"""Generate a query with shredder mitigation."""

import os
import re
import sys
from datetime import date
from datetime import datetime as dt
from datetime import time, timedelta
from enum import Enum
from pathlib import Path
from types import NoneType
from typing import Any, Optional, Tuple

import attrs
import click
from dateutil import parser
from gcloud.exceptions import NotFound  # type: ignore
from google.cloud import bigquery
from jinja2 import Environment, FileSystemLoader

from bigquery_etl.format_sql.formatter import reformat
from bigquery_etl.metadata.parse_metadata import METADATA_FILE, Metadata
from bigquery_etl.metadata.validate_metadata import SHREDDER_MITIGATION_LABEL
from bigquery_etl.schema import Schema
from bigquery_etl.util.common import extract_last_group_by_from_query, write_sql

PREVIOUS_DATE = (dt.now() - timedelta(days=2)).date()
TEMP_DATASET = "tmp"
THIS_PATH = Path(os.path.dirname(__file__))
DEFAULT_PROJECT_ID = "moz-fx-data-shared-prod"
SHREDDER_MITIGATION_QUERY_NAME = "shredder_mitigation_query"
SHREDDER_MITIGATION_CHECKS_NAME = "shredder_mitigation_checks"
DEFAULT_FOR_NULLS = "??"
WILDCARD_STRING = "???????"
WILDCARD_NUMBER = -9999999
QUERY_FILE_RE = re.compile(
    r"^.*/([a-zA-Z0-9-]+)/([a-zA-Z0-9_]+)/([a-zA-Z0-9_]+(_v[0-9]+)?)/"
    r"(?:query\.sql|shredder_mitigation_query\.sql|part1\.sql|script\.sql|"
    r"query\.py|view\.sql|metadata\.yaml|backfill\.yaml)$"
)


class ColumnType(Enum):
    """Differentiate metric and dimensions."""

    METRIC = "METRIC"
    DIMENSION = "DIMENSION"
    UNDETERMINED = "None"


class ColumnStatus(Enum):
    """Different status of a column during shredder mitigation."""

    COMMON = "COMMON"
    ADDED = "ADDED"
    REMOVED = "REMOVED"
    UNDETERMINED = "None"


class DataTypeGroup(Enum):
    """Data types in BigQuery. Not supported/expected in aggregates: TIMESTAMP, ARRAY, STRUCT."""

    STRING = ("STRING", "BYTES")
    BOOLEAN = "BOOLEAN"
    INTEGER = ("INTEGER", "INT64", "INT", "SMALLINT", "TINYINT", "BYTEINT")
    NUMERIC = (
        "NUMERIC",
        "BIGNUMERIC",
    )
    FLOAT = "FLOAT"
    DATE = "DATE"
    DATETIME = "DATETIME"
    TIME = "TIME"
    TIMESTAMP = "TIMESTAMP"
    UNDETERMINED = "None"

    @staticmethod
    def from_schema_type(schema_type):
        """Convert schema type (string) to the corresponding Type enum."""
        for _type in DataTypeGroup:
            if schema_type in _type.value:
                return _type
        return None


@attrs.define(eq=True)
class Column:
    """Representation of a column in a query, with relevant details for shredder mitigation."""

    name: str
    data_type: DataTypeGroup = attrs.field(
        default=DataTypeGroup.UNDETERMINED,
        validator=attrs.validators.instance_of(DataTypeGroup),
    )
    column_type: ColumnType = attrs.field(
        default=ColumnType.UNDETERMINED,
        validator=attrs.validators.instance_of(ColumnType),
    )
    status: ColumnStatus = attrs.field(
        default=ColumnStatus.UNDETERMINED,
        validator=attrs.validators.instance_of(ColumnStatus),
    )


@attrs.define(eq=True)
class Subset:
    """Representation of a subset/CTEs in the query and the actions related to this subset."""

