
"""simple docstring"""





import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class    _UpperCAmelCase(   unittest.TestCase     ):
		lowercase__      =				MODEL_FOR_MASKED_LM_MAPPING
		lowercase__      =				TF_MODEL_FOR_MASKED_LM_MAPPING



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							if is_torch_available():
												import torch

												torch.cuda.empty_cache()



		@require_tf
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		top_k=2    ,		framework='''tf''')
							_UpperCamelCase										=  unmasker('''My name is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 3_80_15, '''token_str''': ''' grouped'''},
							        {'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 2_55_06, '''token_str''': ''' accuser'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''The largest city in France is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {
							            '''sequence''': '''The largest city in France is grouped''',
							            '''score''': 2.1e-05,
							            '''token''': 3_80_15,
							            '''token_str''': ''' grouped''',
							        },
							        {
							            '''sequence''': '''The largest city in France is accuser''',
							            '''score''': 2.1e-05,
							            '''token''': 2_55_06,
							            '''token_str''': ''' accuser''',
							        },
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask>'''    ,		targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,		top_k=3)
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
							        {'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
							        {'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 29_41, '''token_str''': ''' Te'''},
							    ]    ,		)



		@require_torch
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		top_k=2    ,		framework='''pt''')

							_UpperCamelCase										=  unmasker('''My name is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 3_56_76, '''token_str''': ''' Maul'''},
							        {'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''The largest city in France is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {
							            '''sequence''': '''The largest city in France is Maul''',
							            '''score''': 2.2e-05,
							            '''token''': 3_56_76,
							            '''token_str''': ''' Maul''',
							        },
							        {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask>'''    ,		targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,		top_k=3)
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is Patrick''', '''score''': 2.1e-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
							        {'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 29_41, '''token_str''': ''' Te'''},
							        {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask> <mask>'''    ,		top_k=2)

							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        [
							            {
							                '''score''': 2.2e-05,
							                '''token''': 3_56_76,
							                '''token_str''': ''' Maul''',
							                '''sequence''': '''<s>My name is Maul<mask></s>''',
							            },
							            {'''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
							        ],
							        [
							            {
							                '''score''': 2.2e-05,
							                '''token''': 3_56_76,
							                '''token_str''': ''' Maul''',
							                '''sequence''': '''<s>My name is<mask> Maul</s>''',
							            },
							            {'''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
							        ],
							    ]    ,		)



		@require_torch_gpu
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline('''fill-mask'''    ,		model='''hf-internal-testing/tiny-random-distilbert'''    ,		device=0    ,		framework='''pt''')

							# convert model to fp16
							pipe.model.half()

							_UpperCamelCase										=  pipe('''Paris is the [MASK] of France.''')
							# We actually don't care about the result, we just want to make sure
							# it works, meaning the float16 tensor got casted back to float32
							# for postprocessing.
							self.assertIsInstance(__a    ,		__a)



		@slow
		@require_torch
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''distilroberta-base'''    ,		top_k=2    ,		framework='''pt''')
							self.run_large_test(__a)



		@slow
		@require_tf
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''distilroberta-base'''    ,		top_k=2    ,		framework='''tf''')
							self.run_large_test(__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  unmasker('''My name is <mask>''')
							self.assertEqual(
							    nested_simplify(__a)    ,		[
							        {'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 6_10, '''token_str''': ''' John'''},
							        {'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 15_73, '''token_str''': ''' Chris'''},
							    ]    ,		)
							_UpperCamelCase										=  unmasker('''The largest city in France is <mask>''')
							self.assertEqual(
							    nested_simplify(__a)    ,		[
							        {
							            '''sequence''': '''The largest city in France is Paris''',
							            '''score''': 0.251,
							            '''token''': 22_01,
							            '''token_str''': ''' Paris''',
							        },
							        {
							            '''sequence''': '''The largest city in France is Lyon''',
							            '''score''': 0.214,
							            '''token''': 1_27_90,
							            '''token_str''': ''' Lyon''',
							        },
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask>'''    ,		targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,		top_k=3)
							self.assertEqual(
							    nested_simplify(__a)    ,		[
							        {'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 34_99, '''token_str''': ''' Patrick'''},
							        {'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
							        {'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 29_41, '''token_str''': ''' Te'''},
							    ]    ,		)



		@require_torch
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		framework='''pt''')
							_UpperCamelCase										=  None
							_UpperCamelCase										=  None
							self.run_pipeline_test(__a    ,		[])



		@require_tf
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		framework='''tf''')
							_UpperCamelCase										=  None
							_UpperCamelCase										=  None
							self.run_pipeline_test(__a    ,		[])



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Tuple:






							'''simple docstring'''
							if tokenizer is None or tokenizer.mask_token_id is None:
												self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)''')

							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  [
							    F'''This is another {tokenizer.mask_token} test''',
							]
							return fill_masker, examples



