

"""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     snake_case	(				unittest.TestCase  ):

				@slow
				def 							lowercase_     (	self							:				Tuple)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base")

						__lowerCAmelCase:       Optional[Any]          =			{
						    "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),
						}

						__lowerCAmelCase:       List[str]          =			model(UpperCamelCase__)["last_hidden_state"]
						__lowerCAmelCase:       Optional[Any]          =			tf.TensorShape((1, 6, 7_6_8))
						self.assertEqual(output.shape     ,  UpperCamelCase__)
						# compare the actual values for a slice.
						__lowerCAmelCase:       int          =			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))





"""simple docstring"""


from numpy import exp, pi, sqrt






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = 0.0					, __SCREAMING_SNAKE_CASE = 1.0 )	->	int:
		return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


