





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						(EulerDiscreteScheduler,)
							snake_case_							 =						10

							def      A_  (      self				:     Union[str, Any]							,	**a				:     Union[str, Any]    )	->int:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					{
													    "num_train_timesteps": 11_00,
													    "beta_start": 0.0001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													}

													config.update(**a    )
													return config

							def      A_  (      self				:     List[str]    )	->Any:
													for timesteps in [10, 50, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=a    )

							def      A_  (      self				:     List[str]    )	->Tuple:
													for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,	[0.0002, 0.002, 0.02]    ):
																			self.check_over_configs(beta_start=a							,	beta_end=a    )

							def      A_  (      self				:     str    )	->Union[str, Any]:
													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=a    )

							def      A_  (      self				:     Optional[int]    )	->Tuple:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=a    )

							def      A_  (      self				:     Union[str, Any]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__     :				List[str]   =					scheduler_class(**a    )

													scheduler.set_timesteps(self.num_inference_steps    )

													SCREAMING_SNAKE_CASE__     :				int   =					torch.manual_seed(0    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.dummy_model()
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.dummy_sample_deter * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					sample.to(a    )

													for i, t in enumerate(scheduler.timesteps    ):
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					scheduler.scale_model_input(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					scheduler.step(a							,	a							,	a							,	generator=a    )
																			SCREAMING_SNAKE_CASE__     :				str   =					output.prev_sample

													SCREAMING_SNAKE_CASE__     :				Any   =					torch.sum(torch.abs(a    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.mean(torch.abs(a    )    )

													assert abs(result_sum.item() - 10.0807    ) < 1E-2
													assert abs(result_mean.item() - 0.0131    ) < 1E-3

							def      A_  (      self				:     List[Any]    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				str   =					self.get_scheduler_config(prediction_type="v_prediction"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					scheduler_class(**a    )

													scheduler.set_timesteps(self.num_inference_steps    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.manual_seed(0    )

													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.dummy_model()
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.dummy_sample_deter * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE__     :				int   =					sample.to(a    )

													for i, t in enumerate(scheduler.timesteps    ):
																			SCREAMING_SNAKE_CASE__     :				Any   =					scheduler.scale_model_input(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				str   =					model(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				Dict   =					scheduler.step(a							,	a							,	a							,	generator=a    )
																			SCREAMING_SNAKE_CASE__     :				Any   =					output.prev_sample

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.sum(torch.abs(a    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					torch.mean(torch.abs(a    )    )

													assert abs(result_sum.item() - 0.0002    ) < 1E-2
													assert abs(result_mean.item() - 2.2676E-06    ) < 1E-3

							def      A_  (      self				:     int    )	->str:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__     :				Any   =					scheduler_class(**a    )

													scheduler.set_timesteps(self.num_inference_steps							,	device=a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.manual_seed(0    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					self.dummy_model()
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													SCREAMING_SNAKE_CASE__     :				Tuple   =					sample.to(a    )

													for t in scheduler.timesteps:
																			SCREAMING_SNAKE_CASE__     :				str   =					scheduler.scale_model_input(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				Any   =					model(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				int   =					scheduler.step(a							,	a							,	a							,	generator=a    )
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					output.prev_sample

													SCREAMING_SNAKE_CASE__     :				str   =					torch.sum(torch.abs(a    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.mean(torch.abs(a    )    )

													assert abs(result_sum.item() - 10.0807    ) < 1E-2
													assert abs(result_mean.item() - 0.0131    ) < 1E-3






							def      A_  (      self				:     List[str]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				str   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__     :				List[str]   =					scheduler_class(**a							,	use_karras_sigmas=a    )

													scheduler.set_timesteps(self.num_inference_steps							,	device=a    )

													SCREAMING_SNAKE_CASE__     :				Any   =					torch.manual_seed(0    )

													SCREAMING_SNAKE_CASE__     :				int   =					self.dummy_model()
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													SCREAMING_SNAKE_CASE__     :				Tuple   =					sample.to(a    )

													for t in scheduler.timesteps:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					scheduler.scale_model_input(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(a							,	a    )

																			SCREAMING_SNAKE_CASE__     :				Dict   =					scheduler.step(a							,	a							,	a							,	generator=a    )
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					output.prev_sample

													SCREAMING_SNAKE_CASE__     :				Any   =					torch.sum(torch.abs(a    )    )
													SCREAMING_SNAKE_CASE__     :				int   =					torch.mean(torch.abs(a    )    )

													assert abs(result_sum.item() - 124.52_2994_9951_1719    ) < 1E-2
													assert abs(result_mean.item() - 0.1_6213_9326_3339_9963    ) < 1E-3






import numpy



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     numpy.ndarray							,	a				:     numpy.ndarray    )	->None:
													SCREAMING_SNAKE_CASE__     :				Any   =					input_array

													# Random initial weights are assigned where first argument is the
													# number of nodes in previous layer and second argument is the
													# number of nodes in the next layer.

