





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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					List[str]:
				lowerCamelCase			=np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__			)]			)
				lowerCamelCase			=np.array(UpperCamelCase__			)
				lowerCamelCase			=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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					str:
				lowerCamelCase			=(1, 2, 1)
				lowerCamelCase			=(1, 1, 0, 7)
				lowerCamelCase			=SARIMAX(
				    UpperCamelCase__     , exog=UpperCamelCase__     , order=UpperCamelCase__     , seasonal_order=UpperCamelCase__			)
				lowerCamelCase			=model.fit(disp=UpperCamelCase__     , maxiter=6_00     , method="""nm"""			)
				lowerCamelCase			=model_fit.predict(1     , len(UpperCamelCase__			)     , exog=[test_match]			)
				return result[0]





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:
				lowerCamelCase			=SVR(kernel="""rbf"""     , C=1     , gamma=0.1     , epsilon=0.1			)
				regressor.fit(UpperCamelCase__     , UpperCamelCase__			)
				lowerCamelCase			=regressor.predict(UpperCamelCase__			)
				return y_pred[0]





def        _lowercase   (  _UpperCAmelCase			)  ->					Any:
				train_user.sort()
				lowerCamelCase			=np.percentile(UpperCamelCase__     , 25			)
				lowerCamelCase			=np.percentile(UpperCamelCase__     , 75			)
				lowerCamelCase			=qa - qa
				lowerCamelCase			=qa - (iqr * 0.1)
				return low_lim





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Any:
				lowerCamelCase			=0
				lowerCamelCase			=0
				for i in list_vote:
								if i > actual_result:
												lowerCamelCase			=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)
							UpperCAmelCase__  :     Optional[Any]   			=[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
							UpperCAmelCase__  :     Optional[Any]   			=pd.DataFrame(
							    data_input, columns=['''total_user''', '''total_even''', '''days''']
							)
							UpperCAmelCase__  :     List[str]   			=Normalizer().fit_transform(data_input_df.values)
							# split data
							UpperCAmelCase__  :     List[str]   			=normalize_df[:, 2].tolist()
							UpperCAmelCase__  :     List[str]   			=normalize_df[:, 0].tolist()
							UpperCAmelCase__  :     int   			=normalize_df[:, 1].tolist()

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

							# for linear regression & sarimax
							UpperCAmelCase__  :     Union[str, Any]   			=total_date[: len(total_date) - 1]
							UpperCAmelCase__  :     Union[str, Any]   			=total_user[: len(total_user) - 1]
							UpperCAmelCase__  :     Union[str, Any]   			=total_match[: len(total_match) - 1]

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

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












import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class  __A       (  a      ,   unittest.TestCase      ):
				__A								=			BioGptTokenizer
				__A								=			False


				def _snake_case    (						self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase			=[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase			=dict(zip(UpperCAmelCase_				,	range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase			=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file				,	"""w"""							) as fp:
												fp.write(json.dumps(UpperCAmelCase_							)							)
								with open(self.merges_file				,	"""w"""							) as fp:
												fp.write("""\n""".join(UpperCAmelCase_							)							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""lower newer"""
								lowerCamelCase			="""lower newer"""
								return input_text, output_text


				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer(self.vocab_file				,	self.merges_file							)

								lowerCamelCase			="""lower"""
								lowerCamelCase			=["""low""", """er</w>"""]
								lowerCamelCase			=tokenizer.tokenize(UpperCAmelCase_							)
								self.assertListEqual(UpperCAmelCase_				,	UpperCAmelCase_							)

								lowerCamelCase			=tokens + ["""<unk>"""]
								lowerCamelCase			=[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)				,	UpperCAmelCase_							)


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)

								lowerCamelCase			=tokenizer.encode("""sequence builders"""				,	add_special_tokens=UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.encode("""multi-sequence build"""				,	add_special_tokens=UpperCAmelCase_							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,	UpperCAmelCase_							)

								self.assertTrue(encoded_sentence == [2] + text							)
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)





