




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       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list					):


      '''simple docstring'''



      lowerCamelCase_							=   np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowercase					)]					)
      lowerCamelCase_							=   np.array(lowercase					)
      lowerCamelCase_							=   np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()     ,							lowercase					)					)     ,							x.transpose()					)     ,							lowercase					)
      return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list					):


      '''simple docstring'''



      lowerCamelCase_							=   (1, 2, 1)
      lowerCamelCase_							=   (1, 1, 0, 7)
      lowerCamelCase_							=   SARIMAX(
          lowercase     ,							exog=lowercase     ,							order=lowercase     ,							seasonal_order=lowercase					)
      lowerCamelCase_							=   model.fit(disp=lowercase     ,							maxiter=6_00     ,							method='nm'					)
      lowerCamelCase_							=   model_fit.predict(1     ,							len(lowercase					)     ,							exog=[test_match]					)
      return result[0]

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	list     ,							lowercase     :	list					):


      '''simple docstring'''



      lowerCamelCase_							=   SVR(kernel='rbf'     ,							C=1     ,							gamma=0.1     ,							epsilon=0.1					)
      regressor.fit(lowercase     ,							lowercase					)
      lowerCamelCase_							=   regressor.predict(lowercase					)
      return y_pred[0]

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list					):


      '''simple docstring'''



      train_user.sort()
      lowerCamelCase_							=   np.percentile(lowercase     ,							25					)
      lowerCamelCase_							=   np.percentile(lowercase     ,							75					)
      lowerCamelCase_							=   qa - qa
      lowerCamelCase_							=   qa - (iqr * 0.1)
      return low_lim

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	list     ,							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   0
      lowerCamelCase_							=   0
      for i in list_vote:
            if i > actual_result:
                  lowerCamelCase_							=   not_safe + 1
            else:
                  if abs(abs(lowercase					) - abs(lowercase					)					) <= 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)
 lowerCamelCase    :		Any			   =       [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
 lowerCamelCase    :		List[Any]			   =       pd.DataFrame(
     data_input, columns=["total_user", "total_even", "days"]
 )
 lowerCamelCase    :		List[Any]			   =       Normalizer().fit_transform(data_input_df.values)
 # split data
 lowerCamelCase    :		str			   =       normalize_df[:, 2].tolist()
 lowerCamelCase    :		List[str]			   =       normalize_df[:, 0].tolist()
 lowerCamelCase    :		Tuple			   =       normalize_df[:, 1].tolist()

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

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

 lowerCamelCase    :		Dict			   =       total_date[len(total_date) - 1 :]
 lowerCamelCase    :		Optional[Any]			   =       total_user[len(total_user) - 1 :]
 lowerCamelCase    :		Dict			   =       total_match[len(total_match) - 1 :]

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









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1_000 / max_iters))



