

"""simple docstring"""





from typing import Any

import numpy as np




def   _A  (  UpperCamelCase_					:	Tuple)     ->						bool:
   '''simple docstring'''


   return np.array_equal(__SCREAMING_SNAKE_CASE,      matrix.conjugate().T)




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Any)     ->						Any:
   '''simple docstring'''


   __lowercase								=						v.conjugate().T
   __lowercase								=						v_star.dot(__SCREAMING_SNAKE_CASE)
   assert isinstance(__SCREAMING_SNAKE_CASE,      np.ndarray)
   return (v_star_dot.dot(__SCREAMING_SNAKE_CASE)) / (v_star.dot(__SCREAMING_SNAKE_CASE))




def   _A  (  )     ->						None:
   '''simple docstring'''


   __lowercase								=						np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]])
   __lowercase								=						np.array([[1], [2], [3]])
   assert is_hermitian(__SCREAMING_SNAKE_CASE), F"""{a} is not hermitian."""
   print(rayleigh_quotient(__SCREAMING_SNAKE_CASE,      __SCREAMING_SNAKE_CASE))

   __lowercase								=						np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]])
   assert is_hermitian(__SCREAMING_SNAKE_CASE), F"""{a} is not hermitian."""
   assert rayleigh_quotient(__SCREAMING_SNAKE_CASE,      __SCREAMING_SNAKE_CASE) == float(3)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   tests()







"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel




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







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4     )     )




