import numpy


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				List[Any]     ,   __magic_name__       :				numpy.ndarray     ,   __magic_name__       :				numpy.ndarray      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     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_      =     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_      =     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_      =     numpy.random.rand(3     ,   1      )

        # Real output values provided.
        SCREAMING_SNAKE_CASE_      =     output_array

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







  def       __A   (							self       :				List[Any]      )     ->		numpy.ndarray:
        SCREAMING_SNAKE_CASE_      =     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_      =     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_      =     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       :				Any      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     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_      =     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_      =     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       :				List[Any]     ,   __magic_name__       :				numpy.ndarray     ,   __magic_name__       :				int     ,   __magic_name__       :				bool      )     ->		None:
        for iteration in range(1     ,   iterations + 1      ):
              SCREAMING_SNAKE_CASE_      =     self.feedforward()
              self.back_propagation()
              if give_loss:
                    SCREAMING_SNAKE_CASE_      =     numpy.mean(numpy.square(output - self.feedforward()      )      )
                    print(F'''Iteration {iteration} Loss: {loss}'''      )







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				numpy.ndarray      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     input_arr

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

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

        SCREAMING_SNAKE_CASE_      =     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__       (						__UpperCamelCase ):
      return 1 / (1 + numpy.exp(-value ))


def    a__       (						__UpperCamelCase ):
      return (value) * (1 - (value))


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     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_      =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,					dtype=numpy.floataa )

      # Calling neural network class.
      SCREAMING_SNAKE_CASE_      =     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()



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


A   :     Tuple       =				get_tests_dir("fixtures")


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Union[str, Any]      )     ->		Union[str, Any]:
        # A mock response for an HTTP head request to emulate server down
        SCREAMING_SNAKE_CASE_      =     mock.Mock()
        SCREAMING_SNAKE_CASE_      =     500
        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     HTTPError
        SCREAMING_SNAKE_CASE_      =     {}

        # Download this model to make sure it's in the cache.
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
        # Under the mock environment we get a 500 error when trying to reach the model.
        with mock.patch("requests.Session.request"     ,   return_value=__magic_name__      ) as mock_head:
              SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"      )
              # This check we did call the fake head request
              mock_head.assert_called()







  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        # This test is for deprecated behavior and can be removed in v5
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(
            "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"      )



@is_staging_test
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  @classmethod
  def       __A   (							cls       :				Any      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     TOKEN
        HfFolder.save_token(__magic_name__      )







  @classmethod
  def       __A   (							cls       :				int      )     ->		Optional[Any]:
        try:
              delete_repo(token=cls._token     ,   repo_id="test-feature-extractor"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="valid_org/test-feature-extractor-org"      )
        except HTTPError:
              pass

        try:
              delete_repo(token=cls._token     ,   repo_id="test-dynamic-feature-extractor"      )
        except HTTPError:
              pass







  def       __A   (							self       :				str      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="test-feature-extractor"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				Dict      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained(__magic_name__      )
        feature_extractor.push_to_hub("valid_org/test-feature-extractor"     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )

        # Reset repo
        delete_repo(token=self._token     ,   repo_id="valid_org/test-feature-extractor"      )

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(
                  __magic_name__     ,   repo_id="valid_org/test-feature-extractor-org"     ,   push_to_hub=__magic_name__     ,   use_auth_token=self._token      )

        SCREAMING_SNAKE_CASE_      =     WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"      )
        for k, v in feature_extractor.__dict__.items():
              self.assertEqual(__magic_name__     ,   getattr(__magic_name__     ,   __magic_name__      )      )







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        CustomFeatureExtractor.register_for_auto_class()
        SCREAMING_SNAKE_CASE_      =     CustomFeatureExtractor.from_pretrained(__magic_name__      )

        feature_extractor.push_to_hub("test-dynamic-feature-extractor"     ,   use_auth_token=self._token      )

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map     ,   {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}     ,   )

        SCREAMING_SNAKE_CASE_      =     AutoFeatureExtractor.from_pretrained(
            F'''{USER}/test-dynamic-feature-extractor'''     ,   trust_remote_code=__magic_name__      )
        # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
        self.assertEqual(new_feature_extractor.__class__.__name__     ,   "CustomFeatureExtractor"      )


