address as key if placeholder is missing or empty
        key = placeholder if placeholder and placeholder.lower() != 'nan' else laconic_address

        # Skip the row if both 'Placeholder' and 'Laconic Address' are missing or invalid
        if not key or key.lower() == 'nan':
            continue

        # If key is the laconic address, validate that it's a valid bech32 address
        if key == laconic_address:
            hrp, data = bech32_decode(laconic_address)
            if hrp is None or data is None or not hrp.startswith("laconic"):
                print(f"Skipping invalid Laconic address: {laconic_address}")
                continue

        entry = {
            'total_lps_allocation': to_number(row[TOTAL_LPS_ALLOCATION_COLUMN]),
            'lock_months': row[LOCK_MONTHS_COLUMN] if not pd.isna(row[LOCK_MONTHS_COLUMN]) else None,
            'vest_months': row[VEST_MONTHS_COLUMN] if not pd.isna(row[VEST_MONTHS_COLUMN]) else None,
            'laconic_address': row[LACONIC_ADDRESS_COLUMN] if not pd.isna(row[LACONIC_ADDRESS_COLUMN]) else None
        }

        result[key] = entry

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

def main():
    parser = argparse.ArgumentParser(description='Generate LPS distribution JSON from CSV or Google Sheet')
    parser.add_argument('--input', '-i', required=True, help='Input: Google Sheet URL or local CSV file path')
    parser.add_argument('--output', '-o', default='distribution.json', help='Output JSON file path (default: distribution.json)')
    
    args = parser.parse_args()
    
    if args.input.startswith('https://'):
        csv_url = get_csv_download_url(args.input)
        csv_path = 'sheet.csv'
        print(f'Downloading CSV file from: {csv_url}')
        download_csv(csv_url, csv_path)
    else:
        csv_path = args.input
        print(f'Using CSV file at path: {csv_path}')
    
    print('Converting CSV to JSON...')
    convert_csv_to_json(csv_path, args.output)
    print(f'JSON saved to {args.output}')

if __name__ == '__main__':
    main()