



'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      List[str]        =							np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase_  )]  )
		_a     :      Optional[int]        =							np.array(lowerCAmelCase_  )
		_a     :      Optional[int]        =							np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()						,			lowerCAmelCase_  )  )						,			x.transpose()  )						,			lowerCAmelCase_  )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      Any        =							(1, 2, 1)
		_a     :      List[Any]        =							(1, 1, 0, 7)
		_a     :      int        =							SARIMAX(
		    lowerCAmelCase_						,			exog=lowerCAmelCase_						,			order=lowerCAmelCase_						,			seasonal_order=lowerCAmelCase_  )
		_a     :      Optional[int]        =							model.fit(disp=lowerCAmelCase_						,			maxiter=600						,			method='nm'  )
		_a     :      Union[str, Any]        =							model_fit.predict(1						,			len(lowerCAmelCase_  )						,			exog=[test_match]  )
		return result[0]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      Any        =							SVR(kernel='rbf'						,			C=1						,			gamma=0.1						,			epsilon=0.1  )
		regressor.fit(lowerCAmelCase_						,			lowerCAmelCase_  )
		_a     :      Optional[int]        =							regressor.predict(lowerCAmelCase_  )
		return y_pred[0]



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      float:
		train_user.sort()
		_a     :      Optional[Any]        =							np.percentile(lowerCAmelCase_						,			25  )
		_a     :      Optional[int]        =							np.percentile(lowerCAmelCase_						,			75  )
		_a     :      str        =							qa - qa
		_a     :      Optional[Any]        =							qa - (iqr * 0.1)
		return low_lim



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      bool:
		_a     :      str        =							0
		_a     :      List[Any]        =							0
		for i in list_vote:
				if i > actual_result:
						_a     :      Optional[int]        =							not_safe + 1
				else:
						if abs(abs(lowerCAmelCase_  ) - abs(lowerCAmelCase_  )  ) <= 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)
							__lowerCAmelCase									=   [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
							__lowerCAmelCase									=   pd.DataFrame(
							    data_input, columns=['''total_user''', '''total_even''', '''days''']
							)
							__lowerCAmelCase									=   Normalizer().fit_transform(data_input_df.values)
							# split data
							__lowerCAmelCase									=   normalize_df[:, 2].tolist()
							__lowerCAmelCase									=   normalize_df[:, 0].tolist()
							__lowerCAmelCase									=   normalize_df[:, 1].tolist()

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

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

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

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







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

