





import numpy



class 							_UpperCamelCase					:

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  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_          =  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_          =  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_          =  numpy.random.rand(3					,   1		)

              # Real output values provided.
              UpperCamelCase_          =  output_array

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

       def lowercase (		self: Union[str, Any]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  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_          =  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_          =  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 lowercase (		self: Union[str, Any]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  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_          =  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_          =  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 lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: List[Any]		)       -> Tuple:




              """simple docstring"""


              for iteration in range(1					,   iterations + 1		):
                     UpperCamelCase_          =  self.feedforward()
                     self.back_propagation()
                     if give_loss:
                            UpperCamelCase_          =  numpy.mean(numpy.square(output - self.feedforward()		)		)
                            print(f'''Iteration {iteration} Loss: {loss}'''		)



       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: str		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  input_arr

              UpperCamelCase_          =  sigmoid(
                  numpy.dot(self.array					,   self.input_layer_and_first_hidden_layer_weights		)		)

              UpperCamelCase_          =  sigmoid(
                  numpy.dot(
                      self.layer_between_input_and_first_hidden_layer					,   self.first_hidden_layer_and_second_hidden_layer_weights					,   )		)

              UpperCamelCase_          =  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_ (							UpperCamelCase_  )     ->		numpy.ndarray:
       return 1 / (1 + numpy.exp(-value  ))




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		numpy.ndarray:
       return (value) * (1 - (value))




def  lowerCAmelCase_ (							)     ->		int:
       UpperCamelCase_          =  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_          =  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])					,		dtype=numpy.floataa  )

       # Calling neural network class.
       UpperCamelCase_          =  TwoHiddenLayerNeuralNetwork(
           input_array=__SCREAMING_SNAKE_CASE					,		output_array=__SCREAMING_SNAKE_CASE  )

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

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


if __name__ == "__main__":
       example()








import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=None					,		UpperCamelCase_="no"					,		UpperCamelCase_="29500"  )     ->		Optional[Any]:
       UpperCamelCase_          =  False
       UpperCamelCase_          =  False
       if any(key.startswith("KAGGLE"  ) for key in os.environ.keys()  ):
              UpperCamelCase_          =  True
       elif "IPython" in sys.modules:
              UpperCamelCase_          =  "google.colab" in str(sys.modules["IPython"].get_ipython()  )

       try:
              UpperCamelCase_          =  PrecisionType(mixed_precision.lower()  )
       except ValueError:
              raise ValueError(
                  F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''  )

       if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"					,		UpperCamelCase_  ) is not None):
              # TPU launch
              import torch_xla.distributed.xla_multiprocessing as xmp

              if len(AcceleratorState._shared_state  ) > 0:
                     raise ValueError(
                         "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                         "your training function. Restart your notebook and make sure no cells initializes an "
                         "`Accelerator`."  )
              if num_processes is None:
                     UpperCamelCase_          =  8

              UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="TPU"  )
              print(F'''Launching a training on {num_processes} TPU cores.'''  )
              xmp.spawn(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
       elif in_colab:
              # No need for a distributed launch otherwise as it's either CPU or one GPU.
              if torch.cuda.is_available():
                     print("Launching training on one GPU."  )
              else:
                     print("Launching training on one CPU."  )
              function(*UpperCamelCase_  )
       else:
              if num_processes is None:
                     raise ValueError(
                         "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."  )

              if num_processes > 1:
                     # Multi-GPU launch
                     from torch.multiprocessing import start_processes
                     from torch.multiprocessing.spawn import ProcessRaisedException

                     if len(AcceleratorState._shared_state  ) > 0:
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                                "inside your training function. Restart your notebook and make sure no cells initializes an "
                                "`Accelerator`."  )

                     if torch.cuda.is_initialized():
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                                "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                                "function."  )

                     # torch.distributed will expect a few environment variable to be here. We set the ones common to each
                     # process here (the other ones will be set be the launcher).
                     with patch_environment(
                         world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port=UpperCamelCase_					,		mixed_precision=UpperCamelCase_  ):
                            UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="MULTI_GPU"  )
                            print(F'''Launching training on {num_processes} GPUs.'''  )
                            try:
                                   start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
                            except ProcessRaisedException as e:
                                   if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                                          raise RuntimeError(
                                              "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                                              "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                                              "Please review your imports and test them when running the `notebook_launcher()` to identify "
                                              "which one is problematic."  ) from e

              else:
                     # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
                     if is_mps_available():
                            UpperCamelCase_          =  "1"
                            print("Launching training on MPS."  )
                     elif torch.cuda.is_available():
                            print("Launching training on one GPU."  )
                     else:
                            print("Launching training on CPU."  )
                     function(*UpperCamelCase_  )







def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=2  )     ->		Optional[Any]:
       from torch.multiprocessing import start_processes

       with tempfile.NamedTemporaryFile() as tmp_file:
              # torch.distributed will expect a few environment variable to be here. We set the ones common to each
              # process here (the other ones will be set be the launcher).
              with patch_environment(
                  world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port="29500"					,		accelerate_mixed_precision="no"					,		accelerate_debug_rdv_file=tmp_file.name					,		accelerate_use_cpu="yes"					,		):
                     UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		debug=UpperCamelCase_  )
                     start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )

