






"""simple docstring"""
import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    List[str]						     = 10





	def  lowerCamelCase(self				):
		A_		:    str						     = [1, 2, 3, 4]
		A_		:    Any						     = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
		self.assertEqual(truncate_or_pad(lowerCAmelCase_  ,  self.block_size  ,  0				)  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
		A_		:    int						     = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
		self.assertEqual(truncate_or_pad(lowerCAmelCase_  ,  self.block_size  ,  0				)  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
		A_		:    int						     = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
		self.assertEqual(truncate_or_pad(lowerCAmelCase_  ,  self.block_size  ,  0				)  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = """It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
		A_     ,  A_		:    List[str]						     = process_story(lowerCAmelCase_				)
		self.assertEqual(lowerCAmelCase_  ,  []				)





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = """"""
		A_     ,  A_		:    Optional[Any]						     = process_story(lowerCAmelCase_				)
		self.assertEqual(lowerCAmelCase_  ,  []				)
		self.assertEqual(lowerCAmelCase_  ,  []				)





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = (
		    """It was the year of Our Lord one thousand seven hundred and """
		    """seventy-five\n\nSpiritual revelations were conceded to England """
		    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
		)
		A_     ,  A_		:    int						     = process_story(lowerCAmelCase_				)

		A_		:    str						     = [
		    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
		    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
		]
		self.assertEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    Dict						     = ["""It was the best of times."""]
		self.assertEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = torch.tensor([1, 2, 3, 4]				)
		A_		:    List[Any]						     = torch.tensor([1, 1, 1, 1]				)
		np.testing.assert_array_equal(build_mask(lowerCAmelCase_  ,  0				).numpy()  ,  expected.numpy()				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = torch.tensor([1, 2, 3, 4, 23, 23, 23]				)
		A_		:    Optional[Any]						     = torch.tensor([1, 1, 1, 1, 0, 0, 0]				)
		np.testing.assert_array_equal(build_mask(lowerCAmelCase_  ,  23				).numpy()  ,  expected.numpy()				)





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = torch.tensor([8, 2, 3, 4, 1, 1, 1]				)
		A_		:    Optional[Any]						     = torch.tensor([1, 1, 1, 1, 0, 0, 0]				)
		np.testing.assert_array_equal(build_mask(lowerCAmelCase_  ,  1				).numpy()  ,  expected.numpy()				)





	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = 101
		A_		:    Any						     = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]				)
		A_		:    str						     = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]				)

		A_		:    Optional[Any]						     = compute_token_type_ids(lowerCAmelCase_  ,  lowerCAmelCase_				)
		np.testing.assert_array_equal(lowerCAmelCase_  ,  lowerCAmelCase_				)









"""simple docstring"""
import numpy as np






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[1]
	# Ensure proper dimensionality.
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[0]
	# Ensure inputs are either both complex or both real
	assert np.iscomplexobj(snake_case__       ) == np.iscomplexobj(snake_case__       )
	A_		:    Union[str, Any]						     = np.iscomplexobj(snake_case__       )
	if is_complex:
		# Ensure complex input_matrix is Hermitian
		assert np.array_equal(snake_case__ , 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.

	A_		:    List[str]						     = False
	A_		:    int						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Tuple						     = 1E12

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

		# Check convergence.
		A_		:    Any						     = np.abs(lambda_ - lambda_previous       ) / lambda_
		iterations += 1

		if error <= error_tol or iterations >= max_iterations:
			A_		:    Optional[Any]						     = True

		A_		:    Union[str, Any]						     = lambda_

	if is_complex:
		A_		:    str						     = np.real(lambda_       )

	return lambda_, vector






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]       )
	A_		:    Tuple						     = np.array([41, 4, 20]       )
	A_		:    Tuple						     = real_input_matrix.astype(np.complexaaa       )
	A_		:    List[Any]						     = np.triu(1J * complex_input_matrix , 1       )
	complex_input_matrix += imag_matrix
	complex_input_matrix += -1 * imag_matrix.T
	A_		:    Any						     = np.array([41, 4, 20]       ).astype(np.complexaaa       )

	for problem_type in ["real", "complex"]:
		if problem_type == "real":
			A_		:    Optional[int]						     = real_input_matrix
			A_		:    List[str]						     = real_vector
		elif problem_type == "complex":
			A_		:    List[Any]						     = complex_input_matrix
			A_		:    str						     = complex_vector

		# Our implementation.
		A_     ,  A_		:    Dict						     = power_iteration(snake_case__ , snake_case__       )

		# Numpy implementation.

		# Get eigenvalues and eigenvectors using built-in numpy
		# eigh (eigh used for symmetric or hermetian matrices).
		A_     ,  A_		:    Optional[Any]						     = np.linalg.eigh(snake_case__       )
		# Last eigenvalue is the maximum one.
		A_		:    List[Any]						     = eigen_values[-1]
		# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
		A_		:    Optional[Any]						     = 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(snake_case__       ) - np.abs(snake_case__       )       ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()