													# Random initial weights are assigned.
													# self.input_array.shape[1] is used to represent number of nodes in input layer.
													# First hidden layer consists of 4 nodes.
													SCREAMING_SNAKE_CASE__     :				int   =					numpy.random.rand(
													    self.input_array.shape[1]							,	4    )

													# Random initial values for the first hidden layer.
													# First hidden layer has 4 nodes.
													# Second hidden layer has 3 nodes.
													SCREAMING_SNAKE_CASE__     :				Dict   =					numpy.random.rand(
													    4							,	3    )

													# Random initial values for the second hidden layer.
													# Second hidden layer has 3 nodes.
													# Output layer has 1 node.
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					numpy.random.rand(3							,	1    )

													# Real output values provided.
													SCREAMING_SNAKE_CASE__     :				str   =					output_array

													# Predicted output values by the neural network.
													# Predicted_output array initially consists of zeroes.
													SCREAMING_SNAKE_CASE__     :				Tuple   =					numpy.zeros(output_array.shape    )

							def      A_  (      self				:     Union[str, Any]    )	->numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					sigmoid(
													    numpy.dot(self.input_array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
													# connecting the first hidden set of nodes with the second hidden set of nodes.
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													# layer_between_second_hidden_layer_and_output is the layer connecting
													# second hidden layer with the output node.
													SCREAMING_SNAKE_CASE__     :				int   =					sigmoid(
													    numpy.dot(
													        self.layer_between_first_hidden_layer_and_second_hidden_layer							,	self.second_hidden_layer_and_output_layer_weights							,	)    )

													return self.layer_between_second_hidden_layer_and_output

							def      A_  (      self				:     int    )	->None:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					numpy.dot(
													    self.layer_between_first_hidden_layer_and_second_hidden_layer.T							,	2
													    * (self.output_array - self.predicted_output)
													    * sigmoid_derivative(self.predicted_output    )							,	)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					numpy.dot(
													    self.layer_between_input_and_first_hidden_layer.T							,	numpy.dot(
													        2
													        * (self.output_array - self.predicted_output)
													        * sigmoid_derivative(self.predicted_output    )							,	self.second_hidden_layer_and_output_layer_weights.T							,	)
													    * sigmoid_derivative(
													        self.layer_between_first_hidden_layer_and_second_hidden_layer    )							,	)
													SCREAMING_SNAKE_CASE__     :				int   =					numpy.dot(
													    self.input_array.T							,	numpy.dot(
													        numpy.dot(
													            2
													            * (self.output_array - self.predicted_output)
													            * sigmoid_derivative(self.predicted_output    )							,	self.second_hidden_layer_and_output_layer_weights.T							,	)
													        * sigmoid_derivative(
													            self.layer_between_first_hidden_layer_and_second_hidden_layer    )							,	self.first_hidden_layer_and_second_hidden_layer_weights.T							,	)
													    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )							,	)

													self.input_layer_and_first_hidden_layer_weights += (
													    updated_input_layer_and_first_hidden_layer_weights
													)
													self.first_hidden_layer_and_second_hidden_layer_weights += (
													    updated_first_hidden_layer_and_second_hidden_layer_weights
													)
													self.second_hidden_layer_and_output_layer_weights += (
													    updated_second_hidden_layer_and_output_layer_weights
													)

							def      A_  (      self				:     int							,	a				:     numpy.ndarray							,	a				:     int							,	a				:     bool    )	->None:
													for iteration in range(1							,	iterations + 1    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.feedforward()
																			self.back_propagation()
																			if give_loss:
																									SCREAMING_SNAKE_CASE__     :				int   =					numpy.mean(numpy.square(output - self.feedforward()    )    )
																									print(f"""Iteration {iteration} Loss: {loss}"""    )






							def      A_  (      self				:     Tuple							,	a				:     numpy.ndarray    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_arr

													SCREAMING_SNAKE_CASE__     :				Dict   =					sigmoid(
													    numpy.dot(self.array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													SCREAMING_SNAKE_CASE__     :				Any   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sigmoid(
													    numpy.dot(
													        self.layer_between_first_hidden_layer_and_second_hidden_layer							,	self.second_hidden_layer_and_output_layer_weights							,	)    )

													return int(self.layer_between_second_hidden_layer_and_output > 0.6    )







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return 1 / (1 + numpy.exp(-value     ))







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return (value) * (1 - (value))







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )			,   dtype=numpy.floataa			,   )

						# True output values for the given input values.
						SCREAMING_SNAKE_CASE__     :				Any   =					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,   dtype=numpy.floataa     )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					TwoHiddenLayerNeuralNetwork(
						    input_array=_lowerCamelCase			,   output_array=_lowerCamelCase     )

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=_lowerCamelCase			,   iterations=10			,   give_loss=_lowerCamelCase     )

						return neural_network.predict(numpy.array(([1, 1, 1])			,   dtype=numpy.floataa     )     )


if __name__ == "__main__":
				example()