




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class       _A				(		_UpperCAmelCase     ):
		__a					    =       (IPNDMScheduler,)
		__a					    =       (("""num_inference_steps""", 50),)






		def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			{"""num_train_timesteps""": 1000}
								config.update(**lowercase_			)
								return config






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=0						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			dict(self.forward_default_kwargs			)
								_UpperCAmelCase      =			kwargs.pop("""num_inference_steps"""						,		lowercase_			)
								_UpperCAmelCase      =			self.dummy_sample
								_UpperCAmelCase      =			0.1 * sample
								_UpperCAmelCase      =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample
														_UpperCAmelCase      =			new_scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample

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

														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample
														_UpperCAmelCase      =			new_scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample

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






		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=0						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			dict(self.forward_default_kwargs			)
								_UpperCAmelCase      =			kwargs.pop("""num_inference_steps"""						,		lowercase_			)
								_UpperCAmelCase      =			self.dummy_sample
								_UpperCAmelCase      =			0.1 * sample
								_UpperCAmelCase      =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase      =			self.get_scheduler_config()
														_UpperCAmelCase      =			scheduler_class(**lowercase_			)
														scheduler.set_timesteps(lowercase_			)

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

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

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

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

														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample
														_UpperCAmelCase      =			new_scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample

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

														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample
														_UpperCAmelCase      =			new_scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample

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






		def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.scheduler_classes[0]
								_UpperCAmelCase      =			self.get_scheduler_config(**lowercase_			)
								_UpperCAmelCase      =			scheduler_class(**lowercase_			)

								_UpperCAmelCase      =			10
								_UpperCAmelCase      =			self.dummy_model()
								_UpperCAmelCase      =			self.dummy_sample_deter
								scheduler.set_timesteps(lowercase_			)

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

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

								return sample






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			dict(self.forward_default_kwargs			)

								_UpperCAmelCase      =			kwargs.pop("""num_inference_steps"""						,		lowercase_			)

								for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase      =			self.get_scheduler_config()
														_UpperCAmelCase      =			scheduler_class(**lowercase_			)

														_UpperCAmelCase      =			self.dummy_sample
														_UpperCAmelCase      =			0.1 * sample

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

														# copy over dummy past residuals (must be done after set_timesteps)
														_UpperCAmelCase      =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														_UpperCAmelCase      =			dummy_past_residuals[:]

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

														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample
														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample

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

														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample
														_UpperCAmelCase      =			scheduler.step(lowercase_						,		lowercase_						,		lowercase_						,		**lowercase_			).prev_sample

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






		def 				UpperCAmelCase (    self			):
								for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase_						,		time_step=lowercase_			)






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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.full_loop()
								_UpperCAmelCase      =			torch.mean(torch.abs(lowercase_			)			)

								assert abs(result_mean.item() - 254_0529			) < 10





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						return (-y * np.log(snake_case       ) - (1 - y) * np.log(1 - h       )).mean()




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_UpperCAmelCase      =			np.zeros(x.shape[1]       )

						for iterations in range(snake_case       ):
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			np.dot(x.T			,     h - y       ) / y.size
												_UpperCAmelCase      =			theta - alpha * gradient  # updating the weights
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			cost_function(snake_case			,     snake_case       )
												if iterations % 1_0_0 == 0:
																		print(f"loss: {j} \t"       )  # printing the loss after every 100 iterations
						return theta


# In[68]:

if __name__ == "__main__":
				a													=			datasets.load_iris()
				a													=			iris.data[:, :2]
				a													=			(iris.target != 0) * 1

				a													=			0.1
				a													=			logistic_reg(alpha, x, y, max_iterations=70000)
				print("theta: ", theta)  # printing the theta i.e our weights vector




				def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
										return sigmoid_function(
										    np.dot(snake_case			,     snake_case       )       )  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
				((a)							,  (a))													=			(x[:, 0].min(), x[:, 0].max())
				((a)							,  (a))													=			(x[:, 1].min(), x[:, 1].max())
				((a)							,  (a))													=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				a													=			np.c_[xxa.ravel(), xxa.ravel()]
				a													=			predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

				plt.legend()

				plt.show()