



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__ :list ,					UpperCAmelCase__ :list ,					UpperCAmelCase__ :list ,					UpperCAmelCase__ :list ,					UpperCAmelCase__ :list					):






 '''simple docstring'''

 a						=		np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase__					)]					)
 a						=		np.array(UpperCAmelCase__					)
 a						=		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__ :list ,					UpperCAmelCase__ :list ,					UpperCAmelCase__ :list					):






 '''simple docstring'''

 a						=		(1, 2, 1)
 a						=		(1, 1, 0, 7)
 a						=		SARIMAX(
     UpperCAmelCase__ ,					exog=UpperCAmelCase__ ,					order=UpperCAmelCase__ ,					seasonal_order=UpperCAmelCase__					)
 a						=		model.fit(disp=UpperCAmelCase__ ,					maxiter=6_00 ,					method="nm"					)
 a						=		model_fit.predict(1 ,					len(UpperCAmelCase__					) ,					exog=[test_match]					)
 return result[0]


def 	UpperCAmelCase__      (      UpperCAmelCase__ :list ,					UpperCAmelCase__ :list ,					UpperCAmelCase__ :list					):






 '''simple docstring'''

 a						=		SVR(kernel="rbf" ,					C=1 ,					gamma=0.1 ,					epsilon=0.1					)
 regressor.fit(UpperCAmelCase__ ,					UpperCAmelCase__					)
 a						=		regressor.predict(UpperCAmelCase__					)
 return y_pred[0]


def 	UpperCAmelCase__      (      UpperCAmelCase__ :list					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :list ,					UpperCAmelCase__ :float					):






 '''simple docstring'''

 a						=		0
 a						=		0
 for i in list_vote:
  if i > actual_result:
   a						=		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)
     A_		:     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]]
     A_		:     Dict        =     pd.DataFrame(
         data_input, columns=['''total_user''', '''total_even''', '''days''']
     )
     A_		:     str        =     Normalizer().fit_transform(data_input_df.values)
     # split data
     A_		:     int        =     normalize_df[:, 2].tolist()
     A_		:     List[str]        =     normalize_df[:, 0].tolist()
     A_		:     List[Any]        =     normalize_df[:, 1].tolist()

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

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

     A_		:     List[str]        =     total_date[len(total_date) - 1 :]
     A_		:     str        =     total_user[len(total_user) - 1 :]
     A_		:     Any        =     total_match[len(total_match) - 1 :]

     # voting system with forecasting
     A_		:     Tuple        =     [
         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
     A_		:     Tuple        =     '''''' 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


A_		:     int        =     logging.get_logger(__name__)

A_		:     str        =     {
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''focalnet'''

   def __init__(      self			:       int							,   __lowerCAmelCase			:       Optional[Any]=224							,   __lowerCAmelCase			:       Any=4							,   __lowerCAmelCase			:       Any=3							,   __lowerCAmelCase			:       Tuple=96							,   __lowerCAmelCase			:       Dict=False							,   __lowerCAmelCase			:       Optional[int]=[192, 384, 768, 768]							,   __lowerCAmelCase			:       Union[str, Any]=[2, 2, 6, 2]							,   __lowerCAmelCase			:       Optional[int]=[2, 2, 2, 2]							,   __lowerCAmelCase			:       Union[str, Any]=[3, 3, 3, 3]							,   __lowerCAmelCase			:       str="gelu"							,   __lowerCAmelCase			:       Any=4.0							,   __lowerCAmelCase			:       Optional[int]=0.0							,   __lowerCAmelCase			:       Tuple=0.1							,   __lowerCAmelCase			:       str=False							,   __lowerCAmelCase			:       Optional[int]=1E-4							,   __lowerCAmelCase			:       Optional[Any]=False							,   __lowerCAmelCase			:       Union[str, Any]=False							,   __lowerCAmelCase			:       str=False							,   __lowerCAmelCase			:       Any=0.0_2							,   __lowerCAmelCase			:       str=1E-5							,   __lowerCAmelCase			:       Optional[Any]=32							,   __lowerCAmelCase			:       Dict=None							,   __lowerCAmelCase			:       str=None							,   **__lowerCAmelCase			:       Any							,   )       ->				List[str]:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase							)

    a						=		image_size
    a						=		patch_size
    a						=		num_channels
    a						=		embed_dim
    a						=		use_conv_embed
    a						=		hidden_sizes
    a						=		depths
    a						=		focal_levels
    a						=		focal_windows
    a						=		hidden_act
    a						=		mlp_ratio
    a						=		hidden_dropout_prob
    a						=		drop_path_rate
    a						=		use_layerscale
    a						=		layerscale_value
    a						=		use_post_layernorm
    a						=		use_post_layernorm_in_modulation
    a						=		normalize_modulator
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		encoder_stride
    a						=		["stem"] + [f"""stage{idx}""" for idx in range(1							,   len(self.depths							) + 1							)]
    a		,  a						=		get_aligned_output_features_output_indices(
        out_features=__lowerCAmelCase							,   out_indices=__lowerCAmelCase							,   stage_names=self.stage_names							)

