"""Queries y operaciones de datos para Google BigQuery."""

from .client import BQClient
from google.cloud import bigquery


def bq_query(
    client: BQClient,
    sql: str,
    params: list[dict] | None = None,
    dry_run: bool = False,
) -> dict:
    """Ejecuta una query SQL en BigQuery con soporte para parametros y dry-run.

    Si dry_run=True, estima el costo sin ejecutar la query. Si params no es
    None, los convierte a ScalarQueryParameter usando la sintaxis @nombre en SQL.

    Args:
        client: Cliente autenticado de BigQuery.
        sql: Query SQL a ejecutar. Usar @nombre para referencias a parametros.
        params: Lista de parametros. Cada elemento: {"name": "x", "type": "STRING", "value": "hello"}.
                Tipos soportados: STRING, INT64, FLOAT64, BOOL, DATE, TIMESTAMP.
        dry_run: Si True, retorna estimacion de bytes sin ejecutar.

    Returns:
        Si dry_run=True: {"total_bytes_processed": N, "total_bytes_billed": N}.
        Si dry_run=False: {"columns": [...], "rows": [[...], ...],
                           "total_rows": N, "bytes_processed": N, "cache_hit": bool}.

    Raises:
        google.api_core.exceptions.GoogleAPICallError: Si la query falla.
        ValueError: Si un tipo de parametro no es soportado.

    Example:
        >>> result = bq_query(client, "SELECT COUNT(*) as n FROM `project.dataset.table`")
        >>> print(result["rows"])
        >>> # Con parametros:
        >>> result = bq_query(client, "SELECT * FROM t WHERE status = @s",
        ...     params=[{"name": "s", "type": "STRING", "value": "active"}])
    """
    job_config = bigquery.QueryJobConfig()

    if params:
        query_params = []
        for p in params:
            query_params.append(
                bigquery.ScalarQueryParameter(p["name"], p["type"], p["value"])
            )
        job_config.query_parameters = query_params

    if dry_run:
        job_config.dry_run = True
        job_config.use_query_cache = False
        job = client._client.query(sql, job_config=job_config)
        return {
            "total_bytes_processed": job.total_bytes_processed,
            "total_bytes_billed": job.total_bytes_billed,
        }

    job = client._client.query(sql, job_config=job_config)
    result = job.result()

    columns = [field.name for field in result.schema]
    rows = [list(row.values()) for row in result]

    return {
        "columns": columns,
        "rows": rows,
        "total_rows": result.total_rows,
        "bytes_processed": job.total_bytes_processed,
        "cache_hit": job.cache_hit,
    }


def bq_insert_rows(
    client: BQClient,
    dataset_id: str,
    table_id: str,
    rows: list[dict],
) -> dict:
    """Inserta filas en una tabla BigQuery usando el streaming insert.

    Usa la API de streaming (insert_rows_json) para insercion en tiempo real.
    Los errores de filas individuales se retornan en el campo "errors" sin
    lanzar excepcion — revisar siempre ese campo.

    Args:
        client: Cliente autenticado de BigQuery.
        dataset_id: ID del dataset de destino.
        table_id: ID de la tabla de destino.
        rows: Lista de dicts con los datos a insertar. Cada dict debe tener
              claves que coincidan con las columnas de la tabla.

    Returns:
        {"inserted": N, "errors": [...]} donde errors es la lista de errores
        de streaming insert retornada por la API (vacia si todo OK).

    Raises:
        google.api_core.exceptions.NotFound: Si la tabla no existe.
        google.api_core.exceptions.GoogleAPICallError: Si la API falla.