    @staticmethod
    def attr_not_null(instance, attribute, value):
        """Raise an exception if the value is None or empty."""
        if value is None or value == "":
            raise click.ClickException(
                f"{attribute.name} not given and it's required to continue."
            )

    client: bigquery.Client
    destination_table: str = attrs.field(
        default="",
        validator=attr_not_null,
    )
    query_cte: str = attrs.field(default="")
    dataset: str = attrs.field(default=TEMP_DATASET)
    project_id: str = attrs.field(default=DEFAULT_PROJECT_ID)
    expiration_days: Optional[float] = attrs.field(default=None)

    @property
    def version(self):
        """Return the version of the destination table."""
        match = re.search(r"v(\d+)$", self.destination_table)
        try:
            version = int(match.group(1))
            if not isinstance(version, int):
                raise click.ClickException(
                    f"{self.destination_table} must end with a positive integer."
                )
            return version
        except (AttributeError, TypeError) as e:
            raise click.ClickException(
                f"Invalid or missing table version in {self.destination_table}."
            ) from e

    @property
    def full_table_id(self):
        """Return the full id of the destination table."""
        return f"{self.project_id}.{self.dataset}.{self.destination_table}"

    @property
    def query_path(self):
        """Return the full path of the query.sql file associated with the subset."""
        sql_path = (
            Path("sql")
            / self.project_id
            / self.dataset
            / self.destination_table
            / "query.sql"
        )
        match = QUERY_FILE_RE.match(str(sql_path))
        if match and os.path.isfile(sql_path):
            return sql_path
        click.echo(click.style(f"File not found: {sql_path}.", fg="yellow"))
        return None

    @property
    def partitioning(self):
        """Return the partition details of the destination table."""
        metadata = Metadata.from_file(
            Path("sql")
            / self.project_id
            / self.dataset
            / self.destination_table
            / METADATA_FILE
        )
        if metadata.bigquery and metadata.bigquery.time_partitioning:
            partitioning = {
                "type": metadata.bigquery.time_partitioning.type.name,
                "field": metadata.bigquery.time_partitioning.field,
            }
        else:
            partitioning = {"type": None, "field": None}
        return partitioning

    @property
    def labels(self):
        """Return the labels in the metadata of the destination table."""
        metadata = Metadata.from_file(
            Path("sql")
            / self.project_id
            / self.dataset
            / self.destination_table
            / METADATA_FILE
        )
        return metadata.labels

    def generate_query(
        self,
        select_list,
        from_clause,
        where_clause=None,
        group_by_clause=None,
        order_by_clause=None,
        having_clause=None,
    ):
        """Build query to populate the table."""
        if not select_list or not from_clause:
            raise click.ClickException(
                f"Missing required clause to generate query.\n"
                f"Actual: SELECT: {select_list}, FROM: {self.full_table_id}"
            )
        query = f"SELECT {', '.join(map(str, select_list))}"
        query += f" FROM {from_clause}" if from_clause is not None else ""
        query += f" WHERE {where_clause}" if where_clause is not None else ""
        query += f" GROUP BY {group_by_clause}" if group_by_clause is not None else ""
        query += (
            f" HAVING {having_clause}"
            if having_clause is not None and group_by_clause is not None
            else ""
        )
        query += f" ORDER BY {order_by_clause}" if order_by_clause is not None else ""
        return query

    def get_query_path_results(
        self,
        backfill_date: date = PREVIOUS_DATE,
        row_limit: Optional[int] = None,
        **kwargs,
    ) -> list[dict[str, Any]]:
        """Run the query in sql_path & return result or number of rows requested."""
        having_clause = None
        for key, value in kwargs.items():
            if key.lower() == "having_clause":
                having_clause = f"{value}"

        with open(self.query_path, "r") as file:
            sql_text = file.read().strip()

        if sql_text.endswith(";"):
            sql_text = sql_text[:-1]
        if having_clause:
            sql_text = f"{sql_text} {having_clause}"
        if row_limit:
            sql_text = f"{sql_text} LIMIT {row_limit}"

        partition_field = self.partitioning["field"]
        partition_type = (
            "DATE" if self.partitioning["type"] == "DAY" else self.partitioning["type"]
        )
        parameters = []
        if partition_field is not None:
            parameters = [
                bigquery.ScalarQueryParameter(
                    partition_field, partition_type, backfill_date
                ),
            ]

