"""bigquery-etl CLI monitoring command."""

import ast
import json
import os
import re
import sys
from collections import defaultdict
from pathlib import Path
from typing import Optional

import click
import sqlglot
from bigeye_sdk.authentication.api_authentication import APIKeyAuth
from bigeye_sdk.bigconfig_validation.validation_context import _BIGEYE_YAML_FILE_IX
from bigeye_sdk.client.datawatch_client import datawatch_client_factory
from bigeye_sdk.client.enum import Method
from bigeye_sdk.controller.metric_suite_controller import MetricSuiteController
from bigeye_sdk.exceptions.exceptions import FileLoadException
from bigeye_sdk.generated.com.bigeye.models.generated import MetricRunStatus
from bigeye_sdk.model.big_config import (
    BigConfig,
    ColumnSelector,
    RowCreationTimes,
    TagDeployment,
    TagDeploymentSuite,
)
from bigeye_sdk.model.protobuf_message_facade import (
    SimpleCollection,
    SimpleConstantThreshold,
    SimpleMetricDefinition,
    SimpleMetricSchedule,
    SimpleNamedSchedule,
    SimplePredefinedMetric,
    SimplePredefinedMetricName,
)

from bigquery_etl.config import ConfigLoader
from bigquery_etl.metadata.parse_metadata import METADATA_FILE, Metadata

from ..cli.utils import paths_matching_name_pattern, project_id_option, sql_dir_option
from ..util import extract_from_query_path
from ..util.common import render as render_template

BIGCONFIG_FILE = "bigconfig.yml"
CUSTOM_RULES_FILE = "bigeye_custom_rules.sql"
QUERY_FILE = "query.sql"
VIEW_FILE = "view.sql"
METRIC_STATUS_FAILURES = [
    MetricRunStatus.METRIC_RUN_STATUS_UPPERBOUND_CRITICAL,
    MetricRunStatus.METRIC_RUN_STATUS_LOWERBOUND_CRITICAL,
    MetricRunStatus.METRIC_RUN_STATUS_GROUPS_CRITICAL,
    MetricRunStatus.METRIC_RUN_STATUS_MUTABLE_UPPERBOUND_CRITICAL,
    MetricRunStatus.METRIC_RUN_STATUS_MUTABLE_LOWERBOUND_CRITICAL,
    MetricRunStatus.METRIC_RUN_STATUS_GROUPS_LIMIT_FAILED,
]


@click.group(
    help="""
        Commands for managing monitoring of datasets.
        """
)
@click.pass_context
def monitoring(ctx):
    """Create the CLI group for the monitoring command."""
    pass


@monitoring.command(
    help="""
    Deploy monitors defined in the BigConfig files to Bigeye.

    Requires BigConfig API key to be set via BIGEYE_API_KEY env variable.
    """
)
@click.argument("name")
@project_id_option("moz-fx-data-shared-prod")
@sql_dir_option
@click.option(
    "--workspace",
    default=463,
    help="Bigeye workspace to use when authenticating to API.",
)
@click.option(
    "--base-url",
    "--base_url",
    default="https://app.bigeye.com",
    help="Bigeye base URL.",
)
@click.option(
    "--dry-run",
    "--dry_run",
    "--dryrun",
    is_flag=True,
    help="Dry run the BigConfig files without applying them.",
    default=False,
)
@click.pass_context
def deploy(
    ctx,
    name: str,
    sql_dir: Optional[str],
    project_id: Optional[str],
    workspace: str,
    base_url: str,
    dry_run: bool,
) -> None:
    """Deploy Bigeye config."""
    api_key = os.environ.get("BIGEYE_API_KEY")
    if api_key is None:
        click.echo(
            "Bigeye API token needs to be set via `BIGEYE_API_KEY` env variable."
        )
        sys.exit(1)

