
"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Optional[Any]          =   (UniPCMultistepScheduler,)
							_lowerCamelCase					:			List[Any]          =   (("""num_inference_steps""", 25),)
							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										    "solver_type": "bh2",
										}

										config.update(**_lowerCamelCase       )
										return config
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase=0  ,    **_lowerCamelCase       )			->							Any:




										'''simple docstring'''
										__lowercase       =       dict(self.forward_default_kwargs       )
										__lowercase       =       kwargs.pop("num_inference_steps"  ,    _lowerCamelCase       )
										__lowercase       =       self.dummy_sample
										__lowercase       =       0.1 * sample
										__lowercase       =       [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
													__lowercase       =       self.get_scheduler_config(**_lowerCamelCase       )
													__lowercase       =       scheduler_class(**_lowerCamelCase       )
													scheduler.set_timesteps(_lowerCamelCase       )
													# copy over dummy past residuals
													__lowercase       =       dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_lowerCamelCase       )
																__lowercase       =       scheduler_class.from_pretrained(_lowerCamelCase       )
																new_scheduler.set_timesteps(_lowerCamelCase       )
																# copy over dummy past residuals
																__lowercase       =       dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowercase   ,					__lowercase       =       sample, sample
													for t in range(_lowerCamelCase  ,    time_step + scheduler.config.solver_order + 1       ):
																__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       ).prev_sample
																__lowercase       =       new_scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       ).prev_sample

																assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase=0  ,    **_lowerCamelCase       )			->							Any:




										'''simple docstring'''
										__lowercase       =       dict(self.forward_default_kwargs       )
										__lowercase       =       kwargs.pop("num_inference_steps"  ,    _lowerCamelCase       )
										__lowercase       =       self.dummy_sample
										__lowercase       =       0.1 * sample
										__lowercase       =       [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
													__lowercase       =       self.get_scheduler_config()
													__lowercase       =       scheduler_class(**_lowerCamelCase       )
													scheduler.set_timesteps(_lowerCamelCase       )

													# copy over dummy past residuals (must be after setting timesteps)
													__lowercase       =       dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_lowerCamelCase       )
																__lowercase       =       scheduler_class.from_pretrained(_lowerCamelCase       )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(_lowerCamelCase       )

																# copy over dummy past residual (must be after setting timesteps)
																__lowercase       =       dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       ).prev_sample
													__lowercase       =       new_scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       ).prev_sample

													assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase=None  ,    **_lowerCamelCase       )			->							List[Any]:




										'''simple docstring'''
										if scheduler is None:
													__lowercase       =       self.scheduler_classes[0]
													__lowercase       =       self.get_scheduler_config(**_lowerCamelCase       )
													__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config(**_lowerCamelCase       )
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       10
										__lowercase       =       self.dummy_model()
										__lowercase       =       self.dummy_sample_deter
										scheduler.set_timesteps(_lowerCamelCase       )

										for i, t in enumerate(scheduler.timesteps       ):
													__lowercase       =       model(_lowerCamelCase  ,    _lowerCamelCase       )
													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       ).prev_sample

										return sample
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       dict(self.forward_default_kwargs       )

										__lowercase       =       kwargs.pop("num_inference_steps"  ,    _lowerCamelCase       )

										for scheduler_class in self.scheduler_classes:
													__lowercase       =       self.get_scheduler_config()
													__lowercase       =       scheduler_class(**_lowerCamelCase       )

													__lowercase       =       self.dummy_sample
													__lowercase       =       0.1 * sample

													if num_inference_steps is not None and hasattr(_lowerCamelCase  ,    "set_timesteps"       ):
																scheduler.set_timesteps(_lowerCamelCase       )
													elif num_inference_steps is not None and not hasattr(_lowerCamelCase  ,    "set_timesteps"       ):
																__lowercase       =       num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__lowercase       =       [residual + 0.2, residual + 0.15, residual + 0.10]
													__lowercase       =       dummy_past_residuals[: scheduler.config.solver_order]

