
"""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__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 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							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 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.''')







"""simple docstring"""






import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase							=				logging.get_logger(__name__)

_lowercase							=				{
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

_lowercase							=				{
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

_lowercase							=				{
    '''vinai/phobert-base''': 256,
    '''vinai/phobert-large''': 256,
}



def      lowerCAmelCase__			(		__magic_name__      )						->Optional[int]:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char

			__lowercase       =       set(__magic_name__      )
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			List[str]          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			Optional[Any]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    **_lowerCamelCase  ,    )			->							Tuple:




										'''simple docstring'''
										super().__init__(
										    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

										__lowercase       =       vocab_file
										__lowercase       =       merges_file

										__lowercase       =       {}
										__lowercase       =       0
										__lowercase       =       1
										__lowercase       =       2
										__lowercase       =       3

										self.add_from_file(_lowerCamelCase       )

										__lowercase       =       {v: k for k, v in self.encoder.items()}

										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as merges_handle:
													__lowercase       =       merges_handle.read().split("\n"       )[:-1]
										__lowercase       =       [tuple(merge.split()[:-1]       ) for merge in merges]
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )
										__lowercase       =       {}
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''

										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]
										__lowercase       =       [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''

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

										if token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       tuple(list(word[:-1]       ) + [word[-1] + "</w>"]       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

										while True:
													__lowercase       =       min(_lowerCamelCase  ,    key=lambda _lowerCamelCase       : self.bpe_ranks.get(_lowerCamelCase  ,    float("inf"       )       )       )
													if bigram not in self.bpe_ranks:
																break
													__lowercase   ,					__lowercase       =       bigram
													__lowercase       =       []
													__lowercase       =       0
													while i < len(_lowerCamelCase       ):
																try:
																			__lowercase       =       word.index(_lowerCamelCase  ,    _lowerCamelCase       )
																except ValueError:
																			new_word.extend(word[i:]       )
																			break
																else:
																			new_word.extend(word[i:j]       )
																			__lowercase       =       j

																if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       "@@ ".join(_lowerCamelCase       )
										__lowercase       =       word[:-4]
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       []

										__lowercase       =       re.findall(R"\S+\n?"  ,    _lowerCamelCase       )

										for token in words:
													split_tokens.extend(list(self.bpe(_lowerCamelCase       ).split(" "       )       )       )
										return split_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[int]:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase  ,    self.unk_token       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       " ".join(_lowerCamelCase       ).replace("@@ "  ,    ""       ).strip()
										return out_string
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

										if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowerCamelCase       ):
													copyfile(self.vocab_file  ,    _lowerCamelCase       )

										if os.path.abspath(self.merges_file       ) != os.path.abspath(_lowerCamelCase       ):
													copyfile(self.merges_file  ,    _lowerCamelCase       )

										return out_vocab_file, out_merge_file





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Dict:




										'''simple docstring'''
										if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													try:
																with open(_lowerCamelCase  ,    "r"  ,    encoding="utf-8"       ) as fd:
																			self.add_from_file(_lowerCamelCase       )
													except FileNotFoundError as fnfe:
																raise fnfe
													except UnicodeError:
																raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''       )
													return

										__lowercase       =       f.readlines()
										for lineTmp in lines:
													__lowercase       =       lineTmp.strip()
													__lowercase       =       line.rfind(" "       )
													if idx == -1:
																raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"       )
													__lowercase       =       line[:idx]
													__lowercase       =       len(self.encoder       )





