# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import datetime
import decimal
import os
import textwrap
from typing import Optional
from unittest import mock
import uuid

import dateutil
import dateutil.relativedelta
from google.adk.integrations.bigquery import BigQueryCredentialsConfig
from google.adk.integrations.bigquery import BigQueryToolset
from google.adk.integrations.bigquery import client as bq_client_lib
from google.adk.integrations.bigquery import query_tool
from google.adk.integrations.bigquery.config import BigQueryToolConfig
from google.adk.integrations.bigquery.config import WriteMode
from google.adk.tools.base_tool import BaseTool
from google.adk.tools.tool_context import ToolContext
import google.auth
from google.auth.exceptions import DefaultCredentialsError
from google.cloud import bigquery
from google.oauth2.credentials import Credentials
import pytest


async def get_tool(
    name: str, tool_settings: Optional[BigQueryToolConfig] = None
) -> BaseTool:
  """Get a tool from BigQuery toolset.

  This method gets the tool view that an Agent using the BigQuery toolset would
  see.

  Returns:
    The tool.
  """
  credentials_config = BigQueryCredentialsConfig(
      client_id="abc", client_secret="def"
  )

  toolset = BigQueryToolset(
      credentials_config=credentials_config,
      tool_filter=[name],
      bigquery_tool_config=tool_settings,
  )

  tools = await toolset.get_tools()
  assert tools is not None
  assert len(tools) == 1
  return tools[0]


@pytest.mark.parametrize(
    ("tool_settings",),
    [
        pytest.param(None, id="no-config"),
        pytest.param(BigQueryToolConfig(), id="default-config"),
        pytest.param(
            BigQueryToolConfig(write_mode=WriteMode.BLOCKED),
            id="explicit-no-write",
        ),
    ],
)
@pytest.mark.asyncio
async def test_execute_sql_declaration_read_only(tool_settings):
  """Test BigQuery execute_sql tool declaration in read-only mode.

  This test verifies that the execute_sql tool declaration reflects the
  read-only capability.
  """
  tool_name = "execute_sql"
  tool = await get_tool(tool_name, tool_settings)
  assert tool.name == tool_name
  assert tool.description == textwrap.dedent("""\
    Run a BigQuery or BigQuery ML SQL query in the project and return the result.

    Args:
        project_id (str): The GCP project id in which the query should be
          executed.
        query (str): The BigQuery SQL query to be executed.
        credentials (Credentials): The credentials to use for the request.
        settings (BigQueryToolConfig): The settings for the tool.
        tool_context (ToolContext): The context for the tool.
        dry_run (bool, default False): If True, the query will not be executed.
          Instead, the query will be validated and information about the query
          will be returned. Defaults to False.

    Returns:
        dict: If `dry_run` is False, dictionary representing the result of the
              query. If the result contains the key "result_is_likely_truncated"
              with value True, it means that there may be additional rows matching
              the query not returned in the result.
              If `dry_run` is True, dictionary with "dry_run_info" field
              containing query information returned by BigQuery.

    Examples:
        Fetch data or insights from a table:

            >>> execute_sql("my_project",
            ... "SELECT island, COUNT(*) AS population "
            ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island")
            {
              "status": "SUCCESS",
              "rows": [
                  {
                      "island": "Dream",
                      "population": 124
                  },
                  {
                      "island": "Biscoe",
                      "population": 168
                  },
                  {
                      "island": "Torgersen",
                      "population": 52
                  }
              ]
            }

        Validate a query and estimate costs without executing it:

            >>> execute_sql(
            ...     "my_project",
            ...     "SELECT island FROM "
            ...     "`bigquery-public-data`.`ml_datasets`.`penguins`",
            ...     dry_run=True
            ... )
            {
              "status": "SUCCESS",
              "dry_run_info": {
                "configuration": {
                  "dryRun": True,
                  "jobType": "QUERY",
                  "query": {
                    "destinationTable": {
                      "datasetId": "_...",
                      "projectId": "my_project",
                      "tableId": "anon..."
                    },
                    "priority": "INTERACTIVE",
                    "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`",
                    "useLegacySql": False,
                    "writeDisposition": "WRITE_TRUNCATE"
                  }
                },
                "jobReference": {
                  "location": "US",
                  "projectId": "my_project"
                }
              }
            }""")


@pytest.mark.parametrize(
    ("tool_settings",),
    [
        pytest.param(
            BigQueryToolConfig(write_mode=WriteMode.ALLOWED),
            id="explicit-all-write",
        ),
    ],
)
@pytest.mark.asyncio
async def test_execute_sql_declaration_write(tool_settings):
  """Test BigQuery execute_sql tool declaration with all writes enabled.

  This test verifies that the execute_sql tool declaration reflects the write
  capability.
  """
  tool_name = "execute_sql"
  tool = await get_tool(tool_name, tool_settings)
  assert tool.name == tool_name
  assert tool.description == textwrap.dedent("""\
    Run a BigQuery or BigQuery ML SQL query in the project and return the result.

    Args:
        project_id (str): The GCP project id in which the query should be
          executed.
        query (str): The BigQuery SQL query to be executed.
        credentials (Credentials): The credentials to use for the request.
        settings (BigQueryToolConfig): The settings for the tool.
        tool_context (ToolContext): The context for the tool.
        dry_run (bool, default False): If True, the query will not be executed.
          Instead, the query will be validated and information about the query
          will be returned. Defaults to False.

    Returns:
        dict: If `dry_run` is False, dictionary representing the result of the
              query. If the result contains the key "result_is_likely_truncated"
              with value True, it means that there may be additional rows matching
              the query not returned in the result.
              If `dry_run` is True, dictionary with "dry_run_info" field
              containing query information returned by BigQuery.

    Examples:
        Fetch data or insights from a table:

            >>> execute_sql("my_project",
            ... "SELECT island, COUNT(*) AS population "
            ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island")
            {
              "status": "SUCCESS",
              "rows": [
                  {
                      "island": "Dream",
                      "population": 124
                  },
                  {
                      "island": "Biscoe",
                      "population": 168
                  },
                  {
                      "island": "Torgersen",
                      "population": 52
                  }
              ]
            }

        Validate a query and estimate costs without executing it:

            >>> execute_sql(
            ...     "my_project",
            ...     "SELECT island FROM "
            ...     "`bigquery-public-data`.`ml_datasets`.`penguins`",
            ...     dry_run=True
            ... )
            {
              "status": "SUCCESS",
              "dry_run_info": {
                "configuration": {
                  "dryRun": True,
                  "jobType": "QUERY",
                  "query": {
                    "destinationTable": {
                      "datasetId": "_...",
                      "projectId": "my_project",
                      "tableId": "anon..."
                    },
                    "priority": "INTERACTIVE",
                    "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`",
                    "useLegacySql": False,
                    "writeDisposition": "WRITE_TRUNCATE"
                  }
                },
                "jobReference": {
                  "location": "US",
                  "projectId": "my_project"
                }
              }
            }

        Create a table with schema prescribed:

            >>> execute_sql("my_project",
            ... "CREATE TABLE `my_project`.`my_dataset`.`my_table` "
            ... "(island STRING, population INT64)")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Insert data into an existing table:

            >>> execute_sql("my_project",
            ... "INSERT INTO `my_project`.`my_dataset`.`my_table` (island, population) "
            ... "VALUES ('Dream', 124), ('Biscoe', 168)")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Create a table from the result of a query:

            >>> execute_sql("my_project",
            ... "CREATE TABLE `my_project`.`my_dataset`.`my_table` AS "
            ... "SELECT island, COUNT(*) AS population "
            ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Delete a table:

            >>> execute_sql("my_project",
            ... "DROP TABLE `my_project`.`my_dataset`.`my_table`")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Copy a table to another table:

            >>> execute_sql("my_project",
            ... "CREATE TABLE `my_project`.`my_dataset`.`my_table_clone` "
            ... "CLONE `my_project`.`my_dataset`.`my_table`")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Create a snapshot (a lightweight, read-optimized copy) of en existing
        table:

