





import unittest

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


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase							(					unittest.TestCase			):
 @slow
 def      A_	(      self  :      int	)			->	Any:
     lowerCamelCase__    :      Optional[Any]					 =		AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'			,     return_dict=UpperCAmelCase	).to(UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		AutoTokenizer.from_pretrained('google/mt5-small'	)

     lowerCamelCase__    :      Any					 =		tokenizer('Hello there'			,     return_tensors='pt'	).input_ids
     lowerCamelCase__    :      Tuple					 =		tokenizer('Hi I am'			,     return_tensors='pt'	).input_ids

     lowerCamelCase__    :      Optional[int]					 =		model(input_ids.to(UpperCAmelCase	)			,     labels=labels.to(UpperCAmelCase	)	).loss
     lowerCamelCase__    :      Dict					 =		-(labels.shape[-1] * loss.item())

     lowerCamelCase__    :      Optional[int]					 =		-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)











'''simple docstring'''

import re


def 		__magic_name__    (	__UpperCAmelCase	)		->					bool:

					'''simple docstring'''

					snake_case_									=				re.compile(
					    r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$'''	)

					return bool(re.search(__UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							a  :				Any									=  '0094702343221'

							print(is_sri_lankan_phone_number(phone))

