

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     (IPNDMScheduler,)
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     (('''num_inference_steps''', 50),)




				def        a				(   self ,  **snake_case			):
											snake_case_     							=       {'num_train_timesteps': 1000}
											config.update(**snake_case			)
											return config




				def        a				(   self ,  snake_case=0 ,  **snake_case			):
											snake_case_     							=       dict(self.forward_default_kwargs			)
											snake_case_     							=       kwargs.pop('num_inference_steps' ,  snake_case			)
											snake_case_     							=       self.dummy_sample
											snake_case_     							=       0.1 * sample
											snake_case_     							=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       new_scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       new_scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample

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




				def        a				(   self			):
											pass




				def        a				(   self ,  snake_case=0 ,  **snake_case			):
											snake_case_     							=       dict(self.forward_default_kwargs			)
											snake_case_     							=       kwargs.pop('num_inference_steps' ,  snake_case			)
											snake_case_     							=       self.dummy_sample
											snake_case_     							=       0.1 * sample
											snake_case_     							=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																		snake_case_     							=       self.get_scheduler_config()
																		snake_case_     							=       scheduler_class(**snake_case			)
																		scheduler.set_timesteps(snake_case			)

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

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

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

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       new_scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       new_scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample

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




				def        a				(   self ,  **snake_case			):
											snake_case_     							=       self.scheduler_classes[0]
											snake_case_     							=       self.get_scheduler_config(**snake_case			)
											snake_case_     							=       scheduler_class(**snake_case			)

											snake_case_     							=       10
											snake_case_     							=       self.dummy_model()
											snake_case_     							=       self.dummy_sample_deter
											scheduler.set_timesteps(snake_case			)

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

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

											return sample




				def        a				(   self			):
											snake_case_     							=       dict(self.forward_default_kwargs			)

											snake_case_     							=       kwargs.pop('num_inference_steps' ,  snake_case			)

											for scheduler_class in self.scheduler_classes:
																		snake_case_     							=       self.get_scheduler_config()
																		snake_case_     							=       scheduler_class(**snake_case			)

																		snake_case_     							=       self.dummy_sample
																		snake_case_     							=       0.1 * sample

																		if num_inference_steps is not None and hasattr(snake_case ,  'set_timesteps'			):
																									scheduler.set_timesteps(snake_case			)
																		elif num_inference_steps is not None and not hasattr(snake_case ,  'set_timesteps'			):
																									snake_case_     							=       num_inference_steps

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

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample

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

																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample
																		snake_case_     							=       scheduler.step(snake_case ,  snake_case ,  snake_case ,  **snake_case			).prev_sample

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




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




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




				def        a				(   self			):
											snake_case_     							=       self.full_loop()
											snake_case_     							=       torch.mean(torch.abs(snake_case			)			)

											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    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							return 1 / (1 + np.exp(-z ))





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							return (-y * np.log(UpperCamelCase__ ) - (1 - y) * np.log(1 - h )).mean()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
							return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase__ ) ) )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=70000 ):







							'''simple docstring'''






							snake_case_     							=       np.zeros(x.shape[1] )

							for iterations in range(UpperCamelCase__ ):
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       np.dot(x.T  ,				h - y ) / y.size
														snake_case_     							=       theta - alpha * gradient  # updating the weights
														snake_case_     							=       np.dot(UpperCamelCase__  ,				UpperCamelCase__ )
														snake_case_     							=       sigmoid_function(UpperCamelCase__ )
														snake_case_     							=       cost_function(UpperCamelCase__  ,				UpperCamelCase__ )
														if iterations % 100 == 0:
																					print(F'''loss: {j} \t''' )  # printing the loss after every 100 iterations
							return theta


# In[68]:

if __name__ == "__main__":
					_UpperCAmelCase						:  str									=			datasets.load_iris()
					_UpperCAmelCase						:  Union[str, Any]									=			iris.data[:, :2]
					_UpperCAmelCase						:  List[str]									=			(iris.target != 0) * 1

					_UpperCAmelCase						:  Optional[int]									=			0.1
					_UpperCAmelCase						:  Dict									=			logistic_reg(alpha, x, y, max_iterations=7_0000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def    __lowerCamelCase      (					UpperCamelCase__ ):







												'''simple docstring'''






												return sigmoid_function(
												    np.dot(UpperCamelCase__  ,				UpperCamelCase__ ) )  # 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""")
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Any									=			(x[:, 0].min(), x[:, 0].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  Union[str, Any]									=			(x[:, 1].min(), x[:, 1].max())
					((_UpperCAmelCase)							,		(_UpperCAmelCase))						:  int									=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					_UpperCAmelCase						:  List[Any]									=			np.c_[xxa.ravel(), xxa.ravel()]
					_UpperCAmelCase						:  Dict									=			predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()


					plt.show()




