


'''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       a				(      __a			, __a			, __a			, __a			, __a       )  ->     float:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			np.array([[1, item, train_mtch[i]] for i, item in enumerate(__a       )]       )
					UpperCamelCase__       :List[Any]							 =			np.array(__a       )
					UpperCamelCase__       :str							 =			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       a				(      __a			, __a			, __a       )  ->     float:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			(1, 2, 1)
					UpperCamelCase__       :str							 =			(1, 1, 0, 7)
					UpperCamelCase__       :Optional[int]							 =			SARIMAX(
					    __a			, exog=__a			, order=__a			, seasonal_order=__a       )
					UpperCamelCase__       :Optional[Any]							 =			model.fit(disp=__a			, maxiter=600			, method='''nm'''       )
					UpperCamelCase__       :Union[str, Any]							 =			model_fit.predict(1			, len(__a       )			, exog=[test_match]       )
					return result[0]


def       a				(      __a			, __a			, __a       )  ->     float:



					'''simple docstring'''






					UpperCamelCase__       :Optional[int]							 =			SVR(kernel='''rbf'''			, C=1			, gamma=0.1			, epsilon=0.1       )
					regressor.fit(__a			, __a       )
					UpperCamelCase__       :Optional[int]							 =			regressor.predict(__a       )
					return y_pred[0]


def       a				(      __a       )  ->     float:



					'''simple docstring'''






					train_user.sort()
					UpperCamelCase__       :Any							 =			np.percentile(__a			, 25       )
					UpperCamelCase__       :int							 =			np.percentile(__a			, 75       )
					UpperCamelCase__       :Tuple							 =			qa - qa
					UpperCamelCase__       :Tuple							 =			qa - (iqr * 0.1)
					return low_lim


def       a				(      __a			, __a       )  ->     bool:



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			0
					UpperCamelCase__       :Optional[Any]							 =			0
					for i in list_vote:
										if i > actual_result:
															UpperCamelCase__       :Optional[Any]							 =			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)
		__snake_case											=					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 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.''')






'''simple docstring'''
from math import ceil


def       a				(      __a			, __a       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			list(range(0			, __a       )       )

					UpperCamelCase__       :Optional[int]							 =			[item for sublist in list(device_map.values()       ) for item in sublist]

					# Duplicate check
					UpperCamelCase__       :Optional[int]							 =			[]
					for i in device_map_blocks:
										if device_map_blocks.count(__a       ) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(__a       )
    # Missing blocks
					UpperCamelCase__       :List[str]							 =			[i for i in blocks if i not in device_map_blocks]
					UpperCamelCase__       :Optional[Any]							 =			[i for i in device_map_blocks if i not in blocks]

					if len(__a       ) != 0:
										raise ValueError(
										    '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
										    ''' These attention blocks were specified more than once: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
										    '''blocks to a device on the device_map: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
										    + str(__a       )       )







def       a				(      __a			, __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			list(range(__a       )       )
					UpperCamelCase__       :Any							 =			int(ceil(n_layers / len(__a       )       )       )
					UpperCamelCase__       :List[Any]							 =			[layers[i : i + n_blocks] for i in range(0			, __a			, __a       )]

					return dict(zip(__a			, __a       )       )