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#!/usr/bin/env python3
"""
Checks for the validity of numbers following the Luhn algorithm

Samuel Prevost 2021-04-01
"""

import os
import sys
import numpy as np

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from typing import Callable, Union
from enum import Enum
from argparse import ArgumentTypeError as ArgumentError
import os

class ValType(Enum):
    ANY = 0
    FILE = 1
    DIR = 2

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# Pshape is now a fully fledged Python library !
# Check it out at https://github.com/sam1902/pshape
# and install it anywhere with
#
# pip3 install pshape

import inspect, re

def pshape(arr):
    frame = inspect.currentframe()

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# Samuel Prevost 2021-02-25
# Released under GNU General Public License v3.0

import os
import functools
from datetime import datetime

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import tensorflow as tf

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#!/usr/bin/env python3
# Distributed under GNU GPLv3
"""Compute the receptive field of a CNN from any starting depth"""

import numpy as np
import torch
from torch import nn

from numba import njit
from numba.types import uint64

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from PIL import Image
import io
from ipywidgets import widgets
import IPython.display as display

def view(*arrs):
    ims = []
    for arr in arrs:
        output = io.BytesIO()
        im = Image.fromarray(arr).resize((500,500), Image.NEAREST)

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#!/usr/bin/env bash

if [ "$#" -lt 1 ] || [ ! -f "$1" ]; then
    echo "Usage: $0 1.jpg 2.jpg ..."
    exit 1
fi

sizes=$(identify -format "%h %w\n" "$@" | awk -F ' ' '(NR==1){minh=$1;minw=$2;maxh=$1;maxw=$2}; (NR>0){if(minh>$1) minh=$1; if(maxh<$1) maxh=$1;if(minw>$2) minw=$2; if(maxw<$2) maxw=$2;}; {height += $1} {width += $2} END {print height/NR; print width/NR; print maxh; print minh; print maxw; print minw;}')

h=$(echo "$sizes" | sed -n 1p)

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wgetGdrive() {
    if [ $# -lt 2 ]; then
        echo "Usage $0 FILE_ID filename"
        echo "FILE_ID looks like 1UkzAZMBG1U8kTqO3yhO2nTtoRNtEvyRq"
        exit 1
    fi
    # This is the proper way to create temp files
    trap 'rm -f "$TMPFILE"' EXIT
    COOKIES=$(mktemp) || exit 1
    

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#!/usr/bin/env python3
"""Tests PyTorch CUDA capabilities"""

import sys

import torch


def main():
    """Main function"""

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import argparse
import gym
import numpy as np
from itertools import count

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical