# Contains code from https://github.com/ibis-project/ibis/blob/9.2.0/ibis/backends/bigquery/__init__.py


"""BigQuery public API."""

from __future__ import annotations

import concurrent.futures
import contextlib
import glob
import os
from typing import Any, Optional, TYPE_CHECKING

import bigframes_vendored.ibis
from bigframes_vendored.ibis import util
from bigframes_vendored.ibis.backends import CanCreateDatabase, CanCreateSchema
from bigframes_vendored.ibis.backends.bigquery.client import (
    bigquery_param,
    parse_project_and_dataset,
    rename_partitioned_column,
    schema_from_bigquery_table,
)
from bigframes_vendored.ibis.backends.bigquery.datatypes import (
    BigQuerySchema,
    BigQueryType,
)
from bigframes_vendored.ibis.backends.sql import SQLBackend
import bigframes_vendored.ibis.backends.sql.compilers as sc
import bigframes_vendored.ibis.common.exceptions as com
import bigframes_vendored.ibis.expr.operations as ops
import bigframes_vendored.ibis.expr.schema as sch
import bigframes_vendored.ibis.expr.types as ir
import bigframes_vendored.sqlglot as sg
import bigframes_vendored.sqlglot.expressions as sge
import google.api_core.exceptions
import google.auth.credentials
import google.cloud.bigquery as bq
import google.cloud.bigquery_storage_v1 as bqstorage
import pydata_google_auth
from pydata_google_auth import cache

if TYPE_CHECKING:
    from collections.abc import Iterable, Mapping
    from pathlib import Path
    from urllib.parse import ParseResult

    import pandas as pd
    import polars as pl
    import pyarrow as pa


SCOPES = ["https://www.googleapis.com/auth/bigquery"]
EXTERNAL_DATA_SCOPES = [
    "https://www.googleapis.com/auth/bigquery",
    "https://www.googleapis.com/auth/cloud-platform",
    "https://www.googleapis.com/auth/drive",
]
CLIENT_ID = "546535678771-gvffde27nd83kfl6qbrnletqvkdmsese.apps.googleusercontent.com"
CLIENT_SECRET = "iU5ohAF2qcqrujegE3hQ1cPt"  # noqa: S105


def _create_user_agent(application_name: str) -> str:
    user_agent = []

    if application_name:
        user_agent.append(application_name)

    user_agent_default_template = f"ibis/{bigframes_vendored.ibis.__version__}"
    user_agent.append(user_agent_default_template)

    return " ".join(user_agent)


def _create_client_info(application_name):
    from google.api_core.client_info import ClientInfo

    return ClientInfo(user_agent=_create_user_agent(application_name))


def _create_client_info_gapic(application_name):
    from google.api_core.gapic_v1.client_info import ClientInfo

    return ClientInfo(user_agent=_create_user_agent(application_name))


def _force_quote_table(table: sge.Table) -> sge.Table:
    """Force quote all the parts of a bigquery path.

    https://cloud.google.com/bigquery/docs/reference/standard-sql/lexical#identifiers

    my-table is OK, but not mydataset.my-table

    mytable-287 is OK, but not mytable-287a

    Just quote everything.
    """
    for key in ("this", "db", "catalog"):
        if (val := table.args[key]) is not None:
            if isinstance(val, sg.exp.Identifier) and not val.quoted:
                val.args["quoted"] = True
            else:
                table.args[key] = sg.to_identifier(val, quoted=True)
    return table


class Backend(SQLBackend, CanCreateDatabase, CanCreateSchema):
    name = "bigquery"
    compiler = sc.bigquery.compiler
    supports_in_memory_tables = True
    supports_python_udfs = False

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.__session_dataset: bq.DatasetReference | None = None
        self._query_cache.lookup = lambda name: self.table(
            name,
            database=(self._session_dataset.project, self._session_dataset.dataset_id),
        ).op()

    @property
    def _session_dataset(self):
        if self.__session_dataset is None:
            self.__session_dataset = self._make_session()
        return self.__session_dataset

    def _read_file(
        self,
        path: str | Path,
        *,
        table_name: str | None = None,
        job_config: bq.LoadJobConfig,
    ) -> ir.Table:
        self._make_session()

        if table_name is None:
            table_name = util.gen_name(f"bq_read_{job_config.source_format}")

        table_ref = self._session_dataset.table(table_name)

        database = self._session_dataset.dataset_id
        catalog = self._session_dataset.project

