





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list     )      ->						float:
  _lowercase							= np.array([[1, item, train_mtch[i]] for i, item in enumerate(snake_case__     )]     )
  _lowercase							= np.array(snake_case__     )
  _lowercase							= np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,							snake_case__     )     )			,							x.transpose()     )			,							snake_case__     )
  return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list     )      ->						float:
  _lowercase							= (1, 2, 1)
  _lowercase							= (1, 1, 0, 7)
  _lowercase							= SARIMAX(
      snake_case__			,							exog=snake_case__			,							order=snake_case__			,							seasonal_order=snake_case__     )
  _lowercase							= model.fit(disp=snake_case__			,							maxiter=600			,							method='nm'     )
  _lowercase							= model_fit.predict(1			,							len(snake_case__     )			,							exog=[test_match]     )
  return result[0]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :list			,							snake_case__  :list     )      ->						float:
  _lowercase							= SVR(kernel='rbf'			,							C=1			,							gamma=0.1			,							epsilon=0.1     )
  regressor.fit(snake_case__			,							snake_case__     )
  _lowercase							= regressor.predict(snake_case__     )
  return y_pred[0]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list     )      ->						float:
  train_user.sort()
  _lowercase							= np.percentile(snake_case__			,							25     )
  _lowercase							= np.percentile(snake_case__			,							75     )
  _lowercase							= qa - qa
  _lowercase							= qa - (iqr * 0.1)
  return low_lim

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :list			,							snake_case__  :float     )      ->						bool:
  _lowercase							= 0
  _lowercase							= 0
  for i in list_vote:
    if i > actual_result:
      _lowercase							= not_safe + 1
    else:
      if abs(abs(snake_case__     ) - abs(snake_case__     )     ) <= 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)
      snake_case 				=       [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
      snake_case 				=       pd.DataFrame(
          data_input, columns=["""total_user""", """total_even""", """days"""]
      )
      snake_case 				=       Normalizer().fit_transform(data_input_df.values)
      # split data
      snake_case 				=       normalize_df[:, 2].tolist()
      snake_case 				=       normalize_df[:, 0].tolist()
      snake_case 				=       normalize_df[:, 1].tolist()

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

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

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

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
      import torch

      from transformers.generation import DisjunctiveConstraint





@require_torch
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         # For consistency across different places the DisjunctiveConstraint is called,
         # dc.token_ids is a list of integers. It is also initialized only by integers.

         _lowercase							= [[1, 2, 4], [1, 2, 3, 4]]
         _lowercase							= DisjunctiveConstraint(__A  )
         self.assertTrue(isinstance(dc.token_ids						,__A  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					str:
         # We can't have constraints that are complete subsets of another. This leads to a preverse
         # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
         # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
         # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
         # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
         _lowercase							= [[1, 2], [1, 2, 3, 4]]

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(__A  )  # fails here







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[Any]:
         _lowercase							= [[1, 2, 3], [1, 2, 4]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(3  )
         _lowercase							= stepped is True and completed is True and reset is False
         self.assertTrue(__A  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 3]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(4  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2, 4]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

         dc.reset()

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 3  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 2  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.remaining() == 0  )
         self.assertTrue(dc.current_seq == [1, 2, 5]  )