

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   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  float:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  bool:




      """simple docstring"""

      snake_case_	=				0
      snake_case_	=				0
      for i in list_vote:
            if i > actual_result:
                  snake_case_	=				not_safe + 1
            else:
                  if abs(abs(SCREAMING_SNAKE_CASE ) - abs(SCREAMING_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)
   UpperCAmelCase      =   [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
   UpperCAmelCase      =   pd.DataFrame(
       data_input, columns=["""total_user""", """total_even""", """days"""]
   )
   UpperCAmelCase      =   Normalizer().fit_transform(data_input_df.values)
   # split data
   UpperCAmelCase      =   normalize_df[:, 2].tolist()
   UpperCAmelCase      =   normalize_df[:, 0].tolist()
   UpperCAmelCase      =   normalize_df[:, 1].tolist()

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

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

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

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



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "convnextv2"



 def __init__(     self		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-12		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=2_24		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase )

       snake_case_	=				num_channels
       snake_case_	=				patch_size
       snake_case_	=				num_stages
       snake_case_	=				[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
       snake_case_	=				[3, 3, 9, 3] if depths is None else depths
       snake_case_	=				hidden_act
       snake_case_	=				initializer_range
       snake_case_	=				layer_norm_eps
       snake_case_	=				drop_path_rate
       snake_case_	=				image_size
       snake_case_	=				['''stem'''] + [F'''stage{idx}''' for idx in range(1		,				len(self.depths ) + 1 )]
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(
           out_features=_UpperCAmelCase		,				out_indices=_UpperCAmelCase		,				stage_names=self.stage_names )