    """Deploy monitors to Bigeye."""
    metadata_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["metadata.yaml"]
    )

    if len(metadata_files) == 0:
        print(f"No metadata files matching {name}")
        return

    for metadata_file in list(set(metadata_files)):
        project, dataset, table = extract_from_query_path(metadata_file)
        try:
            metadata = Metadata.from_file(metadata_file)
            if metadata.monitoring and metadata.monitoring.enabled:
                ctx.invoke(
                    update,
                    name=metadata_file.parent,
                    sql_dir=sql_dir,
                    project_id=project_id,
                )
                api_auth = APIKeyAuth(base_url=base_url, api_key=api_key)
                client = datawatch_client_factory(api_auth, workspace_id=workspace)
                mc = MetricSuiteController(client=client)

                ctx.invoke(
                    validate,
                    name=metadata_file.parent,
                    sql_dir=sql_dir,
                    project_id=project_id,
                )

                if (
                    metadata_file.parent / VIEW_FILE
                ).exists() or "public_bigquery" in metadata.labels:
                    # monitoring to be deployed on a view
                    # Bigeye requires to explicitly set the partition column for views
                    if metadata.monitoring.partition_column and not dry_run:
                        ctx.invoke(
                            set_partition_column,
                            name=metadata_file.parent,
                            sql_dir=sql_dir,
                            project_id=project_id,
                        )

                if (metadata_file.parent / CUSTOM_RULES_FILE).exists():
                    if dry_run:
                        print(
                            f"Dry run; Skip deploying custom SQL rule {metadata_file.parent}"
                        )
                    else:
                        ctx.invoke(
                            deploy_custom_rules,
                            name=metadata_file.parent,
                            sql_dir=sql_dir,
                            project_id=project_id,
                        )

        except FileNotFoundError:
            print("No metadata file for: {}.{}.{}".format(project, dataset, table))

    try:
        # Deploy BigConfig files at once.
        # Deploying BigConfig files separately can lead to previously deployed metrics being removed.
        mc.execute_bigconfig(
            input_path=[
                metadata_file.parent / BIGCONFIG_FILE
                for metadata_file in list(set(metadata_files))
            ]
            + [f"{sql_dir}/{BIGCONFIG_FILE}"],
            output_path=Path(sql_dir).parent if sql_dir else None,
            apply=not dry_run,
            recursive=False,
            strict_mode=True,
            auto_approve=True,
        )
    except Exception as e:
        if dry_run:
            bigconfig_result = Path(f"{project_id}_PLAN.yml")
        else:
            bigconfig_result = Path(f"{project_id}_APPLY.yml")
        click.echo(bigconfig_result.read_text())
        raise e


def _sql_rules_from_file(custom_rules_file, project, dataset, table) -> list:
    """Extract the SQL rules from the custom rules file."""
    jinja_params = {
        **{
            "dataset_id": dataset,
            "table_name": table,
            "project_id": project,
            "format": False,
        },
    }

    rendered_result = render_template(
        custom_rules_file.name,
        template_folder=str(custom_rules_file.parent),
        templates_dir="",
        **jinja_params,
    )

    statements = []
    for statement in sqlglot.parse(rendered_result, read="bigquery"):
        if statement is None:
            continue

        for select_statement in statement.find_all(sqlglot.exp.Select):
            statements.append(select_statement)

    return statements


@monitoring.command(
    help="""
    Deploy custom SQL rules.
    """
)
@click.argument("name")
@project_id_option()
@sql_dir_option
@click.option(
    "--workspace",
    default=463,
    help="Bigeye workspace to use when authenticating to API.",
)
@click.option(
    "--base-url",
    "--base_url",
    default="https://app.bigeye.com",
    help="Bigeye base URL.",
)
def deploy_custom_rules(
    name: str,
    sql_dir: Optional[str],
    project_id: Optional[str],
    base_url: str,
    workspace: int,
) -> None:
    """Deploy custom SQL rules for files."""
    api_key = os.environ.get("BIGEYE_API_KEY")
    if api_key is None:
        click.echo(
            "Bigeye API token needs to be set via `BIGEYE_API_KEY` env variable."
        )
        sys.exit(1)

    custom_rules_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=[CUSTOM_RULES_FILE]
    )

    api_auth = APIKeyAuth(base_url=base_url, api_key=api_key)
    client = datawatch_client_factory(api_auth, workspace_id=workspace)
    collections = client.get_collections()
    warehouse_id = ConfigLoader.get("monitoring", "bigeye_warehouse_id")
    existing_rules = client.get_rules_for_source(warehouse_id=warehouse_id)
    existing_rules_sql = [rule.custom_rule.sql for rule in existing_rules.custom_rules]
    existing_schedules = {
        schedule.name: schedule.id
        for schedule in client.get_named_schedule().named_schedules
    }
    url = "/api/v1/custom-rules"

