
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        lowercase	(      unittest.TestCase	):




							@slow
							def 				A__   (       self):
												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(_SCREAMING_SNAKE_CASE)['''last_hidden_state''']
												lowercase					=		tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape     ,_SCREAMING_SNAKE_CASE)
												# compare the actual values for a slice.
												lowercase					=		tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]     ,dtype=tf.floataa     ,)

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






from numpy import exp, pi, sqrt



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0	):
					'''simple docstring'''
					return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