            >>> execute_sql("my_project",
            ... "CREATE SNAPSHOT TABLE `my_project`.`my_dataset`.`my_table_snapshot` "
            ... "CLONE `my_project`.`my_dataset`.`my_table`")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Create a BigQuery ML linear regression model:

            >>> execute_sql("my_project",
            ... "CREATE MODEL `my_dataset`.`my_model` "
            ... "OPTIONS (model_type='linear_reg', input_label_cols=['body_mass_g']) AS "
            ... "SELECT * FROM `bigquery-public-data`.`ml_datasets`.`penguins` "
            ... "WHERE body_mass_g IS NOT NULL")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Evaluate BigQuery ML model:

            >>> execute_sql("my_project",
            ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset`.`my_model`)")
            {
              "status": "SUCCESS",
              "rows": [{'mean_absolute_error': 227.01223667447218,
                        'mean_squared_error': 81838.15989216768,
                        'mean_squared_log_error': 0.0050704473735013,
                        'median_absolute_error': 173.08081641661738,
                        'r2_score': 0.8723772534253441,
                        'explained_variance': 0.8723772534253442}]
            }

        Evaluate BigQuery ML model on custom data:

            >>> execute_sql("my_project",
            ... "SELECT * FROM ML.EVALUATE(MODEL `my_dataset`.`my_model`, "
            ... "(SELECT * FROM `my_dataset`.`my_table`))")
            {
              "status": "SUCCESS",
              "rows": [{'mean_absolute_error': 227.01223667447218,
                        'mean_squared_error': 81838.15989216768,
                        'mean_squared_log_error': 0.0050704473735013,
                        'median_absolute_error': 173.08081641661738,
                        'r2_score': 0.8723772534253441,
                        'explained_variance': 0.8723772534253442}]
            }

        Predict using BigQuery ML model:

            >>> execute_sql("my_project",
            ... "SELECT * FROM ML.PREDICT(MODEL `my_dataset`.`my_model`, "
            ... "(SELECT * FROM `my_dataset`.`my_table`))")
            {
              "status": "SUCCESS",
              "rows": [
                  {
                    "predicted_body_mass_g": "3380.9271650847013",
                    ...
                  }, {
                    "predicted_body_mass_g": "3873.6072435386004",
                    ...
                  },
                  ...
              ]
            }

        Delete a BigQuery ML model:

            >>> execute_sql("my_project", "DROP MODEL `my_dataset`.`my_model`")
            {
              "status": "SUCCESS",
              "rows": []
            }

    Notes:
        - If a destination table already exists, there are a few ways to overwrite
        it:
            - Use "CREATE OR REPLACE TABLE" instead of "CREATE TABLE".
            - First run "DROP TABLE", followed by "CREATE TABLE".
        - If a model already exists, there are a few ways to overwrite it:
            - Use "CREATE OR REPLACE MODEL" instead of "CREATE MODEL".
            - First run "DROP MODEL", followed by "CREATE MODEL".""")


@pytest.mark.parametrize(
    ("tool_settings",),
    [
        pytest.param(
            BigQueryToolConfig(write_mode=WriteMode.PROTECTED),
            id="explicit-protected-write",
        ),
    ],
)
@pytest.mark.asyncio
async def test_execute_sql_declaration_protected_write(tool_settings):
  """Test BigQuery execute_sql tool declaration with protected writes enabled.

  This test verifies that the execute_sql tool declaration reflects the
  protected write capability.
  """
  tool_name = "execute_sql"
  tool = await get_tool(tool_name, tool_settings)
  assert tool.name == tool_name
  assert tool.description == textwrap.dedent("""\
    Run a BigQuery or BigQuery ML SQL query in the project and return the result.

    Args:
        project_id (str): The GCP project id in which the query should be
          executed.
        query (str): The BigQuery SQL query to be executed.
        credentials (Credentials): The credentials to use for the request.
        settings (BigQueryToolConfig): The settings for the tool.
        tool_context (ToolContext): The context for the tool.
        dry_run (bool, default False): If True, the query will not be executed.
          Instead, the query will be validated and information about the query
          will be returned. Defaults to False.

    Returns:
        dict: If `dry_run` is False, dictionary representing the result of the
              query. If the result contains the key "result_is_likely_truncated"
              with value True, it means that there may be additional rows matching
              the query not returned in the result.
              If `dry_run` is True, dictionary with "dry_run_info" field
              containing query information returned by BigQuery.

    Examples:
        Fetch data or insights from a table:

            >>> execute_sql("my_project",
            ... "SELECT island, COUNT(*) AS population "
            ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island")
            {
              "status": "SUCCESS",
              "rows": [
                  {
                      "island": "Dream",
                      "population": 124
                  },
                  {
                      "island": "Biscoe",
                      "population": 168
                  },
                  {
                      "island": "Torgersen",
                      "population": 52
                  }
              ]
            }

        Validate a query and estimate costs without executing it:

            >>> execute_sql(
            ...     "my_project",
            ...     "SELECT island FROM "
            ...     "`bigquery-public-data`.`ml_datasets`.`penguins`",
            ...     dry_run=True
            ... )
            {
              "status": "SUCCESS",
              "dry_run_info": {
                "configuration": {
                  "dryRun": True,
                  "jobType": "QUERY",
                  "query": {
                    "destinationTable": {
                      "datasetId": "_...",
                      "projectId": "my_project",
                      "tableId": "anon..."
                    },
                    "priority": "INTERACTIVE",
                    "query": "SELECT island FROM `bigquery-public-data`.`ml_datasets`.`penguins`",
                    "useLegacySql": False,
                    "writeDisposition": "WRITE_TRUNCATE"
                  }
                },
                "jobReference": {
                  "location": "US",
                  "projectId": "my_project"
                }
              }
            }

        Create a temporary table with schema prescribed:

            >>> execute_sql("my_project",
            ... "CREATE TEMP TABLE `my_table` (island STRING, population INT64)")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Insert data into an existing temporary table:

            >>> execute_sql("my_project",
            ... "INSERT INTO `my_table` (island, population) "
            ... "VALUES ('Dream', 124), ('Biscoe', 168)")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Create a temporary table from the result of a query:

            >>> execute_sql("my_project",
            ... "CREATE TEMP TABLE `my_table` AS "
            ... "SELECT island, COUNT(*) AS population "
            ... "FROM `bigquery-public-data`.`ml_datasets`.`penguins` GROUP BY island")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Delete a temporary table:

            >>> execute_sql("my_project", "DROP TABLE `my_table`")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Copy a temporary table to another temporary table:

            >>> execute_sql("my_project",
            ... "CREATE TEMP TABLE `my_table_clone` CLONE `my_table`")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Create a temporary BigQuery ML linear regression model:

            >>> execute_sql("my_project",
            ... "CREATE TEMP MODEL `my_model` "
            ... "OPTIONS (model_type='linear_reg', input_label_cols=['body_mass_g']) AS"
            ... "SELECT * FROM `bigquery-public-data`.`ml_datasets`.`penguins` "
            ... "WHERE body_mass_g IS NOT NULL")
            {
              "status": "SUCCESS",
              "rows": []
            }

        Evaluate BigQuery ML model:

            >>> execute_sql("my_project", "SELECT * FROM ML.EVALUATE(MODEL `my_model`)")
            {
              "status": "SUCCESS",
              "rows": [{'mean_absolute_error': 227.01223667447218,
                        'mean_squared_error': 81838.15989216768,
                        'mean_squared_log_error': 0.0050704473735013,
                        'median_absolute_error': 173.08081641661738,
                        'r2_score': 0.8723772534253441,
                        'explained_variance': 0.8723772534253442}]
            }

        Evaluate BigQuery ML model on custom data:

            >>> execute_sql("my_project",
            ... "SELECT * FROM ML.EVALUATE(MODEL `my_model`, "
            ... "(SELECT * FROM `my_dataset`.`my_table`))")
            {
              "status": "SUCCESS",
              "rows": [{'mean_absolute_error': 227.01223667447218,
                        'mean_squared_error': 81838.15989216768,
                        'mean_squared_log_error': 0.0050704473735013,
                        'median_absolute_error': 173.08081641661738,
                        'r2_score': 0.8723772534253441,
                        'explained_variance': 0.8723772534253442}]
            }

