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# "License"); you may not use this file except in compliance
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# pylint:disable=redefined-outer-name,logging-format-interpolation


import logging
import argparse

import numpy as np
import onnx

logger = logging.getLogger(__name__)
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                    datefmt = '%m/%d/%Y %H:%M:%S',
                    level = logging.WARN)

if __name__ == "__main__":
    logger.info("Evaluating ONNXRuntime full precision accuracy and performance:")
    parser = argparse.ArgumentParser(
        description="Mobilenet_v3 fine-tune examples for image classification tasks.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument(
        '--model_path',
        type=str,
        default='mobilenet_v3.onnx',
        help="Pre-trained mobilenet_v2 model on onnx file"
    )
    parser.add_argument(
        '--benchmark',
        action='store_true', \
        default=False
    )
    parser.add_argument(
        '--tune',
        action='store_true', \
        default=False,
        help="whether quantize the model"
    )
    parser.add_argument(
        '--config',
        type=str,
        default='mobilenet_v3.yaml',
        help="config yaml path"
    )
    parser.add_argument(
        '--output_model',
        type=str,
        default='mobilenet_v3_tune.onnx',
        help="output model path"
    )
    parser.add_argument(
        '--mode',
        type=str,
        default='performance',
        help="benchmark mode of performance or accuracy"
    )

    args = parser.parse_args()
    
    model = onnx.load(args.model_path)

    if args.benchmark:
        from lpot.experimental import Benchmark, common
        evaluator = Benchmark(args.config)
        evaluator.model = common.Model(model)
        evaluator(args.mode)

    if args.tune:
        from lpot.experimental import Quantization, common

        quantize = Quantization(args.config)
        quantize.model = common.Model(model)
        q_model = quantize()
        q_model.save(args.output_model)
        
        if args.benchmark:
            from lpot.experimental import Benchmark
            evaluator = Benchmark(args.config)
            evaluator.model = common.Model(q_model)
            evaluator(args.mode)

