

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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      float:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    np.array([[1, item, train_mtch[i]] for i, item in enumerate(SCREAMING_SNAKE_CASE				)]				)
     _lowerCAmelCase   :					Dict        =    np.array(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Union[str, Any]        =    np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()		,							SCREAMING_SNAKE_CASE				)				)		,							x.transpose()				)		,							SCREAMING_SNAKE_CASE				)
     return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      float:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      float:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      float:

     """simple docstring"""







     train_user.sort()
     _lowerCAmelCase   :					str        =    np.percentile(SCREAMING_SNAKE_CASE		,							25				)
     _lowerCAmelCase   :					Tuple        =    np.percentile(SCREAMING_SNAKE_CASE		,							75				)
     _lowerCAmelCase   :					Optional[int]        =    qa - qa
     _lowerCAmelCase   :					Optional[Any]        =    qa - (iqr * 0.1)
     return low_lim







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      bool:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    0
     _lowerCAmelCase   :					int        =    0
     for i in list_vote:
          if i > actual_result:
               _lowerCAmelCase   :					List[Any]        =    not_safe + 1
          else:
               if abs(abs(SCREAMING_SNAKE_CASE				) - abs(SCREAMING_SNAKE_CASE				)				) <= 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_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 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 unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__UpperCAmelCase    				=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'





class A__    ( unittest.TestCase  ,		A ):



 """simple docstring"""







 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    load_tool("text-question-answering"       )
      self.tool.setup()
      _lowerCAmelCase   :					int        =    load_tool("text-question-answering"     ,						remote=A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.tool(A_     ,						"What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.remote_tool(A_     ,						"What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.tool(text=A_     ,						question="What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )


 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.remote_tool(text=A_     ,						question="What did Hugging Face do in April 2021?"       )
      self.assertEqual(A_     ,						"launched the BigScience Research Workshop"       )






