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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

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

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from typing import Optional, List, Dict
from transformers import AutoTokenizer, AutoModel
from torch.nn import functional as F


class ZeroShotLearning:
    """
    Zero-Shot Learning

    ...

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import tempfile

import numpy as np
import torch
from torch.nn import Module
from scipy.io.wavfile import write
from playsound import playsound


# Constant Variables

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from itertools import permutations

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import networkx as nx
import osmnx as ox


ox.config(log_console=True, use_cache=True)

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'''
Task: Operations Research
PYIKEA wants to maximize its profit of selling armchair, wingchair, and Lovet-table. The profit of selling armchair is 150 DKK, wingchair 100 DKK, and Lovet-table 250 DKK. 

It takes:
    15 planks of wood and 5 hours of labour to make one armchair
    12 planks of wood and 2 hours of labour to make one wingchair
    18 planks of wood and 8 hours of labour to make one Lovet-table

The store needs at least 4 of each chair, and a table. The total amount of woods pieces in storage is 450 and labour budget is of 120 hours only.

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import easyocr
import requests


# API Defaults
API_URL = "https://www.tjekbil.dk/api/v2/nummerplade"
HEADERS = {"Content-Type": "application/json; charset=utf-8"}


def check_vehical(image_path: str, language: str = "en") -> dict:

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from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression

# assumes classification problem data split with numeric, categorical and text features
# [Salary, Bonus, Age, Gender, Descriptions Target]

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import warnings


# custom warning
class ParentalAdvisoryWarning(UserWarning):
	pass


# custom exception
class InputError(Exception):

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'''
Example of Wrappers
'''

from functools import wraps
from datetime import datetime
import time

def verbose_timeit(func):
    @wraps(func)

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# Advance Python:  Tips and Tricks
# Author: Prayson W. Daniel (github.com/proteusiq)
# Objective: We want functions to be activate/executed when we experience change in X
# Note: we can run functions in threads or asynchronic to avoid blocking (if that's wanted behavior)


from events import Events