        Predict using BigQuery ML model:

            >>> execute_sql("my_project",
            ... "SELECT * FROM ML.PREDICT(MODEL `my_model`, "
            ... "(SELECT * FROM `my_dataset`.`my_table`))")
            {
              "status": "SUCCESS",
              "rows": [
                  {
                    "predicted_body_mass_g": "3380.9271650847013",
                    ...
                  }, {
                    "predicted_body_mass_g": "3873.6072435386004",
                    ...
                  },
                  ...
              ]
            }

        Delete a BigQuery ML model:

            >>> execute_sql("my_project", "DROP MODEL `my_model`")
            {
              "status": "SUCCESS",
              "rows": []
            }

    Notes:
        - If a destination table already exists, there are a few ways to overwrite
        it:
            - Use "CREATE OR REPLACE TEMP TABLE" instead of "CREATE TEMP TABLE".
            - First run "DROP TABLE", followed by "CREATE TEMP TABLE".
        - Only temporary tables can be created, inserted into or deleted. Please
        do not try creating a permanent table (non-TEMP table), inserting into or
        deleting one.
        - If a destination model already exists, there are a few ways to overwrite
        it:
            - Use "CREATE OR REPLACE TEMP MODEL" instead of "CREATE TEMP MODEL".
            - First run "DROP MODEL", followed by "CREATE TEMP MODEL".
        - Only temporary models can be created or deleted. Please do not try
        creating a permanent model (non-TEMP model) or deleting one.""")


@pytest.mark.parametrize(
    ("write_mode",),
    [
        pytest.param(WriteMode.BLOCKED, id="blocked"),
        pytest.param(WriteMode.PROTECTED, id="protected"),
        pytest.param(WriteMode.ALLOWED, id="allowed"),
    ],
)
def test_execute_sql_select_stmt(write_mode):
  """Test execute_sql tool for SELECT query when writes are blocked."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  query_result = [{"num": 123}]
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=write_mode)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = (
      "test-bq-session-id",
      "_anonymous_dataset",
  )

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job

    # Simulate the result of query_and_wait API
    bq_client.query_and_wait.return_value = query_result

    # Test the tool
    result = query_tool.execute_sql(
        project, query, credentials, tool_settings, tool_context
    )
    assert result == {"status": "SUCCESS", "rows": query_result}


@pytest.mark.parametrize(
    ("query", "statement_type"),
    [
        pytest.param(
            "CREATE TABLE my_dataset.my_table AS SELECT 123 AS num",
            "CREATE_AS_SELECT",
            id="create-as-select",
        ),
        pytest.param(
            "DROP TABLE my_dataset.my_table",
            "DROP_TABLE",
            id="drop-table",
        ),
        pytest.param(
            "CREATE MODEL my_dataset.my_model (model_type='linear_reg',"
            " input_label_cols=['label_col']) AS SELECT * FROM"
            " my_dataset.my_table",
            "CREATE_MODEL",
            id="create-model",
        ),
        pytest.param(
            "DROP MODEL my_dataset.my_model",
            "DROP_MODEL",
            id="drop-model",
        ),
    ],
)
def test_execute_sql_non_select_stmt_write_allowed(query, statement_type):
  """Test execute_sql tool for non-SELECT query when writes are blocked."""
  project = "my_project"
  query_result = []
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=WriteMode.ALLOWED)
  tool_context = mock.create_autospec(ToolContext, instance=True)

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job

    # Simulate the result of query_and_wait API
    bq_client.query_and_wait.return_value = query_result

    # Test the tool
    result = query_tool.execute_sql(
        project, query, credentials, tool_settings, tool_context
    )
    assert result == {"status": "SUCCESS", "rows": query_result}


@pytest.mark.parametrize(
    ("query", "statement_type"),
    [
        pytest.param(
            "CREATE TABLE my_dataset.my_table AS SELECT 123 AS num",
            "CREATE_AS_SELECT",
            id="create-as-select",
        ),
        pytest.param(
            "DROP TABLE my_dataset.my_table",
            "DROP_TABLE",
            id="drop-table",
        ),
        pytest.param(
            "CREATE MODEL my_dataset.my_model (model_type='linear_reg',"
            " input_label_cols=['label_col']) AS SELECT * FROM"
            " my_dataset.my_table",
            "CREATE_MODEL",
            id="create-model",
        ),
        pytest.param(
            "DROP MODEL my_dataset.my_model",
            "DROP_MODEL",
            id="drop-model",
        ),
    ],
)
def test_execute_sql_non_select_stmt_write_blocked(query, statement_type):
  """Test execute_sql tool for non-SELECT query when writes are blocked."""
  project = "my_project"
  query_result = []
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=WriteMode.BLOCKED)
  tool_context = mock.create_autospec(ToolContext, instance=True)

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job

    # Simulate the result of query_and_wait API
    bq_client.query_and_wait.return_value = query_result

    # Test the tool
    result = query_tool.execute_sql(
        project, query, credentials, tool_settings, tool_context
    )
    assert result == {
        "status": "ERROR",
        "error_details": "Read-only mode only supports SELECT statements.",
    }


@pytest.mark.parametrize(
    ("query", "statement_type"),
    [
        pytest.param(
            "CREATE TEMP TABLE my_table AS SELECT 123 AS num",
            "CREATE_AS_SELECT",
            id="create-as-select",
        ),
        pytest.param(
            "DROP TABLE my_table",
            "DROP_TABLE",
            id="drop-table",
        ),
        pytest.param(
            "CREATE TEMP MODEL my_model (model_type='linear_reg',"
            " input_label_cols=['label_col']) AS SELECT * FROM"
            " my_dataset.my_table",
            "CREATE_MODEL",
            id="create-model",
        ),
        pytest.param(
            "DROP MODEL my_model",
            "DROP_MODEL",
            id="drop-model",
        ),
    ],
)
def test_execute_sql_non_select_stmt_write_protected(query, statement_type):
  """Test execute_sql tool for non-SELECT query when writes are protected."""
  project = "my_project"
  query_result = []
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = (
      "test-bq-session-id",
      "_anonymous_dataset",
  )

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    query_job.destination.dataset_id = "_anonymous_dataset"
    bq_client.query.return_value = query_job

    # Simulate the result of query_and_wait API
    bq_client.query_and_wait.return_value = query_result

    # Test the tool
    result = query_tool.execute_sql(
        project, query, credentials, tool_settings, tool_context
    )
    assert result == {"status": "SUCCESS", "rows": query_result}


@pytest.mark.parametrize(
    ("query", "statement_type"),
    [
        pytest.param(
            "CREATE TABLE my_dataset.my_table AS SELECT 123 AS num",
            "CREATE_AS_SELECT",
            id="create-as-select",
        ),
        pytest.param(
            "DROP TABLE my_dataset.my_table",
            "DROP_TABLE",
            id="drop-table",
        ),
        pytest.param(
            "CREATE MODEL my_dataset.my_model (model_type='linear_reg',"
            " input_label_cols=['label_col']) AS SELECT * FROM"
            " my_dataset.my_table",
            "CREATE_MODEL",
            id="create-model",
        ),
        pytest.param(
            "DROP MODEL my_dataset.my_model",
            "DROP_MODEL",
            id="drop-model",
        ),
    ],
)
def test_execute_sql_non_select_stmt_write_protected_persistent_target(
    query, statement_type
):
  """Test execute_sql tool for non-SELECT query when writes are protected.

