






import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
	import sqlitea

	import sqlalchemy




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a = None       ,			a = None       ,			a = False       ,			**a       ,			)       ->    Any:
								super().__init__(features=a       ,			cache_dir=a       ,			keep_in_memory=a       ,			**a					)
								snake_case_				=							Sql(
								    cache_dir=a       ,			features=a       ,			sql=a       ,			con=a       ,			**a       ,			)






	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_				=							None
								snake_case_				=							None
								snake_case_				=							None
								snake_case_				=							None

								self.builder.download_and_prepare(
								    download_config=a       ,			download_mode=a       ,			verification_mode=a       ,			base_path=a       ,			)

								# Build dataset for splits
								snake_case_				=							self.builder.as_dataset(
								    split='train'       ,			verification_mode=a       ,			in_memory=self.keep_in_memory					)
								return dataset






class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a       ,			a = None       ,			a = None       ,			**a       ,			)       ->    Union[str, Any]:
								if num_proc is not None and num_proc <= 0:
															raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''					)

								snake_case_				=							dataset
								snake_case_				=							name
								snake_case_				=							con
								snake_case_				=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								snake_case_				=							num_proc
								snake_case_				=							to_sql_kwargs




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							self.to_sql_kwargs.pop('sql'       ,			a					)
								snake_case_				=							self.to_sql_kwargs.pop('con'       ,			a					)
								snake_case_				=							self.to_sql_kwargs.pop('index'       ,			a					)

								snake_case_				=							self._write(index=a       ,			**self.to_sql_kwargs					)
								return written




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Dict:
								snake_case_			,		snake_case_			,		snake_case_				=							args
								snake_case_				=							{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
								snake_case_				=							query_table(
								    table=self.dataset.data       ,			key=slice(a       ,			offset + self.batch_size					)       ,			indices=self.dataset._indices       ,			)
								snake_case_				=							batch.to_pandas()
								snake_case_				=							df.to_sql(self.name       ,			self.con       ,			index=a       ,			**a					)
								return num_rows or len(a					)






	def 							_UpperCamelCase			(       self       ,			a       ,			**a					)       ->    int:
								snake_case_				=							0

								if self.num_proc is None or self.num_proc == 1:
															for offset in logging.tqdm(
															    range(0       ,			len(self.dataset					)       ,			self.batch_size					)       ,			unit='ba'       ,			disable=not logging.is_progress_bar_enabled()       ,			desc='Creating SQL from Arrow format'       ,			):
																						written += self._batch_sql((offset, index, to_sql_kwargs)					)
								else:
															snake_case_			,		snake_case_				=							len(self.dataset					), self.batch_size
															with multiprocessing.Pool(self.num_proc					) as pool:
																						for num_rows in logging.tqdm(
																						    pool.imap(
																						        self._batch_sql       ,			[(offset, index, to_sql_kwargs) for offset in range(0       ,			a       ,			a					)]       ,			)       ,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size       ,			unit='ba'       ,			disable=not logging.is_progress_bar_enabled()       ,			desc='Creating SQL from Arrow format'       ,			):
																													written += num_rows

								return written













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_):
							snake_case_				=							np.array([[1, item, train_mtch[i]] for i, item in enumerate(a_)])
							snake_case_				=							np.array(a_)
							snake_case_				=							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_):
							snake_case_				=							(1, 2, 1)
							snake_case_				=							(1, 1, 0, 7)
							snake_case_				=							SARIMAX(
							    a_							,     exog=a_							,     order=a_							,     seasonal_order=a_)
							snake_case_				=							model.fit(disp=a_							,     maxiter=6_00							,     method='nm')
							snake_case_				=							model_fit.predict(1							,     len(a_)							,     exog=[test_match])
							return result[0]





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							SVR(kernel='rbf'							,     C=1							,     gamma=0.1							,     epsilon=0.1)
							regressor.fit(a_							,     a_)
							snake_case_				=							regressor.predict(a_)
							return y_pred[0]





def 						__UpperCAmelCase			(     a_):
							train_user.sort()
							snake_case_				=							np.percentile(a_							,     25)
							snake_case_				=							np.percentile(a_							,     75)
							snake_case_				=							qa - qa
							snake_case_				=							qa - (iqr * 0.1)
							return low_lim





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							0
							snake_case_				=							0
							for i in list_vote:
														if i > actual_result:
																					snake_case_				=							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)
	lowercase  							=					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
	lowercase  							=					pd.DataFrame(
	    data_input, columns=["total_user", "total_even", "days"]
	)
	lowercase  							=					Normalizer().fit_transform(data_input_df.values)
	# split data
	lowercase  							=					normalize_df[:, 2].tolist()
	lowercase  							=					normalize_df[:, 0].tolist()
	lowercase  							=					normalize_df[:, 1].tolist()

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

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

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

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