        # drop the table if it exists
        #
        # we could do this with write_disposition = WRITE_TRUNCATE but then the
        # concurrent append jobs aren't possible
        #
        # dropping the table first means all write_dispositions can be
        # WRITE_APPEND
        self.drop_table(table_name, database=(catalog, database), force=True)

        if os.path.isdir(path):
            raise NotImplementedError("Reading from a directory is not supported.")
        elif str(path).startswith("gs://"):
            load_job = self.client.load_table_from_uri(
                path, table_ref, job_config=job_config
            )
            load_job.result()
        else:

            def load(file: str) -> None:
                with open(file, mode="rb") as f:
                    load_job = self.client.load_table_from_file(
                        f, table_ref, job_config=job_config
                    )
                    load_job.result()

            job_config.write_disposition = bq.WriteDisposition.WRITE_APPEND

            with concurrent.futures.ThreadPoolExecutor() as executor:
                for fut in concurrent.futures.as_completed(
                    executor.submit(load, file) for file in glob.glob(str(path))
                ):
                    fut.result()

        return self.table(table_name, database=(catalog, database))

    def read_parquet(
        self, path: str | Path, table_name: str | None = None, **kwargs: Any
    ):
        """Read Parquet data into a BigQuery table.

        Parameters
        ----------
        path
            Path to a Parquet file on GCS or the local filesystem. Globs are supported.
        table_name
            Optional table name
        kwargs
            Additional keyword arguments passed to `google.cloud.bigquery.LoadJobConfig`.

        Returns
        -------
        Table
            An Ibis table expression

        """
        return self._read_file(
            path,
            table_name=table_name,
            job_config=bq.LoadJobConfig(
                source_format=bq.SourceFormat.PARQUET, **kwargs
            ),
        )

    def read_csv(
        self, path: str | Path, table_name: str | None = None, **kwargs: Any
    ) -> ir.Table:
        """Read CSV data into a BigQuery table.

        Parameters
        ----------
        path
            Path to a CSV file on GCS or the local filesystem. Globs are supported.
        table_name
            Optional table name
        kwargs
            Additional keyword arguments passed to
            `google.cloud.bigquery.LoadJobConfig`.

        Returns
        -------
        Table
            An Ibis table expression

        """
        job_config = bq.LoadJobConfig(
            source_format=bq.SourceFormat.CSV,
            autodetect=True,
            skip_leading_rows=1,
            **kwargs,
        )
        return self._read_file(path, table_name=table_name, job_config=job_config)

    def read_json(
        self, path: str | Path, table_name: str | None = None, **kwargs: Any
    ) -> ir.Table:
        """Read newline-delimited JSON data into a BigQuery table.

        Parameters
        ----------
        path
            Path to a newline-delimited JSON file on GCS or the local
            filesystem. Globs are supported.
        table_name
            Optional table name
        kwargs
            Additional keyword arguments passed to
            `google.cloud.bigquery.LoadJobConfig`.

        Returns
        -------
        Table
            An Ibis table expression

        """
        job_config = bq.LoadJobConfig(
            source_format=bq.SourceFormat.NEWLINE_DELIMITED_JSON,
            autodetect=True,
            **kwargs,
        )
        return self._read_file(path, table_name=table_name, job_config=job_config)

    def _from_url(self, url: ParseResult, **kwargs):
        return self.connect(
            project_id=url.netloc or kwargs.get("project_id", [""])[0],
            dataset_id=url.path[1:] or kwargs.get("dataset_id", [""])[0],
            **kwargs,
        )

    def do_connect(
        self,
        project_id: str | None = None,
        dataset_id: str = "",
        credentials: google.auth.credentials.Credentials | None = None,
        application_name: str | None = None,
        auth_local_webserver: bool = True,
        auth_external_data: bool = False,
        auth_cache: str = "default",
        partition_column: str | None = "PARTITIONTIME",
        client: bq.Client | None = None,
        storage_client: bqstorage.BigQueryReadClient | None = None,
        location: str | None = None,
    ) -> Backend:
        """Create a `Backend` for use with Ibis.

