'''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_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list    )    ->    float:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase    )]    )
       _UpperCAmelCase     :  List[str]							      =    np.array(_UpperCAmelCase    )
       _UpperCAmelCase     :  str							      =    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_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list    )    ->    float:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    (1, 2, 1)
       _UpperCAmelCase     :  List[str]							      =    (1, 1, 0, 7)
       _UpperCAmelCase     :  Optional[Any]							      =    SARIMAX(
           _UpperCAmelCase			,	exog=_UpperCAmelCase			,	order=_UpperCAmelCase			,	seasonal_order=_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    model.fit(disp=_UpperCAmelCase			,	maxiter=600			,	method="nm"    )
       _UpperCAmelCase     :  int							      =    model_fit.predict(1			,	len(_UpperCAmelCase    )			,	exog=[test_match]    )
       return result[0]

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list    )    ->    float:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    SVR(kernel="rbf"			,	C=1			,	gamma=0.1			,	epsilon=0.1    )
       regressor.fit(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[Any]							      =    regressor.predict(_UpperCAmelCase    )
       return y_pred[0]

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list    )    ->    float:


       """simple docstring"""







       train_user.sort()
       _UpperCAmelCase     :  str							      =    np.percentile(_UpperCAmelCase			,	25    )
       _UpperCAmelCase     :  Tuple							      =    np.percentile(_UpperCAmelCase			,	75    )
       _UpperCAmelCase     :  Tuple							      =    qa - qa
       _UpperCAmelCase     :  str							      =    qa - (iqr * 0.1)
       return low_lim

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      float    )    ->    bool:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    0
       _UpperCAmelCase     :  Any							      =    0
       for i in list_vote:
              if i > actual_result:
                     _UpperCAmelCase     :  int							      =    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)
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
  __SCREAMING_SNAKE_CASE       :     Dict     =  pd.DataFrame(
      data_input, columns=["""total_user""", """total_even""", """days"""]
  )
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  Normalizer().fit_transform(data_input_df.values)
  # split data
  __SCREAMING_SNAKE_CASE       :     List[Any]     =  normalize_df[:, 2].tolist()
  __SCREAMING_SNAKE_CASE       :     int     =  normalize_df[:, 0].tolist()
  __SCREAMING_SNAKE_CASE       :     str     =  normalize_df[:, 1].tolist()

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

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

  __SCREAMING_SNAKE_CASE       :     int     =  total_date[len(total_date) - 1 :]
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  total_user[len(total_user) - 1 :]
  __SCREAMING_SNAKE_CASE       :     List[Any]     =  total_match[len(total_match) - 1 :]

  # voting system with forecasting
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  [
      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
  __SCREAMING_SNAKE_CASE       :     List[str]     =  """""" if data_safety_checker(res_vote, tst_user) else """not """
  print("""Today's data is {not_str}safe.""")


'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Tuple     =  ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Dict     =  [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE       :     Any     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