        try:
            query_job = self.client.query(
                query=sql_text,
                job_config=bigquery.QueryJobConfig(
                    query_parameters=parameters,
                    use_legacy_sql=False,
                    dry_run=False,
                    use_query_cache=False,
                ),
            )
            query_results = query_job.result()
        except NotFound as e:
            raise click.ClickException(
                f"Unable to query data for {backfill_date}. Table {self.full_table_id} not found"
                f" or partitioning in metadata missing."
            ) from e
        rows = [dict(row) for row in query_results]
        return rows


def get_bigquery_type(value) -> DataTypeGroup:
    """Find the datatype of a value, grouping similar types."""
    if isinstance(value, dt):
        return DataTypeGroup.DATETIME
    try:
        if isinstance(dt.strptime(value, "%H:%M:%S").time(), time):
            return DataTypeGroup.TIME
    except (ValueError, TypeError, AttributeError):
        pass
    try:
        value_parsed = parser.isoparse(value.replace(" UTC", "Z").replace(" ", "T"))
        if (
            isinstance(value_parsed, dt)
            and value_parsed.time() == time(0, 0)
            and isinstance(dt.strptime(value, "%Y-%m-%d"), date)
        ):
            return DataTypeGroup.DATE
        if isinstance(value_parsed, dt) and value_parsed.tzinfo is None:
            return DataTypeGroup.DATETIME
        if isinstance(value_parsed, dt) and value_parsed.tzinfo is not None:
            return DataTypeGroup.TIMESTAMP
    except (ValueError, TypeError, AttributeError):
        pass
    if isinstance(value, time):
        return DataTypeGroup.TIME
    if isinstance(value, date):
        return DataTypeGroup.DATE
    if isinstance(value, bool):
        return DataTypeGroup.BOOLEAN
    if isinstance(value, int):
        return DataTypeGroup.INTEGER
    if isinstance(value, float):
        return DataTypeGroup.FLOAT
    if isinstance(value, (str, bytes)):
        return DataTypeGroup.STRING
    if isinstance(value, NoneType):
        return DataTypeGroup.UNDETERMINED
    raise ValueError(f"Unsupported data type: {type(value)}")


def validate_types(columns, schema, sample_data):
    """Return dtype from schema file or if not available, from bigquery."""
    results = {}

    for dim in columns:
        schema_field = next((field for field in schema if field.name == dim), None)

        bigquery_type = get_bigquery_type(sample_data.get(dim))
        # If the column is in the schema
        if schema_field:
            schema_type = DataTypeGroup.from_schema_type(schema_field.field_type)
            results[dim] = schema_type or bigquery_type
        # If the column is _not_ in the schema
        else:
            if bigquery_type:
                results[dim] = bigquery_type
                click.echo(
                    f"Column {dim} not in schema!! Using type {bigquery_type} from sample data."
                )
            else:
                results[dim] = DataTypeGroup.UNDETERMINED
                click.echo(
                    f"Data type for {dim} could not be retrieved from schema or BigQuery."
                    f" Set to UNDETERMINED."
                )
                continue
    return results


def classify_columns(
    new_row: dict,
    existing_dimension_columns: list,
    new_dimension_columns: list,
    existing_schema: bigquery.schema.SchemaField,
    new_schema: bigquery.schema.SchemaField,
) -> tuple[list[Column], list[Column], list[Column], list[Column], list[Column]]:
    """Compare new row with existing columns & return common, added & removed columns."""
    common_dimensions = []
    added_dimensions = []
    removed_dimensions = []
    metrics = []
    undefined = []

    if not new_row or not existing_dimension_columns or not new_dimension_columns:
        raise click.ClickException(
            f"\n\nMissing one or more required parameters. Received:"
            f"\nnew_row= {new_row}"
            f"\nexisting_dimension_columns= {existing_dimension_columns},"
            f"\nnew_dimension_columns= {new_dimension_columns}."
        )

    missing_dimensions = [
        dimension for dimension in new_dimension_columns if dimension not in new_row
    ]
    if not len(missing_dimensions) == 0:
        raise click.ClickException(
            f"Existing dimensions don't match columns retrieved by query."
            f" Missing {missing_dimensions}."
        )
    existing_dimension_columns_types = validate_types(
        existing_dimension_columns, existing_schema, new_row
    )
    new_dimension_columns_types = validate_types(
        new_dimension_columns, new_schema, new_row
    )

