# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

"""Metric Learning Recognition convert onnx model to TRT engine."""

import logging
import os
import tempfile

from nvidia_tao_deploy.cv.metric_learning_recognition.engine_builder import MLRecogEngineBuilder
from nvidia_tao_deploy.cv.common.hydra.hydra_runner import hydra_runner
from nvidia_tao_deploy.cv.metric_learning_recognition.hydra_config.default_config import ExperimentConfig
from nvidia_tao_deploy.cv.common.decorators import monitor_status

logging.basicConfig(format='%(asctime)s [TAO Toolkit] [%(levelname)s] %(name)s %(lineno)d: %(message)s',
                    level="INFO")
logger = logging.getLogger(__name__)
spec_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))


@hydra_runner(
    config_path=os.path.join(spec_root, "specs"),
    config_name="export", schema=ExperimentConfig
)
@monitor_status(name='ml_recog', mode='gen_trt_engine')
def main(cfg: ExperimentConfig) -> None:
    """Convert encrypted uff or onnx model to TRT engine."""
    if cfg.gen_trt_engine.results_dir is not None:
        results_dir = cfg.gen_trt_engine.results_dir
    else:
        results_dir = os.path.join(cfg.results_dir, "gen_trt_engine")
    os.makedirs(results_dir, exist_ok=True)

    engine_file = cfg.gen_trt_engine.trt_engine

    data_type = cfg.gen_trt_engine.tensorrt.data_type
    workspace_size = cfg.gen_trt_engine.tensorrt.workspace_size
    min_batch_size = cfg.gen_trt_engine.tensorrt.min_batch_size
    opt_batch_size = cfg.gen_trt_engine.tensorrt.opt_batch_size
    max_batch_size = cfg.gen_trt_engine.tensorrt.max_batch_size
    batch_size = cfg.gen_trt_engine.batch_size
    num_channels = cfg.model.input_channels
    input_width = cfg.model.input_width
    input_height = cfg.model.input_height

    # INT8 related configs
    img_std = cfg.dataset.pixel_std
    img_mean = cfg.dataset.pixel_mean
    calib_input = list(cfg.gen_trt_engine.tensorrt.calibration.get('cal_image_dir', []))
    calib_cache = cfg.gen_trt_engine.tensorrt.calibration.get('cal_cache_file', None)

    if batch_size is None or batch_size == -1:
        input_batch_size = 1
        is_dynamic = True
    else:
        input_batch_size = batch_size
        is_dynamic = False

    if engine_file is not None:
        if engine_file is None:
            engine_handle, temp_engine_path = tempfile.mkstemp()
            os.close(engine_handle)
            output_engine_path = temp_engine_path
        else:
            output_engine_path = engine_file

        builder = MLRecogEngineBuilder(
            workspace=workspace_size // 1024,
            input_dims=(input_batch_size, num_channels, input_height, input_width),
            is_dynamic=is_dynamic,
            min_batch_size=min_batch_size,
            opt_batch_size=opt_batch_size,
            max_batch_size=max_batch_size,
            img_std=img_std,
            img_mean=img_mean)

        builder.create_network(cfg.gen_trt_engine.onnx_file, "onnx")
        builder.create_engine(
            output_engine_path,
            data_type,
            calib_input=calib_input,
            calib_cache=calib_cache,
            calib_num_images=cfg.gen_trt_engine.tensorrt.calibration.cal_batch_size * cfg.gen_trt_engine.tensorrt.calibration.cal_batches,
            calib_batch_size=cfg.gen_trt_engine.tensorrt.calibration.cal_batch_size
        )

    logging.info("Export finished successfully.")


if __name__ == '__main__':
    main()
