import warnings
from datetime import datetime

import dagster as dg
from dagster import EnvVar
from dagster_snowflake import SnowflakeResource

# Using asset key references to avoid import chain issues
from src.utils.automation_conditions import hourly_cron_with_eager_historical_backfill_condition
from src.utils.snowflake.constants import TIME_WINDOW_FRESHNESS_POLICY_WARN_1H_FAIL_2H, PartitionExpr, Warehouse
from src.utils.snowflake.query import JinjaSQLFormatter, PythonStringSQLFormatter

warnings.filterwarnings("ignore", category=dg.BetaWarning)

DIM_HOOK_START_DATE = datetime.strptime('2025-05-12', '%Y-%m-%d')
DIM_HOOK_TABLE_NAME = "DIM_HOOK"


class DimHookConfig(dg.Config):
    dim_hook_table_name: str = DIM_HOOK_TABLE_NAME
    warehouse: str = Warehouse.DIM_HOOK_MEDIUM.value


@dg.asset(
    name="dim_hook",
    description="Dimension table for hook/video metadata and properties, including AI-generated features from Gemini analysis.",
    group_name="hooks",
    partitions_def=dg.HourlyPartitionsDefinition(start_date=DIM_HOOK_START_DATE, end_offset=-1),
    deps=[
        dg.AssetDep("rds_video_hook"),
        dg.AssetDep("bk_nonrealtime_event"),
        dg.AssetDep("app_event"),
        dg.AssetDep("ddb_item_info"),
        dg.AssetDep(["PROD", "dim_clip"]),
    ],
    backfill_policy=dg.BackfillPolicy.multi_run(max_partitions_per_run=24*7),
    owners=["team:core-pod"],
    metadata={
        "database": EnvVar("SNOWFLAKE_DB").get_value(),
        "schema": EnvVar("SNOWFLAKE_SCHEMA").get_value(),
        "table_name": DIM_HOOK_TABLE_NAME,
        "data_start_date": DIM_HOOK_START_DATE.strftime("%Y-%m-%d"),
        "cluster_by": "[p_date, p_hour]",
        "partition_expr": PartitionExpr.HOURLY.value,
        "transient": True,
        "sla_minutes": 120,
    },
    automation_condition=hourly_cron_with_eager_historical_backfill_condition,
    freshness_policy=TIME_WINDOW_FRESHNESS_POLICY_WARN_1H_FAIL_2H,
)
def dim_hook(context: dg.AssetExecutionContext, snowflake: SnowflakeResource, config: DimHookConfig) -> dg.MaterializeResult:
    run_id = context.run.run_id
    logger = dg.get_dagster_logger()
    jinja_formatter = JinjaSQLFormatter()
    python_formatter = PythonStringSQLFormatter()

    # Get partition time window for processing
    partition_start = context.partition_time_window.start
    partition_end = context.partition_time_window.end

    fetch_params = {
        "partition_start_date": partition_start.strftime("%Y-%m-%d"),
        "partition_end_date": partition_end.strftime("%Y-%m-%d"),
        "partition_start_hour": partition_start.hour,
        "partition_end_hour": partition_end.hour,
        "dim_hook_table_name": config.dim_hook_table_name,
    }

    logger.info(f"Processing dim_hook for partition: {partition_start} to {partition_end}")
    logger.info(f"Fetch params: {fetch_params}")

    with snowflake.get_connection() as conn:
        cursor = conn.cursor()
        logger.info(f"Using warehouse {config.warehouse}")
        warehouse_query = python_formatter.load("src/utils/snowflake/queries/use_warehouse.sql", params={"warehouse": config.warehouse}, logger=logger)
        cursor.execute(warehouse_query)

        # 1. Ensure the target table exists
        logger.info(f"Creating table {config.dim_hook_table_name}...")
        create_table_query = jinja_formatter.load("src/assets/snowflake/dim/hook/table.sql", params=fetch_params, logger=logger)
        cursor.execute(create_table_query)

        # 2. Delete existing data from the target table for the partition window
        logger.info(f"Deleting existing data from {config.dim_hook_table_name} for partition window {partition_start} to {partition_end}.")
        delete_query = python_formatter.load("src/utils/snowflake/queries/delete_hourly_partitions.sql", params={**fetch_params, "delete_partition_table_name": config.dim_hook_table_name}, logger=logger)
        cursor.execute(delete_query)

        # 3. Insert new hook dimension data
        logger.info(f"Inserting new hook dimension data into {config.dim_hook_table_name}.")
        insert_query = jinja_formatter.load("src/assets/snowflake/dim/hook/insert_dim_hook.sql", params=fetch_params, logger=logger)
        cursor.execute(insert_query)

        # 4. Commit the transaction
        conn.commit()
        rows_inserted = cursor.rowcount

    logger.info(f"Successfully processed partition. Inserted {rows_inserted} rows.")

    return dg.MaterializeResult(
        metadata={
            "run_id": dg.MetadataValue.text(run_id),
            "table_name": config.dim_hook_table_name,
            "partition_time_window_start": dg.MetadataValue.text(partition_start.isoformat()),
            "partition_time_window_end": dg.MetadataValue.text(partition_end.isoformat()),
            "dagster/row_count": rows_inserted if not context.has_partition_key_range else 0,
        },
    )


# dim_hook_hourly_insert = dg.define_asset_job(
#     name="dim_hook_hourly_insert",
#     description="Hourly job to insert dim_hook asset data",
#     selection=dg.AssetSelection.assets(dim_hook),
#     partitions_def=dg.HourlyPartitionsDefinition(start_date=DIM_HOOK_START_DATE, end_offset=-2),
#     tags={"team": "core-pod", "domain": "dimensions", "type": "asset-based"},
# )

# # Hourly schedule for the job
# dim_hook_hourly_schedule = dg.ScheduleDefinition(
#     name="dim_hook_hourly_schedule",
#     cron_schedule="0 * * * *",  # Every hour at minute 0
#     job=dim_hook_hourly_insert,
#     description="Schedule to run dim_hook_hourly_insert every hour",
#     tags={"team": "core-pod", "cadence": "hourly", "domain": "dimensions"},
# )