

import numpy

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		None:
         UpperCAmelCase_      :							Tuple       =      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_      :							List[str]       =      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_      :							Union[str, Any]       =      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_      :							int       =      numpy.random.rand(3       ,1  )

         # Real output values provided.
         UpperCAmelCase_      :							Union[str, Any]       =      output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         UpperCAmelCase_      :							Optional[int]       =      numpy.zeros(output_array.shape  )





    def 							a__				(				self  )			->		numpy.ndarray:
         UpperCAmelCase_      :							Union[str, 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.
         UpperCAmelCase_      :							List[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_      :							Union[str, Any]       =      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  )			->		None:
         UpperCAmelCase_      :							Any       =      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_      :							int       =      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_      :							List[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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		None:
         for iteration in range(1       ,iterations + 1  ):
              UpperCAmelCase_      :							Union[str, Any]       =      self.feedforward()
              self.back_propagation()
              if give_loss:
                   UpperCAmelCase_      :							Dict       =      numpy.mean(numpy.square(output - self.feedforward()  )  )
                   print(f'''Iteration {iteration} Loss: {loss}'''  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[str]       =      input_arr

         UpperCAmelCase_      :							Any       =      sigmoid(
             numpy.dot(self.array       ,self.input_layer_and_first_hidden_layer_weights  )  )

         UpperCAmelCase_      :							Optional[int]       =      sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer       ,self.first_hidden_layer_and_second_hidden_layer_weights       ,)  )

         UpperCAmelCase_      :							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 lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      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_      :							Dict       =      numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,						dtype=numpy.floataa				)

     # Calling neural network class.
     UpperCAmelCase_      :							int       =      TwoHiddenLayerNeuralNetwork(
         input_array=__snake_case			,						output_array=__snake_case				)

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

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


if __name__ == "__main__":
     example()





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				(IPNDMScheduler,)
    lowerCAmelCase        =				(('''num_inference_steps''', 50),)





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Tuple       =      {'''num_train_timesteps''': 1_000}
         config.update(**_SCREAMING_SNAKE_CASE  )
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      self.dummy_sample
         UpperCAmelCase_      :							Optional[Any]       =      0.1 * sample
         UpperCAmelCase_      :							Optional[int]       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							Tuple       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              # copy over dummy past residuals
              UpperCAmelCase_      :							Optional[Any]       =      dummy_past_residuals[:]

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
                   # copy over dummy past residuals
                   UpperCAmelCase_      :							Optional[int]       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							str       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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

              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[int]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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





    def 							a__				(				self  )			->		List[Any]:
         pass





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      self.dummy_sample
         UpperCAmelCase_      :							Tuple       =      0.1 * sample
         UpperCAmelCase_      :							int       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							str       =      self.get_scheduler_config()
              UpperCAmelCase_      :							str       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

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

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Optional[int]       =      scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

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

              UpperCAmelCase_      :							Tuple       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[int]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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

              UpperCAmelCase_      :							str       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							List[str]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							Tuple       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[Any]       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      10
         UpperCAmelCase_      :							Tuple       =      self.dummy_model()
         UpperCAmelCase_      :							List[str]       =      self.dummy_sample_deter
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         return sample





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      dict(self.forward_default_kwargs  )

         UpperCAmelCase_      :							Any       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							int       =      self.get_scheduler_config()
              UpperCAmelCase_      :							List[Any]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Optional[int]       =      self.dummy_sample
              UpperCAmelCase_      :							List[Any]       =      0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,'''set_timesteps'''  ):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,'''set_timesteps'''  ):
                   UpperCAmelCase_      :							Union[str, Any]       =      num_inference_steps

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

              UpperCAmelCase_      :							List[Any]       =      scheduler.timesteps[5]
              UpperCAmelCase_      :							Dict       =      scheduler.timesteps[6]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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





    def 							a__				(				self  )			->		Any:
         for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         for t, num_inference_steps in zip([1, 5, 10]       ,[10, 50, 100]  ):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.full_loop()
         UpperCAmelCase_      :							str       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

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