    for custom_rule_file in list(set(custom_rules_files)):
        project, dataset, table = extract_from_query_path(custom_rule_file)
        try:
            metadata = Metadata.from_file(custom_rule_file.parent / METADATA_FILE)
            if metadata.monitoring and metadata.monitoring.enabled:
                # Convert all the Airflow params to jinja usable dict.
                for select_statement in _sql_rules_from_file(
                    custom_rule_file, project, dataset, table
                ):
                    sql = select_statement.sql(dialect="bigquery")
                    if sql in existing_rules_sql:
                        continue

                    # parse config values from SQL comment
                    config = {}
                    if select_statement.comments:
                        config = ast.literal_eval("".join(select_statement.comments))

                    payload = {
                        "name": config.get(
                            "name", f"{project}_{dataset}_{table}_bqetl_check"
                        ),
                        "sql": sql,
                        "warehouseId": warehouse_id,
                        "thresholdType": "CUSTOM_RULES_THRESHOLD_TYPE_"
                        + config.get("alert_type", "count").upper(),
                    }

                    if "range" in config:
                        if "min" in config["range"]:
                            payload["lowerThreshold"] = config["range"]["min"]
                        if "max" in config["range"]:
                            payload["upperThreshold"] = config["range"]["max"]

                    if "owner" in config:
                        payload["owner"] = {"email": config["owner"]}

                    if "collections" in config:
                        collection_ids = [
                            collection.id
                            for collection in collections.collections
                            if collection.name in config["collections"]
                        ]
                        payload["collectionIds"] = collection_ids

                    if (
                        "schedule" in config
                        and config["schedule"] in existing_schedules
                    ):
                        payload["metricSchedule"] = {
                            "namedSchedule": {
                                "id": existing_schedules[config["schedule"]]
                            }
                        }

                    try:
                        response = client._call_datawatch(
                            Method.POST,
                            url=url,
                            body=json.dumps({"customRule": payload}),
                        )

                        if "id" in response:
                            click.echo(
                                f"Created custom rule {response['id']} for `{project}.{dataset}.{table}`"
                            )
                    except Exception as e:
                        if "There was an error processing your request" in str(e):
                            # API throws an error when partition column was already set.
                            # There is no API endpoint to check for partition columns though
                            click.echo(
                                f"Partition column for `{project}.{dataset}.{table}` already set."
                            )
                        else:
                            raise e

        except FileNotFoundError:
            print("No metadata file for: {}.{}.{}".format(project, dataset, table))


def _update_bigconfig(
    bigconfig,
    metadata,
    project,
    dataset,
    table,
    default_metrics,
):
    """Update the BigConfig file to monitor a view."""
    tag_deployment_metrics = [
        {
            "predefined_metric": (
                metric.metric_type.predefined_metric.upper()
                if metric.metric_type
                else None
            ),
            "saved_metric_id": (
                metric.saved_metric_id.upper().removesuffix("_FAIL")
                if metric.saved_metric_id
                else None
            ),
            "metric_name": (metric.metric_name.upper() if metric.metric_name else None),
        }
        for collection in bigconfig.tag_deployments
        for deployment in collection.deployments
        for metric in deployment.metrics
    ]

    table_deployment_metrics = [
        {
            "predefined_metric": (
                table_metric.metric_type.predefined_metric.upper()
                if table_metric.metric_type
                else None
            ),
            "saved_metric_id": (
                table_metric.saved_metric_id.upper().removesuffix("_FAIL")
                if table_metric.saved_metric_id
                else None
            ),
            "metric_name": (
                table_metric.metric_name.upper() if table_metric.metric_name else None
            ),
        }
        for collection in bigconfig.table_deployments
        for deployment in collection.deployments
        for table_metric in deployment.table_metrics
    ]

    for deployment_metric in tag_deployment_metrics + table_deployment_metrics:
        config_metric = (
            deployment_metric["predefined_metric"]
            or deployment_metric["saved_metric_id"]
            or deployment_metric["metric_name"]
        )

        if config_metric in default_metrics:
            default_metrics.remove(config_metric)