  This is a special case when the destination table is a persistent/permanent
  one and the protected write is enabled. In this case the operation should
  fail.
  """
  project = "my_project"
  query_result = []
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = (
      "test-bq-session-id",
      "_anonymous_dataset",
  )

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    query_job.destination.dataset_id = "my_dataset"
    bq_client.query.return_value = query_job

    # Simulate the result of query_and_wait API
    bq_client.query_and_wait.return_value = query_result

    # Test the tool
    result = query_tool.execute_sql(
        project, query, credentials, tool_settings, tool_context
    )
    assert result == {
        "status": "ERROR",
        "error_details": (
            "Protected write mode only supports SELECT statements, or write"
            " operations in the anonymous dataset of a BigQuery session."
        ),
    }


def test_execute_sql_dry_run_true():
  """Test execute_sql tool with dry_run=True."""
  project = "my_project"
  query = "SELECT 123 AS num"
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=WriteMode.ALLOWED)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  api_repr = {
      "configuration": {"dryRun": True, "query": {"query": query}},
      "jobReference": {"projectId": project, "location": "US"},
  }

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value

    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.to_api_repr.return_value = api_repr
    bq_client.query.return_value = query_job

    result = query_tool.execute_sql(
        project, query, credentials, tool_settings, tool_context, dry_run=True
    )
    assert result == {"status": "SUCCESS", "dry_run_info": api_repr}
    bq_client.query.assert_called_once()
    _, mock_kwargs = bq_client.query.call_args
    assert mock_kwargs["job_config"].dry_run == True
    bq_client.query_and_wait.assert_not_called()


@pytest.mark.parametrize(
    ("write_mode",),
    [
        pytest.param(WriteMode.BLOCKED, id="blocked"),
        pytest.param(WriteMode.PROTECTED, id="protected"),
        pytest.param(WriteMode.ALLOWED, id="allowed"),
    ],
)
@mock.patch.dict(os.environ, {}, clear=True)
@mock.patch.object(bigquery.Client, "query_and_wait", autospec=True)
@mock.patch.object(bigquery.Client, "query", autospec=True)
@mock.patch.object(google.auth, "default", autospec=True)
def test_execute_sql_no_default_auth(
    mock_default_auth, mock_query, mock_query_and_wait, write_mode
):
  """Test execute_sql tool invocation does not involve calling default auth."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  query_result = [{"num": 123}]
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(write_mode=write_mode)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = (
      "test-bq-session-id",
      "_anonymous_dataset",
  )

  # Simulate the behavior of default auth - on purpose throw exception when
  # the default auth is called
  mock_default_auth.side_effect = DefaultCredentialsError(
      "Your default credentials were not found"
  )

  # Simulate the result of query API
  query_job = mock.create_autospec(bigquery.QueryJob)
  query_job.statement_type = statement_type
  mock_query.return_value = query_job

  # Simulate the result of query_and_wait API
  mock_query_and_wait.return_value = query_result

  # Test the tool worked without invoking default auth
  result = query_tool.execute_sql(
      project, query, credentials, tool_settings, tool_context
  )
  assert result == {"status": "SUCCESS", "rows": query_result}
  mock_default_auth.assert_not_called()


@pytest.mark.parametrize(
    ("query", "query_result", "tool_result_rows"),
    [
        pytest.param(
            "SELECT [1,2,3] AS x",
            [{"x": [1, 2, 3]}],
            [{"x": [1, 2, 3]}],
            id="ARRAY",
        ),
        pytest.param(
            "SELECT TRUE AS x", [{"x": True}], [{"x": True}], id="BOOL"
        ),
        pytest.param(
            "SELECT b'Hello World!' AS x",
            [{"x": b"Hello World!"}],
            [{"x": "b'Hello World!'"}],
            id="BYTES",
        ),
        pytest.param(
            "SELECT DATE '2025-07-21' AS x",
            [{"x": datetime.date(2025, 7, 21)}],
            [{"x": "2025-07-21"}],
            id="DATE",
        ),
        pytest.param(
            "SELECT DATETIME '2025-07-21 14:30:45' AS x",
            [{"x": datetime.datetime(2025, 7, 21, 14, 30, 45)}],
            [{"x": "2025-07-21 14:30:45"}],
            id="DATETIME",
        ),
        pytest.param(
            "SELECT ST_GEOGFROMTEXT('POINT(-122.21 47.48)') as x",
            [{"x": "POINT(-122.21 47.48)"}],
            [{"x": "POINT(-122.21 47.48)"}],
            id="GEOGRAPHY",
        ),
        pytest.param(
            "SELECT INTERVAL 10 DAY as x",
            [{"x": dateutil.relativedelta.relativedelta(days=10)}],
            [{"x": "relativedelta(days=+10)"}],
            id="INTERVAL",
        ),
        pytest.param(
            "SELECT JSON_OBJECT('name', 'Alice', 'age', 30) AS x",
            [{"x": {"age": 30, "name": "Alice"}}],
            [{"x": {"age": 30, "name": "Alice"}}],
            id="JSON",
        ),
        pytest.param("SELECT 1 AS x", [{"x": 1}], [{"x": 1}], id="INT64"),
        pytest.param(
            "SELECT CAST(1.2 AS NUMERIC) AS x",
            [{"x": decimal.Decimal("1.2")}],
            [{"x": "1.2"}],
            id="NUMERIC",
        ),
        pytest.param(
            "SELECT CAST(1.2 AS BIGNUMERIC) AS x",
            [{"x": decimal.Decimal("1.2")}],
            [{"x": "1.2"}],
            id="BIGNUMERIC",
        ),
        pytest.param(
            "SELECT 1.23 AS x", [{"x": 1.23}], [{"x": 1.23}], id="FLOAT64"
        ),
        pytest.param(
            "SELECT RANGE(DATE '2023-01-01', DATE '2023-01-31') as x",
            [{
                "x": {
                    "start": datetime.date(2023, 1, 1),
                    "end": datetime.date(2023, 1, 31),
                }
            }],
            [{
                "x": (
                    "{'start': datetime.date(2023, 1, 1), 'end':"
                    " datetime.date(2023, 1, 31)}"
                )
            }],
            id="RANGE",
        ),
        pytest.param(
            "SELECT 'abc' AS x", [{"x": "abc"}], [{"x": "abc"}], id="STRING"
        ),
        pytest.param(
            "SELECT STRUCT('Alice' AS name, 30 AS age) as x",
            [{"x": {"name": "Alice", "age": 30}}],
            [{"x": {"name": "Alice", "age": 30}}],
            id="STRUCT",
        ),
        pytest.param(
            "SELECT TIME '10:30:45' as x",
            [{"x": datetime.time(10, 30, 45)}],
            [{"x": "10:30:45"}],
            id="TIME",
        ),
        pytest.param(
            "SELECT TIMESTAMP '2025-07-21 10:30:45-07:00' as x",
            [{
                "x": datetime.datetime(
                    2025, 7, 21, 17, 30, 45, tzinfo=datetime.timezone.utc
                )
            }],
            [{"x": "2025-07-21 17:30:45+00:00"}],
            id="TIMESTAMP",
        ),
        pytest.param(
            "SELECT NULL AS x", [{"x": None}], [{"x": None}], id="NULL"
        ),
    ],
)
@mock.patch.dict(os.environ, {}, clear=True)
@mock.patch.object(bigquery.Client, "query_and_wait", autospec=True)
@mock.patch.object(bigquery.Client, "query", autospec=True)
def test_execute_sql_result_dtype(
    mock_query, mock_query_and_wait, query, query_result, tool_result_rows
):
  """Test execute_sql tool invocation for various BigQuery data types.