    Example:
        >>> result = bq_insert_rows(client, "my_dataset", "my_table", [
        ...     {"id": 1, "name": "Alice", "created_at": "2024-01-01"},
        ...     {"id": 2, "name": "Bob",   "created_at": "2024-01-02"},
        ... ])
        >>> if result["errors"]:
        ...     print("Errores:", result["errors"])
    """
    table_ref = client._client.dataset(dataset_id).table(table_id)
    errors = client._client.insert_rows_json(table_ref, rows)
    return {
        "inserted": len(rows) - len(errors),
        "errors": errors,
    }


def bq_load_from_gcs(
    client: BQClient,
    uri: str | list[str],
    dataset_id: str,
    table_id: str,
    source_format: str = "CSV",
    write_disposition: str = "WRITE_APPEND",
    autodetect: bool = True,
    skip_leading_rows: int = 0,
) -> dict:
    """Carga datos desde Google Cloud Storage a una tabla BigQuery.

    Configura y ejecuta un LoadJob desde uno o varios URIs de GCS. Espera
    la finalizacion del job con job.result().

    Args:
        client: Cliente autenticado de BigQuery.
        uri: URI de GCS. Puede ser un string ("gs://bucket/file.csv") o una
             lista de strings para multiples archivos.
        dataset_id: ID del dataset de destino.
        table_id: ID de la tabla de destino.
        source_format: Formato del archivo: "CSV", "NEWLINE_DELIMITED_JSON",
                       "AVRO", "PARQUET", "ORC". Default: "CSV".
        write_disposition: Comportamiento si la tabla existe: "WRITE_APPEND",
                           "WRITE_TRUNCATE", "WRITE_EMPTY". Default: "WRITE_APPEND".
        autodetect: Si True, detecta el schema automaticamente. Default: True.
        skip_leading_rows: Numero de filas a saltar al inicio (cabeceras CSV).

    Returns:
        {"job_id": str, "rows_loaded": N, "status": "DONE"|"FAILED"}.

    Raises:
        google.api_core.exceptions.GoogleAPICallError: Si el job falla.
        google.cloud.exceptions.NotFound: Si el bucket o dataset no existe.

    Example:
        >>> result = bq_load_from_gcs(
        ...     client, "gs://my-bucket/data/*.csv",
        ...     "my_dataset", "my_table",
        ...     skip_leading_rows=1,
        ... )
        >>> print(f"Cargadas {result['rows_loaded']} filas, job: {result['job_id']}")
    """
    format_map = {
        "CSV": bigquery.SourceFormat.CSV,
        "NEWLINE_DELIMITED_JSON": bigquery.SourceFormat.NEWLINE_DELIMITED_JSON,
        "AVRO": bigquery.SourceFormat.AVRO,
        "PARQUET": bigquery.SourceFormat.PARQUET,
        "ORC": bigquery.SourceFormat.ORC,
    }
    disposition_map = {
        "WRITE_APPEND": bigquery.WriteDisposition.WRITE_APPEND,
        "WRITE_TRUNCATE": bigquery.WriteDisposition.WRITE_TRUNCATE,
        "WRITE_EMPTY": bigquery.WriteDisposition.WRITE_EMPTY,
    }

    job_config = bigquery.LoadJobConfig(
        source_format=format_map.get(source_format, bigquery.SourceFormat.CSV),
        write_disposition=disposition_map.get(source_format, bigquery.WriteDisposition.WRITE_APPEND),
        autodetect=autodetect,
        skip_leading_rows=skip_leading_rows,
    )
    job_config.write_disposition = disposition_map.get(write_disposition, bigquery.WriteDisposition.WRITE_APPEND)

    table_ref = client._client.dataset(dataset_id).table(table_id)
    uris = uri if isinstance(uri, list) else [uri]

    job = client._client.load_table_from_uri(uris, table_ref, job_config=job_config)
    result = job.result()

    return {
        "job_id": job.job_id,
        "rows_loaded": result.output_rows,
        "status": "DONE" if job.state == "DONE" and not job.errors else "FAILED",
    }


def bq_load_from_file(
    client: BQClient,
    file_path: str,
    dataset_id: str,
    table_id: str,
    source_format: str = "CSV",
    write_disposition: str = "WRITE_APPEND",
    autodetect: bool = True,
    skip_leading_rows: int = 0,
) -> dict:
    """Carga datos desde un archivo local a una tabla BigQuery.

