
import numpy

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       str						,	snake_case_ :       numpy.ndarray						,	snake_case_ :       numpy.ndarray      ):
							UpperCamelCase_:							str	=						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_:							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.
							UpperCamelCase_:							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.
							UpperCamelCase_:							List[Any]	=						numpy.random.rand(3						,	1      )

							# Real output values provided.
							UpperCamelCase_:							List[Any]	=						output_array

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

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Optional[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.
							UpperCamelCase_:							Union[str, 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.
							UpperCamelCase_:							List[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       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Tuple	=						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_:							Optional[int]	=						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_:							Any	=						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       lowerCAmelCase__	(       self :       int						,	snake_case_ :       numpy.ndarray						,	snake_case_ :       int						,	snake_case_ :       bool      ):
							for iteration in range(1						,	iterations + 1      ):
											UpperCamelCase_:							Tuple	=						self.feedforward()
											self.back_propagation()
											if give_loss:
															UpperCamelCase_:							int	=						numpy.mean(numpy.square(output - self.feedforward()      )      )
															print(f'''Iteration {iteration} Loss: {loss}'''      )




			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       numpy.ndarray      ):
							UpperCamelCase_:							Optional[Any]	=						input_arr

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

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

							UpperCamelCase_:							List[str]	=						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:
				UpperCamelCase_:							str	=						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_:							Union[str, Any]	=						numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,    dtype=numpy.floataa						)

				# Calling neural network class.
				UpperCamelCase_:							Optional[Any]	=						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 unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = BarthezTokenizer
			__UpperCamelCase      :						str					      = BarthezTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							super().setUp()

							UpperCamelCase_:							Tuple	=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""      )
							tokenizer.save_pretrained(self.tmpdirname      )
							tokenizer.save_pretrained(self.tmpdirname						,	legacy_format=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						"""<pad>"""
							UpperCamelCase_:							int	=						1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_      )						,	snake_case_      )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_      )						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]						,	"""<s>"""      )
							self.assertEqual(vocab_keys[1]						,	"""<pad>"""      )
							self.assertEqual(vocab_keys[-1]						,	"""<mask>"""      )
							self.assertEqual(len(snake_case_      )						,	10_1122      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	10_1122      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Union[str, Any]	=						[0, 57, 3018, 7_0307, 91, 2]

							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer(
							    snake_case_						,	max_length=len(snake_case_      )						,	padding=snake_case_						,	truncation=snake_case_						,	return_tensors="""pt"""      )
							self.assertIsInstance(snake_case_						,	snake_case_      )

							self.assertEqual((2, 6)						,	batch.input_ids.shape      )
							self.assertEqual((2, 6)						,	batch.attention_mask.shape      )
							UpperCamelCase_:							Any	=						batch.input_ids.tolist()[0]
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()

							UpperCamelCase_:							str	=						"""I was born in 92000, and this is falsé."""

							UpperCamelCase_:							str	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[str]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Tuple	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							Tuple	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       int      ):
							# fmt: off
							UpperCamelCase_:							Optional[Any]	=						{"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# moussaKam/mbarthez is a french model. So we also use french texts.
							UpperCamelCase_:							str	=						[
							    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
							    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
							    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
							    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
							    """telles que la traduction et la synthèse de texte.""",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case_						,	model_name="""moussaKam/mbarthez"""						,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,	sequences=snake_case_						,	)