    for key in existing_dimension_columns:
        if key not in new_dimension_columns:
            removed_dimensions.append(
                Column(
                    key,
                    existing_dimension_columns_types[key],
                    ColumnType.UNDETERMINED,
                    ColumnStatus.REMOVED,
                )
            )

    for key, _ in new_row.items():
        if key in existing_dimension_columns:
            common_dimensions.append(
                Column(
                    key,
                    new_dimension_columns_types[key],
                    ColumnType.DIMENSION,
                    ColumnStatus.COMMON,
                )
            )
        elif key not in existing_dimension_columns and key in new_dimension_columns:
            added_dimensions.append(
                Column(
                    key,
                    new_dimension_columns_types[key],
                    ColumnType.DIMENSION,
                    ColumnStatus.ADDED,
                )
            )
        elif key not in existing_dimension_columns and key not in new_dimension_columns:
            value_type = validate_types([key], new_schema, new_row)
            if value_type[key] in (
                DataTypeGroup.INTEGER,
                DataTypeGroup.FLOAT,
                DataTypeGroup.NUMERIC,
            ):
                # A column that's not a dimensions nor a grouping columns is classified as metric.
                metrics.append(
                    Column(
                        key,
                        value_type[key],
                        ColumnType.METRIC,
                        ColumnStatus.COMMON,
                    )
                )
            else:
                undefined.append(
                    Column(
                        key,
                        value_type[key],
                        ColumnType.UNDETERMINED,
                        ColumnStatus.UNDETERMINED,
                    )
                )

    common_dimensions_sorted = sorted(common_dimensions, key=lambda column: column.name)
    added_dimensions_sorted = sorted(added_dimensions, key=lambda column: column.name)
    removed_dimensions_sorted = sorted(
        removed_dimensions, key=lambda column: column.name
    )
    metrics_sorted = sorted(metrics, key=lambda column: column.name)
    undefined_sorted = sorted(undefined, key=lambda column: column.name)

    return (
        common_dimensions_sorted,
        added_dimensions_sorted,
        removed_dimensions_sorted,
        metrics_sorted,
        undefined_sorted,
    )


def generate_query_with_shredder_mitigation(
    client,
    project_id,
    dataset,
    destination_table,
    staging_table_name,
    backfill_date=PREVIOUS_DATE,
) -> Tuple[Path, str]:
    """Generate a query to backfill with shredder mitigation."""
    query_with_mitigation_path = Path("sql") / project_id

    # Find query files and grouping of previous and new queries.
    new = Subset(client, destination_table, "new_version", dataset, project_id, None)

    if SHREDDER_MITIGATION_LABEL not in new.labels:
        click.echo(
            click.style(
                "The required label `shredder_mitigation` is missing in the metadata of the "
                "table. The process will now terminate.",
                fg="yellow",
            )
        )
        sys.exit(1)

    if new.version < 2:
        raise click.ClickException(
            f"The new version of the table is expected >= 2. Actual is {new.version}."
        )

    destination_table_previous_version = (
        f"{destination_table[:-len(str(new.version))]}{new.version-1}"
    )
    previous = Subset(
        client,
        destination_table_previous_version,
        "previous",
        dataset,
        project_id,
        None,
    )
    new_group_by = extract_last_group_by_from_query(sql_path=new.query_path)
    previous_group_by = extract_last_group_by_from_query(sql_path=previous.query_path)

    # Check that previous query exists and GROUP BYs are valid in both queries.
    integers_in_group_by = False
    for e in previous_group_by + new_group_by:
        try:
            int(e)
            integers_in_group_by = True
        except ValueError:
            continue
    if (
        "ALL" in previous_group_by
        or "ALL" in new_group_by
        or not all(isinstance(e, str) for e in previous_group_by)
        or not all(isinstance(e, str) for e in new_group_by)
        or not previous_group_by
        or not new_group_by
        or integers_in_group_by
    ):
        raise click.ClickException(
            "GROUP BY must use an explicit list of columns. "
            "Avoid expressions like `GROUP BY ALL` or `GROUP BY 1, 2, 3`."
        )