    if len(default_metrics) > 0:
        deployments = [
            TagDeployment(
                column_selectors=[
                    ColumnSelector(name=f"{project}.{project}.{dataset}.{table}.*")
                ],
                metrics=[
                    SimpleMetricDefinition(
                        metric_name=(
                            f"{metric.name}"
                            if "volume" not in metric.name.lower()
                            else f"{metric.name} [fail]"
                        ),
                        metric_type=SimplePredefinedMetric(
                            type="PREDEFINED", predefined_metric=metric
                        ),
                        metric_schedule=SimpleMetricSchedule(
                            named_schedule=SimpleNamedSchedule(
                                name="Default Schedule - 13:00 UTC"
                            )
                        ),
                    )
                    for metric in default_metrics
                ],
            )
        ]

        collection = None
        if metadata.monitoring.collection:
            collection = SimpleCollection(name=metadata.monitoring.collection)

        bigconfig.tag_deployments += [
            TagDeploymentSuite(deployments=deployments, collection=collection)
        ]

        if metadata.monitoring.partition_column:
            bigconfig.row_creation_times = RowCreationTimes(
                column_selectors=[
                    ColumnSelector(
                        name=f"{project}.{project}.{dataset}.{table}.{metadata.monitoring.partition_column}"
                    )
                ]
            )


@monitoring.command(
    help="""
    Update BigConfig files based on monitoring metadata.

    Requires BigConfig credentials to be set via BIGEYE_API_CRED_FILE env variable.
    """
)
@click.argument("name")
@project_id_option()
@sql_dir_option
def update(name: str, sql_dir: Optional[str], project_id: Optional[str]) -> None:
    """Update BigConfig files based on monitoring metadata."""
    metadata_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["metadata.yaml"]
    )

    for metadata_file in list(set(metadata_files)):
        project, dataset, table = extract_from_query_path(metadata_file)
        try:
            metadata = Metadata.from_file(metadata_file)
            if metadata.monitoring and metadata.monitoring.enabled:
                bigconfig_file = metadata_file.parent / BIGCONFIG_FILE

                if bigconfig_file.exists():
                    bigconfig = BigConfig.load(bigconfig_file)
                else:
                    bigconfig = BigConfig(type="BIGCONFIG_FILE")

                if (
                    metadata_file.parent / QUERY_FILE
                ).exists() and "public_bigquery" not in metadata.labels:
                    _update_bigconfig(
                        bigconfig=bigconfig,
                        metadata=metadata,
                        project=project,
                        dataset=dataset,
                        table=table,
                        default_metrics=[
                            SimplePredefinedMetricName.FRESHNESS,
                            SimplePredefinedMetricName.VOLUME,
                        ],
                    )

                bigconfig.save(
                    output_path=bigconfig_file.parent,
                    default_file_name=bigconfig_file.stem,
                )

        except FileNotFoundError:
            print("No metadata file for: {}.{}.{}".format(project, dataset, table))


@monitoring.command(
    help="""
    Validate BigConfig files.
    """
)
@click.argument("name")
@project_id_option()
@sql_dir_option
def validate(name: str, sql_dir: Optional[str], project_id: Optional[str]) -> None:
    """Validate BigConfig file."""
    bigconfig_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["bigconfig.yml"]
    )

    invalid = False

    for bigconfig_file in list(set(bigconfig_files)):
        try:
            BigConfig.load(bigconfig_file)
        except FileLoadException as e:
            if "Duplicate" in e.message:
                pass
            else:
                click.echo(f"Invalid BigConfig file {bigconfig_file}: {e}")
                invalid = True
        except Exception as e:
            click.echo(f"Invalid BigConfig file {bigconfig_file}: {e}")
            invalid = True

        if (bigconfig_file.parent / VIEW_FILE).exists():
            try:
                metadata = Metadata.from_file(bigconfig_file.parent / METADATA_FILE)
                if metadata.monitoring and metadata.monitoring.enabled:
                    if not metadata.monitoring.partition_column_set:
                        invalid = True
                        click.echo(
                            "If montoring is enabled for views, then the partition_column needs to be configured "
                            + f"explicitly in the `monitoring` metadata for {bigconfig_file.parent}. "
                            + "Set `partition_column: null` for views that do not reference a partitioned table."
                        )
            except FileNotFoundError:
                pass  # view has no metadata file, so monitoring is disabled

    if invalid:
        sys.exit(1)

    click.echo("All BigConfig files are valid.")


