import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     DebertaTokenizer
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     DebertaTokenizerFast







							def 				snake_case  (				self     :    List[str]		):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase						:Dict						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										lowerCamelCase						:List[str]						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:Any						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:Dict						  =     {'''unk_token''': '''[UNK]'''}

										lowerCamelCase						:Any						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:List[str]						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''merges_file''']		)
										with open(self.vocab_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write(json.dumps(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    str	,					**__snake_case     :    Dict		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    int		):
										lowerCamelCase						:List[Any]						  =     '''lower newer'''
										lowerCamelCase						:List[str]						  =     '''lower newer'''
										return input_text, output_text







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     self.get_tokenizer()
										lowerCamelCase						:Union[str, Any]						  =     '''lower newer'''
										lowerCamelCase						:str						  =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowerCamelCase						:Optional[int]						  =     tokenizer.tokenize(__snake_case		)
										self.assertListEqual(__snake_case	,					__snake_case		)

										lowerCamelCase						:List[str]						  =     tokens + [tokenizer.unk_token]
										lowerCamelCase						:Optional[int]						  =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case		)	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:List[str]						  =     self.get_tokenizer()
										lowerCamelCase						:Optional[int]						  =     tokenizer('''Hello'''	,					'''World'''		)
										lowerCamelCase						:List[str]						  =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']	,					__snake_case		)







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Union[str, Any]						  =     self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

										lowerCamelCase						:Optional[Any]						  =     tokenizer.encode('''sequence builders'''	,					add_special_tokens=__snake_case		)
										lowerCamelCase						:Tuple						  =     tokenizer.encode('''multi-sequence build'''	,					add_special_tokens=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(
										    '''sequence builders'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.encode(
										    '''sequence builders'''	,					'''multi-sequence build'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)

										lowerCamelCase						:Any						  =     tokenizer.build_inputs_with_special_tokens(__snake_case		)
										lowerCamelCase						:Dict						  =     tokenizer.build_inputs_with_special_tokens(__snake_case	,					__snake_case		)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:List[str]						  =     [self.tokenizer_class]
										if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class		)

										for tokenizer_class in tokenizer_classes:
													lowerCamelCase						:int						  =     tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

													lowerCamelCase						:Tuple						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													lowerCamelCase						:List[Any]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [tokenizer.decode(__snake_case	,					skip_special_tokens=__snake_case		) for seq in encoding['''input_ids''']]

													# fmt: off
													lowerCamelCase						:Any						  =     {
													    '''input_ids''': [
													        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 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, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
													    ],
													    '''token_type_ids''': [
													        [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, 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]
													    ],
													    '''attention_mask''': [
													        [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],
													        [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],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
													    ]
													}
													# fmt: on

													lowerCamelCase						:Optional[int]						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													self.assertDictEqual(encoding.data	,					__snake_case		)

													for expected, decoded in zip(__snake_case	,					__snake_case		):
																self.assertEqual(__snake_case	,					__snake_case		)




import numpy
class   _lowerCAmelCase		:







							def __init__(				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:Dict						  =     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.
										lowerCamelCase						:Dict						  =     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.
										lowerCamelCase						:Dict						  =     numpy.random.rand(
										    4	,					3		)

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

										# Real output values provided.
										lowerCamelCase						:Union[str, Any]						  =     output_array

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







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						: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.
										lowerCamelCase						: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.
										lowerCamelCase						:Dict						  =     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		):
										lowerCamelCase						:Union[str, Any]						  =     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		)	,					)
										lowerCamelCase						:Dict						  =     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		)	,					)
										lowerCamelCase						:int						  =     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     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    int	,					__snake_case     :    bool		):
										for iteration in range(1	,					iterations + 1		):
													lowerCamelCase						:Union[str, Any]						  =     self.feedforward()
													self.back_propagation()
													if give_loss:
																lowerCamelCase						:Tuple						  =     numpy.mean(numpy.square(output - self.feedforward()		)		)
																print(F"Iteration {iteration} Loss: {loss}"		)







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:int						  =     input_arr

										lowerCamelCase						:Union[str, Any]						  =     sigmoid(
										    numpy.dot(self.array	,					self.input_layer_and_first_hidden_layer_weights		)		)

										lowerCamelCase						:Optional[Any]						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_input_and_first_hidden_layer	,					self.first_hidden_layer_and_second_hidden_layer_weights	,					)		)

										lowerCamelCase						:Optional[int]						  =     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_				: numpy.ndarray):
			return 1 / (1 + numpy.exp(-value))





def  _lowerCamelCase					(   a_				: numpy.ndarray):
			return (value) * (1 - (value))





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[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.
			lowerCamelCase						:int						  =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,			dtype=numpy.floataa)

			# Calling neural network class.
			lowerCamelCase						:List[Any]						  =     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()