    # Identify columns common to both queries and columns new. This excludes removed columns.
    existing_schema = Schema.from_schema_file(
        query_with_mitigation_path
        / dataset
        / destination_table_previous_version
        / "schema.yaml"
    ).to_bigquery_schema()
    new_schema = Schema.from_schema_file(
        query_with_mitigation_path / dataset / destination_table / "schema.yaml"
    ).to_bigquery_schema()

    sample_rows = new.get_query_path_results(
        backfill_date=backfill_date,
        row_limit=1,
        having_clause=f"HAVING {' IS NOT NULL AND '.join(new_group_by)} IS NOT NULL",
    )
    if not sample_rows:
        sample_rows = new.get_query_path_results(
            backfill_date=backfill_date, row_limit=1
        )
    try:
        new_table_row = sample_rows[0]
        (
            common_dimensions,
            added_dimensions,
            removed_dimensions,
            metrics,
            undetermined_columns,
        ) = classify_columns(
            new_table_row, previous_group_by, new_group_by, existing_schema, new_schema
        )
    except TypeError:
        raise click.ClickException(
            f"Table {destination_table} did not return any rows for {backfill_date}."
        )

    if not common_dimensions or not added_dimensions or not metrics:
        raise click.ClickException(
            "The process requires that previous & query have at least one dimension in common,"
            " one dimension added and one metric."
        )

    # Find the new version of the query.
    with open(new.query_path, "r") as file:
        new_query = file.read().strip()

    # Aggregate previous data and new query results using common dimensions.
    new_agg = Subset(
        client, destination_table, "new_agg", TEMP_DATASET, project_id, None
    )
    previous_agg = Subset(
        client,
        destination_table_previous_version,
        "previous_agg",
        TEMP_DATASET,
        project_id,
        None,
    )
    common_select = (
        [previous.partitioning["field"]]
        + [
            f"IF({dim.name} IS NULL OR {dim.name} = '{DEFAULT_FOR_NULLS}', '{WILDCARD_STRING}',"
            f" {dim.name}) AS {dim.name}"
            for dim in common_dimensions
            if (
                dim.name != previous.partitioning["field"]
                and dim.data_type == DataTypeGroup.STRING
            )
        ]
        + [
            f"COALESCE({dim.name}, {WILDCARD_NUMBER}) AS {dim.name}"
            for dim in common_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type in (DataTypeGroup.INTEGER, DataTypeGroup.FLOAT)
            )
        ]
        + [
            dim.name
            for dim in common_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type in (DataTypeGroup.BOOLEAN, DataTypeGroup.DATE)
            )
        ]
        + [f"SUM({metric.name}) AS {metric.name}" for metric in metrics]
    )
    new_agg_query = new_agg.generate_query(
        select_list=common_select,
        from_clause=f"{new.query_cte}",
        group_by_clause="ALL",
    )
    previous_agg_query = previous_agg.generate_query(
        select_list=common_select,
        from_clause=f"`{previous.full_table_id}`",
        where_clause=f"{previous.partitioning['field']} = @{previous.partitioning['field']}",
        group_by_clause="ALL",
    )

    # Generate the subset that will calculate shredder impact.
    shredded = Subset(
        client, destination_table, "shredded", TEMP_DATASET, project_id, None
    )

