
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class    a_ (			_a    ):
			a	:			Dict					   =							(IPNDMScheduler,)
			a	:			str					   =							(('''num_inference_steps''', 5_0),)






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									_lowercase					       =					{"""num_train_timesteps""": 1_000}
									config.update(**__UpperCamelCase  )
									return config






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0				,			**__UpperCamelCase  ):
									_lowercase					       =					dict(self.forward_default_kwargs  )
									_lowercase					       =					kwargs.pop("""num_inference_steps"""				,			__UpperCamelCase  )
									_lowercase					       =					self.dummy_sample
									_lowercase					       =					0.1 * sample
									_lowercase					       =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
															_lowercase					       =					self.get_scheduler_config(**__UpperCamelCase  )
															_lowercase					       =					scheduler_class(**__UpperCamelCase  )
															scheduler.set_timesteps(__UpperCamelCase  )
															# copy over dummy past residuals
															_lowercase					       =					dummy_past_residuals[:]

															if time_step is None:
																					_lowercase					       =					scheduler.timesteps[len(scheduler.timesteps  ) // 2]

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

															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample
															_lowercase					       =					new_scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample
															_lowercase					       =					new_scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"






			def    UpperCamelCase_				(      self  ):
									pass






			def    UpperCamelCase_				(      self				,			__UpperCamelCase=0				,			**__UpperCamelCase  ):
									_lowercase					       =					dict(self.forward_default_kwargs  )
									_lowercase					       =					kwargs.pop("""num_inference_steps"""				,			__UpperCamelCase  )
									_lowercase					       =					self.dummy_sample
									_lowercase					       =					0.1 * sample
									_lowercase					       =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
															_lowercase					       =					self.get_scheduler_config()
															_lowercase					       =					scheduler_class(**__UpperCamelCase  )
															scheduler.set_timesteps(__UpperCamelCase  )

															# copy over dummy past residuals (must be after setting timesteps)
															_lowercase					       =					dummy_past_residuals[:]

															if time_step is None:
																					_lowercase					       =					scheduler.timesteps[len(scheduler.timesteps  ) // 2]

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

																					# copy over dummy past residual (must be after setting timesteps)
																					_lowercase					       =					dummy_past_residuals[:]

															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample
															_lowercase					       =					new_scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample
															_lowercase					       =					new_scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample

															assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									_lowercase					       =					self.scheduler_classes[0]
									_lowercase					       =					self.get_scheduler_config(**__UpperCamelCase  )
									_lowercase					       =					scheduler_class(**__UpperCamelCase  )

									_lowercase					       =					10
									_lowercase					       =					self.dummy_model()
									_lowercase					       =					self.dummy_sample_deter
									scheduler.set_timesteps(__UpperCamelCase  )

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

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

									return sample






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					dict(self.forward_default_kwargs  )

									_lowercase					       =					kwargs.pop("""num_inference_steps"""				,			__UpperCamelCase  )

									for scheduler_class in self.scheduler_classes:
															_lowercase					       =					self.get_scheduler_config()
															_lowercase					       =					scheduler_class(**__UpperCamelCase  )

															_lowercase					       =					self.dummy_sample
															_lowercase					       =					0.1 * sample

															if num_inference_steps is not None and hasattr(__UpperCamelCase				,			"""set_timesteps"""  ):
																					scheduler.set_timesteps(__UpperCamelCase  )
															elif num_inference_steps is not None and not hasattr(__UpperCamelCase				,			"""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 + 0.1, residual + 0.05]
															_lowercase					       =					dummy_past_residuals[:]

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

															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample
															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample

															self.assertEqual(output_a.shape				,			sample.shape  )
															self.assertEqual(output_a.shape				,			output_a.shape  )

															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample
															_lowercase					       =					scheduler.step(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			**__UpperCamelCase  ).prev_sample

															self.assertEqual(output_a.shape				,			sample.shape  )
															self.assertEqual(output_a.shape				,			output_a.shape  )






			def    UpperCamelCase_				(      self  ):
									for timesteps in [100, 1_000]:
															self.check_over_configs(num_train_timesteps=__UpperCamelCase				,			time_step=__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									for t, num_inference_steps in zip([1, 5, 10]				,			[10, 50, 100]  ):
															self.check_over_forward(num_inference_steps=__UpperCamelCase				,			time_step=__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.full_loop()
									_lowercase					       =					torch.mean(torch.abs(__UpperCamelCase  )  )

									assert abs(result_mean.item() - 2_540_529  ) < 10

import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


A		: Union[str, Any]								=     PolynomialFeatures(degree=4)
A		: str								=     poly_reg.fit_transform(X)
A		: Union[str, Any]								=     LinearRegression()
pol_reg.fit(X_poly, y)




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_       )       )		,   color="""blue"""       )
						plt.title("""Truth or Bluff (Linear Regression)"""       )
						plt.xlabel("""Position level"""       )
						plt.ylabel("""Salary"""       )
						plt.show()


if __name__ == "__main__":
		viz_polymonial()

		# Predicting a new result with Polymonial Regression
		pol_reg.predict(poly_reg.fit_transform([[5.5]]))
		# output should be 132148.43750003