imports.py

import io,operator,sys,os,re,mimetypes,csv,itertools,json,shutil,glob,pickle,tarfile,collections
import hashlib,itertools,types,inspect,functools,random,time,math,bz2,typing,numbers,string
import multiprocessing,threading,urllib,tempfile,concurrent.futures,matplotlib,warnings,zipfile

from concurrent.futures import as_completed
from functools import partial,reduce
from itertools import starmap,dropwhile,takewhile,zip_longest
from copy import copy,deepcopy
from multiprocessing import Lock,Process,Queue,queues
from datetime import datetime
from contextlib import redirect_stdout,contextmanager
from collections.abc import Iterable,Iterator,Generator,Sequence
from typing import Union,Optional
from types import SimpleNamespace
from pathlib import Path
from collections import OrderedDict,defaultdict,Counter,namedtuple
from enum import Enum,IntEnum
from warnings import warn
from textwrap import TextWrapper
from operator import itemgetter,attrgetter,methodcaller
from urllib.request import urlopen

# External modules
import requests,yaml,matplotlib.pyplot as plt,numpy as np,pandas as pd,scipy
from pandas.api.types import is_categorical_dtype,is_numeric_dtype
from numpy import array,ndarray
from scipy import ndimage
from IPython.core.debugger import set_trace
from fastcore.all import *
from fastprogress.fastprogress import progress_bar,master_bar

try:
    from types import WrapperDescriptorType,MethodWrapperType,MethodDescriptorType
except ImportError:
    WrapperDescriptorType = type(object.__init__)
    MethodWrapperType = type(object().__str__)
    MethodDescriptorType = type(str.join)
from types import BuiltinFunctionType,BuiltinMethodType,MethodType,FunctionType,LambdaType

pd.options.display.max_colwidth = 600
NoneType = type(None)
string_classes = (str,bytes)
mimetypes.init()

# PyTorch warnings
warnings.filterwarnings("ignore", message='.*nonzero.*', category=UserWarning)
warnings.filterwarnings("ignore", message='.*grid_sample.*', category=UserWarning)
warnings.filterwarnings("ignore", message='.*Distutils.*', category=UserWarning)

def is_iter(o):
    "Test whether `o` can be used in a `for` loop"
    #Rank 0 tensors in PyTorch are not really iterable
    return isinstance(o, (Iterable,Generator)) and getattr(o,'ndim',1)

def is_coll(o):
    "Test whether `o` is a collection (i.e. has a usable `len`)"
    #Rank 0 tensors in PyTorch do not have working `len`
    return hasattr(o, '__len__') and getattr(o,'ndim',1)

def all_equal(a,b):
    "Compares whether `a` and `b` are the same length and have the same contents"
    if not is_iter(b): return False
    return all(equals(a_,b_) for a_,b_ in itertools.zip_longest(a,b))

def noop (x=None, *args, **kwargs):
    "Do nothing"
    return x

def noops(self, x=None, *args, **kwargs):
    "Do nothing (method)"
    return x

def one_is_instance(a, b, t): return isinstance(a,t) or isinstance(b,t)

def equals(a,b):
    "Compares `a` and `b` for equality; supports sublists, tensors and arrays too"
    if one_is_instance(a,b,type): return a==b
    if hasattr(a, '__array_eq__'): return a.__array_eq__(b)
    if hasattr(b, '__array_eq__'): return b.__array_eq__(a)
    cmp = (np.array_equal if one_is_instance(a, b, ndarray       ) else
           operator.eq    if one_is_instance(a, b, (str,dict,set)) else
           all_equal      if is_iter(a) or is_iter(b) else
           operator.eq)
    return cmp(a,b)


def pv(text, verbose):
    if verbose: print(text)