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


def 		_UpperCAmelCase (     _lowerCamelCase  :				list     ,					_lowerCamelCase  :				list     ,					_lowerCamelCase  :				list						)			->  float:
  _lowerCAmelCase				:     Tuple       =    SVR(kernel="""rbf"""     ,					C=1     ,					gamma=0.1     ,					epsilon=0.1						)
  regressor.fit(UpperCamelCase__     ,					UpperCamelCase__						)
  _lowerCAmelCase				:     Optional[int]       =    regressor.predict(UpperCamelCase__						)
  return y_pred[0]


def 		_UpperCAmelCase (     _lowerCamelCase  :				list						)			->  float:
  train_user.sort()
  _lowerCAmelCase				:     int       =    np.percentile(UpperCamelCase__     ,					25						)
  _lowerCAmelCase				:     Dict       =    np.percentile(UpperCamelCase__     ,					75						)
  _lowerCAmelCase				:     str       =    qa - qa
  _lowerCAmelCase				:     Any       =    qa - (iqr * 0.1)
  return low_lim


def 		_UpperCAmelCase (     _lowerCamelCase  :				list     ,					_lowerCamelCase  :				float						)			->  bool:
  _lowerCAmelCase				:     List[str]       =    0
  _lowerCAmelCase				:     Union[str, Any]       =    0
  for i in list_vote:
    if i > actual_result:
      _lowerCAmelCase				:     Dict       =    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_   	=				[[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_   	=				pd.DataFrame(
         data_input, columns=["""total_user""", """total_even""", """days"""]
     )
     UpperCamelCase_   	=				Normalizer().fit_transform(data_input_df.values)
     # split data
     UpperCamelCase_   	=				normalize_df[:, 2].tolist()
     UpperCamelCase_   	=				normalize_df[:, 0].tolist()
     UpperCamelCase_   	=				normalize_df[:, 1].tolist()

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

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

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

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









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


a__:      Optional[int]									=		logging.get_logger(__name__)

a__:      int									=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a__:      Optional[Any]									=		{
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

a__:      List[str]									=		{
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

a__:      Optional[Any]									=		{
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	BertTokenizer



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase="[UNK]",__lowerCamelCase="[SEP]",__lowerCamelCase="[PAD]",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(
            __lowerCamelCase,tokenizer_file=__lowerCamelCase,do_lower_case=__lowerCamelCase,unk_token=__lowerCamelCase,sep_token=__lowerCamelCase,pad_token=__lowerCamelCase,cls_token=__lowerCamelCase,mask_token=__lowerCamelCase,tokenize_chinese_chars=__lowerCamelCase,strip_accents=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get('''lowercase''',__lowerCamelCase ) != do_lower_case
            or normalizer_state.get('''strip_accents''',__lowerCamelCase ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars''',__lowerCamelCase ) != tokenize_chinese_chars
        ):
               A__											=						getattr(__lowerCamelCase,normalizer_state.pop('''type''' ) )
               A__											=						do_lower_case
               A__											=						strip_accents
               A__											=						tokenize_chinese_chars
               A__											=						normalizer_class(**__lowerCamelCase )

        A__											=						do_lower_case



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        A__											=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

        return output



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[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 ) * [0] + len(token_ids_a + sep ) * [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase )
        return tuple(__lowerCamelCase )





