



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						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase						)]						)
		__UpperCAmelCase    :     Union[str, Any]													=	np.array(_UpperCAmelCase						)
		__UpperCAmelCase    :     Dict													=	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        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	(1, 2, 1)
		__UpperCAmelCase    :     Union[str, Any]													=	(1, 1, 0, 7)
		__UpperCAmelCase    :     List[str]													=	SARIMAX(
		    _UpperCAmelCase ,   exog=_UpperCAmelCase ,   order=_UpperCAmelCase ,   seasonal_order=_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	model.fit(disp=_UpperCAmelCase ,   maxiter=6_00 ,   method='''nm'''						)
		__UpperCAmelCase    :     int													=	model_fit.predict(1 ,   len(_UpperCAmelCase						) ,   exog=[test_match]						)
		return result[0]

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	SVR(kernel='''rbf''' ,   C=1 ,   gamma=0.1 ,   epsilon=0.1						)
		regressor.fit(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	regressor.predict(_UpperCAmelCase						)
		return y_pred[0]

def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		train_user.sort()
		__UpperCAmelCase    :     Optional[int]													=	np.percentile(_UpperCAmelCase ,   25						)
		__UpperCAmelCase    :     Optional[int]													=	np.percentile(_UpperCAmelCase ,   75						)
		__UpperCAmelCase    :     Optional[int]													=	qa - qa
		__UpperCAmelCase    :     List[str]													=	qa - (iqr * 0.1)
		return low_lim

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					float						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	0
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i in list_vote:
				if i > actual_result:
						__UpperCAmelCase    :     Tuple													=	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)
				__A   			=[[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
				__A   			=pd.DataFrame(
				    data_input, columns=["total_user", "total_even", "days"]
				)
				__A   			=Normalizer().fit_transform(data_input_df.values)
				# split data
				__A   			=normalize_df[:, 2].tolist()
				__A   			=normalize_df[:, 0].tolist()
				__A   			=normalize_df[:, 1].tolist()

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

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

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

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










import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A   			=logging.get_logger(__name__)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""input_ids""", """attention_mask"""]




			def __init__(   self							: List[str]       ,       a_							: List[Any]="</s>"       ,       a_							: str="<unk>"       ,       a_							: Tuple="<pad>"       ,       a_							: List[Any]=1_25       ,       a_							: Any=None       ,       **a_							: Tuple       ,       ):
					'''simple docstring'''





					if extra_ids > 0 and additional_special_tokens is None:
							__UpperCAmelCase    :     Optional[Any]													=	[F'<extra_id_{i}>' for i in range(a_ )]
					elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							__UpperCAmelCase    :     Optional[Any]													=	len(set(filter(lambda a_ : bool('''extra_id''' in str(a_ ) )       ,       a_ ) ) )
							if extra_tokens != extra_ids:
									raise ValueError(
									    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
									    ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
									    ''' extra_ids tokens''' )

					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else pad_token
					__UpperCAmelCase    :     str													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else eos_token
					__UpperCAmelCase    :     int													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else unk_token

					super().__init__(
					    eos_token=a_       ,       unk_token=a_       ,       pad_token=a_       ,       extra_ids=a_       ,       additional_special_tokens=a_       ,       **a_       ,       )

					__UpperCAmelCase    :     Dict													=	extra_ids

					__UpperCAmelCase    :     Any													=	2**8  # utf is 8 bits

					# define special tokens dict
					__UpperCAmelCase    :     Dict[int, str]													=	{
					    self.pad_token: 0,
					    self.eos_token: 1,
					    self.unk_token: 2,
					}
					__UpperCAmelCase    :     Optional[int]													=	len(self.special_tokens_encoder )
					__UpperCAmelCase    :     Union[str, Any]													=	len(a_ )
					for i, token in enumerate(a_ ):
							__UpperCAmelCase    :     Union[str, Any]													=	self.vocab_size + i - n
					__UpperCAmelCase    :     Dict[str, int]													=	{v: k for k, v in self.special_tokens_encoder.items()}




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




			def  snake_case__     (   self							: Any       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None       ,       a_							: bool = False ):
					'''simple docstring'''





					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=a_       ,       token_ids_a=a_       ,       already_has_special_tokens=a_ )

					# normal case: some special tokens
					if token_ids_a is None:
							return ([0] * len(a_ )) + [1]
					return ([0] * len(a_ )) + [1] + ([0] * len(a_ )) + [1]




			def  snake_case__     (   self							: Any       ,       a_							: List[int] ):
					'''simple docstring'''





					if len(a_ ) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
							    ''' eos tokens being added.''' )
							return token_ids
					else:
							return token_ids + [self.eos_token_id]




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[self.eos_token_id]

					if token_ids_a is None:
							return len(token_ids_a + eos ) * [0]
					return len(token_ids_a + eos + token_ids_a + eos ) * [0]




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self._add_eos_if_not_present(a_ )
					if token_ids_a is None:
							return token_ids_a
					else:
							__UpperCAmelCase    :     List[str]													=	self._add_eos_if_not_present(a_ )
							return token_ids_a + token_ids_a




			def  snake_case__     (   self							: List[Any]       ,       a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	[chr(a_ ) for i in text.encode('''utf-8''' )]
					return tokens




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int ):
					'''simple docstring'''





					if token in self.special_tokens_encoder:
							__UpperCAmelCase    :     Union[str, Any]													=	self.special_tokens_encoder[token]
					elif token in self.added_tokens_encoder:
							__UpperCAmelCase    :     Optional[Any]													=	self.added_tokens_encoder[token]
					elif len(a_ ) != 1:
							__UpperCAmelCase    :     int													=	self.unk_token_id
					else:
							__UpperCAmelCase    :     List[str]													=	ord(a_ ) + self._num_special_tokens
					return token_id




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Optional[int] ):
					'''simple docstring'''





					if index in self.special_tokens_decoder:
							__UpperCAmelCase    :     Dict													=	self.special_tokens_decoder[index]
					else:
							__UpperCAmelCase    :     List[Any]													=	chr(index - self._num_special_tokens )
					return token




			def  snake_case__     (   self							: int       ,       a_							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	b''''''
					for token in tokens:
							if token in self.special_tokens_decoder:
									__UpperCAmelCase    :     Optional[int]													=	self.special_tokens_decoder[token].encode('''utf-8''' )
							elif token in self.added_tokens_decoder:
									__UpperCAmelCase    :     List[str]													=	self.special_tokens_decoder[token].encode('''utf-8''' )
							elif token in self.special_tokens_encoder:
									__UpperCAmelCase    :     Tuple													=	token.encode('''utf-8''' )
							elif token in self.added_tokens_encoder:
									__UpperCAmelCase    :     int													=	token.encode('''utf-8''' )
							else:
									__UpperCAmelCase    :     Any													=	bytes([ord(a_ )] )
							bstring += tok_string
					__UpperCAmelCase    :     Dict													=	bstring.decode('''utf-8'''       ,       errors='''ignore''' )
					return string




			def  snake_case__     (   self							: Tuple       ,       a_							: str       ,       a_							: Optional[str] = None ):
					'''simple docstring'''





					return ()