  See all the supported BigQuery data types at
  https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#data_type_list.
  """
  project = "my_project"
  statement_type = "SELECT"
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig()
  tool_context = mock.create_autospec(ToolContext, instance=True)

  # Simulate the result of query API
  query_job = mock.create_autospec(bigquery.QueryJob)
  query_job.statement_type = statement_type
  mock_query.return_value = query_job

  # Simulate the result of query_and_wait API
  mock_query_and_wait.return_value = query_result

  # Test the tool worked without invoking default auth
  result = query_tool.execute_sql(
      project, query, credentials, tool_settings, tool_context
  )
  assert result == {"status": "SUCCESS", "rows": tool_result_rows}


@mock.patch.dict(os.environ, {}, clear=True)
@mock.patch.object(bigquery.Client, "query_and_wait", autospec=True)
@mock.patch.object(bigquery.Client, "query", autospec=True)
def test_execute_sql_result_dtype_circular_reference(
    mock_query, mock_query_and_wait
):
  """Test execute_sql converts circular values to strings."""
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig()
  tool_context = mock.create_autospec(ToolContext, instance=True)
  query_job = mock.create_autospec(bigquery.QueryJob)
  query_job.statement_type = "SELECT"
  mock_query.return_value = query_job
  circular_value = []
  circular_value.append(circular_value)
  mock_query_and_wait.return_value = [{"x": circular_value}]

  result = query_tool.execute_sql(
      "my_project", "SELECT 1", credentials, tool_settings, tool_context
  )

  assert result == {
      "status": "SUCCESS",
      "rows": [{"x": str(circular_value)}],
  }


@mock.patch.object(bq_client_lib, "get_bigquery_client", autospec=True)
def test_execute_sql_bq_client_creation(mock_get_bigquery_client):
  """Test BigQuery client creation params during execute_sql tool invocation."""
  project = "my_project_id"
  query = "SELECT 1"
  credentials = mock.create_autospec(Credentials, instance=True)
  application_name = "my-agent"
  tool_settings = BigQueryToolConfig(application_name=application_name)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  query_tool.execute_sql(
      project, query, credentials, tool_settings, tool_context
  )
  mock_get_bigquery_client.assert_called_once()
  assert len(mock_get_bigquery_client.call_args.kwargs) == 4
  assert mock_get_bigquery_client.call_args.kwargs["project"] == project
  assert mock_get_bigquery_client.call_args.kwargs["credentials"] == credentials
  assert mock_get_bigquery_client.call_args.kwargs["user_agent"] == [
      application_name,
      "execute_sql",
  ]


def test_execute_sql_unexpected_project_id():
  """Test execute_sql tool invocation with unexpected project id."""
  compute_project_id = "compute_project_id"
  tool_call_project_id = "project_id"
  query = "SELECT 1"
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(compute_project_id=compute_project_id)
  tool_context = mock.create_autospec(ToolContext, instance=True)

  result = query_tool.execute_sql(
      tool_call_project_id, query, credentials, tool_settings, tool_context
  )
  assert result == {
      "status": "ERROR",
      "error_details": (
          f"Cannot execute query in the project {tool_call_project_id}, as the"
          " tool is restricted to execute queries only in the project"
          f" {compute_project_id}."
      ),
  }


# AI.Forecast calls _execute_sql with a specific query statement. We need to
# test that the query is properly constructed and call _execute_sql with the
# correct parameters exactly once.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
def test_forecast_with_table_id(mock_execute_sql):
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig()
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)

  query_tool.forecast(
      project_id="test-project",
      history_data="test-dataset.test-table",
      timestamp_col="ts_col",
      data_col="data_col",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      horizon=20,
      id_cols=["id1", "id2"],
  )

  expected_query = """
  SELECT * FROM AI.FORECAST(
    TABLE `test-dataset.test-table`,
    data_col => 'data_col',
    timestamp_col => 'ts_col',
    model => 'TimesFM 2.0',
    id_cols => ['id1', 'id2'],
    horizon => 20,
    confidence_level => 0.95
  )
  """
  mock_execute_sql.assert_called_once_with(
      project_id="test-project",
      query=expected_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="forecast",
  )


# AI.Forecast calls _execute_sql with a specific query statement. We need to
# test that the query is properly constructed and call _execute_sql with the
# correct parameters exactly once.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
def test_forecast_with_query_statement(mock_execute_sql):
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig()
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)

  history_data_query = "SELECT * FROM `test-dataset.test-table`"
  query_tool.forecast(
      project_id="test-project",
      history_data=history_data_query,
      timestamp_col="ts_col",
      data_col="data_col",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_query = f"""
  SELECT * FROM AI.FORECAST(
    ({history_data_query}),
    data_col => 'data_col',
    timestamp_col => 'ts_col',
    model => 'TimesFM 2.0',
    horizon => 10,
    confidence_level => 0.95
  )
  """
  mock_execute_sql.assert_called_once_with(
      project_id="test-project",
      query=expected_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="forecast",
  )


def test_forecast_with_invalid_id_cols():
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig()
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)

  result = query_tool.forecast(
      project_id="test-project",
      history_data="test-dataset.test-table",
      timestamp_col="ts_col",
      data_col="data_col",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      id_cols=["id1", 123],
  )

  assert result["status"] == "ERROR"
  assert "All elements in id_cols must be strings." in result["error_details"]


# analyze_contribution calls _execute_sql twice. We need to test that the
# queries are properly constructed and call _execute_sql with the correct
# parameters exactly twice.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
@mock.patch.object(uuid, "uuid4", autospec=True)
def test_analyze_contribution_with_table_id(mock_uuid, mock_execute_sql):
  """Test analyze_contribution tool invocation with a table id."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
  mock_uuid.return_value = "test_uuid"
  mock_execute_sql.return_value = {"status": "SUCCESS"}
  query_tool.analyze_contribution(
      project_id="test-project",
      input_data="test-dataset.test-table",
      dimension_id_cols=["dim1", "dim2"],
      contribution_metric="SUM(metric)",
      is_test_col="is_test",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_create_model_query = """
  CREATE TEMP MODEL contribution_analysis_model_test_uuid
    OPTIONS (MODEL_TYPE = 'CONTRIBUTION_ANALYSIS', CONTRIBUTION_METRIC = 'SUM(metric)', IS_TEST_COL = 'is_test', DIMENSION_ID_COLS = ['dim1', 'dim2'], TOP_K_INSIGHTS_BY_APRIORI_SUPPORT = 30, PRUNING_METHOD = 'PRUNE_REDUNDANT_INSIGHTS')
  AS SELECT * FROM `test-dataset.test-table`
  """

  expected_get_insights_query = """
  SELECT * FROM ML.GET_INSIGHTS(MODEL contribution_analysis_model_test_uuid)
  """

  assert mock_execute_sql.call_count == 2
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_create_model_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="analyze_contribution",
  )
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_get_insights_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="analyze_contribution",
  )


# analyze_contribution calls _execute_sql twice. We need to test that the
# queries are properly constructed and call _execute_sql with the correct
# parameters exactly twice.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
@mock.patch.object(uuid, "uuid4", autospec=True)
def test_analyze_contribution_with_query_statement(mock_uuid, mock_execute_sql):
  """Test analyze_contribution tool invocation with a query statement."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
  mock_uuid.return_value = "test_uuid"
  mock_execute_sql.return_value = {"status": "SUCCESS"}
  input_data_query = "SELECT * FROM `test-dataset.test-table`"
  query_tool.analyze_contribution(
      project_id="test-project",
      input_data=input_data_query,
      dimension_id_cols=["dim1", "dim2"],
      contribution_metric="SUM(metric)",
      is_test_col="is_test",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_create_model_query = f"""
  CREATE TEMP MODEL contribution_analysis_model_test_uuid
    OPTIONS (MODEL_TYPE = 'CONTRIBUTION_ANALYSIS', CONTRIBUTION_METRIC = 'SUM(metric)', IS_TEST_COL = 'is_test', DIMENSION_ID_COLS = ['dim1', 'dim2'], TOP_K_INSIGHTS_BY_APRIORI_SUPPORT = 30, PRUNING_METHOD = 'PRUNE_REDUNDANT_INSIGHTS')
  AS ({input_data_query})
  """

  expected_get_insights_query = """
  SELECT * FROM ML.GET_INSIGHTS(MODEL contribution_analysis_model_test_uuid)
  """

  assert mock_execute_sql.call_count == 2
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_create_model_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="analyze_contribution",
  )
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_get_insights_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="analyze_contribution",
  )


def test_analyze_contribution_with_invalid_dimension_id_cols():
  """Test analyze_contribution tool invocation with invalid dimension_id_cols."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig()
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)

  result = query_tool.analyze_contribution(
      project_id="test-project",
      input_data="test-dataset.test-table",
      dimension_id_cols=["dim1", 123],
      contribution_metric="metric",
      is_test_col="is_test",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  assert result["status"] == "ERROR"
  assert (
      "All elements in dimension_id_cols must be strings."
      in result["error_details"]
  )


