


"""simple docstring"""







import numpy






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A						) ->					None:
     a					=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.
     a					=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.
     a					=numpy.random.rand(
         4			,   3						)

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

     # Real output values provided.
     a					=output_array

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					numpy.ndarray:
     a					=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.
     a					=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.
     a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					None:
     a					=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						)			,   )
     a					=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						)			,   )
     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					None:
     for iteration in range(1			,   iterations + 1						):
         a					=self.feedforward()
         self.back_propagation()
         if give_loss:
             a					=numpy.mean(numpy.square(output - self.feedforward()						)						)
             print(f'''Iteration {iteration} Loss: {loss}'''						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
     a					=input_arr

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

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

     a					=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						(						lowercase		):







    """simple docstring"""
    return 1 / (1 + numpy.exp(-value		))
def _A						(						lowercase		):







    """simple docstring"""
    return (value) * (1 - (value))
def _A						(						):







    """simple docstring"""
    a					=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.
    a					=numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	,   dtype=numpy.floataa		)

    # Calling neural network class.
    a					=TwoHiddenLayerNeuralNetwork(
        input_array=lowercase	,   output_array=lowercase		)

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

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


if __name__ == "__main__":
       example()



"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


lowerCamelCase_       :       str      						=  ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_       :       List[str]      						=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():






       class 				__A    (	tf.keras.Model      ):






        """simple docstring"""




        def __init__(		self			,   __A						) ->					Dict:
            super().__init__()
            a					=tokenizer
            a					=AutoConfig.from_pretrained(__A						)
            a					=TFAutoModel.from_config(__A						)





        def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
            a					=self.tokenizer(__A						)
            a					=self.bert(**__A						)
            return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     super().setUp()

     a					=[
         BertTokenizer.from_pretrained(__A						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     a					=[TFBertTokenizer.from_pretrained(__A						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(__A			,   use_fast_bert_tokenizer=__A						)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers						) == len(self.tf_tokenizers						)

     a					=[
         '''This is a straightforward English test sentence.''',
         '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
         '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
         '''And some much more rare Chinese: 齉 堃 齉堃''',
         '''Je vais aussi écrire en français pour tester les accents''',
         '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
     ]
     a					=list(zip(self.test_sentences			,   self.test_sentences[::-1]						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     for tokenizer, tf_tokenizer in zip(self.tokenizers			,   self.tf_tokenizers						):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tokenizer(__A			,   return_tensors='''tf'''			,   padding='''longest'''						)
             a					=tf_tokenizer(__A						)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,   tf.intaa						) == tf_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf_tokenizer(self.paired_sentences						)
         a					=tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences]			,   text_pair=[sentence[1] for sentence in self.paired_sentences]			,   )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,   tf.intaa						) == separated_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf.function(__A						)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tf.constant(__A						)
             a					=compiled_tokenizer(__A						)
             a					=tf_tokenizer(__A						)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for tf_tokenizer in self.tf_tokenizers:
         a					=ModelToSave(tokenizer=__A						)
         a					=tf.convert_to_tensor(self.test_sentences						)
         a					=model(__A						)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             a					=Path(__A						) / '''saved.model'''
             model.save(__A						)
             a					=tf.keras.models.load_model(__A						)
         a					=loaded_model(__A						)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)			,   1E-5						)