# Copyright 2023 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.

"""Core operations for BQML based models"""

from __future__ import annotations

import dataclasses
import datetime
import typing
from typing import Callable, Iterable, Mapping, Optional, Union
import uuid

from google.cloud import bigquery

import bigframes.constants as constants
import bigframes.formatting_helpers as formatting_helpers
from bigframes.ml import sql as ml_sql
import bigframes.pandas as bpd
import bigframes.session


class BaseBqml:
    """Base class for BQML functionalities."""

    def __init__(self, session: bigframes.session.Session):
        self._session = session
        self._sql_generator = ml_sql.BaseSqlGenerator()

    def ai_forecast(
        self,
        input_data: bpd.DataFrame,
        options: Mapping[str, Union[str, int, float, Iterable[str]]],
    ) -> bpd.DataFrame:
        result_sql = self._sql_generator.ai_forecast(
            source_sql=input_data.sql, options=options
        )

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(result_sql, allow_large_results=True)


class BqmlModel(BaseBqml):
    """Represents an existing BQML model in BigQuery.

    Wraps the BQML API and SQL interface to expose the functionality needed for
    BigQuery DataFrames ML.
    """

    @dataclasses.dataclass
    class TvfDef:
        tvf: Callable[[BqmlModel, bpd.DataFrame, dict], bpd.DataFrame]
        status_col: str

    def __init__(self, session: bigframes.Session, model: bigquery.Model):
        self._session = session
        self._model = model
        model_ref = self._model.reference
        assert model_ref is not None
        self._sql_generator: ml_sql.ModelManipulationSqlGenerator = (
            ml_sql.ModelManipulationSqlGenerator(model_ref)
        )

    def _apply_ml_tvf(
        self,
        input_data: bpd.DataFrame,
        apply_sql_tvf: Callable[[str], str],
    ) -> bpd.DataFrame:
        # Used for predict, transform, distance
        """Helper to wrap a dataframe in a SQL query, keeping the index intact.

        Args:
            session (bigframes.Session):
                the active bigframes.Session

            input_data (bigframes.dataframe.DataFrame):
                the dataframe to be wrapped

            func (function):
                Takes an input sql table value and applies a prediction tvf. The
                resulting table value must include all input columns, with new
                columns appended to the end.
        """
        # TODO: Preserve ordering information?
        input_sql, index_col_ids, index_labels = input_data._to_sql_query(
            include_index=True
        )

        result_sql = apply_sql_tvf(input_sql)
        df = self._session.read_gbq_query(
            result_sql,
            index_col=index_col_ids,
            # Many ML methods use nested JSON, which isn't yet compatible with
            # joining local results. Also, there is a chance that the results
            # are greater than 10 GB.
            # TODO(b/395912450): Once the limitations with local data are
            # resolved, consider setting allow_large_results only when expected
            # data size is large.
            allow_large_results=True,
        )
        if df._has_index:
            df.index.names = index_labels
        # Restore column labels
        df.rename(
            columns={
                label: original_label
                for label, original_label in zip(
                    df.columns.values, input_data.columns.values
                )
            }
        )
        return df

    def _keys(self):
        return (self._session, self._model)

    def __eq__(self, other):
        return isinstance(other, self.__class__) and self._keys() == other._keys()

    def __hash__(self):
        return hash(self._keys())

    @property
    def session(self) -> bigframes.Session:
        """Get the BigQuery DataFrames session that this BQML model wrapper is tied to"""
        return self._session

    @property
    def model_name(self) -> str:
        """Get the fully qualified name of the model, i.e. project_id.dataset_id.model_id"""
        return f"{self._model.project}.{self._model.dataset_id}.{self._model.model_id}"

