
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			(	a_		,   a_		,   a_		,   a_		,   a_						)		->	float:







				"""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 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	float:







				"""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=6_0_0		,   method="nm"						)
				__A   			= model_fit.predict(1		,   len(a_						)		,   exog=[test_match]						)
				return result[0]


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	float:







				"""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 						UpperCAmelCase			(	a_						)		->	float:







				"""simple docstring"""



				train_user.sort()
				__A   			= np.percentile(a_		,   2_5						)
				__A   			= np.percentile(a_		,   7_5						)
				__A   			= qa - qa
				__A   			= qa - (iqr * 0.1)
				return low_lim


def 						UpperCAmelCase			(	a_		,   a_						)		->	bool:







				"""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)
					SCREAMING_SNAKE_CASE							:List[str]							 =				[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				pd.DataFrame(
					    data_input, columns=['total_user', 'total_even', 'days']
					)
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				Normalizer().fit_transform(data_input_df.values)
					# split data
					SCREAMING_SNAKE_CASE							:int							 =				normalize_df[:, 2].tolist()
					SCREAMING_SNAKE_CASE							:List[str]							 =				normalize_df[:, 0].tolist()
					SCREAMING_SNAKE_CASE							:List[Any]							 =				normalize_df[:, 1].tolist()

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

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

					SCREAMING_SNAKE_CASE							:int							 =				total_date[len(total_date) - 1 :]
					SCREAMING_SNAKE_CASE							:Tuple							 =				total_user[len(total_user) - 1 :]
					SCREAMING_SNAKE_CASE							:int							 =				total_match[len(total_match) - 1 :]

					# voting system with forecasting
					SCREAMING_SNAKE_CASE							: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
					SCREAMING_SNAKE_CASE							:Dict							 =				'' if data_safety_checker(res_vote, tst_user) else 'not '
					print('Today\'s data is {not_str}safe.')


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							:List[Any]							 =				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				['BartphoTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
