import os
from getpass import getpass

import pandas as pd
from sqlalchemy import create_engine, text


def build_url(prefix: str) -> str:
    user = os.getenv(f"{prefix}_PG_USER", "log_admin")
    password = os.getenv(f"{prefix}_PG_PASSWORD")
    if not password:
        password = getpass(f"{prefix} database password: ")
    host = os.getenv(f"{prefix}_PG_HOST", "blv-rd.tech")
    port = os.getenv(f"{prefix}_PG_PORT", "15433")
    default_db = "log_platform" if prefix == "SOURCE" else "test"
    database = os.getenv(f"{prefix}_PG_DB", default_db)
    if not database:
        raise ValueError(f"{prefix}_PG_DB is required")
    return f"postgresql+psycopg2://{user}:{password}@{host}:{port}/{database}"


LOOPS_COLUMNS = [
    "id",
    "loop_name",
    "room_type_id",
    "loop_address",
    "loop_type",
    "type",
    "name",
    "power",
    "rate",
    "temperature",
    "air_type",
    "air_brand",
    "air_model",
    "height",
    "area",
    "heat_loss",
    "remark",
]


def create_target_table(target_conn, target_schema: str, target_table: str):
    target_conn.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"'))
    target_conn.execute(text(f'DROP TABLE IF EXISTS "{target_schema}"."{target_table}"'))

    create_sql = f'''
        CREATE TABLE "{target_schema}"."{target_table}" (
          "id" int4 NOT NULL,
          "loop_name" varchar(255),
          "room_type_id" int4 NOT NULL,
          "loop_address" varchar(255),
          "loop_type" varchar(50),
          "type" varchar(254),
          "name" varchar(254),
          "power" float8,
          "rate" float8,
          "temperature" float8,
          "air_type" varchar(254),
          "air_brand" varchar(254),
          "air_model" varchar(254),
          "height" float8,
          "area" float8,
          "heat_loss" float8,
          "remark" varchar(254),
          "hotel_name" varchar(255),
          "room_type_name" varchar(255),
          "room_name" varchar(255)
        )
    '''
    target_conn.execute(text(create_sql))


def main():
    source_schema = os.getenv("SOURCE_SCHEMA", "temporary_project")
    target_schema = os.getenv("TARGET_SCHEMA", "wh_test")
    target_table = os.getenv("TARGET_TABLE", "loops_type01_enriched")

    # 默认源库：log_platform；默认目标库：test
    source_engine = create_engine(build_url("SOURCE"))
    target_engine = create_engine(build_url("TARGET"))

    with source_engine.connect() as source_conn:
        stats_sql = f'''
            SELECT
                count(*) AS total_rows,
                count(*) FILTER (WHERE "type" IN ('0', '1')) AS exact_match_rows,
                count(*) FILTER (WHERE trim(coalesce("type", '')) IN ('0', '1')) AS normalized_match_rows
            FROM "{source_schema}"."loops"
        '''
        stats = source_conn.execute(text(stats_sql)).mappings().first()

        dist_sql = f'''
            SELECT
                "type",
                count(*) AS cnt
            FROM "{source_schema}"."loops"
            GROUP BY "type"
            ORDER BY cnt DESC, "type" NULLS FIRST
            LIMIT 20
        '''
        type_dist = source_conn.execute(text(dist_sql)).mappings().all()

        loops_select = ",\n                ".join([f'l."{c}"' for c in LOOPS_COLUMNS])

        data_sql = f'''
            SELECT
                {loops_select},
                h."hotel_name" AS hotel_name,
                rt."room_type_name" AS room_type_name,
                NULL::varchar(255) AS room_name
            FROM "{source_schema}"."loops" l
            LEFT JOIN "{source_schema}"."room_type" rt
                ON rt."id" = l."room_type_id"
            LEFT JOIN "{source_schema}"."hotels" h
                ON h."id" = rt."hotel_id"
            WHERE trim(coalesce(l."type", '')) IN ('0', '1')
        '''
        df = pd.read_sql(text(data_sql), source_conn)

    with target_engine.begin() as target_conn:
        create_target_table(target_conn, target_schema, target_table)
        df.to_sql(target_table, target_conn, schema=target_schema, if_exists="append", index=False)

    print(f"Inserted rows: {len(df)}")
    print(f"Target table: {target_schema}.{target_table}")
    print(
        "Source counts: "
        f"total={stats['total_rows']}, "
        f"exact_type_0_1={stats['exact_match_rows']}, "
        f"normalized_type_0_1={stats['normalized_match_rows']}"
    )
    print("Top 20 type distribution:")
    for row in type_dist:
        print(f"type={row['type']!r}, count={row['cnt']}")


if __name__ == "__main__":
    main()