@monitoring.command(
    help="""
    Set partition column for view or table in Bigeye.
    """
)
@click.argument("name")
@project_id_option()
@sql_dir_option
@click.option(
    "--workspace",
    default=463,
    help="Bigeye workspace to use when authenticating to API.",
)
@click.option(
    "--base-url",
    "--base_url",
    default="https://app.bigeye.com",
    help="Bigeye base URL.",
)
def set_partition_column(
    name: str,
    sql_dir: Optional[str],
    project_id: Optional[str],
    base_url: str,
    workspace: int,
) -> None:
    """Validate BigConfig file."""
    api_key = os.environ.get("BIGEYE_API_KEY")
    if api_key is None:
        click.echo(
            "Bigeye API token needs to be set via `BIGEYE_API_KEY` env variable."
        )
        sys.exit(1)

    metadata_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["metadata.yaml"]
    )

    for metadata_file in list(set(metadata_files)):
        project, dataset, table = extract_from_query_path(metadata_file)
        try:
            metadata = Metadata.from_file(metadata_file)
            if metadata.monitoring and metadata.monitoring.enabled:
                if not metadata.monitoring.partition_column_set:
                    click.echo(f"No partition column set for {metadata_file.parent}")
                    continue
                api_auth = APIKeyAuth(base_url=base_url, api_key=api_key)
                client = datawatch_client_factory(api_auth, workspace_id=workspace)

                warehouse_id = ConfigLoader.get("monitoring", "bigeye_warehouse_id")
                table_id = client.get_table_ids(
                    warehouse_id=warehouse_id,
                    schemas=f"{project}.{dataset}",
                    table_name=table,
                )

                if len(table_id) == 0:
                    click.echo(
                        f"Table `{project}.{dataset}.{table}` does not exist in Bigeye."
                    )
                    sys.exit(1)
                table_id = table_id[0]

                columns = client.get_columns(table_ids=[table_id])
                column_id = None
                for column in columns.columns:
                    if column.column.name == metadata.monitoring.partition_column:
                        column_id = column.column.id
                        break

                if column_id is None:
                    click.echo(
                        f"Configured partition column {metadata.monitoring.partition_column} does not exist for table `{project}.{dataset}.{table}`"
                    )
                    sys.exit(1)

                url = f"/api/v1/tables/{table_id}/required-partition-column"
                payload = json.dumps({"columnId": column_id})

                try:
                    response = client._call_datawatch(Method.PUT, url=url, body=payload)

                    if (
                        response.get("datasetId", None) == table_id
                        and response.get("requiredPartitionColumnId") == column_id
                    ):
                        click.echo(
                            f"Set partition column {column_id} for `{project}.{dataset}.{table}`"
                        )
                except Exception as e:
                    if "There was an error processing your request" in str(
                        e
                    ) or "Dataset already requires a partition filter" in str(e):
                        # API throws an error when partition column was already set.
                        # There is no API endpoint to check for partition columns though
                        pass
                    else:
                        raise e

        except FileNotFoundError:
            print("No metadata file for: {}.{}.{}".format(project, dataset, table))


@monitoring.command(
    help="""
    Delete deployed monitors. Use --custom-sql and/or --metrics flags to select which types of monitors to delete.
    """
)
@click.argument("name")
@project_id_option()
@sql_dir_option
@click.option(
    "--workspace",
    default=463,
    help="Bigeye workspace to use when authenticating to API.",
)
@click.option(
    "--base-url",
    "--base_url",
    default="https://app.bigeye.com",
    help="Bigeye base URL.",
)
@click.option(
    "--custom-sql",
    "--custom_sql",
    is_flag=True,
    default=False,
    help="Deletes custom SQL rules.",
)
@click.option(
    "--metrics",
    is_flag=True,
    default=False,
    help="Deletes metrics checks.",
)
def delete(
    name: str,
    sql_dir: Optional[str],
    project_id: Optional[str],
    base_url: str,
    workspace: int,
    custom_sql: bool,
    metrics: bool,
) -> None:
    """Validate BigConfig file."""
    api_key = os.environ.get("BIGEYE_API_KEY")
    if api_key is None:
        click.echo(
            "Bigeye API token needs to be set via `BIGEYE_API_KEY` env variable."
        )
        sys.exit(1)

