



'''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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__A)])
     _a      =  np.array(__A)
     _a      =  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()  ,	__A))  ,	x.transpose())  ,	__A)
     return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  (1, 2, 1)
     _a      =  (1, 1, 0, 7)
     _a      =  SARIMAX(
         __A  ,	exog=__A  ,	order=__A  ,	seasonal_order=__A)
     _a      =  model.fit(disp=__A  ,	maxiter=600  ,	method='''nm''')
     _a      =  model_fit.predict(1  ,	len(__A)  ,	exog=[test_match])
     return result[0]




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  SVR(kernel='''rbf'''  ,	C=1  ,	gamma=0.1  ,	epsilon=0.1)
     regressor.fit(__A  ,	__A)
     _a      =  regressor.predict(__A)
     return y_pred[0]




def lowerCAmelCase  (__A):



     """simple docstring"""



     train_user.sort()
     _a      =  np.percentile(__A  ,	25)
     _a      =  np.percentile(__A  ,	75)
     _a      =  qa - qa
     _a      =  qa - (iqr * 0.1)
     return low_lim




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  0
     _a      =  0
     for i in list_vote:
          if i > actual_result:
               _a      =  not_safe + 1
          else:
               if abs(abs(__A) - abs(__A)) <= 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)
  lowercase_		       =  [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
  lowercase_		       =  pd.DataFrame(
      data_input, columns=["total_user", "total_even", "days"]
  )
  lowercase_		       =  Normalizer().fit_transform(data_input_df.values)
  # split data
  lowercase_		       =  normalize_df[:, 2].tolist()
  lowercase_		       =  normalize_df[:, 0].tolist()
  lowercase_		       =  normalize_df[:, 1].tolist()

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

  # for linear regression & sarimax
  lowercase_		       =  total_date[: len(total_date) - 1]
  lowercase_		       =  total_user[: len(total_user) - 1]
  lowercase_		       =  total_match[: len(total_match) - 1]

  lowercase_		       =  total_date[len(total_date) - 1 :]
  lowercase_		       =  total_user[len(total_user) - 1 :]
  lowercase_		       =  total_match[len(total_match) - 1 :]

  # voting system with forecasting
  lowercase_		       =  [
      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
  lowercase_		       =  "" if data_safety_checker(res_vote, tst_user) else "not "
  print("Today's data is {not_str}safe.")








'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       'MCTCTFeatureExtractor'
 __lowerCamelCase    :       Optional[Any]          =       'AutoTokenizer'



 def __init__(self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      super().__init__(A ,   A       )
      _a      =  self.feature_extractor
      _a      =  False



 def __call__(self ,   *A ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor(*A ,   **A       )

      if "raw_speech" in kwargs:
           warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
           _a      =  kwargs.pop('''raw_speech'''       )
      else:
           _a      =  kwargs.pop('''audio''' ,   A       )
      _a      =  kwargs.pop('''sampling_rate''' ,   A       )
      _a      =  kwargs.pop('''text''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if audio is None and text is None:
           raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

      if audio is not None:
           _a      =  self.feature_extractor(A ,   *A ,   sampling_rate=A ,   **A       )
      if text is not None:
           _a      =  self.tokenizer(A ,   **A       )

      if text is None:
           return inputs
      elif audio is None:
           return encodings
      else:
           _a      =  encodings['''input_ids''']
           return inputs



 def 					a__  (self ,   *A ,   **A       )    ->     int:


      """simple docstring"""



      return self.tokenizer.batch_decode(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor.pad(*A ,   **A       )

      _a      =  kwargs.pop('''input_features''' ,   A       )
      _a      =  kwargs.pop('''labels''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if input_features is not None:
           _a      =  self.feature_extractor.pad(A ,   *A ,   **A       )
      if labels is not None:
           _a      =  self.tokenizer.pad(A ,   **A       )

      if labels is None:
           return input_features
      elif input_features is None:
           return labels
      else:
           _a      =  labels['''input_ids''']
           return input_features



 def 					a__  (self ,   *A ,   **A       )    ->     List[str]:


      """simple docstring"""



      return self.tokenizer.decode(*A ,   **A       )



 @contextmanager
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      warnings.warn(
          '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
          '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
          '''your audio inputs, or in a separate call.'''       )
      _a      =  True
      _a      =  self.tokenizer
      yield
      _a      =  self.feature_extractor
      _a      =  False