# detect_anomalies calls _execute_sql twice. We need to test that
# the queries are properly constructed and call _execute_sql with the correct
# parameters exactly twice.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
@mock.patch.object(uuid, "uuid4", autospec=True)
def test_detect_anomalies_with_table_id(mock_uuid, mock_execute_sql):
  """Test time series anomaly detection tool invocation with a table id."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
  mock_uuid.return_value = "test_uuid"
  mock_execute_sql.return_value = {"status": "SUCCESS"}
  history_data_query = "SELECT * FROM `test-dataset.test-table`"
  query_tool.detect_anomalies(
      project_id="test-project",
      history_data=history_data_query,
      times_series_timestamp_col="ts_timestamp",
      times_series_data_col="ts_data",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_create_model_query = """
  CREATE TEMP MODEL detect_anomalies_model_test_uuid
    OPTIONS (MODEL_TYPE = 'ARIMA_PLUS', TIME_SERIES_TIMESTAMP_COL = 'ts_timestamp', TIME_SERIES_DATA_COL = 'ts_data', HORIZON = 1000)
  AS (SELECT * FROM `test-dataset.test-table`)
  """

  expected_anomaly_detection_query = """
  SELECT * FROM ML.DETECT_ANOMALIES(MODEL detect_anomalies_model_test_uuid, STRUCT(0.95 AS anomaly_prob_threshold)) ORDER BY ts_timestamp
  """

  assert mock_execute_sql.call_count == 2
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_create_model_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_anomaly_detection_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )


# detect_anomalies calls _execute_sql twice. We need to test that
# the queries are properly constructed and call _execute_sql with the correct
# parameters exactly twice.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
@mock.patch.object(uuid, "uuid4", autospec=True)
def test_detect_anomalies_with_custom_params(mock_uuid, mock_execute_sql):
  """Test time series anomaly detection tool invocation with a table id."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
  mock_uuid.return_value = "test_uuid"
  mock_execute_sql.return_value = {"status": "SUCCESS"}
  history_data_query = "SELECT * FROM `test-dataset.test-table`"
  query_tool.detect_anomalies(
      project_id="test-project",
      history_data=history_data_query,
      times_series_timestamp_col="ts_timestamp",
      times_series_data_col="ts_data",
      times_series_id_cols=["dim1", "dim2"],
      horizon=20,
      anomaly_prob_threshold=0.8,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_create_model_query = """
  CREATE TEMP MODEL detect_anomalies_model_test_uuid
    OPTIONS (MODEL_TYPE = 'ARIMA_PLUS', TIME_SERIES_TIMESTAMP_COL = 'ts_timestamp', TIME_SERIES_DATA_COL = 'ts_data', HORIZON = 20, TIME_SERIES_ID_COL = ['dim1', 'dim2'])
  AS (SELECT * FROM `test-dataset.test-table`)
  """

  expected_anomaly_detection_query = """
  SELECT * FROM ML.DETECT_ANOMALIES(MODEL detect_anomalies_model_test_uuid, STRUCT(0.8 AS anomaly_prob_threshold)) ORDER BY dim1, dim2, ts_timestamp
  """

  assert mock_execute_sql.call_count == 2
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_create_model_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_anomaly_detection_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )


# detect_anomalies calls _execute_sql twice. We need to test that
# the queries are properly constructed and call _execute_sql with the correct
# parameters exactly twice.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
@mock.patch.object(uuid, "uuid4", autospec=True)
def test_detect_anomalies_on_target_table(mock_uuid, mock_execute_sql):
  """Test time series anomaly detection tool with target data is provided."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
  mock_uuid.return_value = "test_uuid"
  mock_execute_sql.return_value = {"status": "SUCCESS"}
  history_data_query = "SELECT * FROM `test-dataset.history-table`"
  target_data_query = "SELECT * FROM `test-dataset.target-table`"
  query_tool.detect_anomalies(
      project_id="test-project",
      history_data=history_data_query,
      times_series_timestamp_col="ts_timestamp",
      times_series_data_col="ts_data",
      times_series_id_cols=["dim1", "dim2"],
      horizon=20,
      target_data=target_data_query,
      anomaly_prob_threshold=0.8,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_create_model_query = """
  CREATE TEMP MODEL detect_anomalies_model_test_uuid
    OPTIONS (MODEL_TYPE = 'ARIMA_PLUS', TIME_SERIES_TIMESTAMP_COL = 'ts_timestamp', TIME_SERIES_DATA_COL = 'ts_data', HORIZON = 20, TIME_SERIES_ID_COL = ['dim1', 'dim2'])
  AS (SELECT * FROM `test-dataset.history-table`)
  """

  expected_anomaly_detection_query = """
    SELECT * FROM ML.DETECT_ANOMALIES(MODEL detect_anomalies_model_test_uuid, STRUCT(0.8 AS anomaly_prob_threshold), (SELECT * FROM `test-dataset.target-table`)) ORDER BY dim1, dim2, ts_timestamp
    """

  assert mock_execute_sql.call_count == 2
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_create_model_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_anomaly_detection_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )


# detect_anomalies calls execute_sql twice. We need to test that
# the queries are properly constructed and call execute_sql with the correct
# parameters exactly twice.
@mock.patch.object(query_tool, "_execute_sql", autospec=True)
@mock.patch.object(uuid, "uuid4", autospec=True)
def test_detect_anomalies_with_str_table_id(mock_uuid, mock_execute_sql):
  """Test time series anomaly detection tool invocation with a table id."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
  mock_uuid.return_value = "test_uuid"
  mock_execute_sql.return_value = {"status": "SUCCESS"}
  history_data_query = "SELECT * FROM `test-dataset.test-table`"
  query_tool.detect_anomalies(
      project_id="test-project",
      history_data=history_data_query,
      times_series_timestamp_col="ts_timestamp",
      times_series_data_col="ts_data",
      target_data="test-dataset.target-table",
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  expected_create_model_query = """
  CREATE TEMP MODEL detect_anomalies_model_test_uuid
    OPTIONS (MODEL_TYPE = 'ARIMA_PLUS', TIME_SERIES_TIMESTAMP_COL = 'ts_timestamp', TIME_SERIES_DATA_COL = 'ts_data', HORIZON = 1000)
  AS (SELECT * FROM `test-dataset.test-table`)
  """

  expected_anomaly_detection_query = """
    SELECT * FROM ML.DETECT_ANOMALIES(MODEL detect_anomalies_model_test_uuid, STRUCT(0.95 AS anomaly_prob_threshold), (SELECT * FROM `test-dataset.target-table`)) ORDER BY ts_timestamp
    """

  assert mock_execute_sql.call_count == 2
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_create_model_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )
  mock_execute_sql.assert_any_call(
      project_id="test-project",
      query=expected_anomaly_detection_query,
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
      caller_id="detect_anomalies",
  )


def test_detect_anomalies_with_invalid_id_cols():
  """Test time series anomaly detection tool invocation with invalid times_series_id_cols."""
  mock_credentials = mock.MagicMock(spec=Credentials)
  mock_settings = BigQueryToolConfig()
  mock_tool_context = mock.create_autospec(ToolContext, instance=True)

  result = query_tool.detect_anomalies(
      project_id="test-project",
      history_data="test-dataset.test-table",
      times_series_timestamp_col="ts_timestamp",
      times_series_data_col="ts_data",
      times_series_id_cols=["dim1", 123],
      credentials=mock_credentials,
      settings=mock_settings,
      tool_context=mock_tool_context,
  )

  assert result["status"] == "ERROR"
  assert (
      "All elements in times_series_id_cols must be strings."
      in result["error_details"]
  )