    metadata_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["metadata.yaml"]
    )
    api_auth = APIKeyAuth(base_url=base_url, api_key=api_key)
    client = datawatch_client_factory(api_auth, workspace_id=workspace)
    warehouse_id = ConfigLoader.get("monitoring", "bigeye_warehouse_id")
    existing_rules = {
        rule.custom_rule.sql: rule.id
        for rule in client.get_rules_for_source(warehouse_id=warehouse_id).custom_rules
    }

    for metadata_file in list(set(metadata_files)):
        project, dataset, table = extract_from_query_path(metadata_file)
        if metrics:
            deployed_metrics = client.get_metric_info_batch_post(
                table_name=table,
                schema_name=f"{project}.{dataset}",
                warehouse_ids=[warehouse_id],
            )
            client.delete_metrics(metrics=deployed_metrics.metrics)

        if custom_sql:
            if (metadata_file.parent / CUSTOM_RULES_FILE).exists():
                for select_statement in _sql_rules_from_file(
                    metadata_file.parent / CUSTOM_RULES_FILE, project, dataset, table
                ):
                    sql = select_statement.sql(dialect="bigquery")
                    if sql in existing_rules:
                        client.delete_custom_rule(existing_rules[sql])
                        click.echo(
                            f"Deleted custom rule {existing_rules[sql]} for {project}.{dataset}.{table}"
                        )


@monitoring.command(
    help="""
    Runs Bigeye monitors.

    Example:

    \t./bqetl monitoring run ga_derived.downloads_with_attribution_v2
    """,
    context_settings=dict(
        ignore_unknown_options=True,
        allow_extra_args=True,
    ),
)
@click.argument("name")
@project_id_option()
@sql_dir_option
@click.option(
    "--workspace",
    default=463,
    help="Bigeye workspace to use when authenticating to API.",
)
@click.option(
    "--base-url",
    "--base_url",
    default="https://app.bigeye.com",
    help="Bigeye base URL.",
)
@click.option("--marker", default="", help="Marker to filter checks.")
def run(name, project_id, sql_dir, workspace, base_url, marker):
    """Run Bigeye checks."""
    api_key = os.environ.get("BIGEYE_API_KEY")
    if api_key is None:
        click.echo(
            "Bigeye API token needs to be set via `BIGEYE_API_KEY` env variable."
        )
        sys.exit(1)

    api_auth = APIKeyAuth(base_url=base_url, api_key=api_key)
    client = datawatch_client_factory(api_auth, workspace_id=workspace)
    warehouse_id = ConfigLoader.get("monitoring", "bigeye_warehouse_id")
    existing_rules = {
        rule.custom_rule.sql: {"id": rule.id, "name": rule.custom_rule.name}
        for rule in client.get_rules_for_source(warehouse_id=warehouse_id).custom_rules
        if rule.custom_rule.name.endswith(marker or "")
    }

    metadata_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["metadata.yaml"]
    )

    failed = False

    for metadata_file in list(set(metadata_files)):
        project, dataset, table = extract_from_query_path(metadata_file)
        try:
            metadata = Metadata.from_file(metadata_file)
            if metadata.monitoring and metadata.monitoring.enabled:
                metrics = client.get_metric_info_batch_post(
                    table_name=table,
                    schema_name=f"{project}.{dataset}",
                    warehouse_ids=[warehouse_id],
                ).metrics

                if marker:
                    metrics = [
                        metric for metric in metrics if metric.name.endswith(marker)
                    ]

                metric_ids = [metric.metric_configuration.id for metric in metrics]

                click.echo(
                    f"Trigger metric runs for {project}.{dataset}.{table}: {metric_ids}"
                )
                response = client.run_metric_batch_async(metric_ids=metric_ids)
                for metric_info in response:
                    latest_metric_run = metric_info.latest_metric_runs[-1]
                    if (
                        latest_metric_run
                        and latest_metric_run.status in METRIC_STATUS_FAILURES
                    ):
                        if metric_info.metric_configuration.name.lower().endswith(
                            "[fail]"
                        ):
                            failed = True
                        click.echo(
                            f"Error running check {metric_info.metric_configuration.id}: {metric_info.active_issue.display_name}"
                        )
                        click.echo(
                            f"Check {base_url}/w/{workspace}/catalog/data-sources/metric/{metric_info.metric_configuration.id}/chart for more information."
                        )

