


"""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 TFXLMRobertaModel





@require_tf
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__						(		unittest.TestCase    ):
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""							)

       __lowercase									=	{
           """input_ids""": tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]      ,       dtype=tf.intaa							),  # "My dog is cute"
           """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]      ,       dtype=tf.intaa							),
       }

       __lowercase									=	model(A_							)["""last_hidden_state"""]
       __lowercase									=	tf.TensorShape((1, 6, 7_6_8)							)
       self.assertEqual(output.shape      ,       A_							)
       # compare the actual values for a slice.
       __lowercase									=	tf.convert_to_tensor(
           [
               [
                   [0.0_68_17_62, 0.10_89_44_51, 0.06_77_25_04],
                   [-0.06_42_36_68, 0.02_36_66_15, 0.04_32_93_44],
                   [-0.06_05_72_95, 0.09_97_41_35, -0.00_07_05_84],
               ]
           ]      ,       dtype=tf.floataa      ,       )

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




"""simple docstring"""

from collections.abc import Callable

import numpy as np


def lowerCAmelCase_      (				UpperCamelCase__	: Callable				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float ):






 """simple docstring"""







 __lowercase									=	int(np.ceil((x_end - xa) / step_size ) )
 __lowercase									=	np.zeros((n + 1,) )
 __lowercase									=	ya
 __lowercase									=	xa

 for k in range(UpperCamelCase__ ):
  __lowercase									=	y[k] + step_size * ode_func(UpperCamelCase__				,   y[k] )
  x += step_size

 return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()