    # Cast NULL values to the corresponding type.
    # This doesn't convert data or dtypes, it's only used to cast NULLs for UNION queries.
    shredded_select = (
        [f"{previous_agg.query_cte}.{new.partitioning['field']}"]
        + [
            f"{previous_agg.query_cte}.{dim.name}"
            for dim in common_dimensions
            if (dim.name != new.partitioning["field"])
        ]
        + [
            f"CAST(NULL AS {DataTypeGroup.STRING.name}) AS {dim.name}"
            for dim in added_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type == DataTypeGroup.STRING
            )
        ]
        + [
            f"CAST(NULL AS {DataTypeGroup.BOOLEAN.name}) AS {dim.name}"
            for dim in added_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type == DataTypeGroup.BOOLEAN
            )
        ]
        + [
            f"CAST(NULL AS {DataTypeGroup.DATE.name}) AS {dim.name}"
            for dim in added_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type == DataTypeGroup.DATE
            )
        ]
        + [
            f"CAST(NULL AS {DataTypeGroup.INTEGER.name}) AS {dim.name}"
            for dim in added_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type == DataTypeGroup.INTEGER
            )
        ]
        + [
            f"CAST(NULL AS {DataTypeGroup.FLOAT.name}) AS {dim.name}"
            for dim in added_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type == DataTypeGroup.FLOAT
            )
        ]
        + [
            f"NULL AS {dim.name}"
            for dim in added_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type
                not in (
                    DataTypeGroup.STRING,
                    DataTypeGroup.BOOLEAN,
                    DataTypeGroup.INTEGER,
                    DataTypeGroup.FLOAT,
                )
            )
        ]
        + [
            f"COALESCE({previous_agg.query_cte}.{metric.name}, 0) - "
            f"COALESCE({new_agg.query_cte}.{metric.name}, 0)"
            f" AS {metric.name}"
            for metric in metrics
            if metric.data_type != DataTypeGroup.FLOAT
        ]
        + [
            # Round FLOAT to avoid exponential numbers.
            f"ROUND({previous_agg.query_cte}.{metric.name}, 10) - "
            f"ROUND(COALESCE({new_agg.query_cte}.{metric.name}, 0), 10) AS {metric.name}"
            for metric in metrics
            if metric.data_type == DataTypeGroup.FLOAT
        ]
    )

    shredded_join = " AND ".join(
        [
            f"{previous_agg.query_cte}.{previous.partitioning['field']} ="
            f" {new_agg.query_cte}.{new.partitioning['field']}"
        ]
        + [
            f"{previous_agg.query_cte}.{dim.name} = {new_agg.query_cte}.{dim.name}"
            for dim in common_dimensions
            if dim.name != previous.partitioning["field"]
            and dim.data_type not in (DataTypeGroup.BOOLEAN, DataTypeGroup.DATE)
        ]
        + [
            f"({previous_agg.query_cte}.{dim.name} = {new_agg.query_cte}.{dim.name} OR"
            f" ({previous_agg.query_cte}.{dim.name} IS NULL"
            f" AND {new_agg.query_cte}.{dim.name} IS NULL))"  # Compare null values.
            for dim in common_dimensions
            if dim.name != previous.partitioning["field"]
            and dim.data_type in (DataTypeGroup.BOOLEAN, DataTypeGroup.DATE)
        ]
    )
    shredded_query = shredded.generate_query(
        select_list=shredded_select,
        from_clause=f"{previous_agg.query_cte} LEFT JOIN {new_agg.query_cte} ON {shredded_join} ",
        where_clause=" OR ".join(
            [
                f"COALESCE({previous_agg.query_cte}.{metric.name}, 0) >"
                f" COALESCE({new_agg.query_cte}.{metric.name}, 0)"
                for metric in metrics
            ]
        ),
    )

    combined_list = []

    # Set back to NULL, values that were temporarily set to a wildcard.
    for dim in common_dimensions + added_dimensions:
        if dim.data_type.name == DataTypeGroup.STRING.name:
            combined_list.append(
                f"IF({dim.name} = '{WILDCARD_STRING}', "
                f"CAST(NULL AS {DataTypeGroup.STRING.name}), {dim.name}) AS {dim.name}"
            )
        elif dim.data_type.name == DataTypeGroup.INTEGER.name:
            combined_list.append(
                f"IF({dim.name} = {WILDCARD_NUMBER}, "
                f"CAST(NULL AS {DataTypeGroup.INTEGER.name}), {dim.name}) AS {dim.name}"
            )
        elif dim.data_type.name == DataTypeGroup.NUMERIC.name:
            combined_list.append(
                f"IF({dim.name} = '{WILDCARD_STRING}', "
                f"CAST(NULL AS {DataTypeGroup.NUMERIC.name}), {dim.name}) AS {dim.name}"
            )
        elif dim.data_type.name == DataTypeGroup.FLOAT.name:
            combined_list.append(
                f"IF({dim.name} = '{WILDCARD_STRING}', "
                f"CAST(NULL AS {DataTypeGroup.FLOAT.name}), {dim.name}) AS {dim.name}"
            )
        else:
            combined_list.append(f"{dim.name} AS {dim.name}")

    combined_list = combined_list + [metric.name for metric in metrics]
    final_select = f"{', '.join(combined_list)}"