                if (metadata_file.parent / CUSTOM_RULES_FILE).exists():
                    for select_statement in _sql_rules_from_file(
                        metadata_file.parent / CUSTOM_RULES_FILE,
                        project,
                        dataset,
                        table,
                    ):
                        sql = select_statement.sql(dialect="bigquery")
                        if sql in existing_rules:
                            response = client._call_datawatch(
                                Method.GET,
                                url=f"/api/v1/custom-rules/run/{existing_rules[sql]['id']}",
                            )
                            click.echo(
                                f"Triggered custom rule {existing_rules[sql]['id']} for {project}.{dataset}.{table}"
                            )

                            latest_rule_run = response.get("latestRuns", [])
                            if latest_rule_run and latest_rule_run[-1].get(
                                "status"
                            ) in {status.name for status in METRIC_STATUS_FAILURES}:
                                if (
                                    not existing_rules[sql]["name"]
                                    .lower()
                                    .endswith("[warn]")
                                ):
                                    failed = True

                                click.echo(
                                    f"Error running custom rule {existing_rules[sql]} for {project}.{dataset}.{table}. "
                                    + f"Check {base_url}/w/{workspace}/catalog/data-sources/{warehouse_id}/rules/{existing_rules[sql]['id']}/runs "
                                    + "for more information."
                                )

        except FileNotFoundError:
            print("No metadata file for: {}.{}.{}".format(project, dataset, table))

    if failed:
        sys.exit(1)


# TODO: remove this command once checks have been migrated
@monitoring.command(
    help="""
    Create BigConfig files from ETL check.sql files.

    This is a temporary command and will be removed after checks have been migrated.
    """
)
@click.argument("name")
@project_id_option()
@sql_dir_option
@click.pass_context
def migrate(ctx, name: str, sql_dir: Optional[str], project_id: Optional[str]) -> None:
    """Migrate checks.sql files to BigConfig."""
    check_files = paths_matching_name_pattern(
        name, sql_dir, project_id=project_id, files=["checks.sql"]
    )

    for check_file in list(set(check_files)):
        project, dataset, table = extract_from_query_path(check_file)
        checks_migrated = 0
        try:
            metadata_file = check_file.parent / METADATA_FILE
            metadata = Metadata.from_file(check_file.parent / METADATA_FILE)
            metadata.monitoring = {"enabled": True}
            metadata.write(metadata_file)

            ctx.invoke(
                update,
                name=metadata_file.parent,
                sql_dir=sql_dir,
                project_id=project_id,
            )

            # clearing Bigeye file index after running the `update` command
            # this is necessary as Bigeye throws an exception when it sees the same config file twice,
            # which will be the case after doing `update` previously
            _BIGEYE_YAML_FILE_IX.clear()
            bigconfig_file = check_file.parent / BIGCONFIG_FILE
            if bigconfig_file.exists():
                bigconfig = BigConfig.load(bigconfig_file)
            else:
                bigconfig = BigConfig(type="BIGCONFIG_FILE")

            checks = check_file.read_text()
            metrics_by_column = defaultdict(list)

            # map pre-defined ETL checks to Bigeye checks
            if matches := re.findall(
                r"not_null\(\[([^\]\)]*)\](?:, \"(.*)\")?\)", checks
            ):
                for match in matches:
                    checks_migrated += 1
                    columns = [
                        col.replace('"', "")
                        for col in match[0]
                        .replace("[", "")
                        .replace("]", "")
                        .split(", ")
                    ]
                    metric_definition = SimpleMetricDefinition(
                        metric_type=SimplePredefinedMetric(
                            type="PREDEFINED",
                            predefined_metric=SimplePredefinedMetricName.PERCENT_NOT_NULL,
                        ),
                        threshold=SimpleConstantThreshold(
                            type="CONSTANT", lower_bound=1.0
                        ),
                    )

                    for column in columns:
                        metrics_by_column[
                            f"{project}.{project}.{dataset}.{table}.{column}"
                        ].append(metric_definition)