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  fill_masker.tokenizer
							_UpperCamelCase										=  fill_masker.model

							_UpperCamelCase										=  fill_masker(
							    F'''This is a {tokenizer.mask_token}'''    ,		)
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)

							_UpperCamelCase										=  fill_masker([F'''This is a {tokenizer.mask_token}'''])
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)

							_UpperCamelCase										=  fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.'''])
							self.assertEqual(
							    __a    ,		[
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							    ]    ,		)

							with self.assertRaises(__a):
												fill_masker([None])
							# No mask_token is not supported
							with self.assertRaises(__a):
												fill_masker('''This is''')

							self.run_test_top_k(__a    ,		__a)
							self.run_test_targets(__a    ,		__a)
							self.run_test_top_k_targets(__a    ,		__a)
							self.fill_mask_with_duplicate_targets_and_top_k(__a    ,		__a)
							self.fill_mask_with_multiple_masks(__a    ,		__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  tokenizer.get_vocab()
							_UpperCamelCase										=  sorted(vocab.keys())[:2]
							# Pipeline argument
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a    ,		targets=__a)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}''')
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)
							_UpperCamelCase										=  {vocab[el] for el in targets}
							self.assertEqual({el['''token'''] for el in outputs}    ,		__a)
							_UpperCamelCase										=  [tokenizer.decode([x]) for x in target_ids]
							self.assertEqual({el['''token_str'''] for el in outputs}    ,		set(__a))

							# Call argument
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=__a)
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)
							_UpperCamelCase										=  {vocab[el] for el in targets}
							self.assertEqual({el['''token'''] for el in outputs}    ,		__a)
							_UpperCamelCase										=  [tokenizer.decode([x]) for x in target_ids]
							self.assertEqual({el['''token_str'''] for el in outputs}    ,		set(__a))

							# Score equivalence
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=__a)
							_UpperCamelCase										=  [top_mask['''token_str'''] for top_mask in outputs]
							_UpperCamelCase										=  [top_mask['''score'''] for top_mask in outputs]

							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(__a) == set(__a):
												_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=__a)
												_UpperCamelCase										=  [top_mask['''score'''] for top_mask in unmasked_targets]
												self.assertEqual(nested_simplify(__a)    ,		nested_simplify(__a))

							# Raises with invalid
							with self.assertRaises(__a):
												_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=[])
							# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
							if "" not in tokenizer.get_vocab():
												with self.assertRaises(__a):
																	_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=[''''''])
												with self.assertRaises(__a):
																	_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets='''''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a    ,		top_k=2)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}''')
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)

							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		top_k=2)
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)
							self.assertEqual(nested_simplify(__a)    ,		nested_simplify(__a))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  tokenizer.get_vocab()
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)

							# top_k=2, ntargets=3
							_UpperCamelCase										=  sorted(vocab.keys())[:3]
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		top_k=2    ,		targets=__a)

							# If we use the most probably targets, and filter differently, we should still
							# have the same results
							_UpperCamelCase										=  [el['''token_str'''] for el in sorted(__a    ,		key=lambda __a: x["score"]    ,		reverse=__a)]
							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(__a).issubset(__a):
												_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		top_k=3    ,		targets=__a)
												# They should yield exactly the same result
												self.assertEqual(nested_simplify(__a)    ,		nested_simplify(__a))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  tokenizer.get_vocab()
							# String duplicates + id duplicates
							_UpperCamelCase										=  sorted(vocab.keys())[:3]
							_UpperCamelCase										=  [targets[0], targets[1], targets[0], targets[2], targets[1]]
							_UpperCamelCase										=  fill_masker(F'''My name is {tokenizer.mask_token}'''    ,		targets=__a    ,		top_k=10)

							# The target list contains duplicates, so we can't output more
							# than them
							self.assertEqual(len(__a)    ,		3)




		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)

							_UpperCamelCase										=  fill_masker(
							    F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''    ,		top_k=2)
							self.assertEqual(
							    __a    ,		[
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							    ]    ,		)








"""simple docstring"""





import numpy






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  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										=  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										=  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										=  numpy.random.rand(3    ,		1)

							# Real output values provided.
							_UpperCamelCase										=  output_array

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



		def 						UpperCAmelCase							( self)	->	numpy.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  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										=  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										=  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 						UpperCAmelCase							( self)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  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										=  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										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							for iteration in range(1    ,		iterations + 1):
												_UpperCamelCase										=  self.feedforward()
												self.back_propagation()
												if give_loss:
																	_UpperCamelCase										=  numpy.mean(numpy.square(output - self.feedforward()))
																	print(F'''Iteration {iteration} Loss: {loss}''')




		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  input_arr

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

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

							_UpperCamelCase										=  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__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





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

					# Calling neural network class.
					_UpperCamelCase										=  TwoHiddenLayerNeuralNetwork(
					    input_array=__snake_case,					output_array=__snake_case     )

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

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


if __name__ == "__main__":
				example()






