import re
import subprocess
from itertools import combinations
from pathlib import Path

import pandas as pd

CSV_DIR = None
while CSV_DIR is None:
    user_input = input("Enter the full path to the CSV directory: ").strip()
    if not user_input:
        continue
    csv_path = Path(user_input)
    if not csv_path.is_dir():
        continue
    CSV_DIR = csv_path

OUTPUT_FILE = Path("./schema_comparison_output.txt")
N_ROWS = 1000
SAMPLE_ROWS = 5

output_lines = []


def output(*args, **kwargs):
    line = " ".join(str(a) for a in args)
    print(line, **kwargs)
    output_lines.append(line)


def extract_date(filename: str) -> str:
    match = re.match(r"^(.*?_\d{2}_\d{2}_\d{4})", filename)
    return match.group(1) if match else None


def get_row_count(filepath: Path) -> int:
    result = subprocess.run(
        ["wc", "-l", str(filepath)],
        capture_output=True,
        text=True,
    )
    return int(result.stdout.strip().split()[0]) - 1


def load_schema(filepath: Path, nrows: int = N_ROWS) -> tuple[dict, pd.DataFrame]:
    try:
        df = pd.read_csv(filepath, nrows=nrows)
    except Exception as e:
        output(f"Warning: Could not read {filepath.name}: {e}")
        return {}, pd.DataFrame()
    schema = dict(df.dtypes)
    return schema, df


def compare_schemas(
    schema1: dict,
    df1: pd.DataFrame,
    name1: str,
    schema2: dict,
    df2: pd.DataFrame,
    name2: str,
):
    cols1 = set(schema1.keys())
    cols2 = set(schema2.keys())

    missing_in_1 = cols2 - cols1
    missing_in_2 = cols1 - cols2
    common_cols = cols1 & cols2

    dtype_mismatches = {}
    for col in common_cols:
        if str(schema1[col]) != str(schema2[col]):
            dtype_mismatches[col] = (schema1[col], schema2[col])

    if missing_in_1 or missing_in_2 or dtype_mismatches:
        return False, missing_in_1, missing_in_2, dtype_mismatches
    return True, None, None, None


def print_mismatch(
    name1: str,
    name2: str,
    missing_in_1: set,
    missing_in_2: set,
    dtype_mismatches: dict,
    df1: pd.DataFrame,
    df2: pd.DataFrame,
):
    output(f"\n{'=' * 60}")
    output(f"MISMATCH: {name1} <-> {name2}")
    output(f"{'=' * 60}")

    if missing_in_1:
        output(f"\nColumns in {name2} but not in {name1}: {missing_in_1}")
    if missing_in_2:
        output(f"\nColumns in {name1} but not in {name2}: {missing_in_2}")

    if dtype_mismatches:
        output("\n--- DTYPE MISMATCHES ---")
        for col, (dt1, dt2) in dtype_mismatches.items():
            output(f"  {col}: {name1}={dt1}, {name2}={dt2}")

        output("\n--- SAMPLE DATA FOR MISMATCHED COLUMNS ---")
        for col in dtype_mismatches.keys():
            output(f"\n--- {col} ---")
            output(f"{name1} ({SAMPLE_ROWS} rows):")
            output(df1[col].head(SAMPLE_ROWS).to_list())
            output(f"{name2} ({SAMPLE_ROWS} rows):")
            output(df2[col].head(SAMPLE_ROWS).to_list())


def main():
    global output_lines

    csv_files = list(CSV_DIR.glob("*.csv"))

    date_groups = {}
    for f in csv_files:
        date = extract_date(f.name)
        if date:
            date_groups.setdefault(date, []).append(f)

    file_stats = {}

    for date, files in sorted(date_groups.items()):
        if len(files) < 2:
            output(f"\nSkipping {date} (only 1 file: {files[0].name})")
            for f in files:
                schema, df = load_schema(f)
                file_stats[f.name] = {
                    "row_count": get_row_count(f),
                    "col_count": len(df.columns),
                }
            continue

        output(f"\n{'#' * 60}")
        output(f"DATE GROUP: {date}")

        row_counts = {}
        schemas = {}
        dataframes = {}

        for f in files:
            row_counts[f.name] = get_row_count(f)
            file_stats[f.name] = {
                "row_count": row_counts[f.name],
                "col_count": 0,
            }
            schema, df = load_schema(f)
            schemas[f.name] = schema
            dataframes[f.name] = df
            file_stats[f.name]["col_count"] = len(df.columns)

        file_info = ", ".join(f"{f.name} ({row_counts[f.name]:,} rows)" for f in files)
        output(f"Files: {file_info}")
        output(f"{'#' * 60}")

        has_mismatch = False
        for f1, f2 in combinations(files, 2):
            name1, name2 = f1.name, f2.name
            is_match, missing_in_1, missing_in_2, dtype_mismatches = compare_schemas(
                schemas[name1],
                dataframes[name1],
                name1,
                schemas[name2],
                dataframes[name2],
                name2,
            )

            if not is_match:
                has_mismatch = True
                print_mismatch(
                    name1,
                    name2,
                    missing_in_1,
                    missing_in_2,
                    dtype_mismatches,
                    dataframes[name1],
                    dataframes[name2],
                )

        if not has_mismatch:
            output("\nAll files in this group have matching schemas.")

    output(f"\n{'#' * 60}")
    output("FILE STATISTICS")
    output(f"{'#' * 60}")
    output(f"\n{'File':<60} {'Rows':>12} {'Columns':>10}")
    output("-" * 82)
    grand_total_rows = 0
    for name, stats in sorted(file_stats.items()):
        output(f"{name:<60} {stats['row_count']:>12,} {stats['col_count']:>10}")
        grand_total_rows += stats["row_count"]
    output("-" * 82)
    output(f"{'TOTAL':<60} {grand_total_rows:>12,}")

    OUTPUT_FILE.write_text("\n".join(output_lines))
    print(f"\nOutput saved to {OUTPUT_FILE}")


if __name__ == "__main__":
    main()