


'''simple docstring'''



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 						_snake_case   (       a__		):
					lowerCAmelCase :List[str]						=   (DEISMultistepScheduler,)
					lowerCAmelCase :Optional[Any]						=   (('''num_inference_steps''', 25),)




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """solver_order""": 2,
								}

								config.update(**_lowerCamelCase)
								return config




					def 			snake_case__  ( self							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       dict(self.forward_default_kwargs)
								UpperCAmelCase__					:   List[Any]       				=       kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       self.dummy_sample
								UpperCAmelCase__					:   Any       				=       0.1 * sample
								UpperCAmelCase__					:   List[Any]       				=       [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

											UpperCAmelCase__				,    UpperCAmelCase__					:   Union[str, Any]       				=       sample, sample
											for t in range(_lowerCamelCase							,							time_step + scheduler.config.solver_order + 1):
														UpperCAmelCase__					:   Dict       				=       scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
														UpperCAmelCase__					:   Optional[int]       				=       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 			snake_case__  ( self):
								pass




					def 			snake_case__  ( self							,							_lowerCamelCase=0							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       dict(self.forward_default_kwargs)
								UpperCAmelCase__					:   Union[str, Any]       				=       kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       self.dummy_sample
								UpperCAmelCase__					:   Any       				=       0.1 * sample
								UpperCAmelCase__					:   List[Any]       				=       [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__					:   Optional[int]       				=       self.get_scheduler_config()
											UpperCAmelCase__					:   Dict       				=       scheduler_class(**_lowerCamelCase)
											scheduler.set_timesteps(_lowerCamelCase)

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

											with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(_lowerCamelCase)
														UpperCAmelCase__					:   Dict       				=       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)
														UpperCAmelCase__					:   List[str]       				=       dummy_past_residuals[: new_scheduler.config.solver_order]

											UpperCAmelCase__					:   List[Any]       				=       scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__					:   Optional[int]       				=       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 			snake_case__  ( self							,							_lowerCamelCase=None							,							**_lowerCamelCase):
								if scheduler is None:
											UpperCAmelCase__					:   List[Any]       				=       self.scheduler_classes[0]
											UpperCAmelCase__					:   Optional[Any]       				=       self.get_scheduler_config(**_lowerCamelCase)
											UpperCAmelCase__					:   List[str]       				=       scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__					:   List[str]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   int       				=       self.get_scheduler_config(**_lowerCamelCase)
								UpperCAmelCase__					:   Dict       				=       scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__					:   str       				=       10
								UpperCAmelCase__					:   str       				=       self.dummy_model()
								UpperCAmelCase__					:   Optional[int]       				=       self.dummy_sample_deter
								scheduler.set_timesteps(_lowerCamelCase)

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

								return sample




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       dict(self.forward_default_kwargs)

								UpperCAmelCase__					:   Union[str, Any]       				=       kwargs.pop("""num_inference_steps"""							,							_lowerCamelCase)

								for scheduler_class in self.scheduler_classes:
											UpperCAmelCase__					:   List[Any]       				=       self.get_scheduler_config()
											UpperCAmelCase__					:   Dict       				=       scheduler_class(**_lowerCamelCase)

											UpperCAmelCase__					:   int       				=       self.dummy_sample
											UpperCAmelCase__					:   List[Any]       				=       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"""):
														UpperCAmelCase__					:   int       				=       num_inference_steps

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

											UpperCAmelCase__					:   List[str]       				=       scheduler.timesteps[5]
											UpperCAmelCase__					:   Dict       				=       scheduler.timesteps[6]

											UpperCAmelCase__					:   str       				=       scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample
											UpperCAmelCase__					:   Any       				=       scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase).prev_sample

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




					def 			snake_case__  ( self):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								UpperCAmelCase__					:   Optional[int]       				=       DEISMultistepScheduler(**self.get_scheduler_config())
								UpperCAmelCase__					:   Tuple       				=       self.full_loop(scheduler=_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_mean.item() - 0.23916) < 1e-3

								UpperCAmelCase__					:   Dict       				=       DPMSolverSinglestepScheduler.from_config(scheduler.config)
								UpperCAmelCase__					:   int       				=       DPMSolverMultistepScheduler.from_config(scheduler.config)
								UpperCAmelCase__					:   int       				=       UniPCMultistepScheduler.from_config(scheduler.config)
								UpperCAmelCase__					:   List[str]       				=       DEISMultistepScheduler.from_config(scheduler.config)