        Parameters
        ----------
        project_id
            A BigQuery project id.
        dataset_id
            A dataset id that lives inside of the project indicated by
            `project_id`.
        credentials
            Optional credentials.
        application_name
            A string identifying your application to Google API endpoints.
        auth_local_webserver
            Use a local webserver for the user authentication.  Binds a
            webserver to an open port on localhost between 8080 and 8089,
            inclusive, to receive authentication token. If not set, defaults to
            False, which requests a token via the console.
        auth_external_data
            Authenticate using additional scopes required to `query external
            data sources
            <https://cloud.google.com/bigquery/external-data-sources>`_,
            such as Google Sheets, files in Google Cloud Storage, or files in
            Google Drive. If not set, defaults to False, which requests the
            default BigQuery scopes.
        auth_cache
            Selects the behavior of the credentials cache.

            `'default'``
                Reads credentials from disk if available, otherwise
                authenticates and caches credentials to disk.

            `'reauth'``
                Authenticates and caches credentials to disk.

            `'none'``
                Authenticates and does **not** cache credentials.

            Defaults to `'default'`.
        partition_column
            Identifier to use instead of default `_PARTITIONTIME` partition
            column. Defaults to `'PARTITIONTIME'`.
        client
            A `Client` from the `google.cloud.bigquery` package. If not
            set, one is created using the `project_id` and `credentials`.
        storage_client
            A `BigQueryReadClient` from the
            `google.cloud.bigquery_storage_v1` package. If not set, one is
            created using the `project_id` and `credentials`.
        location
            Default location for BigQuery objects.

        Returns
        -------
        Backend
            An instance of the BigQuery backend.

        """
        default_project_id = client.project if client is not None else project_id

        # Only need `credentials` to create a `client` and
        # `storage_client`, so only one or the other needs to be set.
        if (client is None or storage_client is None) and credentials is None:
            scopes = SCOPES
            if auth_external_data:
                scopes = EXTERNAL_DATA_SCOPES

            if auth_cache == "default":
                credentials_cache = cache.ReadWriteCredentialsCache(
                    filename="ibis.json"
                )
            elif auth_cache == "reauth":
                credentials_cache = cache.WriteOnlyCredentialsCache(
                    filename="ibis.json"
                )
            elif auth_cache == "none":
                credentials_cache = cache.NOOP
            else:
                raise ValueError(
                    f"Got unexpected value for auth_cache = '{auth_cache}'. "
                    "Expected one of 'default', 'reauth', or 'none'."
                )

            credentials, default_project_id = pydata_google_auth.default(
                scopes,
                client_id=CLIENT_ID,
                client_secret=CLIENT_SECRET,
                credentials_cache=credentials_cache,
                use_local_webserver=auth_local_webserver,
            )

        project_id = project_id or default_project_id

        (
            self.data_project,
            self.billing_project,
            self.dataset,
        ) = parse_project_and_dataset(project_id, dataset_id)

        if client is not None:
            self.client = client
        else:
            self.client = bq.Client(
                project=self.billing_project,
                credentials=credentials,
                client_info=_create_client_info(application_name),
                location=location,
            )

        if self.client.default_query_job_config is None:
            self.client.default_query_job_config = bq.QueryJobConfig()

        self.client.default_query_job_config.use_legacy_sql = False
        self.client.default_query_job_config.allow_large_results = True

        if storage_client is not None:
            self.storage_client = storage_client
        else:
            self.storage_client = bqstorage.BigQueryReadClient(
                credentials=credentials,
                client_info=_create_client_info_gapic(application_name),
            )

        self.partition_column = partition_column

    @util.experimental
    @classmethod
    def from_connection(
        cls,
        client: bq.Client,
        partition_column: str | None = "PARTITIONTIME",
        storage_client: bqstorage.BigQueryReadClient | None = None,
        dataset_id: str = "",
    ) -> Backend:
        """Create a BigQuery `Backend` from an existing `Client`.