    @property
    def model(self) -> bigquery.Model:
        """Get the BQML model associated with this wrapper"""
        return self._model

    def recommend(self, input_data: bpd.DataFrame) -> bpd.DataFrame:
        return self._apply_ml_tvf(
            input_data,
            self._sql_generator.ml_recommend,
        )

    def predict(self, input_data: bpd.DataFrame) -> bpd.DataFrame:
        return self._apply_ml_tvf(
            input_data,
            self._sql_generator.ml_predict,
        )

    def explain_predict(
        self, input_data: bpd.DataFrame, options: Mapping[str, int | float]
    ) -> bpd.DataFrame:
        return self._apply_ml_tvf(
            input_data,
            lambda source_sql: self._sql_generator.ml_explain_predict(
                source_sql=source_sql,
                struct_options=options,
            ),
        )

    def global_explain(self, options: Mapping[str, bool]) -> bpd.DataFrame:
        sql = self._sql_generator.ml_global_explain(struct_options=options)
        return (
            # TODO(b/395912450): Once the limitations with local data are
            # resolved, consider setting allow_large_results only when expected
            # data size is large.
            self._session.read_gbq_query(sql, allow_large_results=True)
            .sort_values(by="attribution", ascending=False)
            .set_index("feature")
        )

    def transform(self, input_data: bpd.DataFrame) -> bpd.DataFrame:
        return self._apply_ml_tvf(
            input_data,
            self._sql_generator.ml_transform,
        )

    def generate_text(
        self,
        input_data: bpd.DataFrame,
        options: dict[str, Union[int, float, bool]],
    ) -> bpd.DataFrame:
        options["flatten_json_output"] = True
        return self._apply_ml_tvf(
            input_data,
            lambda source_sql: self._sql_generator.ml_generate_text(
                source_sql=source_sql,
                struct_options=options,
            ),
        )

    generate_text_tvf = TvfDef(generate_text, "ml_generate_text_status")

    def generate_embedding(
        self,
        input_data: bpd.DataFrame,
        options: dict[str, Union[int, float, bool]],
    ) -> bpd.DataFrame:
        options["flatten_json_output"] = True
        return self._apply_ml_tvf(
            input_data,
            lambda source_sql: self._sql_generator.ml_generate_embedding(
                source_sql=source_sql,
                struct_options=options,
            ),
        )

    generate_embedding_tvf = TvfDef(generate_embedding, "ml_generate_embedding_status")

    def generate_table(
        self,
        input_data: bpd.DataFrame,
        options: dict[str, Union[int, float, bool, Mapping]],
    ) -> bpd.DataFrame:
        return self._apply_ml_tvf(
            input_data,
            lambda source_sql: self._sql_generator.ai_generate_table(
                source_sql=source_sql,
                struct_options=options,
            ),
        )

    generate_table_tvf = TvfDef(generate_table, "status")

    def detect_anomalies(
        self, input_data: bpd.DataFrame, options: Mapping[str, int | float]
    ) -> bpd.DataFrame:
        assert self._model.model_type in ("PCA", "KMEANS", "ARIMA_PLUS")

        return self._apply_ml_tvf(
            input_data,
            lambda source_sql: self._sql_generator.ml_detect_anomalies(
                source_sql=source_sql,
                struct_options=options,
            ),
        )

    def forecast(self, options: Mapping[str, int | float]) -> bpd.DataFrame:
        sql = self._sql_generator.ml_forecast(struct_options=options)
        timestamp_col_name = "forecast_timestamp"
        index_cols = [timestamp_col_name]
        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        first_col_name = self._session.read_gbq_query(
            sql, allow_large_results=True
        ).columns.values[0]
        if timestamp_col_name != first_col_name:
            index_cols.append(first_col_name)
        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(
            sql, index_col=index_cols, allow_large_results=True
        ).reset_index()

    def explain_forecast(self, options: Mapping[str, int | float]) -> bpd.DataFrame:
        sql = self._sql_generator.ml_explain_forecast(struct_options=options)
        timestamp_col_name = "time_series_timestamp"
        index_cols = [timestamp_col_name]
        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        first_col_name = self._session.read_gbq_query(
            sql, allow_large_results=True
        ).columns.values[0]
        if timestamp_col_name != first_col_name:
            index_cols.append(first_col_name)
        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(
            sql, index_col=index_cols, allow_large_results=True
        ).reset_index()

    def evaluate(self, input_data: Optional[bpd.DataFrame] = None):
        sql = self._sql_generator.ml_evaluate(
            input_data.sql if (input_data is not None) else None
        )

