



'''simple docstring'''





import numpy






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      int     ,      _a :      numpy.ndarray     ,      _a :      numpy.ndarray  )   ->   None:
    __lowerCamelCase			:			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.
    __lowerCamelCase			:			List[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.
    __lowerCamelCase			:			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.
    __lowerCamelCase			:			Dict       			=					numpy.random.rand(3     ,      1  )

    # Real output values provided.
    __lowerCamelCase			:			Optional[int]       			=					output_array

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




  def 		_lowercase	(					self :      Optional[int]  )   ->   numpy.ndarray:
    __lowerCamelCase			:			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.
    __lowerCamelCase			:			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.
    __lowerCamelCase			:			Optional[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 		_lowercase	(					self :      List[str]  )   ->   None:
    __lowerCamelCase			:			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  )     ,      )
    __lowerCamelCase			:			List[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  )     ,      )
    __lowerCamelCase			:			str       			=					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 :      Optional[int]     ,      _a :      numpy.ndarray     ,      _a :      int     ,      _a :      bool  )   ->   None:

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




  def 		_lowercase	(					self :      Any     ,      _a :      numpy.ndarray  )   ->   int:
    __lowerCamelCase			:			Optional[int]       			=					input_arr

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

    __lowerCamelCase			:			List[str]       			=					sigmoid(
        numpy.dot(
            self.layer_between_input_and_first_hidden_layer     ,      self.first_hidden_layer_and_second_hidden_layer_weights     ,      )  )

    __lowerCamelCase			:			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 			a_	(				_lowerCAmelCase			) -> numpy.ndarray:
  return 1 / (1 + numpy.exp(-value			))

def 			a_	(				_lowerCAmelCase			) -> numpy.ndarray:
  return (value) * (1 - (value))

def 			a_	(				) -> int:
  __lowerCamelCase			:			Optional[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.
  __lowerCamelCase			:			List[Any]       			=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,dtype=numpy.floataa			)

  # Calling neural network class.
  __lowerCamelCase			:			int       			=					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()










import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""  ,				"""False""" ) ) is not True  ,				reason="""Skipping test because should only be run when releasing minor transformers version"""  ,				)
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ] )
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''simple docstring'''

											if self.framework == "pytorch":
																	subprocess.run(
																	    f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()  ,     encoding="utf-8"  ,     check=SCREAMING_SNAKE_CASE  ,     )
											assert hasattr(self  ,     "env"    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Any=1    )      ->						List[str]:




											'''simple docstring'''

											return HuggingFace(
											    entry_point=self.script  ,     source_dir=self.env.test_path  ,     role=self.env.role  ,     image_uri=self.env.image_uri  ,     base_job_name=f'''{self.env.base_job_name}-single'''  ,     instance_count=SCREAMING_SNAKE_CASE  ,     instance_type=self.instance_type  ,     debugger_hook_config=SCREAMING_SNAKE_CASE  ,     hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}  ,     metric_definitions=self.env.metric_definitions  ,     py_version="py36"  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						List[str]:




											'''simple docstring'''

											TrainingJobAnalytics(SCREAMING_SNAKE_CASE    ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											__snake_case				=		TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

											# extract kpis
											__snake_case				=		list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]    )
											__snake_case				=		list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]    )
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											__snake_case				=		(
											    Session().describe_training_job(estimator.latest_training_job.name    ).get("TrainingTimeInSeconds"  ,     9_9_9_9_9_9    )
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy    )
											assert all(t <= self.results["eval_loss"] for t in eval_loss    )

											# dump tests result into json file to share in PR
											with open(f'''{estimator.latest_training_job.name}.json'''  ,     "w"    ) as outfile:
																	json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}  ,     SCREAMING_SNAKE_CASE    )