        Parameters
        ----------
        client
            A `Client` from the `google.cloud.bigquery` package.
        partition_column
            Identifier to use instead of default `_PARTITIONTIME` partition
            column. Defaults to `'PARTITIONTIME'`.
        storage_client
            A `BigQueryReadClient` from the `google.cloud.bigquery_storage_v1`
            package.
        dataset_id
            A dataset id that lives inside of the project attached to `client`.
        """
        return bigframes_vendored.ibis.bigquery.connect(
            client=client,
            partition_column=partition_column,
            storage_client=storage_client,
            dataset_id=dataset_id,
        )

    def disconnect(self) -> None:
        self.client.close()

    def _parse_project_and_dataset(self, dataset) -> tuple[str, str]:
        if isinstance(dataset, sge.Table):
            dataset = dataset.sql(self.dialect)
        if not dataset and not self.dataset:
            raise ValueError("Unable to determine BigQuery dataset.")
        project, _, dataset = parse_project_and_dataset(
            self.billing_project,
            dataset or f"{self.data_project}.{self.dataset}",
        )
        return project, dataset

    @property
    def project_id(self):
        return self.data_project

    @property
    def dataset_id(self):
        return self.dataset

    def create_database(
        self,
        name: str,
        catalog: str | None = None,
        force: bool = False,
        collate: str | None = None,
        **options: Any,
    ) -> None:
        properties = [
            sge.Property(this=sg.to_identifier(name), value=sge.convert(value))
            for name, value in (options or {}).items()
        ]

        if collate is not None:
            properties.append(
                sge.CollateProperty(this=sge.convert(collate), default=True)
            )

        stmt = sge.Create(
            kind="SCHEMA",
            this=sg.table(name, db=catalog),
            exists=force,
            properties=sge.Properties(expressions=properties),
        )

        self.raw_sql(stmt.sql(self.name))

    def drop_database(
        self,
        name: str,
        catalog: str | None = None,
        force: bool = False,
        cascade: bool = False,
    ) -> None:
        """Drop a BigQuery dataset."""
        stmt = sge.Drop(
            kind="SCHEMA",
            this=sg.table(name, db=catalog),
            exists=force,
            cascade=cascade,
        )

        self.raw_sql(stmt.sql(self.name))

    def table(
        self, name: str, database: str | None = None, schema: str | None = None
    ) -> ir.Table:
        table_loc = self._warn_and_create_table_loc(database, schema)
        table = sg.parse_one(f"`{name}`", into=sge.Table, read=self.name)

        # Bigquery, unlike other backends, had existing support for specifying
        # table hierarchy in the table name, e.g. con.table("dataset.table_name")
        # so here we have an extra layer of disambiguation to handle.

        # Default `catalog` to None unless we've parsed it out of the database/schema kwargs
        # Raise if there are path specifications in both the name and as a kwarg
        catalog = table_loc.args["catalog"]  # args access will return None, not ''
        if table.catalog:
            if table_loc.catalog:
                raise com.IbisInputError(
                    "Cannot specify catalog both in the table name and as an argument"
                )
            else:
                catalog = table.catalog

        # Default `db` to None unless we've parsed it out of the database/schema kwargs
        db = table_loc.args["db"]  # args access will return None, not ''
        if table.db:
            if table_loc.db:
                raise com.IbisInputError(
                    "Cannot specify database both in the table name and as an argument"
                )
            else:
                db = table.db

        database = (
            sg.table(None, db=db, catalog=catalog, quoted=False).sql(dialect=self.name)
            or None
        )

        project, dataset = self._parse_project_and_dataset(database)

        bq_table = self.client.get_table(
            bq.TableReference(
                bq.DatasetReference(project=project, dataset_id=dataset),
                table.name,
            )
        )

        node = ops.DatabaseTable(
            table.name,
            # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#filtering_selected_tables_using_table_suffix
            schema=schema_from_bigquery_table(bq_table, wildcard=table.name[-1] == "*"),
            source=self,
            namespace=ops.Namespace(database=dataset, catalog=project),
        )
        table_expr = node.to_expr()
        return rename_partitioned_column(table_expr, bq_table, self.partition_column)

    def _make_session(self) -> tuple[str, str]:
        if (client := getattr(self, "client", None)) is not None:
            job_config = bq.QueryJobConfig(use_query_cache=False)
            query = client.query(
                "SELECT 1", job_config=job_config, project=self.billing_project
            )
            query.result()

            return bq.DatasetReference(
                project=query.destination.project,
                dataset_id=query.destination.dataset_id,
            )
        return None