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(sql, allow_large_results=True)

    def llm_evaluate(
        self,
        input_data: bpd.DataFrame,
        task_type: Optional[str] = None,
    ):
        sql = self._sql_generator.ml_llm_evaluate(input_data.sql, task_type)

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(sql, allow_large_results=True)

    def arima_evaluate(self, show_all_candidate_models: bool = False):
        sql = self._sql_generator.ml_arima_evaluate(show_all_candidate_models)

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(sql, allow_large_results=True)

    def arima_coefficients(self) -> bpd.DataFrame:
        sql = self._sql_generator.ml_arima_coefficients()

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(sql, allow_large_results=True)

    def centroids(self) -> bpd.DataFrame:
        assert self._model.model_type == "KMEANS"

        sql = self._sql_generator.ml_centroids()

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(
            sql, index_col=["centroid_id", "feature"], allow_large_results=True
        ).reset_index()

    def principal_components(self) -> bpd.DataFrame:
        assert self._model.model_type == "PCA"

        sql = self._sql_generator.ml_principal_components()

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(
            sql,
            index_col=["principal_component_id", "feature"],
            allow_large_results=True,
        ).reset_index()

    def principal_component_info(self) -> bpd.DataFrame:
        assert self._model.model_type == "PCA"

        sql = self._sql_generator.ml_principal_component_info()

        # TODO(b/395912450): Once the limitations with local data are
        # resolved, consider setting allow_large_results only when expected
        # data size is large.
        return self._session.read_gbq_query(sql, allow_large_results=True)

    def copy(self, new_model_name: str, replace: bool = False) -> BqmlModel:
        job_config = self._session._prepare_copy_job_config()

        if replace:
            job_config.write_disposition = "WRITE_TRUNCATE"

        copy_job = self._session.bqclient.copy_table(
            self.model_name, new_model_name, job_config=job_config
        )
        _start_generic_job(copy_job)

        new_model = self._session.bqclient.get_model(new_model_name)
        return BqmlModel(self._session, new_model)

    def register(self, vertex_ai_model_id: Optional[str] = None) -> BqmlModel:
        if vertex_ai_model_id is None:
            # vertex id needs to start with letters. https://cloud.google.com/vertex-ai/docs/general/resource-naming
            vertex_ai_model_id = "bigframes_" + typing.cast(str, self._model.model_id)

        # truncate as Vertex ID only accepts 63 characters, easily exceeding the limit for temp models.
        # The possibility of conflicts should be low.
        vertex_ai_model_id = vertex_ai_model_id[:63]
        sql = self._sql_generator.alter_model(
            options={"vertex_ai_model_id": vertex_ai_model_id}
        )
        # Register the model and wait it to finish
        self._session._start_query_ml_ddl(sql)

        self._model = self._session.bqclient.get_model(self.model_name)
        return self


class BqmlModelFactory:
    def __init__(self):
        self._model_creation_sql_generator = ml_sql.ModelCreationSqlGenerator()

    def _create_model_ref(
        self, dataset: bigquery.DatasetReference
    ) -> bigquery.ModelReference:
        return bigquery.ModelReference.from_string(
            f"{dataset.project}.{dataset.dataset_id}.{uuid.uuid4().hex}"
        )

    def _create_model_with_sql(self, session: bigframes.Session, sql: str) -> BqmlModel:
        # fit the model, synchronously
        _, job = session._start_query_ml_ddl(sql)