    # Generate formatted output strings to display generated-query information in console.
    common_output = "".join(
        [
            f"{dim.column_type.name} > {dim.name}:{dim.data_type.name}\n"
            for dim in common_dimensions
        ]
    )
    metrics_output = "".join(
        [
            f"{dim.column_type.name} > {dim.name}:{dim.data_type.name}\n"
            for dim in metrics
        ]
    )
    changed_output = "".join(
        [
            f"{dim.status.name} > {dim.name}:{dim.data_type.name}\n"
            for dim in added_dimensions + removed_dimensions + undetermined_columns
        ]
    )
    click.echo(
        click.style(
            f"Query columns:\n" f"{common_output + metrics_output + changed_output}",
            fg="yellow",
        )
    )

    # Generate query using the template.
    env = Environment(loader=FileSystemLoader(str(THIS_PATH)))
    query_with_mitigation_template = env.get_template(
        f"{SHREDDER_MITIGATION_QUERY_NAME}_template.sql"
    )
    checks_for_mitigation_template = env.get_template(
        f"{SHREDDER_MITIGATION_CHECKS_NAME}_template.sql"
    )

    query_with_mitigation_sql = reformat(
        query_with_mitigation_template.render(
            new_version_cte=new.query_cte,
            new_version=new_query,
            new_agg_cte=new_agg.query_cte,
            new_agg=new_agg_query,
            previous_agg_cte=previous_agg.query_cte,
            previous_agg=previous_agg_query,
            shredded_cte=shredded.query_cte,
            shredded=shredded_query,
            final_select=final_select,
        )
    )
    write_sql(
        output_dir=query_with_mitigation_path,
        full_table_id=new.full_table_id,
        basename=f"{SHREDDER_MITIGATION_QUERY_NAME}.sql",
        sql=query_with_mitigation_sql,
        skip_existing=False,
    )

    # Generate checks to compare versions after each partition backfill.
    checks_select = (
        [new.partitioning["field"]]
        + [
            f"IF({dim.name} IS NULL OR {dim.name} = '{DEFAULT_FOR_NULLS}', '{WILDCARD_STRING}',"
            f" {dim.name}) AS {dim.name}"
            for dim in common_dimensions
            if (
                dim.name != previous.partitioning["field"]
                and dim.data_type == DataTypeGroup.STRING
            )
        ]
        + [
            dim.name
            for dim in common_dimensions
            if (
                dim.name != new.partitioning["field"]
                and dim.data_type != DataTypeGroup.STRING
            )
        ]
        + [f"SUM({metric.name})" f" AS {metric.name}" for metric in metrics]
    )
    previous_checks_query = previous.generate_query(
        select_list=checks_select,
        from_clause=f"`{previous.full_table_id}`",
        where_clause=f"{previous.partitioning['field']} = @{previous.partitioning['field']}",
        group_by_clause="ALL",
    )
    new_checks_query = new.generate_query(
        select_list=checks_select,
        from_clause=f"`{staging_table_name}`",
        where_clause=f"{previous.partitioning['field']} = @{previous.partitioning['field']}",
        group_by_clause="ALL",
    )

    checks_for_mitigation_sql = reformat(
        checks_for_mitigation_template.render(
            previous_version_cte=previous.query_cte,
            previous_version=previous_checks_query,
            new_version_cte=new.query_cte,
            new_version=new_checks_query,
        )
    )
    write_sql(
        output_dir=query_with_mitigation_path,
        full_table_id=new.full_table_id,
        basename=f"{SHREDDER_MITIGATION_CHECKS_NAME}.sql",
        sql=checks_for_mitigation_sql,
        skip_existing=False,
    )

    query_path = Path("sql") / new.project_id / new.dataset / new.destination_table

    click.echo(
        click.style(
            f"Files for shredder mitigation written to path `{query_path}`.\n"
            f"Query: `{SHREDDER_MITIGATION_QUERY_NAME}.sql`\n"
            f"Data checks: `{SHREDDER_MITIGATION_CHECKS_NAME}.sql`\n",
            fg="blue",
        )
    )

    return (
        query_path,
        query_with_mitigation_sql,
    )