								UpperCAmelCase__					:   Union[str, Any]       				=       self.full_loop(scheduler=_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_mean.item() - 0.23916) < 1e-3




					def 			snake_case__  ( self):
								for timesteps in [25, 50, 100, 999, 1000]:
											self.check_over_configs(num_train_timesteps=_lowerCamelCase)




					def 			snake_case__  ( self):
								self.check_over_configs(thresholding=_lowerCamelCase)
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														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							,							algorithm_type="""deis"""							,							solver_order=_lowerCamelCase							,							solver_type=_lowerCamelCase							,							)




					def 			snake_case__  ( self):
								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=_lowerCamelCase)




					def 			snake_case__  ( self):
								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														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							,							algorithm_type=_lowerCamelCase							,							)
																				UpperCAmelCase__					:   str       				=       self.full_loop(
																				    solver_order=_lowerCamelCase							,							solver_type=_lowerCamelCase							,							prediction_type=_lowerCamelCase							,							algorithm_type=_lowerCamelCase							,							)
																				assert not torch.isnan(_lowerCamelCase).any(), "Samples have nan numbers"




					def 			snake_case__  ( self):
								self.check_over_configs(lower_order_final=_lowerCamelCase)
								self.check_over_configs(lower_order_final=_lowerCamelCase)




					def 			snake_case__  ( self):
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
											self.check_over_forward(num_inference_steps=_lowerCamelCase							,							time_step=0)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       self.full_loop()
								UpperCAmelCase__					:   int       				=       torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_mean.item() - 0.23916) < 1e-3




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   int       				=       self.full_loop(prediction_type="""v_prediction""")
								UpperCAmelCase__					:   Union[str, Any]       				=       torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_mean.item() - 0.091) < 1e-3




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Dict       				=       self.get_scheduler_config(thresholding=_lowerCamelCase							,							dynamic_thresholding_ratio=0)
								UpperCAmelCase__					:   Tuple       				=       scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__					:   Any       				=       10
								UpperCAmelCase__					:   Optional[Any]       				=       self.dummy_model()
								UpperCAmelCase__					:   List[Any]       				=       self.dummy_sample_deter.half()
								scheduler.set_timesteps(_lowerCamelCase)

								for i, t in enumerate(scheduler.timesteps):
											UpperCAmelCase__					:   Optional[Any]       				=       model(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__					:   Optional[int]       				=       scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase).prev_sample

								assert sample.dtype == torch.floataa



'''simple docstring'''



import numpy


class 						_snake_case   :




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       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.
								UpperCAmelCase__					:   Optional[Any]       				=       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.
								UpperCAmelCase__					:   Optional[int]       				=       numpy.random.rand(
								    4							,							3)

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

								# Real output values provided.
								UpperCAmelCase__					:   Tuple       				=       output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase__					:   Union[str, Any]       				=       numpy.zeros(output_array.shape)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       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.
								UpperCAmelCase__					:   Any       				=       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.
								UpperCAmelCase__					:   Tuple       				=       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 			snake_case__  ( self):
								UpperCAmelCase__					:   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)							,							)
								UpperCAmelCase__					:   str       				=       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)							,							)
								UpperCAmelCase__					:   Any       				=       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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								for iteration in range(1							,							iterations + 1):
											UpperCAmelCase__					:   Optional[Any]       				=       self.feedforward()
											self.back_propagation()
											if give_loss:
														UpperCAmelCase__					:   str       				=       numpy.mean(numpy.square(output - self.feedforward()))
														print(f'''Iteration {iteration} Loss: {loss}''')




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       input_arr

								UpperCAmelCase__					:   Tuple       				=       sigmoid(
								    numpy.dot(self.array							,							self.input_layer_and_first_hidden_layer_weights))

								UpperCAmelCase__					:   List[Any]       				=       sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer							,							self.first_hidden_layer_and_second_hidden_layer_weights							,							))

								UpperCAmelCase__					:   Optional[Any]       				=       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 (						UpperCamelCase__   ):
			return 1 / (1 + numpy.exp(-value   ))







def       _UpperCamelCase (						UpperCamelCase__   ):
			return (value) * (1 - (value))







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       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.
			UpperCAmelCase__					:   str       				=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	, dtype=numpy.floataa   )

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

			# Calling training function.
			# Set give_loss to True if you want to see loss in every iteration.
			neural_network.train(output=UpperCamelCase__	, iterations=1_0	, give_loss=UpperCamelCase__   )

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


if __name__ == "__main__":
		example()