
"""simple docstring"""

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 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     DebertaTokenizer
	lowercase_				=     True
	lowercase_				=     DebertaTokenizerFast




	def       _UpperCamelCase   (			self )		->							int:
			super().setUp()

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

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




	def       _UpperCamelCase   (			self		,			**UpperCAmelCase_ )		->							Tuple:
			kwargs.update(self.special_tokens_map )
			return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      Tuple          =   'lower newer'
			lowerCamelCase					:      int          =   'lower newer'
			return input_text, output_text




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      Dict          =   self.get_tokenizer()
			lowerCamelCase					:      Any          =   'lower newer'
			lowerCamelCase					:      Optional[Any]          =   ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
			lowerCamelCase					:      int          =   tokenizer.tokenize(UpperCAmelCase_ )
			self.assertListEqual(UpperCAmelCase_		,			UpperCAmelCase_ )

			lowerCamelCase					:      str          =   tokens + [tokenizer.unk_token]
			lowerCamelCase					:      Union[str, Any]          =   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      List[str]          =   self.get_tokenizer()
			lowerCamelCase					:      str          =   tokenizer('Hello'		,			'World' )
			lowerCamelCase					:      Tuple          =   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
			self.assertListEqual(tokd['token_type_ids']		,			UpperCAmelCase_ )




	@slow
	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Dict          =   self.tokenizer_class.from_pretrained('microsoft/deberta-base' )

			lowerCamelCase					:      Dict          =   tokenizer.encode('sequence builders'		,			add_special_tokens=UpperCAmelCase_ )
			lowerCamelCase					:      int          =   tokenizer.encode('multi-sequence build'		,			add_special_tokens=UpperCAmelCase_ )

			lowerCamelCase					:      Tuple          =   tokenizer.encode(
			    'sequence builders'		,			add_special_tokens=UpperCAmelCase_		,			add_prefix_space=UpperCAmelCase_ )
			lowerCamelCase					:      int          =   tokenizer.encode(
			    'sequence builders'		,			'multi-sequence build'		,			add_special_tokens=UpperCAmelCase_		,			add_prefix_space=UpperCAmelCase_ )

			lowerCamelCase					:      Tuple          =   tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_		,			UpperCAmelCase_ )

			assert encoded_sentence == encoded_text_from_decode
			assert encoded_pair == encoded_pair_from_decode




	@slow
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      int          =   [self.tokenizer_class]
			if self.test_rust_tokenizer:
					tokenizer_classes.append(self.rust_tokenizer_class )

			for tokenizer_class in tokenizer_classes:
					lowerCamelCase					:      List[Any]          =   tokenizer_class.from_pretrained('microsoft/deberta-base' )

					lowerCamelCase					:      Optional[Any]          =   [
					    '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					:      Tuple          =   tokenizer(UpperCAmelCase_		,			padding=UpperCAmelCase_ )
					lowerCamelCase					:      int          =   [tokenizer.decode(UpperCAmelCase_		,			skip_special_tokens=UpperCAmelCase_ ) for seq in encoding['input_ids']]

					# fmt: off
					lowerCamelCase					:      Optional[int]          =   {
					    '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					:      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		,			UpperCAmelCase_ )

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








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase (		a_,						a_,						a_ = 1E-12,						a_ = 100,						):





		'''simple docstring'''


		assert np.shape(a_      )[0] == np.shape(a_      )[1]
		# Ensure proper dimensionality.
		assert np.shape(a_      )[0] == np.shape(a_      )[0]
		# Ensure inputs are either both complex or both real
		assert np.iscomplexobj(a_      ) == np.iscomplexobj(a_      )
		lowerCamelCase					:      Optional[int]          =   np.iscomplexobj(a_      )
		if is_complex:
				# Ensure complex input_matrix is Hermitian
				assert np.array_equal(a_,						input_matrix.conj().T      )

		# Set convergence to False. Will define convergence when we exceed max_iterations
		# or when we have small changes from one iteration to next.

		lowerCamelCase					:      Union[str, Any]          =   False
		lowerCamelCase					:      List[str]          =   0
		lowerCamelCase					:      Any          =   0
		lowerCamelCase					:      Dict          =   1E12

		while not convergence:
				# Multiple matrix by the vector.
				lowerCamelCase					:      Optional[int]          =   np.dot(a_,						a_      )
				# Normalize the resulting output vector.
				lowerCamelCase					:      Optional[int]          =   w / np.linalg.norm(a_      )
				# Find rayleigh quotient
				# (faster than usual b/c we know vector is normalized already)
				lowerCamelCase					:      Optional[Any]          =   vector.conj().T if is_complex else vector.T
				lowerCamelCase					:      str          =   np.dot(a_,						np.dot(a_,						a_      )      )

				# Check convergence.
				lowerCamelCase					:      Optional[int]          =   np.abs(lambda_ - lambda_previous      ) / lambda_
				iterations += 1

				if error <= error_tol or iterations >= max_iterations:
						lowerCamelCase					:      int          =   True

				lowerCamelCase					:      Optional[Any]          =   lambda_

		if is_complex:
				lowerCamelCase					:      Any          =   np.real(lambda_      )

		return lambda_, vector
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
		lowerCamelCase					:      str          =   np.array([41, 4, 20]      )
		lowerCamelCase					:      Optional[Any]          =   real_input_matrix.astype(np.complexaaa      )
		lowerCamelCase					:      Dict          =   np.triu(1j * complex_input_matrix,						1      )
		complex_input_matrix += imag_matrix
		complex_input_matrix += -1 * imag_matrix.T
		lowerCamelCase					:      List[Any]          =   np.array([41, 4, 20]      ).astype(np.complexaaa      )

		for problem_type in ["real", "complex"]:
				if problem_type == "real":
						lowerCamelCase					:      str          =   real_input_matrix
						lowerCamelCase					:      Any          =   real_vector
				elif problem_type == "complex":
						lowerCamelCase					:      str          =   complex_input_matrix
						lowerCamelCase					:      Dict          =   complex_vector

				# Our implementation.
				lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   power_iteration(a_,						a_      )

				# Numpy implementation.

				# Get eigenvalues and eigenvectors using built-in numpy
				# eigh (eigh used for symmetric or hermetian matrices).
				lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   np.linalg.eigh(a_      )
				# Last eigenvalue is the maximum one.
				lowerCamelCase					:      Dict          =   eigen_values[-1]
				# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
				lowerCamelCase					:      List[str]          =   eigen_vectors[:, -1]

				# Check our implementation and numpy gives close answers.
				assert np.abs(eigen_value - eigen_value_max      ) <= 1E-6
				# Take absolute values element wise of each eigenvector.
				# as they are only unique to a minus sign.
				assert np.linalg.norm(np.abs(a_      ) - np.abs(a_      )      ) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()






