# Copyright (C) 2022-present MongoDB, Inc.
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"""Cost Model Calibrator entry point."""

import dataclasses
import os
import csv
import json
from typing import Mapping, Sequence
from cost_estimator import ExecutionStats, ModelParameters
from data_generator import DataGenerator
from database_instance import DatabaseInstance
from config import Config
import abt_calibrator
import workload_execution
from workload_execution import Query, QueryParameters
import parameters_extractor
from random_generator_config import distributions

__all__ = []


def save_to_csv(parameters: Mapping[str, Sequence[ModelParameters]], filepath: str) -> None:
    """Save model input parameters to a csv file."""
    abt_type_name = 'abt_type'
    fieldnames = [
        abt_type_name, *[f.name for f in dataclasses.fields(ExecutionStats)],
        *[f.name for f in dataclasses.fields(QueryParameters)]
    ]
    with open(filepath, 'w', newline='') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        for abt_type, type_params_list in parameters.items():
            for type_params in type_params_list:
                fields = dataclasses.asdict(type_params.execution_stats) | dataclasses.asdict(
                    type_params.query_params)
                fields[abt_type_name] = abt_type
                writer.writerow(fields)


def main():
    """Entry point function."""
    script_directory = os.path.abspath(os.path.dirname(__file__))
    os.chdir(script_directory)

    with open("config.json") as config_file:
        config = Config.create(json.load(config_file))

    # 1. Database Instance provides connectivity to a MongoDB instance, it loads data optionally
    # from the dump on creating and stores data optionally to the dump on closing.
    with DatabaseInstance(config.database) as database:

        # 2. Data generation (optional), generates random data and populates collections with it.
        generator = DataGenerator(database, config.data_generator)
        generator.populate_collections()

        # 3. Collecting data for calibration (optional).
        # It runs the pipelines and stores explains to the database.
        requests = []
        for val in distributions['string_choice'].get_values():
            keys_length = len(val) + 2
            for i in range(1, 5):
                requests.append(
                    Query(pipeline=[{'$match': {f'choice{i}': val}}],
                          keys_length_in_bytes=keys_length))

        workload_execution.execute(database, config.workload_execution, generator.collection_infos,
                                   requests)

        # Calibration phase (optional).
        # Reads the explains stored on the previous step (this run and/or previous runs),
        # aparses the explains, nd calibrates the cost model for the ABT nodes.
        models = abt_calibrator.calibrate(
            config.abt_calibrator, database,
            ['IndexScan', 'Seek', 'PhysicalScan', 'ValueScan', 'CoScan', 'Scan'])
        for abt, model in models.items():
            print(abt)
            print(model)

        parameters = parameters_extractor.extract_parameters(config.abt_calibrator, database, [])
        save_to_csv(parameters, 'parameters.csv')

    print("DONE!")


if __name__ == '__main__':
    main()