
"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
			import torch

			from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  _lowerCAmelCase    (    unittest.TestCase    ):



							@slow
							def    _a (   self )				->				Optional[int]:
								_UpperCAmelCase			=    XLMRobertaModel.from_pretrained("xlm-roberta-base" )
								_UpperCAmelCase			=    torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
								# The dog is cute and lives in the garden house

								_UpperCAmelCase			=    torch.Size((1, 12, 768) )  # batch_size, sequence_length, embedding_vector_dim
								_UpperCAmelCase			=    torch.tensor(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
								#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
								#  xlmr.eval()
								#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
								with torch.no_grad():
									_UpperCAmelCase			=    model(__UpperCamelCase )["last_hidden_state"].detach()
								self.assertEqual(output.shape			,    __UpperCamelCase )
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]			,    __UpperCamelCase			,    atol=1e-3 ) )



							@slow
							def    _a (   self )				->				str:
								_UpperCAmelCase			=    XLMRobertaModel.from_pretrained("xlm-roberta-large" )
								_UpperCAmelCase			=    torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] )
								# The dog is cute and lives in the garden house

								_UpperCAmelCase			=    torch.Size((1, 12, 1024) )  # batch_size, sequence_length, embedding_vector_dim
								_UpperCAmelCase			=    torch.tensor(
								    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
								#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
								#  xlmr.eval()
								#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
								with torch.no_grad():
									_UpperCAmelCase			=    model(__UpperCamelCase )["last_hidden_state"].detach()
								self.assertEqual(output.shape			,    __UpperCamelCase )
								# compare the actual values for a slice of last dim
								self.assertTrue(torch.allclose(output[:, :, -1]			,    __UpperCamelCase			,    atol=1e-3 ) )












import numpy as np





def    __lowerCAmelCase   (    __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							)
     __lowerCAmelCase             =       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.

     __lowerCAmelCase             =       False
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       0
     __lowerCAmelCase             =       1E12

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

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

          if error <= error_tol or iterations >= max_iterations:
               __lowerCAmelCase             =       True

          __lowerCAmelCase             =       lambda_

     if is_complex:
          __lowerCAmelCase             =       np.real(lambda_							)

     return lambda_, vector





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
     __lowerCAmelCase             =       np.array([41, 4, 20]							)
     __lowerCAmelCase             =       real_input_matrix.astype(np.complexaaa							)
     __lowerCAmelCase             =       np.triu(1J * complex_input_matrix       ,			1							)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __lowerCAmelCase             =       np.array([41, 4, 20]							).astype(np.complexaaa							)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __lowerCAmelCase             =       real_input_matrix
               __lowerCAmelCase             =       real_vector
          elif problem_type == "complex":
               __lowerCAmelCase             =       complex_input_matrix
               __lowerCAmelCase             =       complex_vector

          # Our implementation.
          __lowerCAmelCase      ,			__lowerCAmelCase             =       power_iteration(__snake_case       ,			__snake_case							)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __lowerCAmelCase      ,			__lowerCAmelCase             =       np.linalg.eigh(__snake_case							)
          # Last eigenvalue is the maximum one.
          __lowerCAmelCase             =       eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __lowerCAmelCase             =       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()