@pytest.mark.parametrize(
    ("write_mode", "dry_run", "query_call_count", "query_and_wait_call_count"),
    [
        pytest.param(WriteMode.ALLOWED, False, 0, 1, id="write-allowed"),
        pytest.param(WriteMode.ALLOWED, True, 1, 0, id="write-allowed-dry-run"),
        pytest.param(WriteMode.BLOCKED, False, 1, 1, id="write-blocked"),
        pytest.param(WriteMode.BLOCKED, True, 2, 0, id="write-blocked-dry-run"),
        pytest.param(WriteMode.PROTECTED, False, 2, 1, id="write-protected"),
        pytest.param(
            WriteMode.PROTECTED, True, 3, 0, id="write-protected-dry-run"
        ),
    ],
)
def test_execute_sql_job_labels(
    write_mode, dry_run, query_call_count, query_and_wait_call_count
):
  """Test execute_sql tool for job label."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_settings = BigQueryToolConfig(
      write_mode=write_mode, application_name="test-app"
  )
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = None

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value

    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job

    query_tool.execute_sql(
        project,
        query,
        credentials,
        tool_settings,
        tool_context,
        dry_run=dry_run,
    )

    assert bq_client.query.call_count == query_call_count
    assert bq_client.query_and_wait.call_count == query_and_wait_call_count
    for call_args_list in [
        bq_client.query.call_args_list,
        bq_client.query_and_wait.call_args_list,
    ]:
      for call_args in call_args_list:
        _, mock_kwargs = call_args
        assert mock_kwargs["job_config"].labels == {
            "adk-bigquery-tool": "execute_sql",
            "adk-bigquery-application-name": "test-app",
        }


@pytest.mark.parametrize(
    ("write_mode", "dry_run", "query_call_count", "query_and_wait_call_count"),
    [
        pytest.param(WriteMode.ALLOWED, False, 0, 1, id="write-allowed"),
        pytest.param(WriteMode.ALLOWED, True, 1, 0, id="write-allowed-dry-run"),
        pytest.param(WriteMode.BLOCKED, False, 1, 1, id="write-blocked"),
        pytest.param(WriteMode.BLOCKED, True, 2, 0, id="write-blocked-dry-run"),
        pytest.param(WriteMode.PROTECTED, False, 2, 1, id="write-protected"),
        pytest.param(
            WriteMode.PROTECTED, True, 3, 0, id="write-protected-dry-run"
        ),
    ],
)
def test_execute_sql_user_job_labels_augment_internal_labels(
    write_mode, dry_run, query_call_count, query_and_wait_call_count
):
  """Test execute_sql tool augments user job_labels with internal labels."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  credentials = mock.create_autospec(Credentials, instance=True)
  user_labels = {"environment": "test", "team": "data"}
  tool_settings = BigQueryToolConfig(
      write_mode=write_mode,
      job_labels=user_labels,
  )
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = None

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value

    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job

    query_tool.execute_sql(
        project,
        query,
        credentials,
        tool_settings,
        tool_context,
        dry_run=dry_run,
    )

    assert bq_client.query.call_count == query_call_count
    assert bq_client.query_and_wait.call_count == query_and_wait_call_count
    # Build expected labels from user_labels + internal label
    expected_labels = {**user_labels, "adk-bigquery-tool": "execute_sql"}
    for call_args_list in [
        bq_client.query.call_args_list,
        bq_client.query_and_wait.call_args_list,
    ]:
      for call_args in call_args_list:
        _, mock_kwargs = call_args
        # Verify user labels are preserved and internal label is added
        assert mock_kwargs["job_config"].labels == expected_labels


@pytest.mark.parametrize(
    ("tool_call", "expected_tool_label"),
    [
        pytest.param(
            lambda tool_context: query_tool.forecast(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                timestamp_col="ts_col",
                data_col="data_col",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(write_mode=WriteMode.ALLOWED),
                tool_context=tool_context,
            ),
            "forecast",
            id="forecast",
        ),
        pytest.param(
            lambda tool_context: query_tool.analyze_contribution(
                project_id="test-project",
                input_data="test-dataset.test-table",
                dimension_id_cols=["dim1", "dim2"],
                contribution_metric="SUM(metric)",
                is_test_col="is_test",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(write_mode=WriteMode.ALLOWED),
                tool_context=tool_context,
            ),
            "analyze_contribution",
            id="analyze-contribution",
        ),
        pytest.param(
            lambda tool_context: query_tool.detect_anomalies(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                times_series_timestamp_col="ts_timestamp",
                times_series_data_col="ts_data",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(write_mode=WriteMode.ALLOWED),
                tool_context=tool_context,
            ),
            "detect_anomalies",
            id="detect-anomalies",
        ),
    ],
)
def test_ml_tool_job_labels(tool_call, expected_tool_label):
  """Test ML tools for job label."""

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value

    tool_context = mock.create_autospec(ToolContext, instance=True)
    tool_context.state.get.return_value = None
    tool_call(tool_context)

    for call_args_list in [
        bq_client.query.call_args_list,
        bq_client.query_and_wait.call_args_list,
    ]:
      for call_args in call_args_list:
        _, mock_kwargs = call_args
        assert mock_kwargs["job_config"].labels == {
            "adk-bigquery-tool": expected_tool_label
        }


@pytest.mark.parametrize(
    ("tool_call", "expected_tool_label"),
    [
        pytest.param(
            lambda tool_context: query_tool.forecast(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                timestamp_col="ts_col",
                data_col="data_col",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(
                    write_mode=WriteMode.ALLOWED, application_name="test-app"
                ),
                tool_context=tool_context,
            ),
            "forecast",
            id="forecast-app-name",
        ),
        pytest.param(
            lambda tool_context: query_tool.analyze_contribution(
                project_id="test-project",
                input_data="test-dataset.test-table",
                dimension_id_cols=["dim1", "dim2"],
                contribution_metric="SUM(metric)",
                is_test_col="is_test",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(
                    write_mode=WriteMode.ALLOWED, application_name="test-app"
                ),
                tool_context=tool_context,
            ),
            "analyze_contribution",
            id="analyze-contribution-app-name",
        ),
        pytest.param(
            lambda tool_context: query_tool.detect_anomalies(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                times_series_timestamp_col="ts_timestamp",
                times_series_data_col="ts_data",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(
                    write_mode=WriteMode.ALLOWED, application_name="test-app"
                ),
                tool_context=tool_context,
            ),
            "detect_anomalies",
            id="detect-anomalies-app-name",
        ),
    ],
)
def test_ml_tool_job_labels_w_application_name(tool_call, expected_tool_label):
  """Test ML tools for job label with application name."""

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value

    tool_context = mock.create_autospec(ToolContext, instance=True)
    tool_context.state.get.return_value = None
    tool_call(tool_context)

    expected_labels = {
        "adk-bigquery-tool": expected_tool_label,
        "adk-bigquery-application-name": "test-app",
    }

    for call_args_list in [
        bq_client.query.call_args_list,
        bq_client.query_and_wait.call_args_list,
    ]:
      for call_args in call_args_list:
        _, mock_kwargs = call_args
        assert mock_kwargs["job_config"].labels == expected_labels


@pytest.mark.parametrize(
    ("tool_call", "expected_labels"),
    [
        pytest.param(
            lambda tool_context: query_tool.forecast(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                timestamp_col="ts_col",
                data_col="data_col",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(
                    write_mode=WriteMode.ALLOWED,
                    job_labels={"environment": "prod", "app": "forecaster"},
                ),
                tool_context=tool_context,
            ),
            {
                "environment": "prod",
                "app": "forecaster",
                "adk-bigquery-tool": "forecast",
            },
            id="forecast",
        ),
        pytest.param(
            lambda tool_context: query_tool.analyze_contribution(
                project_id="test-project",
                input_data="test-dataset.test-table",
                dimension_id_cols=["dim1", "dim2"],
                contribution_metric="SUM(metric)",
                is_test_col="is_test",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(
                    write_mode=WriteMode.ALLOWED,
                    job_labels={"environment": "prod", "app": "analyzer"},
                ),
                tool_context=tool_context,
            ),
            {
                "environment": "prod",
                "app": "analyzer",
                "adk-bigquery-tool": "analyze_contribution",
            },
            id="analyze-contribution",
        ),
        pytest.param(
            lambda tool_context: query_tool.detect_anomalies(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                times_series_timestamp_col="ts_timestamp",
                times_series_data_col="ts_data",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=BigQueryToolConfig(
                    write_mode=WriteMode.ALLOWED,
                    job_labels={"environment": "prod", "app": "detector"},
                ),
                tool_context=tool_context,
            ),
            {
                "environment": "prod",
                "app": "detector",
                "adk-bigquery-tool": "detect_anomalies",
            },
            id="detect-anomalies",
        ),
    ],
)
def test_ml_tool_user_job_labels_augment_internal_labels(
    tool_call, expected_labels
):
  """Test ML tools augment user job_labels with internal labels."""