    def _get_schema_using_query(self, query: str) -> sch.Schema:
        job = self.client.query(
            query,
            job_config=bq.QueryJobConfig(dry_run=True, use_query_cache=False),
            project=self.billing_project,
        )
        return BigQuerySchema.to_ibis(job.schema)

    def raw_sql(self, query: str, params=None, page_size: int | None = None):
        query_parameters = [
            bigquery_param(
                param.type(),
                value,
                (
                    param.get_name()
                    if not isinstance(op := param.op(), ops.Alias)
                    else op.arg.name
                ),
            )
            for param, value in (params or {}).items()
        ]
        with contextlib.suppress(AttributeError):
            query = query.sql(self.dialect)

        job_config = bq.job.QueryJobConfig(query_parameters=query_parameters or [])
        return self.client.query_and_wait(
            query,
            job_config=job_config,
            project=self.billing_project,
            page_size=page_size,
        )

    @property
    def current_catalog(self) -> str:
        return self.data_project

    @property
    def current_database(self) -> str | None:
        return self.dataset

    def compile(
        self,
        expr: ir.Expr,
        limit: str | None = None,
        params=None,
        pretty: bool = True,
        **kwargs: Any,
    ):
        """Compile an Ibis expression to a SQL string."""
        session_dataset = self._session_dataset
        query = self.compiler.to_sqlglot(
            expr,
            limit=limit,
            params=params,
            session_dataset_id=getattr(session_dataset, "dataset_id", None),
            session_project=getattr(session_dataset, "project", None),
            **kwargs,
        )
        queries = query if isinstance(query, list) else [query]
        sql = ";\n".join(query.sql(self.dialect, pretty=pretty) for query in queries)
        self._log(sql)
        return sql

    def execute(self, expr, params=None, limit="default", **kwargs):
        """Compile and execute the given Ibis expression.
        Compile and execute Ibis expression using this backend client
        interface, returning results in-memory in the appropriate object type
        Parameters
        ----------
        expr
            Ibis expression to execute
        limit
            Retrieve at most this number of values/rows. Overrides any limit
            already set on the expression.
        params
            Query parameters
        kwargs
            Extra arguments specific to the backend
        Returns
        -------
        pd.DataFrame | pd.Series | scalar
            Output from execution
        """
        from bigframes_vendored.ibis.backends.bigquery.converter import (
            BigQueryPandasData,
        )

        self._run_pre_execute_hooks(expr)

        schema = expr.as_table().schema() - bigframes_vendored.ibis.schema(
            {"_TABLE_SUFFIX": "string"}
        )

        sql = self.compile(expr, limit=limit, params=params, **kwargs)
        self._log(sql)
        query = self.raw_sql(sql, params=params, **kwargs)

        arrow_t = query.to_arrow(
            progress_bar_type=None, bqstorage_client=self.storage_client
        )

        result = BigQueryPandasData.convert_table(
            arrow_t.to_pandas(timestamp_as_object=True), schema
        )

        return expr.__pandas_result__(result, schema=schema)

    def insert(
        self,
        table_name: str,
        obj: pd.DataFrame | ir.Table | list | dict,
        schema: str | None = None,
        database: str | None = None,
        overwrite: bool = False,
    ):
        """Insert data into a table.

        Parameters
        ----------
        table_name
            The name of the table to which data needs will be inserted
        obj
            The source data or expression to insert
        schema
            The name of the schema that the table is located in
        database
            Name of the attached database that the table is located in.
        overwrite
            If `True` then replace existing contents of table

        """
        table_loc = self._warn_and_create_table_loc(database, schema)
        catalog, db = self._to_catalog_db_tuple(table_loc)
        if catalog is None:
            catalog = self.current_catalog
        if db is None:
            db = self.current_database

        return super().insert(
            table_name,
            obj,
            database=(catalog, db),
            overwrite=overwrite,
        )

    def to_pyarrow(
        self,
        expr: ir.Expr,
        *,
        params: Mapping[ir.Scalar, Any] | None = None,
        limit: int | str | None = None,
        **kwargs: Any,
    ) -> pa.Table:
        self._import_pyarrow()
        sql = self.compile(expr, limit=limit, params=params, **kwargs)
        self._log(sql)
        query = self.raw_sql(sql, params=params, **kwargs)
        table = query.to_arrow(
            progress_bar_type=None, bqstorage_client=self.storage_client
        )
        table = table.rename_columns(list(expr.as_table().schema().names))
        return expr.__pyarrow_result__(table)

