




'''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 	snake_case_					(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 	snake_case_					(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=600				,		method='''nm'''   )
							_a			  =       model_fit.predict(1				,		len(UpperCamelCase   )				,		exog=[test_match]   )
							return result[0]
def 	snake_case_					(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 	snake_case_					(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 	snake_case_					(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)
				_snake_case   :   Any     							=	[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
				_snake_case   :   Dict     							=	pd.DataFrame(
				    data_input, columns=['total_user', 'total_even', 'days']
				)
				_snake_case   :   Tuple     							=	Normalizer().fit_transform(data_input_df.values)
				# split data
				_snake_case   :   int     							=	normalize_df[:, 2].tolist()
				_snake_case   :   List[str]     							=	normalize_df[:, 0].tolist()
				_snake_case   :   List[Any]     							=	normalize_df[:, 1].tolist()

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

				# for linear regression & sarimax
				_snake_case   :   Optional[Any]     							=	total_date[: len(total_date) - 1]
				_snake_case   :   str     							=	total_user[: len(total_user) - 1]
				_snake_case   :   Dict     							=	total_match[: len(total_match) - 1]

				_snake_case   :   List[Any]     							=	total_date[len(total_date) - 1 :]
				_snake_case   :   Tuple     							=	total_user[len(total_user) - 1 :]
				_snake_case   :   Union[str, Any]     							=	total_match[len(total_match) - 1 :]

				# voting system with forecasting
				_snake_case   :   Optional[int]     							=	[
				    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   :   Any     							=	'' if data_safety_checker(res_vote, tst_user) else 'not '
				print('Today\'s data is {not_str}safe.')







'''simple docstring'''


import os

import pytest

from transformers.dynamic_module_utils import get_imports


_snake_case   :   List[str]     							=	'\nimport os\n'

_snake_case   :   Dict     							=	'\ndef foo():\n    import os\n    return False\n'

_snake_case   :   List[Any]     							=	'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

_snake_case   :   List[str]     							=	'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

_snake_case   :   Union[str, Any]     							=	[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case'''				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       os.path.join(UpperCamelCase				,		'''test_file.py'''   )
							with open(UpperCamelCase				,		'''w'''   ) as _tmp_file:
														_tmp_file.write(UpperCamelCase   )

							_a			  =       get_imports(UpperCamelCase   )
							assert parsed_imports == ["os"]