        # real model path in the session specific hidden dataset and table prefix
        model_name_full = f"{job.destination.project}.{job.destination.dataset_id}.{job.destination.table_id}"
        model = bigquery.Model(model_name_full)
        model.expires = (
            datetime.datetime.now(datetime.timezone.utc) + constants.DEFAULT_EXPIRATION
        )
        model = session.bqclient.update_model(model, ["expires"])

        return BqmlModel(session, model)

    def create_model(
        self,
        X_train: bpd.DataFrame,
        y_train: Optional[bpd.DataFrame] = None,
        transforms: Optional[Iterable[str]] = None,
        options: Mapping[str, Union[str, int, float, Iterable[str]]] = {},
    ) -> BqmlModel:
        """Create a session-temporary BQML model with the CREATE OR REPLACE MODEL statement

        Args:
            X_train: features columns for training
            y_train: labels columns for training, if applicable
            transforms: an optional list of SQL expressions that implement preprocessing
                on top of the input data. Generates a BQML TRANSFORM clause
            options: a dict of options to configure the model. Generates a BQML OPTIONS
                clause

        Returns: a BqmlModel, wrapping a trained model in BigQuery
        """
        options = dict(options)
        # Cache dataframes to make sure base table is not a snapshot.
        # Cached dataframe creates a full copy, never uses snapshot.
        # This is a workaround for internal issue b/310266666.
        if y_train is None:
            input_data = X_train.reset_index(drop=True).cache()
        else:
            input_data = (
                X_train.join(y_train, how="outer").reset_index(drop=True).cache()
            )
            options.update({"INPUT_LABEL_COLS": y_train.columns.tolist()})

        session = X_train._session
        if session._bq_kms_key_name:
            options.update({"kms_key_name": session._bq_kms_key_name})

        model_ref = self._create_model_ref(session._anonymous_dataset)

        sql = self._model_creation_sql_generator.create_model(
            source_sql=input_data.sql,
            model_ref=model_ref,
            transforms=transforms,
            options=options,
        )

        return self._create_model_with_sql(session=session, sql=sql)

    def create_llm_remote_model(
        self,
        X_train: bpd.DataFrame,
        y_train: bpd.DataFrame,
        connection_name: str,
        options: Mapping[str, Union[str, int, float, Iterable[str]]] = {},
    ) -> BqmlModel:
        """Create a session-temporary BQML model with the CREATE OR REPLACE MODEL statement

        Args:
            X_train: features columns for training
            y_train: labels columns for training
            options: a dict of options to configure the model. Generates a BQML OPTIONS
                clause
            connection_name:
                a BQ connection to talk with Vertex AI, of the format <PROJECT_NUMBER>.<REGION>.<CONNECTION_NAME>. https://cloud.google.com/bigquery/docs/create-cloud-resource-connection

        Returns: a BqmlModel, wrapping a trained model in BigQuery
        """
        options = dict(options)
        # Cache dataframes to make sure base table is not a snapshot
        # cached dataframe creates a full copy, never uses snapshot
        input_data = X_train.join(y_train, how="outer").cache()
        options.update({"INPUT_LABEL_COLS": y_train.columns.tolist()})

        session = X_train._session

        model_ref = self._create_model_ref(session._anonymous_dataset)

        sql = self._model_creation_sql_generator.create_llm_remote_model(
            source_sql=input_data.sql,
            model_ref=model_ref,
            options=options,
            connection_name=connection_name,
        )

        return self._create_model_with_sql(session=session, sql=sql)

    def create_time_series_model(
        self,
        X_train: bpd.DataFrame,
        y_train: bpd.DataFrame,
        id_col: Optional[bpd.DataFrame] = None,
        transforms: Optional[Iterable[str]] = None,
        options: Mapping[str, Union[str, int, float, Iterable[str]]] = {},
    ) -> BqmlModel:
        assert (
            X_train.columns.size == 1
        ), "Time series timestamp input must only contain 1 column."
        assert (
            y_train.columns.size == 1
        ), "Time stamp data input must only contain 1 column."
        assert id_col is None or (
            id_col is not None and id_col.columns.size == 1
        ), "Time series id input is either None or must only contain 1 column."

