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# Cutting .wav file from start to end
# ffmpeg -i <in_file> -ss <start_sec> -to <end_sec> -c copy <out_file>
# ffmpeg -i 1.mp3 -ss 0 -to 60 -c copy 2.mp3 (example of use)

# Generating mono .wav file
# ffmpeg -i <in_file> -acodec pcm_s16le -ac 1 -ar 16000 <out_file>
# ffmpeg -i 2.mp3 -acodec pcm_s16le -ac 1 -ar 16000 2.wav

# Combine both
# ffmpeg -i 1.mp3 -ss 0 -to 60 -c -acodec pcm_s16le -ac 1 -ar 16000 copy out.wav

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"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.
[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

from utils import plot_images

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import time
import torch

class AverageMeter:
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0

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import time
import torch

class Tag:
    def __init__(self, topk=(1,), verbose=True):
        self.__topk = topk
        self.__verbose = verbose

    def __call__(self, data_loader):
        self.__time_start = time.time()

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# Example:
# $ python argparse_dict_argument.py --env a=b --env aa=bb
# Namespace(env={'a': 'b', 'aa': 'bb'})

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--env', action = type('', (argparse.Action, ), dict(__call__ = lambda a, p, n, v, o: getattr(n, a.dest).update(dict([v.split('=')])))), default = {}) # anonymously subclassing argparse.Action
print parser.parse_args()

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import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms

from activation import load_actF

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from sqlalchemy import create_engine

db = create_engine('postgresql://user:password@localhost:port/database')
for i in range(1,2000):
    conn = db.connect()
    #some simple data operations
    conn.close()
db.dispose()

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import pandas as pd
from sqlalchemy import create_engine

engine = create_engine('postgresql://id:password@localhost:port/database')
df.to_sql('table_name', engine)
pd.read_sql("select * from users limit 1", engine)

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import pandas as pd
import pyodbc
from sqlalchemy import create_engine
engine = create_engine("mssql+pyodbc://DESKTOP-R5QA5N1/test?driver=SQL+Server+Native+Client+11.0")

df = pd.DataFrame([1])
df.to_sql('test', con=engine)

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from PySide2.QtWidgets import QApplication, QMainWindow, QMdiArea, QAction, QMdiSubWindow, QTextEdit
import sys
 
 
 
class MDIWindow(QMainWindow):
 
    count = 0
    def __init__(self):
        super().__init__()