													__lowercase       =       scheduler.timesteps[5]
													__lowercase       =       scheduler.timesteps[6]

													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       ).prev_sample
													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       ).prev_sample

													self.assertEqual(output_a.shape  ,    sample.shape       )
													self.assertEqual(output_a.shape  ,    output_a.shape       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__lowercase       =       UniPCMultistepScheduler(**self.get_scheduler_config()       )
										__lowercase       =       self.full_loop(scheduler=_lowerCamelCase       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_mean.item() - 0.2464       ) < 1e-3

										__lowercase       =       DPMSolverSinglestepScheduler.from_config(scheduler.config       )
										__lowercase       =       DEISMultistepScheduler.from_config(scheduler.config       )
										__lowercase       =       DPMSolverMultistepScheduler.from_config(scheduler.config       )
										__lowercase       =       UniPCMultistepScheduler.from_config(scheduler.config       )

										__lowercase       =       self.full_loop(scheduler=_lowerCamelCase       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_mean.item() - 0.2464       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										for timesteps in [25, 50, 100, 999, 1_000]:
													self.check_over_configs(num_train_timesteps=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										self.check_over_configs(thresholding=_lowerCamelCase       )
										for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    thresholding=_lowerCamelCase  ,    prediction_type=_lowerCamelCase  ,    sample_max_value=_lowerCamelCase  ,    solver_order=_lowerCamelCase  ,    solver_type=_lowerCamelCase  ,    )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																for prediction_type in ["epsilon", "sample"]:
																			self.check_over_configs(
																			    solver_order=_lowerCamelCase  ,    solver_type=_lowerCamelCase  ,    prediction_type=_lowerCamelCase  ,    )
																			__lowercase       =       self.full_loop(
																			    solver_order=_lowerCamelCase  ,    solver_type=_lowerCamelCase  ,    prediction_type=_lowerCamelCase  ,    )
																			assert not torch.isnan(_lowerCamelCase       ).any(), "Samples have nan numbers"
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										self.check_over_configs(lower_order_final=_lowerCamelCase       )
										self.check_over_configs(lower_order_final=_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
													self.check_over_forward(num_inference_steps=_lowerCamelCase  ,    time_step=0       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       self.full_loop()
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_mean.item() - 0.2464       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       self.full_loop(prediction_type="v_prediction"       )
										__lowercase       =       torch.mean(torch.abs(_lowerCamelCase       )       )

										assert abs(result_mean.item() - 0.1014       ) < 1e-3
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										__lowercase       =       self.scheduler_classes[0]
										__lowercase       =       self.get_scheduler_config(thresholding=_lowerCamelCase  ,    dynamic_thresholding_ratio=0       )
										__lowercase       =       scheduler_class(**_lowerCamelCase       )

										__lowercase       =       10
										__lowercase       =       self.dummy_model()
										__lowercase       =       self.dummy_sample_deter.half()
										scheduler.set_timesteps(_lowerCamelCase       )

										for i, t in enumerate(scheduler.timesteps       ):
													__lowercase       =       model(_lowerCamelCase  ,    _lowerCamelCase       )
													__lowercase       =       scheduler.step(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       ).prev_sample

										assert sample.dtype == torch.floataa





							def   SCREAMING_SNAKE_CASE						(     self  ,    **_lowerCamelCase       )			->							str:




										'''simple docstring'''
										for scheduler_class in self.scheduler_classes:
													__lowercase       =       self.get_scheduler_config(**_lowerCamelCase       )
													__lowercase       =       scheduler_class(**_lowerCamelCase       )

													scheduler.set_timesteps(scheduler.config.num_train_timesteps       )
													assert len(scheduler.timesteps.unique()       ) == scheduler.num_inference_steps







"""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      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 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)
					_lowercase							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
					_lowercase							=				pd.DataFrame(
					    data_input, columns=['''total_user''', '''total_even''', '''days''']
					)
					_lowercase							=				Normalizer().fit_transform(data_input_df.values)
					# split data
					_lowercase							=				normalize_df[:, 2].tolist()
					_lowercase							=				normalize_df[:, 0].tolist()
					_lowercase							=				normalize_df[:, 1].tolist()

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

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

					_lowercase							=				total_date[len(total_date) - 1 :]
					_lowercase							=				total_user[len(total_user) - 1 :]
					_lowercase							=				total_match[len(total_match) - 1 :]

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





