





'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= """▁"""

lowercase_					= {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

lowercase_					= {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

lowercase_					= {"""vinai/bartpho-syllable""": 1_024}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = VOCAB_FILES_NAMES
    UpperCamelCase     = PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase     = ['''input_ids''', '''attention_mask''']


    def __init__(		self ,		A ,		A ,		A="<s>" ,		A="</s>" ,		A="</s>" ,		A="<s>" ,		A="<unk>" ,		A="<pad>" ,		A="<mask>" ,		A = None ,		**A ,		)		->   None:
        # Mask token behave like a normal word, i.e. include the space before it
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else mask_token

        _SCREAMING_SNAKE_CASE									=			{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=A ,		eos_token=A ,		unk_token=A ,		sep_token=A ,		cls_token=A ,		pad_token=A ,		mask_token=A ,		sp_model_kwargs=self.sp_model_kwargs ,		**A ,		)

        _SCREAMING_SNAKE_CASE									=			vocab_file
        _SCREAMING_SNAKE_CASE									=			monolingual_vocab_file
        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(str(A       )       )

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        _SCREAMING_SNAKE_CASE									=			{}
        _SCREAMING_SNAKE_CASE									=			0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
            if str(A       ) not in self.fairseq_tokens_to_ids:
                _SCREAMING_SNAKE_CASE									=			cnt
                cnt += 1
        with open(A ,		"""r""" ,		encoding="""utf-8"""       ) as f:
            for line in f.readlines():
                _SCREAMING_SNAKE_CASE									=			line.strip().split()[0]
                _SCREAMING_SNAKE_CASE									=			len(self.fairseq_tokens_to_ids       )
        if str(A       ) not in self.fairseq_tokens_to_ids:
            _SCREAMING_SNAKE_CASE									=			len(self.fairseq_tokens_to_ids       )

        _SCREAMING_SNAKE_CASE									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


    def __getstate__(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.__dict__.copy()
        _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			self.sp_model.serialized_model_proto()
        return state


    def __setstate__(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			d

        # for backward compatibility
        if not hasattr(self ,		"""sp_model_kwargs"""       ):
            _SCREAMING_SNAKE_CASE									=			{}

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:

        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.cls_token_id]
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


    def 			snake_case_(		self ,		A ,		A = None ,		A = False       )		->   List[int]:

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

        if token_ids_a is None:
            return [1] + ([0] * len(A       )) + [1]
        return [1] + ([0] * len(A       )) + [1, 1] + ([0] * len(A       )) + [1]


    def 			snake_case_(		self ,		A ,		A = None       )		->   List[int]:
        _SCREAMING_SNAKE_CASE									=			[self.sep_token_id]
        _SCREAMING_SNAKE_CASE									=			[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 			snake_case_(		self       )		->   int:
        return len(self.fairseq_ids_to_tokens       )


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			{self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
        vocab.update(self.added_tokens_encoder       )
        return vocab


    def 			snake_case_(		self ,		A       )		->   List[str]:
        return self.sp_model.encode(A ,		out_type=A       )


    def 			snake_case_(		self ,		A       )		->   Dict:
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        else:
            return self.unk_token_id


    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        return self.fairseq_ids_to_tokens[index]


    def 			snake_case_(		self ,		A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			"""""".join(A       ).replace(A ,		""" """       ).strip()
        return out_string





    def 			snake_case_(		self ,		A ,		A = None       )		->   Tuple[str]:
        if not os.path.isdir(A       ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
            return
        _SCREAMING_SNAKE_CASE									=			os.path.join(
            A ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
        _SCREAMING_SNAKE_CASE									=			os.path.join(
            A ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] ,		)

        if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ) and os.path.isfile(self.vocab_file       ):
            copyfile(self.vocab_file ,		A       )
        elif not os.path.isfile(self.vocab_file       ):
            with open(A ,		"""wb"""       ) as fi:
                _SCREAMING_SNAKE_CASE									=			self.sp_model.serialized_model_proto()
                fi.write(A       )

        if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
            A       ) and os.path.isfile(self.monolingual_vocab_file       ):
            copyfile(self.monolingual_vocab_file ,		A       )
        elif not os.path.isfile(self.monolingual_vocab_file       ):
            with open(A ,		"""w""" ,		encoding="""utf-8"""       ) as fp:
                for token in self.fairseq_tokens_to_ids:
                    if token not in self.all_special_tokens:
                        fp.write(f'{str(A       )} \n'       )

        return out_vocab_file, out_monolingual_vocab_file











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.")





