# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
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# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================

# Google utils: https://cloud.google.com/storage/docs/reference/libraries

import os
import platform
import subprocess
import time
from pathlib import Path

import torch
import torch.nn as nn


def gsutil_getsize(url=''):
    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
    s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8')
    return eval(s.split(' ')[0]) if len(s) else 0  # bytes


def attempt_download(weights):
    # Attempt to download pretrained weights if not found locally
    weights = weights.strip().replace("'", '')
    file = Path(weights).name

    msg = weights + ' missing, try downloading from https://github.com/WongKinYiu/ScaledYOLOv4/releases/'
    models = ['yolov4-csp.pt', 'yolov4-csp-x.pt']  # available models

    if file in models and not os.path.isfile(weights):

        try:  # GitHub
            url = 'https://github.com/WongKinYiu/ScaledYOLOv4/releases/download/v1.0/' + file
            print('Downloading %s to %s...' % (url, weights))
            torch.hub.download_url_to_file(url, weights)
            assert os.path.exists(weights) and os.path.getsize(weights) > 1E6  # check
        except Exception as e:  # GCP
            print('ERROR: Download failure.')
            print('')
            
            
def attempt_load(weights, map_location=None):
    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
    model = Ensemble()
    for w in weights if isinstance(weights, list) else [weights]:
        attempt_download(w)
        model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval())  # load FP32 model

    if len(model) == 1:
        return model[-1]  # return model
    else:
        print('Ensemble created with %s\n' % weights)
        for k in ['names', 'stride']:
            setattr(model, k, getattr(model[-1], k))
        return model  # return ensemble


def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
    # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()
    t = time.time()

    print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')
    os.remove(name) if os.path.exists(name) else None  # remove existing
    os.remove('cookie') if os.path.exists('cookie') else None

    # Attempt file download
    out = "NUL" if platform.system() == "Windows" else "/dev/null"
    os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out))
    if os.path.exists('cookie'):  # large file
        s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name)
    else:  # small file
        s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id)
    r = os.system(s)  # execute, capture return
    os.remove('cookie') if os.path.exists('cookie') else None

    # Error check
    if r != 0:
        os.remove(name) if os.path.exists(name) else None  # remove partial
        print('Download error ')  # raise Exception('Download error')
        return r

    # Unzip if archive
    if name.endswith('.zip'):
        print('unzipping... ', end='')
        os.system('unzip -q %s' % name)  # unzip
        os.remove(name)  # remove zip to free space

    print('Done (%.1fs)' % (time.time() - t))
    return r


def get_token(cookie="./cookie"):
    with open(cookie) as f:
        for line in f:
            if "download" in line:
                return line.split()[-1]
    return ""


class Ensemble(nn.ModuleList):
    # Ensemble of models
    def __init__(self):
        super(Ensemble, self).__init__()

    def forward(self, x, augment=False):
        y = []
        for module in self:
            y.append(module(x, augment)[0])
        # y = torch.stack(y).max(0)[0]  # max ensemble
        # y = torch.cat(y, 1)  # nms ensemble
        y = torch.stack(y).mean(0)  # mean ensemble
        return y, None  # inference, train output
    
    
# def upload_blob(bucket_name, source_file_name, destination_blob_name):
#     # Uploads a file to a bucket
#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
#
#     storage_client = storage.Client()
#     bucket = storage_client.get_bucket(bucket_name)
#     blob = bucket.blob(destination_blob_name)
#
#     blob.upload_from_filename(source_file_name)
#
#     print('File {} uploaded to {}.'.format(
#         source_file_name,
#         destination_blob_name))
#
#
# def download_blob(bucket_name, source_blob_name, destination_file_name):
#     # Uploads a blob from a bucket
#     storage_client = storage.Client()
#     bucket = storage_client.get_bucket(bucket_name)
#     blob = bucket.blob(source_blob_name)
#
#     blob.download_to_filename(destination_file_name)
#
#     print('Blob {} downloaded to {}.'.format(
#         source_blob_name,
#         destination_file_name))