





"""simple docstring"""




import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Dict										=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase					)]					)
  A_  :       Optional[Any]										=  np.array(__UpperCamelCase					)
  A_  :       Tuple										=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,					__UpperCamelCase					)					) ,					x.transpose()					) ,					__UpperCamelCase					)
  return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Dict										=  (1, 2, 1)
  A_  :       Optional[int]										=  (1, 1, 0, 7)
  A_  :       str										=  SARIMAX(
      __UpperCamelCase ,					exog=__UpperCamelCase ,					order=__UpperCamelCase ,					seasonal_order=__UpperCamelCase					)
  A_  :       List[str]										=  model.fit(disp=__UpperCamelCase ,					maxiter=600 ,					method='nm'					)
  A_  :       Optional[int]										=  model_fit.predict(1 ,					len(__UpperCamelCase					) ,					exog=[test_match]					)
  return result[0]





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  SVR(kernel='rbf' ,					C=1 ,					gamma=0.1 ,					epsilon=0.1					)
  regressor.fit(__UpperCamelCase ,					__UpperCamelCase					)
  A_  :       int										=  regressor.predict(__UpperCamelCase					)
  return y_pred[0]





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  train_user.sort()
  A_  :       Tuple										=  np.percentile(__UpperCamelCase ,					25					)
  A_  :       str										=  np.percentile(__UpperCamelCase ,					75					)
  A_  :       Dict										=  qa - qa
  A_  :       Union[str, Any]										=  qa - (iqr * 0.1)
  return low_lim





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  0
  A_  :       Dict										=  0
  for i in list_vote:
    if i > actual_result:
      A_  :       str										=  not_safe + 1
    else:
      if abs(abs(__UpperCamelCase					) - abs(__UpperCamelCase					)					) <= 0.1:
        safe += 1
      else:
        not_safe += 1
  return safe > not_safe


if __name__ == "__main__":
   # data_input_df = pd.read_csv("ex_data.csv", header=None)
   lowerCamelCase_     :						List[str]						=					[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
   lowerCamelCase_     :						str						=					pd.DataFrame(
       data_input, columns=['total_user', 'total_even', 'days']
   )
   lowerCamelCase_     :						Dict						=					Normalizer().fit_transform(data_input_df.values)
   # split data
   lowerCamelCase_     :						Union[str, Any]						=					normalize_df[:, 2].tolist()
   lowerCamelCase_     :						Any						=					normalize_df[:, 0].tolist()
   lowerCamelCase_     :						int						=					normalize_df[:, 1].tolist()

   # for svr (input variable = total date and total match)
   lowerCamelCase_     :						Optional[int]						=					normalize_df[:, [1, 2]].tolist()
   lowerCamelCase_     :						Optional[int]						=					x[: len(x) - 1]
   lowerCamelCase_     :						Optional[int]						=					x[len(x) - 1 :]

   # for linear regression & sarimax
   lowerCamelCase_     :						Tuple						=					total_date[: len(total_date) - 1]
   lowerCamelCase_     :						Any						=					total_user[: len(total_user) - 1]
   lowerCamelCase_     :						int						=					total_match[: len(total_match) - 1]

   lowerCamelCase_     :						int						=					total_date[len(total_date) - 1 :]
   lowerCamelCase_     :						Optional[Any]						=					total_user[len(total_user) - 1 :]
   lowerCamelCase_     :						str						=					total_match[len(total_match) - 1 :]

   # voting system with forecasting
   lowerCamelCase_     :						str						=					[
       linear_regression_prediction(
           trn_date, trn_user, trn_match, tst_date, tst_match
       ),
       sarimax_predictor(trn_user, trn_match, tst_match),
       support_vector_regressor(x_train, x_test, trn_user),
   ]

   # check the safety of today's data
   lowerCamelCase_     :						List[Any]						=					'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
   print('Today\'s data is {not_str}safe.')
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")