            if matches := re.findall(
                r"min_row_count\(([^,\)]*)(?:, \"(.*)\")?\)", checks
            ):
                for match in matches:
                    checks_migrated += 1
                    metrics_by_column[
                        f"{project}.{project}.{dataset}.{table}.*"
                    ].append(
                        SimpleMetricDefinition(
                            metric_type=SimplePredefinedMetric(
                                type="PREDEFINED",
                                predefined_metric=SimplePredefinedMetricName.COUNT_ROWS,
                            ),
                            threshold=SimpleConstantThreshold(
                                type="CONSTANT", lower_bound=int(match[0])
                            ),
                        )
                    )

            if matches := re.findall(r"is_unique\(([^\]\)]*)(?:, \"(.*)\")?\)", checks):
                for match in matches:
                    checks_migrated += 1
                    columns = [
                        col.replace('"', "")
                        for col in match[0]
                        .replace("[", "")
                        .replace("]", "")
                        .split(", ")
                    ]
                    metric_definition = SimpleMetricDefinition(
                        metric_type=SimplePredefinedMetric(
                            type="PREDEFINED",
                            predefined_metric=SimplePredefinedMetricName.COUNT_DUPLICATES,
                        ),
                        threshold=SimpleConstantThreshold(
                            type="CONSTANT", lower_bound=0.0
                        ),
                    )

                    for column in columns:
                        metrics_by_column[
                            f"{project}.{project}.{dataset}.{table}.{column}"
                        ].append(metric_definition)

            if matches := re.findall(r"in_range\((.*), (.*), (.*), \"(.*)\"\)", checks):
                for match in matches:
                    checks_migrated += 1
                    columns = [
                        col.replace('"', "")
                        for col in match[0]
                        .replace("[", "")
                        .replace("]", "")
                        .split(", ")
                    ]
                    metric_definition = SimpleMetricDefinition(
                        metric_name="Range",
                        metric_type=SimplePredefinedMetric(
                            type="PREDEFINED",
                            predefined_metric=SimplePredefinedMetricName.MIN,
                        ),
                        threshold=SimpleConstantThreshold(
                            type="CONSTANT",
                            lower_bound=int(match[1]) if match[1] != "none" else None,
                            upper_bound=int(match[2]) if match[2] != "none" else None,
                        ),
                    )

                    for column in columns:
                        metrics_by_column[
                            f"{project}.{project}.{dataset}.{table}.{column}"
                        ].append(metric_definition)

            if matches := re.findall(
                r"value_length\(column=\"(.*)\", expected_length=(.*), where=\"(.*)\"\)",
                checks,
            ):
                for match in matches:
                    checks_migrated += 1
                    metric_definition = SimpleMetricDefinition(
                        metric_name="Value Length",
                        metric_type=SimplePredefinedMetric(
                            type="PREDEFINED",
                            predefined_metric=SimplePredefinedMetricName.STRING_LENGTH_MIN,
                        ),
                        threshold=SimpleConstantThreshold(
                            type="CONSTANT",
                            lower_bound=int(match[1]),
                            upper_bound=int(match[1]),
                        ),
                    )

                    for column in columns:
                        metrics_by_column[
                            f"{project}.{project}.{dataset}.{table}.{column}"
                        ].append(metric_definition)

            deployments = []
            for column_selector, metrics in metrics_by_column.items():
                deployments.append(
                    TagDeployment(
                        column_selectors=[ColumnSelector(name=column_selector)],
                        metrics=metrics,
                    )
                )

            bigconfig.tag_deployments += [TagDeploymentSuite(deployments=deployments)]

            bigconfig.save(
                output_path=bigconfig_file.parent,
                default_file_name=bigconfig_file.stem,
            )

            total_checks = checks.count("#fail") + checks.count("#warn")
            click.echo(
                f"Migrated {checks_migrated} of {total_checks} checks to {bigconfig_file}."
            )
            if checks_migrated < total_checks:
                click.echo(
                    f"There might be custom SQL checks that need to be migrated manually for {check_file.parent}"
                )

        except FileNotFoundError:
            print(f"No metadata file for: {check_file.parent}")

    click.echo(
        "Please manually check the migration logic as it might not be 100% correct"
    )