



'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_ :	List[Any]         =  get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			XLMRobertaTokenizer
	lowercase__  				=			XLMRobertaTokenizerFast
	lowercase__  				=			True
	lowercase__  				=			True





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCamelCase					:		Tuple 				=   XLMRobertaTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )
							tokenizer.save_pretrained(self.tmpdirname )





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   '<pad>'
							_UpperCamelCase					:		str 				=   1

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





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   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(lowerCamelCase__ ) ,1002 )





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							self.assertEqual(self.get_tokenizer().vocab_size ,1002 )





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   XLMRobertaTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   tokenizer.tokenize('This is a test' )
							self.assertListEqual(lowerCamelCase__ ,['▁This', '▁is', '▁a', '▁t', 'est'] )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.tokenize('I was born in 92000, and this is falsé.' )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '9',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        'é',
							        '.',
							    ] ,)
							_UpperCamelCase					:		int 				=   tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ] ,)

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '<unk>',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        '<unk>',
							        '.',
							    ] ,)





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

							_UpperCamelCase					:		Tuple 				=   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
																			_UpperCamelCase					:		List[Any] 				=   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
																			_UpperCamelCase					:		Tuple 				=   self.tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )

																			_UpperCamelCase					:		Optional[int] 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		str 				=   tokenizer_r.save_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		Union[str, Any] 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# Checks it save with the same files + the tokenizer.json file for the fast one
																			self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
																			_UpperCamelCase					:		str 				=   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
																			self.assertSequenceEqual(lowerCamelCase__ ,lowerCamelCase__ )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		int 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(lowerCamelCase__ ,lowerCamelCase__ ) )
																									# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																									# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																			shutil.rmtree(lowerCamelCase__ )

																			# Save tokenizer rust, legacy_format=True
																			_UpperCamelCase					:		str 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		Any 				=   tokenizer_r.save_pretrained(lowerCamelCase__ ,legacy_format=lowerCamelCase__ )
																			_UpperCamelCase					:		Optional[Any] 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# Checks it save with the same files
																			self.assertSequenceEqual(lowerCamelCase__ ,lowerCamelCase__ )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		Tuple 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(lowerCamelCase__ ,lowerCamelCase__ ) )

																			shutil.rmtree(lowerCamelCase__ )

																			# Save tokenizer rust, legacy_format=False
																			_UpperCamelCase					:		Tuple 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		Dict 				=   tokenizer_r.save_pretrained(lowerCamelCase__ ,legacy_format=lowerCamelCase__ )
																			_UpperCamelCase					:		int 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# Checks it saved the tokenizer.json file
																			self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		List[Any] 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		Any 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(lowerCamelCase__ ,lowerCamelCase__ ) )

																			shutil.rmtree(lowerCamelCase__ )





	@cached_property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(lowerCamelCase__ ,f.name )
													_UpperCamelCase					:		Optional[Any] 				=   XLMRobertaTokenizer(f.name ,keep_accents=lowerCamelCase__ )
													_UpperCamelCase					:		str 				=   pickle.dumps(lowerCamelCase__ )
							pickle.loads(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							if not self.test_rust_tokenizer:
													return

							_UpperCamelCase					:		int 				=   self.get_tokenizer()
							_UpperCamelCase					:		List[str] 				=   self.get_rust_tokenizer()

							_UpperCamelCase					:		Any 				=   'I was born in 92000, and this is falsé.'

							_UpperCamelCase					:		str 				=   tokenizer.tokenize(lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   rust_tokenizer.tokenize(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   rust_tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   self.get_rust_tokenizer()
							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.encode(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   rust_tokenizer.encode(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )





	@slow
	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   'Hello World!'
							_UpperCamelCase					:		str 				=   [0, 35378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(lowerCamelCase__ ,self.big_tokenizer.encode(lowerCamelCase__ ) )





	@slow
	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   (
							    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
							    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
							)
							_UpperCamelCase					:		str 				=   [
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    179459,
							    124850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    10114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    22376,
							    642,
							    1221,
							    15190,
							    34153,
							    450,
							    5608,
							    959,
							    1119,
							    57702,
							    136,
							    186,
							    47,
							    1098,
							    29367,
							    47,
							    # 4426, # What fairseq tokenizes from "<unk>": "_<"
							    # 3678, # What fairseq tokenizes from "<unk>": "unk"
							    # 2740, # What fairseq tokenizes from "<unk>": ">"
							    3,  # What we tokenize from "<unk>": "<unk>"
							    6,  # Residue from the tokenization: an extra sentencepiece underline
							    4,
							    6044,
							    237,
							    6284,
							    50901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(lowerCamelCase__ ,self.big_tokenizer.encode(lowerCamelCase__ ) )





	@slow
	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							# fmt: off
							_UpperCamelCase					:		Union[str, Any] 				=   {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 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, 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], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 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, 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, 1, 1, 1, 1]], '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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=lowerCamelCase__ ,model_name='xlm-roberta-base' ,revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' ,)








import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							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.
							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.
							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.
							snake_case_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

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






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							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.
							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.
							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        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							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						)       ,	)
							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						)       ,	)
							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        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






							for iteration in range(1       ,	iterations + 1						):
								snake_case_		    =     self.feedforward()
								self.back_propagation()
								if give_loss:
									snake_case_		    =     numpy.mean(numpy.square(output - self.feedforward()						)						)
									print(f"Iteration {iteration} Loss: {loss}"						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

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

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

							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 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return 1 / (1 + numpy.exp(-value							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return (value) * (1 - (value))


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	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.
	snake_case_		    =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,	dtype=numpy.floataa							)

	# Calling neural network class.
	snake_case_		    =     TwoHiddenLayerNeuralNetwork(
	    input_array=_A     ,	output_array=_A							)

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

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


if __name__ == "__main__":
		example()
