#!/usr/bin/env python3
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

import argparse
import os
import pathlib

from .onnx_model_utils import get_optimization_level, optimize_model


def optimize_model_helper():
    parser = argparse.ArgumentParser(f'{os.path.basename(__file__)}:{optimize_model_helper.__name__}',
                                     description='''
                                     Optimize an ONNX model using ONNX Runtime to the specified level.
                                     See https://onnxruntime.ai/docs/performance/graph-optimizations.html for more
                                     details of the optimization levels.'''
                                     )

    parser.add_argument('--opt_level', default='basic',
                        choices=['disable', 'basic', 'extended', 'all'],
                        help="Optimization level to use.")
    parser.add_argument('--log_level', choices=['debug', 'info', 'warning', 'error'], type=str, required=False,
                        default='error',
                        help="Log level. Defaults to Error so we don't get output about unused initializers "
                             "being removed. Warning or Info may be desirable in some scenarios.")

    parser.add_argument('input_model', type=pathlib.Path, help='Provide path to ONNX model to update.')
    parser.add_argument('output_model', type=pathlib.Path, help='Provide path to write optimized ONNX model to.')

    args = parser.parse_args()

    if args.log_level == 'error':
        log_level = 3
    elif args.log_level == 'debug':
        log_level = 0  # ORT verbose level
    elif args.log_level == 'info':
        log_level = 1
    elif args.log_level == 'warning':
        log_level = 2

    optimize_model(args.input_model, args.output_model, get_optimization_level(args.opt_level), log_level)


if __name__ == '__main__':
    optimize_model_helper()