






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    Adafactor,
							    AdamW,
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_inverse_sqrt_schedule,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							)

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       List[str]=1_0				):
			__a						:  Optional[Any]									=     []
			for _ in range(lowerCamelCase_				):
						lrs.append(scheduler.get_lr()[0]				)
						scheduler.step()
			return lrs

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Tuple=1_0				):
			__a						:  Union[str, Any]									=     []
			for step in range(lowerCamelCase_				):
						lrs.append(scheduler.get_lr()[0]				)
						scheduler.step()
						if step == num_steps // 2:
									with tempfile.TemporaryDirectory() as tmpdirname:
												__a						:  List[Any]									=     os.path.join(lowerCamelCase_   ,  'schedule.bin'				)
												torch.save(scheduler.state_dict()   ,  lowerCamelCase_				)

												__a						:  Tuple									=     torch.load(lowerCamelCase_				)
												scheduler.load_state_dict(lowerCamelCase_				)
			return lrs






@require_torch
class 			_UpperCamelCase(       unittest.TestCase		):

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					self.assertEqual(len(SCREAMING_SNAKE_CASE__    )    ,       len(SCREAMING_SNAKE_CASE__    )    )
					for a, b in zip(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ):
								self.assertAlmostEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       delta=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     torch.tensor([0.1, -0.2, -0.1]    ,       requires_grad=SCREAMING_SNAKE_CASE__    )
					__a						:  List[str]									=     torch.tensor([0.4, 0.2, -0.5]    )
					__a						:  List[Any]									=     nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					__a						:  List[Any]									=     AdamW(params=[w]    ,       lr=2e-1    ,       weight_decay=0.0    )
					for _ in range(1_0_0    ):
								__a						:  int									=     criterion(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
								loss.backward()
								optimizer.step()
								w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
								w.grad.zero_()
					self.assertListAlmostEqual(w.tolist()    ,       [0.4, 0.2, -0.5]    ,       tol=1e-2    )



		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  List[str]									=     torch.tensor([0.1, -0.2, -0.1]    ,       requires_grad=SCREAMING_SNAKE_CASE__    )
					__a						:  Union[str, Any]									=     torch.tensor([0.4, 0.2, -0.5]    )
					__a						:  Union[str, Any]									=     nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					__a						:  int									=     Adafactor(
					    params=[w]    ,       lr=1e-2    ,       eps=(1e-30, 1e-3)    ,       clip_threshold=1.0    ,       decay_rate=-0.8    ,       betaa=SCREAMING_SNAKE_CASE__    ,       weight_decay=0.0    ,       relative_step=SCREAMING_SNAKE_CASE__    ,       scale_parameter=SCREAMING_SNAKE_CASE__    ,       warmup_init=SCREAMING_SNAKE_CASE__    ,       )
					for _ in range(1_0_0_0    ):
								__a						:  str									=     criterion(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
								loss.backward()
								optimizer.step()
								w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
								w.grad.zero_()
					self.assertListAlmostEqual(w.tolist()    ,       [0.4, 0.2, -0.5]    ,       tol=1e-2    )






@require_torch
class 			_UpperCamelCase(       unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							nn.Linear(50		,				50		) if is_torch_available() else None
		__SCREAMING_SNAKE_CASE  :       str							=							AdamW(m.parameters()		,				lr=10.0		) if is_torch_available() else None
		__SCREAMING_SNAKE_CASE  :       str							=							10

		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Dict=None    ):



					'''simple docstring'''



					self.assertEqual(len(SCREAMING_SNAKE_CASE__    )    ,       len(SCREAMING_SNAKE_CASE__    )    )
					for a, b in zip(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ):
								self.assertAlmostEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       delta=SCREAMING_SNAKE_CASE__    ,       msg=SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					__a						:  str									=     {'num_warmup_steps': 2, 'num_training_steps': 1_0}
					# schedulers doct format
					# function: (sched_args_dict, expected_learning_rates)
					__a						:  Any									=     {
					    get_constant_schedule: ({}, [10.0] * self.num_steps),
					    get_constant_schedule_with_warmup: (
					        {'num_warmup_steps': 4},
					        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
					    ),
					    get_linear_schedule_with_warmup: (
					        {**common_kwargs},
					        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
					    ),
					    get_cosine_schedule_with_warmup: (
					        {**common_kwargs},
					        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
					    ),
					    get_cosine_with_hard_restarts_schedule_with_warmup: (
					        {**common_kwargs, 'num_cycles': 2},
					        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
					    ),
					    get_polynomial_decay_schedule_with_warmup: (
					        {**common_kwargs, 'power': 2.0, 'lr_end': 1e-7},
					        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
					    ),
					    get_inverse_sqrt_schedule: (
					        {'num_warmup_steps': 2},
					        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
					    ),
					}

					for scheduler_func, data in scheds.items():
								__a						,					__a						:  str									=     data