    def to_pyarrow_batches(
        self,
        expr: ir.Expr,
        *,
        params: Mapping[ir.Scalar, Any] | None = None,
        limit: int | str | None = None,
        chunk_size: int = 1_000_000,
        **kwargs: Any,
    ):
        pa = self._import_pyarrow()

        schema = expr.as_table().schema()

        sql = self.compile(expr, limit=limit, params=params, **kwargs)
        self._log(sql)
        query = self.raw_sql(sql, params=params, page_size=chunk_size, **kwargs)
        batch_iter = query.to_arrow_iterable(bqstorage_client=self.storage_client)
        return pa.ipc.RecordBatchReader.from_batches(schema.to_pyarrow(), batch_iter)

    def _gen_udf_name(self, name: str, schema: Optional[str]) -> str:
        func = ".".join(filter(None, (schema, name)))
        if "." in func:
            return ".".join(f"`{part}`" for part in func.split("."))
        return func

    def get_schema(
        self,
        name,
        *,
        catalog: str | None = None,
        database: str | None = None,
    ):
        table_ref = bq.TableReference(
            bq.DatasetReference(
                project=catalog or self.data_project,
                dataset_id=database or self.current_database,
            ),
            name,
        )
        return schema_from_bigquery_table(
            self.client.get_table(table_ref),
            # https://cloud.google.com/bigquery/docs/querying-wildcard-tables#filtering_selected_tables_using_table_suffix
            wildcard=name[-1] == "*",
        )

    def list_databases(
        self, like: str | None = None, catalog: str | None = None
    ) -> list[str]:
        results = [
            dataset.dataset_id
            for dataset in self.client.list_datasets(
                project=catalog if catalog is not None else self.data_project
            )
        ]
        return self._filter_with_like(results, like)

    def list_tables(
        self,
        like: str | None = None,
        database: tuple[str, str] | str | None = None,
        schema: str | None = None,
    ) -> list[str]:
        """List the tables in the database.

        Parameters
        ----------
        like
            A pattern to use for listing tables.
        database
            The database location to perform the list against.

            By default uses the current `dataset` (`self.current_database`) and
            `project` (`self.current_catalog`).

            To specify a table in a separate BigQuery dataset, you can pass in the
            dataset and project as a string `"dataset.project"`, or as a tuple of
            strings `("dataset", "project")`.

            ::: {.callout-note}
            ## Ibis does not use the word `schema` to refer to database hierarchy.

            A collection of tables is referred to as a `database`.
            A collection of `database` is referred to as a `catalog`.

            These terms are mapped onto the corresponding features in each
            backend (where available), regardless of whether the backend itself
            uses the same terminology.
            :::
        schema
            [deprecated] The schema (dataset) inside `database` to perform the list against.
        """
        table_loc = self._warn_and_create_table_loc(database, schema)

        project, dataset = self._parse_project_and_dataset(table_loc)
        dataset_ref = bq.DatasetReference(project, dataset)
        result = [table.table_id for table in self.client.list_tables(dataset_ref)]
        return self._filter_with_like(result, like)

    def set_database(self, name):
        self.data_project, self.dataset = self._parse_project_and_dataset(name)

    @property
    def version(self):
        return bq.__version__

    def create_table(
        self,
        name: str,
        obj: ir.Table
        | pd.DataFrame
        | pa.Table
        | pl.DataFrame
        | pl.LazyFrame
        | None = None,
        *,
        schema: sch.SchemaLike | None = None,
        database: str | None = None,
        temp: bool = False,
        overwrite: bool = False,
        default_collate: str | None = None,
        partition_by: str | None = None,
        cluster_by: Iterable[str] | None = None,
        options: Mapping[str, Any] | None = None,
    ) -> ir.Table:
        """Create a table in BigQuery.