    Abre el archivo y usa load_table_from_file del SDK. Equivalente a
    bq_load_from_gcs pero para archivos en disco local.

    Args:
        client: Cliente autenticado de BigQuery.
        file_path: Ruta absoluta o relativa al archivo local a cargar.
        dataset_id: ID del dataset de destino.
        table_id: ID de la tabla de destino.
        source_format: Formato del archivo: "CSV", "NEWLINE_DELIMITED_JSON",
                       "AVRO", "PARQUET", "ORC". Default: "CSV".
        write_disposition: Comportamiento si la tabla existe: "WRITE_APPEND",
                           "WRITE_TRUNCATE", "WRITE_EMPTY". Default: "WRITE_APPEND".
        autodetect: Si True, detecta el schema automaticamente. Default: True.
        skip_leading_rows: Numero de filas a saltar al inicio (cabeceras CSV).

    Returns:
        {"job_id": str, "rows_loaded": N, "status": "DONE"|"FAILED"}.

    Raises:
        FileNotFoundError: Si el archivo local no existe.
        google.api_core.exceptions.GoogleAPICallError: Si el job falla.

    Example:
        >>> result = bq_load_from_file(
        ...     client, "/tmp/data.csv",
        ...     "my_dataset", "my_table",
        ...     skip_leading_rows=1,
        ... )
        >>> print(f"Cargadas {result['rows_loaded']} filas")
    """
    format_map = {
        "CSV": bigquery.SourceFormat.CSV,
        "NEWLINE_DELIMITED_JSON": bigquery.SourceFormat.NEWLINE_DELIMITED_JSON,
        "AVRO": bigquery.SourceFormat.AVRO,
        "PARQUET": bigquery.SourceFormat.PARQUET,
        "ORC": bigquery.SourceFormat.ORC,
    }
    disposition_map = {
        "WRITE_APPEND": bigquery.WriteDisposition.WRITE_APPEND,
        "WRITE_TRUNCATE": bigquery.WriteDisposition.WRITE_TRUNCATE,
        "WRITE_EMPTY": bigquery.WriteDisposition.WRITE_EMPTY,
    }

    job_config = bigquery.LoadJobConfig(
        source_format=format_map.get(source_format, bigquery.SourceFormat.CSV),
        write_disposition=disposition_map.get(write_disposition, bigquery.WriteDisposition.WRITE_APPEND),
        autodetect=autodetect,
        skip_leading_rows=skip_leading_rows,
    )

    table_ref = client._client.dataset(dataset_id).table(table_id)

    with open(file_path, "rb") as f:
        job = client._client.load_table_from_file(f, table_ref, job_config=job_config)

    result = job.result()

    return {
        "job_id": job.job_id,
        "rows_loaded": result.output_rows,
        "status": "DONE" if job.state == "DONE" and not job.errors else "FAILED",
    }


def bq_export_to_gcs(
    client: BQClient,
    dataset_id: str,
    table_id: str,
    destination_uri: str,
    destination_format: str = "CSV",
    compression: str = "NONE",
) -> dict:
    """Exporta una tabla BigQuery a Google Cloud Storage.

    Usa extract_table del SDK para crear un ExtractJob. Espera la finalizacion
    del job con job.result().

    Args:
        client: Cliente autenticado de BigQuery.
        dataset_id: ID del dataset de origen.
        table_id: ID de la tabla de origen.
        destination_uri: URI de destino en GCS. Para multiples archivos usar
                         wildcard: "gs://bucket/prefix_*.csv".
        destination_format: Formato de exportacion: "CSV", "NEWLINE_DELIMITED_JSON",
                            "AVRO", "PARQUET". Default: "CSV".
        compression: Compresion: "NONE", "GZIP", "SNAPPY", "DEFLATE". Default: "NONE".

    Returns:
        {"job_id": str, "destination_uri": str, "status": "DONE"|"FAILED"}.

