# 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.

"""Standalone TensorRT evaluation."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import logging

import os
import json
import numpy as np

from collections import defaultdict
from tqdm.auto import tqdm

from nvidia_tao_deploy.cv.common.decorators import monitor_status
from nvidia_tao_deploy.cv.multitask_classification.inferencer import MClassificationInferencer
from nvidia_tao_deploy.cv.multitask_classification.dataloader import MClassificationLoader
from nvidia_tao_deploy.cv.multitask_classification.proto.utils import load_proto


logging.getLogger('PIL').setLevel(logging.WARNING)
logging.basicConfig(format='%(asctime)s [TAO Toolkit] [%(levelname)s] %(name)s %(lineno)d: %(message)s',
                    level="INFO")
logger = logging.getLogger(__name__)


@monitor_status(name='multitask_classification', mode='evaluation')
def main(args):
    """Multitask Classification TRT evaluation."""
    es = load_proto(args.experiment_spec)
    interpolation = es.model_config.resize_interpolation_method if es.model_config.resize_interpolation_method else 0
    interpolation_map = {
        0: "bilinear",
        1: "bicubic"
    }
    interpolation_method = interpolation_map[interpolation]

    # Override spec file if argument is provided
    image_dirs = args.image_dir if args.image_dir else es.dataset_config.image_directory_path
    batch_size = args.batch_size if args.batch_size else es.eval_config.batch_size

    trt_infer = MClassificationInferencer(args.model_path, batch_size=batch_size)

    if trt_infer._input_shape[0] in [1, 3]:
        data_format = "channels_first"
    else:
        data_format = "channels_last"

    dl = MClassificationLoader(
        trt_infer._input_shape,
        [image_dirs],
        es.dataset_config.val_csv_path,
        data_format=data_format,
        interpolation_method=interpolation_method,
        batch_size=batch_size,
        dtype=trt_infer.inputs[0].host.dtype)

    tp = defaultdict(list)
    for imgs, labels in tqdm(dl, total=len(dl), desc="Producing predictions"):
        y_pred = trt_infer.infer(imgs)
        assert len(dl.class_dict_list_sorted) == len(labels) == len(y_pred), "Output size mismatch!"
        for (c, _), gt, pred in zip(dl.class_dict_list_sorted, labels, y_pred):
            tp[c].append((np.argmax(gt, axis=1) == np.argmax(pred, axis=1)).sum())

    # Get acc for each task
    eval_results = {}
    for k, v in tp.items():
        acc = sum(v) / len(dl.image_paths)
        logging.info("%s accuracy: %s", k, acc)
        eval_results[k] = float(acc)

    # Store evaluation results into JSON
    if args.results_dir is None:
        results_dir = os.path.dirname(args.model_path)
    else:
        results_dir = args.results_dir
    with open(os.path.join(results_dir, "results.json"), "w", encoding="utf-8") as f:
        json.dump(eval_results, f)
    logging.info("Finished evaluation.")


def build_command_line_parser(parser=None):
    """Build the command line parser using argparse.

    Args:
        parser (subparser): Provided from the wrapper script to build a chained
                parser mechanism.
    Returns:
        parser
    """
    if parser is None:
        parser = argparse.ArgumentParser(prog='eval', description='Evaluate with a Multitask Classification TRT model.')

    parser.add_argument(
        '-i',
        '--image_dir',
        type=str,
        required=False,
        default=None,
        help='Input directory of images')
    parser.add_argument(
        '-m',
        '--model_path',
        type=str,
        required=True,
        help='Path to the Classification TensorRT engine.'
    )
    parser.add_argument(
        '-e',
        '--experiment_spec',
        type=str,
        required=True,
        help='Path to the experiment spec file.'
    )
    parser.add_argument(
        '-b',
        '--batch_size',
        type=int,
        required=False,
        default=1,
        help='Batch size.')
    parser.add_argument(
        '-r',
        '--results_dir',
        type=str,
        required=True,
        default=None,
        help='Output directory where the log is saved.'
    )
    return parser


def parse_command_line_arguments(args=None):
    """Simple function to parse command line arguments."""
    parser = build_command_line_parser(args)
    return parser.parse_args(args)


if __name__ == '__main__':
    args = parse_command_line_arguments()
    main(args)