								__a						:  Tuple									=     scheduler_func(self.optimizer    ,       **SCREAMING_SNAKE_CASE__    )
								self.assertEqual(len([scheduler.get_lr()[0]]    )    ,       1    )
								__a						:  List[Any]									=     unwrap_schedule(SCREAMING_SNAKE_CASE__    ,       self.num_steps    )
								self.assertListAlmostEqual(
								    SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       tol=1e-2    ,       msg=f'''failed for {scheduler_func} in normal scheduler'''    ,       )

								__a						:  Dict									=     scheduler_func(self.optimizer    ,       **SCREAMING_SNAKE_CASE__    )
								if scheduler_func.__name__ != "get_constant_schedule":
											LambdaScheduleWrapper.wrap_scheduler(SCREAMING_SNAKE_CASE__    )  # wrap to test picklability of the schedule
								__a						:  Optional[int]									=     unwrap_and_save_reload_schedule(SCREAMING_SNAKE_CASE__    ,       self.num_steps    )
								self.assertListEqual(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       msg=f'''failed for {scheduler_func} in save and reload'''    )






class 			_UpperCamelCase:

		def __init__(					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  str									=     fn

		def __call__(					self		:    int    ,       *SCREAMING_SNAKE_CASE__		:    Dict    ,       **SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					return self.fn(*SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )



		@classmethod
		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  List[Any]									=     list(map(self    ,       scheduler.lr_lambdas    )    )












"""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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase			)]			)
 lowerCAmelCase    				= np.array(_UpperCAmelCase			)
 lowerCAmelCase    				= 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= (1, 2, 1)
 lowerCAmelCase    				= (1, 1, 0, 7)
 lowerCAmelCase    				= SARIMAX(
     _UpperCAmelCase  ,     exog=_UpperCAmelCase  ,     order=_UpperCAmelCase  ,     seasonal_order=_UpperCAmelCase			)
 lowerCAmelCase    				= model.fit(disp=_UpperCAmelCase  ,     maxiter=600  ,     method='nm'			)
 lowerCAmelCase    				= model_fit.predict(1  ,     len(_UpperCAmelCase			)  ,     exog=[test_match]			)
 return result[0]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= SVR(kernel='rbf'  ,     C=1  ,     gamma=0.1  ,     epsilon=0.1			)
 regressor.fit(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= regressor.predict(_UpperCAmelCase			)
 return y_pred[0]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list			):
 train_user.sort()
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     25			)
 lowerCAmelCase    				= np.percentile(_UpperCAmelCase  ,     75			)
 lowerCAmelCase    				= qa - qa
 lowerCAmelCase    				= qa - (iqr * 0.1)
 return low_lim

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list  ,     _UpperCAmelCase					:    float			):
 lowerCAmelCase    				= 0
 lowerCAmelCase    				= 0
 for i in list_vote:
  if i > actual_result:
   lowerCAmelCase    				= 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     :						Optional[Any]						       =  [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
       __UpperCamelCase     :						Any						       =  pd.DataFrame(
           data_input, columns=['''total_user''', '''total_even''', '''days''']
       )
       __UpperCamelCase     :						Dict						       =  Normalizer().fit_transform(data_input_df.values)
       # split data
       __UpperCamelCase     :						Dict						       =  normalize_df[:, 2].tolist()
       __UpperCamelCase     :						Union[str, Any]						       =  normalize_df[:, 0].tolist()
       __UpperCamelCase     :						List[str]						       =  normalize_df[:, 1].tolist()

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

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

       __UpperCamelCase     :						Optional[Any]						       =  total_date[len(total_date) - 1 :]
       __UpperCamelCase     :						str						       =  total_user[len(total_user) - 1 :]
       __UpperCamelCase     :						str						       =  total_match[len(total_match) - 1 :]

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