        Parameters
        ----------
        name
            Name of the table to create
        obj
            The data with which to populate the table; optional, but one of `obj`
            or `schema` must be specified
        schema
            The schema of the table to create; optional, but one of `obj` or
            `schema` must be specified
        database
            The BigQuery *dataset* in which to create the table; optional
        temp
            Whether the table is temporary
        overwrite
            If `True`, replace the table if it already exists, otherwise fail if
            the table exists
        default_collate
            Default collation for string columns. See BigQuery's documentation
            for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/collation-concepts
        partition_by
            Partition the table by the given expression. See BigQuery's documentation
            for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#partition_expression
        cluster_by
            List of columns to cluster the table by. See BigQuery's documentation
            for more details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#clustering_column_list
        options
            BigQuery-specific table options; see the BigQuery documentation for
            details: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#table_option_list

        Returns
        -------
        Table
            The table that was just created

        """
        if obj is None and schema is None:
            raise com.IbisError("One of the `schema` or `obj` parameter is required")
        if schema is not None:
            schema = bigframes_vendored.ibis.schema(schema)

        if isinstance(obj, ir.Table) and schema is not None:
            if not schema.equals(obj.schema()):
                raise com.IbisTypeError(
                    "Provided schema and Ibis table schema are incompatible. Please "
                    "align the two schemas, or provide only one of the two arguments."
                )

        project_id, dataset = self._parse_project_and_dataset(database)

        properties = []

        if default_collate is not None:
            properties.append(
                sge.CollateProperty(this=sge.convert(default_collate), default=True)
            )

        if partition_by is not None:
            properties.append(
                sge.PartitionedByProperty(
                    this=sge.Tuple(
                        expressions=list(map(sg.to_identifier, partition_by))
                    )
                )
            )

        if cluster_by is not None:
            properties.append(
                sge.Cluster(expressions=list(map(sg.to_identifier, cluster_by)))
            )

        properties.extend(
            sge.Property(this=sg.to_identifier(name), value=sge.convert(value))
            for name, value in (options or {}).items()
        )

        if obj is not None and not isinstance(obj, ir.Table):
            obj = bigframes_vendored.ibis.memtable(obj, schema=schema)

        if temp:
            dataset = self._session_dataset.dataset_id
            if database is not None:
                raise com.IbisInputError("Cannot specify database for temporary table")
            database = self._session_dataset.project
        else:
            dataset = database or self.current_database

        try:
            table = sg.parse_one(name, into=sge.Table, read="bigquery")
        except sg.ParseError:
            table = sg.table(
                name,
                db=dataset,
                catalog=project_id,
                quoted=self.compiler.quoted,
            )
        else:
            if table.args["db"] is None:
                table.args["db"] = dataset

            if table.args["catalog"] is None:
                table.args["catalog"] = project_id

        table = _force_quote_table(table)

        column_defs = [
            sge.ColumnDef(
                this=sg.to_identifier(name, quoted=self.compiler.quoted),
                kind=BigQueryType.from_ibis(typ),
                constraints=(
                    None
                    if typ.nullable or typ.is_array()
                    else [sge.ColumnConstraint(kind=sge.NotNullColumnConstraint())]
                ),
            )
            for name, typ in (schema or {}).items()
        ]

        stmt = sge.Create(
            kind="TABLE",
            this=sge.Schema(this=table, expressions=column_defs or None),
            replace=overwrite,
            properties=sge.Properties(expressions=properties),
            expression=None if obj is None else self.compile(obj),
        )

        sql = stmt.sql(self.name)

        self.raw_sql(sql)
        return self.table(table.name, database=(table.catalog, table.db))

    def drop_table(
        self,
        name: str,
        *,
        schema: str | None = None,
        database: tuple[str | str] | str | None = None,
        force: bool = False,
    ) -> None:
        table_loc = self._warn_and_create_table_loc(database, schema)
        catalog, db = self._to_catalog_db_tuple(table_loc)
        stmt = sge.Drop(
            kind="TABLE",
            this=sg.table(
                name,
                db=db or self.current_database,
                catalog=catalog or self.billing_project,
            ),
            exists=force,
        )
        self.raw_sql(stmt.sql(self.name))

    def create_view(
        self,
        name: str,
        obj: ir.Table,
        *,
        schema: str | None = None,
        database: str | None = None,
        overwrite: bool = False,
    ) -> ir.Table:
        table_loc = self._warn_and_create_table_loc(database, schema)
        catalog, db = self._to_catalog_db_tuple(table_loc)