    Raises:
        google.api_core.exceptions.NotFound: Si la tabla no existe.
        google.api_core.exceptions.GoogleAPICallError: Si el job falla.

    Example:
        >>> result = bq_export_to_gcs(
        ...     client, "my_dataset", "my_table",
        ...     "gs://my-bucket/export/data_*.csv",
        ...     compression="GZIP",
        ... )
        >>> print(f"Exportado a {result['destination_uri']}, job: {result['job_id']}")
    """
    format_map = {
        "CSV": bigquery.DestinationFormat.CSV,
        "NEWLINE_DELIMITED_JSON": bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON,
        "AVRO": bigquery.DestinationFormat.AVRO,
        "PARQUET": bigquery.DestinationFormat.PARQUET,
    }
    compression_map = {
        "NONE": bigquery.Compression.NONE,
        "GZIP": bigquery.Compression.GZIP,
        "SNAPPY": bigquery.Compression.SNAPPY,
        "DEFLATE": bigquery.Compression.DEFLATE,
    }

    job_config = bigquery.ExtractJobConfig(
        destination_format=format_map.get(destination_format, bigquery.DestinationFormat.CSV),
        compression=compression_map.get(compression, bigquery.Compression.NONE),
    )

    table_ref = client._client.dataset(dataset_id).table(table_id)
    job = client._client.extract_table(table_ref, destination_uri, job_config=job_config)
    job.result()

    return {
        "job_id": job.job_id,
        "destination_uri": destination_uri,
        "status": "DONE" if job.state == "DONE" and not job.errors else "FAILED",
    }


def bq_copy_table(
    client: BQClient,
    source_dataset: str,
    source_table: str,
    dest_dataset: str,
    dest_table: str,
    write_disposition: str = "WRITE_EMPTY",
) -> dict:
    """Copia una tabla BigQuery a otro dataset o tabla dentro del mismo proyecto.

    Usa copy_table del SDK para crear un CopyJob. Espera la finalizacion
    del job con job.result(). No copia datos entre proyectos distintos.

    Args:
        client: Cliente autenticado de BigQuery.
        source_dataset: ID del dataset de origen.
        source_table: ID de la tabla de origen.
        dest_dataset: ID del dataset de destino.
        dest_table: ID de la tabla de destino.
        write_disposition: Comportamiento si la tabla destino existe: "WRITE_EMPTY"
                           (falla si existe), "WRITE_APPEND", "WRITE_TRUNCATE".
                           Default: "WRITE_EMPTY".

    Returns:
        {"job_id": str, "status": "DONE"|"FAILED"}.

    Raises:
        google.api_core.exceptions.NotFound: Si la tabla de origen no existe.
        google.api_core.exceptions.Conflict: Si destino existe y write_disposition es WRITE_EMPTY.
        google.api_core.exceptions.GoogleAPICallError: Si el job falla.

    Example:
        >>> result = bq_copy_table(
        ...     client,
        ...     "production", "users",
        ...     "staging", "users_backup",
        ...     write_disposition="WRITE_TRUNCATE",
        ... )
        >>> print(f"Copia completada: {result['status']}, job: {result['job_id']}")
    """
    disposition_map = {
        "WRITE_APPEND": bigquery.WriteDisposition.WRITE_APPEND,
        "WRITE_TRUNCATE": bigquery.WriteDisposition.WRITE_TRUNCATE,
        "WRITE_EMPTY": bigquery.WriteDisposition.WRITE_EMPTY,
    }

    job_config = bigquery.CopyJobConfig(
        write_disposition=disposition_map.get(write_disposition, bigquery.WriteDisposition.WRITE_EMPTY),
    )

    source_ref = client._client.dataset(source_dataset).table(source_table)
    dest_ref = client._client.dataset(dest_dataset).table(dest_table)

    job = client._client.copy_table(source_ref, dest_ref, job_config=job_config)
    job.result()

    return {
        "job_id": job.job_id,
        "status": "DONE" if job.state == "DONE" and not job.errors else "FAILED",
    }