




import numpy

class __snake_case		:
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				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.
           lowercase							:					Tuple	     =				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.
           lowercase							:					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.
           lowercase							:					Any	     =				numpy.random.rand(3		,1				)

           # Real output values provided.
           lowercase							:					Optional[Any]	     =				output_array

           # Predicted output values by the neural network.
           # Predicted_output array initially consists of zeroes.
           lowercase							:					List[Any]	     =				numpy.zeros(output_array.shape				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[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.
           lowercase							:					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.
           lowercase							:					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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					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				)		,)
           lowercase							:					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				)		,)
           lowercase							:					Tuple	     =				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		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				input_arr

           lowercase							:					Optional[Any]	     =				sigmoid(
               numpy.dot(self.array		,self.input_layer_and_first_hidden_layer_weights				)				)

           lowercase							:					Union[str, Any]	     =				sigmoid(
               numpy.dot(
                   self.layer_between_input_and_first_hidden_layer		,self.first_hidden_layer_and_second_hidden_layer_weights		,)				)

           lowercase							:					Tuple	     =				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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   numpy.ndarray:
       return 1 / (1 + numpy.exp(-value    ))







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   numpy.ndarray:
       return (value) * (1 - (value))







def 						_snake_case(  )			->   int:
       lowercase							:					Union[str, 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.
       lowercase							:					Optional[int]	     =				numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) ,     dtype=numpy.floataa    )

       # Calling neural network class.
       lowercase							:					Union[str, Any]	     =				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 unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Optional[int]=    None
    _a		:						Optional[Any]=    BloomTokenizerFast
    _a		:						Tuple=    BloomTokenizerFast
    _a		:						str=    True
    _a		:						Optional[int]=    False
    _a		:						List[Any]=    "tokenizer_file"
    _a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map				)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_rust_tokenizer()

           lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

           lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					Optional[Any]	     =				[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

                         lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
           lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

           lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
           lowercase							:					str	     =				list(sample_data.values()				)

           lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
           lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)