        options = dict(options)
        # Cache dataframes to make sure base table is not a snapshot
        # cached dataframe creates a full copy, never uses snapshot
        input_data = X_train.join(y_train, how="outer")
        if id_col is not None:
            input_data = input_data.join(id_col, how="outer")
        input_data = input_data.cache()
        options.update({"TIME_SERIES_TIMESTAMP_COL": X_train.columns.tolist()[0]})
        options.update({"TIME_SERIES_DATA_COL": y_train.columns.tolist()[0]})
        if id_col is not None:
            options.update({"TIME_SERIES_ID_COL": id_col.columns.tolist()[0]})

        session = X_train._session
        model_ref = self._create_model_ref(session._anonymous_dataset)

        sql = self._model_creation_sql_generator.create_model(
            source_sql=input_data.sql,
            model_ref=model_ref,
            transforms=transforms,
            options=options,
        )

        return self._create_model_with_sql(session=session, sql=sql)

    def create_remote_model(
        self,
        session: bigframes.Session,
        connection_name: str,
        input: Mapping[str, str] = {},
        output: Mapping[str, str] = {},
        options: Mapping[str, Union[str, int, float, Iterable[str]]] = {},
    ) -> BqmlModel:
        """Create a session-temporary BQML remote model with the CREATE OR REPLACE MODEL statement

        Args:
            connection_name:
                a BQ connection to talk with Vertex AI, of the format <PROJECT_NUMBER>.<REGION>.<CONNECTION_NAME>. https://cloud.google.com/bigquery/docs/create-cloud-resource-connection
            input:
                input schema for general remote models
            output:
                output schema for general remote models
            options:
                a dict of options to configure the model. Generates a BQML OPTIONS clause

        Returns:
            BqmlModel: a BqmlModel wrapping a trained model in BigQuery
        """
        model_ref = self._create_model_ref(session._anonymous_dataset)
        sql = self._model_creation_sql_generator.create_remote_model(
            connection_name=connection_name,
            model_ref=model_ref,
            input=input,
            output=output,
            options=options,
        )

        return self._create_model_with_sql(session=session, sql=sql)

    def create_imported_model(
        self,
        session: bigframes.Session,
        options: Mapping[str, Union[str, int, float, Iterable[str]]] = {},
    ) -> BqmlModel:
        """Create a session-temporary BQML imported model with the CREATE OR REPLACE MODEL statement

        Args:
            options: a dict of options to configure the model. Generates a BQML OPTIONS
                clause

        Returns: a BqmlModel, wrapping a trained model in BigQuery
        """
        model_ref = self._create_model_ref(session._anonymous_dataset)
        sql = self._model_creation_sql_generator.create_imported_model(
            model_ref=model_ref,
            options=options,
        )

        return self._create_model_with_sql(session=session, sql=sql)

    def create_xgboost_imported_model(
        self,
        session: bigframes.Session,
        input: Mapping[str, str] = {},
        output: Mapping[str, str] = {},
        options: Mapping[str, Union[str, int, float, Iterable[str]]] = {},
    ) -> BqmlModel:
        """Create a session-temporary BQML imported model with the CREATE OR REPLACE MODEL statement

        Args:
            input:
                input schema for imported xgboost models
            output:
                output schema for imported xgboost models
            options: a dict of options to configure the model. Generates a BQML OPTIONS
                clause

        Returns: a BqmlModel, wrapping a trained model in BigQuery
        """
        model_ref = self._create_model_ref(session._anonymous_dataset)

        sql = self._model_creation_sql_generator.create_xgboost_imported_model(
            model_ref=model_ref,
            input=input,
            output=output,
            options=options,
        )

        return self._create_model_with_sql(session=session, sql=sql)


def _start_generic_job(job: formatting_helpers.GenericJob):
    if bigframes.options.display.progress_bar is not None:
        formatting_helpers.wait_for_job(
            job, bigframes.options.display.progress_bar
        )  # Wait for the job to complete
    else:
        job.result()