        stmt = sge.Create(
            kind="VIEW",
            this=sg.table(
                name,
                db=db or self.current_database,
                catalog=catalog or self.billing_project,
            ),
            expression=self.compile(obj),
            replace=overwrite,
        )
        self.raw_sql(stmt.sql(self.name))
        return self.table(name, database=(catalog, database))

    def drop_view(
        self,
        name: str,
        *,
        schema: str | None = None,
        database: str | None = None,
        force: bool = False,
    ) -> None:
        table_loc = self._warn_and_create_table_loc(database, schema)
        catalog, db = self._to_catalog_db_tuple(table_loc)

        stmt = sge.Drop(
            kind="VIEW",
            this=sg.table(
                name,
                db=db or self.current_database,
                catalog=catalog or self.billing_project,
            ),
            exists=force,
        )
        self.raw_sql(stmt.sql(self.name))

    def _drop_cached_table(self, name):
        self.drop_table(
            name,
            database=(self._session_dataset.project, self._session_dataset.dataset_id),
            force=True,
        )

    def _register_udfs(self, expr: ir.Expr) -> None:
        """No op because UDFs made with CREATE TEMPORARY FUNCTION must be followed by a query."""

    @contextlib.contextmanager
    def _safe_raw_sql(self, *args, **kwargs):
        yield self.raw_sql(*args, **kwargs)

    # TODO: remove when the schema kwarg is removed
    def _warn_and_create_table_loc(self, database=None, schema=None):
        if schema is not None:
            self._warn_schema()
        if database is not None and schema is not None:
            if isinstance(database, str):
                table_loc = f"{database}.{schema}"
            elif isinstance(database, tuple):
                table_loc = database + schema
        elif schema is not None:
            table_loc = schema
        elif database is not None:
            table_loc = database
        else:
            table_loc = None

        table_loc = self._to_sqlglot_table(table_loc)

        if table_loc is not None:
            if (sg_cat := table_loc.args["catalog"]) is not None:
                sg_cat.args["quoted"] = False
            if (sg_db := table_loc.args["db"]) is not None:
                sg_db.args["quoted"] = False

        return table_loc


def compile(expr, params=None, **kwargs):
    """Compile an expression for BigQuery."""
    backend = Backend()
    return backend.compile(expr, params=params, **kwargs)


def connect(
    project_id: str | None = None,
    dataset_id: str = "",
    credentials: google.auth.credentials.Credentials | None = None,
    application_name: str | None = None,
    auth_local_webserver: bool = False,
    auth_external_data: bool = False,
    auth_cache: str = "default",
    partition_column: str | None = "PARTITIONTIME",
) -> Backend:
    """Create a :class:`Backend` for use with Ibis.

    Parameters
    ----------
    project_id
        A BigQuery project id.
    dataset_id
        A dataset id that lives inside of the project indicated by
        `project_id`.
    credentials
        Optional credentials.
    application_name
        A string identifying your application to Google API endpoints.
    auth_local_webserver
        Use a local webserver for the user authentication.  Binds a
        webserver to an open port on localhost between 8080 and 8089,
        inclusive, to receive authentication token. If not set, defaults
        to False, which requests a token via the console.
    auth_external_data
        Authenticate using additional scopes required to `query external
        data sources
        <https://cloud.google.com/bigquery/external-data-sources>`_,
        such as Google Sheets, files in Google Cloud Storage, or files in
        Google Drive. If not set, defaults to False, which requests the
        default BigQuery scopes.
    auth_cache
        Selects the behavior of the credentials cache.

        `'default'``
            Reads credentials from disk if available, otherwise
            authenticates and caches credentials to disk.

        `'reauth'``
            Authenticates and caches credentials to disk.

        `'none'``
            Authenticates and does **not** cache credentials.

        Defaults to `'default'`.
    partition_column
        Identifier to use instead of default `_PARTITIONTIME` partition
        column. Defaults to `'PARTITIONTIME'`.

    Returns
    -------
    Backend
        An instance of the BigQuery backend

    """
    backend = Backend()
    return backend.connect(
        project_id=project_id,
        dataset_id=dataset_id,
        credentials=credentials,
        application_name=application_name,
        auth_local_webserver=auth_local_webserver,
        auth_external_data=auth_external_data,
        auth_cache=auth_cache,
        partition_column=partition_column,
    )


__all__ = [
    "Backend",
    "compile",
    "connect",
]
