



'''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        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Dict           = np.array([[1, item, train_mtch[i]] for i, item in enumerate(_lowerCAmelCase       )]       )
  __lowerCamelCase       :       Tuple           = np.array(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()       ,_lowerCAmelCase       )       )       ,x.transpose()       )       ,_lowerCAmelCase       )
  return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Tuple           = (1, 2, 1)
  __lowerCamelCase       :       Union[str, Any]           = (1, 1, 0, 7)
  __lowerCamelCase       :       str           = SARIMAX(
      _lowerCAmelCase       ,exog=_lowerCAmelCase       ,order=_lowerCAmelCase       ,seasonal_order=_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = model.fit(disp=_lowerCAmelCase       ,maxiter=600       ,method='nm'       )
  __lowerCamelCase       :       Union[str, Any]           = model_fit.predict(1       ,len(_lowerCAmelCase       )       ,exog=[test_match]       )
  return result[0]

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       int           = SVR(kernel='rbf'       ,C=1       ,gamma=0.1       ,epsilon=0.1       )
  regressor.fit(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       int           = regressor.predict(_lowerCAmelCase       )
  return y_pred[0]

def        a_     (							_lowerCAmelCase       )      ->      float:
  train_user.sort()
  __lowerCamelCase       :       Optional[Any]           = np.percentile(_lowerCAmelCase       ,25       )
  __lowerCamelCase       :       List[Any]           = np.percentile(_lowerCAmelCase       ,75       )
  __lowerCamelCase       :       Optional[int]           = qa - qa
  __lowerCamelCase       :       List[str]           = qa - (iqr * 0.1)
  return low_lim

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      bool:
  __lowerCamelCase       :       Optional[int]           = 0
  __lowerCamelCase       :       Union[str, Any]           = 0
  for i in list_vote:
    if i > actual_result:
      __lowerCamelCase       :       str           = not_safe + 1
    else:
      if abs(abs(_lowerCAmelCase       ) - abs(_lowerCAmelCase       )       ) <= 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											=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 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.')











'''simple docstring'''





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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_mbart import MBartTokenizer
else:
   _UpperCamelCase											=						None


_UpperCamelCase											=						logging.get_logger(__name__)


_UpperCamelCase											=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase											=						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
_UpperCamelCase											=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=["""input_ids""", """attention_mask"""]
  a_												=MBartTokenizer

  a_												=[]
  a_												=[]




  def __init__( self      :	Optional[Any]			,	_a      :	Optional[int]=None			,	_a      :	Any=None			,	_a      :	Any="<s>"			,	_a      :	Optional[Any]="</s>"			,	_a      :	List[str]="</s>"			,	_a      :	List[Any]="<s>"			,	_a      :	Union[str, Any]="<unk>"			,	_a      :	str="<pad>"			,	_a      :	Any="<mask>"			,	_a      :	Optional[Any]=None			,	_a      :	str=None			,	_a      :	Tuple=None			,	**_a      :	Dict			,	)					->					List[str]:
    # Mask token behave like a normal word, i.e. include the space before it
    __lowerCamelCase       :       List[Any]           = AddedToken(_a			,	lstrip=_a			,	rstrip=_a   ) if isinstance(_a			,	_a   ) else mask_token

    super().__init__(
        vocab_file=_a			,	tokenizer_file=_a			,	bos_token=_a			,	eos_token=_a			,	sep_token=_a			,	cls_token=_a			,	unk_token=_a			,	pad_token=_a			,	mask_token=_a			,	src_lang=_a			,	tgt_lang=_a			,	additional_special_tokens=_a			,	**_a			,	)

    __lowerCamelCase       :       Optional[Any]           = vocab_file
    __lowerCamelCase       :       List[str]           = False if not self.vocab_file else True

    __lowerCamelCase       :       str           = FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

    self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
    __lowerCamelCase       :       Optional[Any]           = {
        lang_code: self.convert_tokens_to_ids(_a   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    __lowerCamelCase       :       Optional[Any]           = src_lang if src_lang is not None else 'en_XX'
    __lowerCamelCase       :       int           = self.convert_tokens_to_ids(self._src_lang   )
    __lowerCamelCase       :       List[str]           = tgt_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  @property
  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    return self._src_lang




  @src_lang.setter
  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = new_src_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




  def 						_lowercase     ( self      :	int			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    __lowerCamelCase       :       Optional[int]           = [self.sep_token_id]
    __lowerCamelCase       :       Union[str, Any]           = [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]




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Optional[Any]			,	_a      :	str			,	_a      :	Optional[str]			,	_a      :	Optional[str]			,	**_a      :	Optional[int]   )					->					Any:
    if src_lang is None or tgt_lang is None:
      raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
    __lowerCamelCase       :       Optional[Any]           = src_lang
    __lowerCamelCase       :       Dict           = self(_a			,	add_special_tokens=_a			,	return_tensors=_a			,	**_a   )
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = tgt_lang_id
    return inputs




  def 						_lowercase     ( self      :	Any			,	_a      :	List[str]			,	_a      :	str = "en_XX"			,	_a      :	Optional[List[str]] = None			,	_a      :	str = "ro_RO"			,	**_a      :	Tuple			,	)					->					BatchEncoding:
    __lowerCamelCase       :       List[Any]           = src_lang
    __lowerCamelCase       :       str           = tgt_lang
    return super().prepare_seqaseq_batch(_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    return self.set_src_lang_special_tokens(self.src_lang   )




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	List[str]   )					->					None:
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       Dict           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Optional[Any]           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Union[str, Any]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       int           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       int           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Any           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Optional[int]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Any			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    if not self.can_save_slow_tokenizer:
      raise ValueError(
          'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
          'tokenizer.'   )

    if not os.path.isdir(_a   ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory.'   )
      return
    __lowerCamelCase       :       List[str]           = os.path.join(
        _a			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

    return (out_vocab_file,)