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value

    tool_context = mock.create_autospec(ToolContext, instance=True)
    tool_context.state.get.return_value = None
    tool_call(tool_context)

    for call_args_list in [
        bq_client.query.call_args_list,
        bq_client.query_and_wait.call_args_list,
    ]:
      for call_args in call_args_list:
        _, mock_kwargs = call_args
        # Verify user labels are preserved and internal label is added
        assert mock_kwargs["job_config"].labels == expected_labels


def test_execute_sql_max_rows_config():
  """Test execute_sql tool respects max_query_result_rows from config."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  query_result = [{"num": i} for i in range(20)]  # 20 rows
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_config = BigQueryToolConfig(max_query_result_rows=10)
  tool_context = mock.create_autospec(ToolContext, instance=True)

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job
    bq_client.query_and_wait.return_value = query_result[:10]

    result = query_tool.execute_sql(
        project, query, credentials, tool_config, tool_context
    )

    # Check that max_results was called with config value
    bq_client.query_and_wait.assert_called_once()
    call_args = bq_client.query_and_wait.call_args
    assert call_args.kwargs["max_results"] == 10

    # Check truncation flag is set
    assert result["status"] == "SUCCESS"
    assert result["result_is_likely_truncated"] is True


def test_execute_sql_no_truncation():
  """Test execute_sql tool when results are not truncated."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  query_result = [{"num": i} for i in range(3)]  # Only 3 rows
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_config = BigQueryToolConfig(max_query_result_rows=10)
  tool_context = mock.create_autospec(ToolContext, instance=True)

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job
    bq_client.query_and_wait.return_value = query_result

    result = query_tool.execute_sql(
        project, query, credentials, tool_config, tool_context
    )

    # Check no truncation flag when fewer rows than limit
    assert result["status"] == "SUCCESS"
    assert "result_is_likely_truncated" not in result


def test_execute_sql_maximum_bytes_billed_config():
  """Test execute_sql tool respects maximum_bytes_billed from config."""
  project = "my_project"
  query = "SELECT 123 AS num"
  statement_type = "SELECT"
  credentials = mock.create_autospec(Credentials, instance=True)
  tool_config = BigQueryToolConfig(maximum_bytes_billed=11_000_000)
  tool_context = mock.create_autospec(ToolContext, instance=True)

  with mock.patch.object(bigquery, "Client", autospec=True) as Client:
    bq_client = Client.return_value
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.statement_type = statement_type
    bq_client.query.return_value = query_job

    query_tool.execute_sql(
        project, query, credentials, tool_config, tool_context
    )

    # Check that maximum_bytes_billed was called with config value
    bq_client.query_and_wait.assert_called_once()
    call_args = bq_client.query_and_wait.call_args
    assert call_args.kwargs["job_config"].maximum_bytes_billed == 11_000_000


@pytest.mark.parametrize(
    ("tool_call",),
    [
        pytest.param(
            lambda settings, tool_context: query_tool.execute_sql(
                project_id="test-project",
                query="SELECT * FROM `test-dataset.test-table`",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="execute-sql",
        ),
        pytest.param(
            lambda settings, tool_context: query_tool.forecast(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                timestamp_col="ts_col",
                data_col="data_col",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="forecast",
        ),
        pytest.param(
            lambda settings, tool_context: query_tool.analyze_contribution(
                project_id="test-project",
                input_data="test-dataset.test-table",
                dimension_id_cols=["dim1", "dim2"],
                contribution_metric="SUM(metric)",
                is_test_col="is_test",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="analyze-contribution",
        ),
        pytest.param(
            lambda settings, tool_context: query_tool.detect_anomalies(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                times_series_timestamp_col="ts_timestamp",
                times_series_data_col="ts_data",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="detect-anomalies",
        ),
    ],
)
def test_tool_call_doesnt_change_global_settings(tool_call):
  """Test query tools don't change global settings."""
  settings = BigQueryToolConfig(write_mode=WriteMode.ALLOWED)
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = (
      "test-bq-session-id",
      "_anonymous_dataset",
  )

  with mock.patch("google.cloud.bigquery.Client", autospec=False) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.destination.dataset_id = "_anonymous_dataset"
    bq_client.query.return_value = query_job
    bq_client.query_and_wait.return_value = []

    # Test settings write mode before
    assert settings.write_mode == WriteMode.ALLOWED

    # Call the tool
    result = tool_call(settings, tool_context)

    # Test successfull executeion of the tool
    assert result == {"status": "SUCCESS", "rows": []}

    # Test settings write mode after
    assert settings.write_mode == WriteMode.ALLOWED


@pytest.mark.parametrize(
    ("tool_call",),
    [
        pytest.param(
            lambda settings, tool_context: query_tool.execute_sql(
                project_id="test-project",
                query="SELECT * FROM `test-dataset.test-table`",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="execute-sql",
        ),
        pytest.param(
            lambda settings, tool_context: query_tool.forecast(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                timestamp_col="ts_col",
                data_col="data_col",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="forecast",
        ),
        pytest.param(
            lambda settings, tool_context: query_tool.analyze_contribution(
                project_id="test-project",
                input_data="test-dataset.test-table",
                dimension_id_cols=["dim1", "dim2"],
                contribution_metric="SUM(metric)",
                is_test_col="is_test",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="analyze-contribution",
        ),
        pytest.param(
            lambda settings, tool_context: query_tool.detect_anomalies(
                project_id="test-project",
                history_data="SELECT * FROM `test-dataset.test-table`",
                times_series_timestamp_col="ts_timestamp",
                times_series_data_col="ts_data",
                credentials=mock.create_autospec(Credentials, instance=True),
                settings=settings,
                tool_context=tool_context,
            ),
            id="detect-anomalies",
        ),
    ],
)
def test_tool_call_doesnt_mutate_job_labels(tool_call):
  """Test query tools don't mutate job_labels in global settings."""
  original_labels = {"environment": "test", "team": "data"}
  settings = BigQueryToolConfig(
      write_mode=WriteMode.ALLOWED,
      job_labels=original_labels.copy(),
  )
  tool_context = mock.create_autospec(ToolContext, instance=True)
  tool_context.state.get.return_value = (
      "test-bq-session-id",
      "_anonymous_dataset",
  )

  with mock.patch("google.cloud.bigquery.Client", autospec=False) as Client:
    # The mock instance
    bq_client = Client.return_value

    # Simulate the result of query API
    query_job = mock.create_autospec(bigquery.QueryJob)
    query_job.destination.dataset_id = "_anonymous_dataset"
    bq_client.query.return_value = query_job
    bq_client.query_and_wait.return_value = []

    # Test job_labels before
    assert settings.job_labels == original_labels
    assert "adk-bigquery-tool" not in settings.job_labels

    # Call the tool
    result = tool_call(settings, tool_context)

    # Test successful execution of the tool
    assert result == {"status": "SUCCESS", "rows": []}

    # Test job_labels remain unchanged after tool call
    assert settings.job_labels == original_labels
    assert "adk-bigquery-tool" not in settings.job_labels
