


"""simple docstring"""






import unittest

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


if is_torch_available():
					import torch

					from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





		@slow
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	XLMRobertaModel.from_pretrained('''xlm-roberta-base'''   )
					_UpperCamelCase					:Union[str, Any]					     =	torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
					# The dog is cute and lives in the garden house

					_UpperCamelCase					:int					     =	torch.Size((1, 12, 7_68)   )  # batch_size, sequence_length, embedding_vector_dim
					_UpperCamelCase					:int					     =	torch.tensor(
					    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]   )
					#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
					#  xlmr.eval()
					#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
					with torch.no_grad():
								_UpperCamelCase					:Dict					     =	model(SCREAMING_SNAKE_CASE__   )['''last_hidden_state'''].detach()
					self.assertEqual(output.shape					,				SCREAMING_SNAKE_CASE__   )
					# compare the actual values for a slice of last dim
					self.assertTrue(torch.allclose(output[:, :, -1]					,				SCREAMING_SNAKE_CASE__					,				atol=1E-3   )   )





		@slow
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	XLMRobertaModel.from_pretrained('''xlm-roberta-large'''   )
					_UpperCamelCase					:str					     =	torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
					# The dog is cute and lives in the garden house

					_UpperCamelCase					:Tuple					     =	torch.Size((1, 12, 10_24)   )  # batch_size, sequence_length, embedding_vector_dim
					_UpperCamelCase					:str					     =	torch.tensor(
					    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]   )
					#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
					#  xlmr.eval()
					#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
					with torch.no_grad():
								_UpperCamelCase					:str					     =	model(SCREAMING_SNAKE_CASE__   )['''last_hidden_state'''].detach()
					self.assertEqual(output.shape					,				SCREAMING_SNAKE_CASE__   )
					# compare the actual values for a slice of last dim
					self.assertTrue(torch.allclose(output[:, :, -1]					,				SCREAMING_SNAKE_CASE__					,				atol=1E-3   )   )





"""simple docstring"""






import os





def 				A_       ( )					->  Any:
			with open(os.path.dirname(snake_case__		) + '''/p022_names.txt'''		) as file:
						_UpperCamelCase					:Optional[Any]					     =	str(file.readlines()[0]		)
						_UpperCamelCase					:Dict					     =	names.replace('''"'''       ,					''''''		).split(''','''		)

			names.sort()

			_UpperCamelCase					:str					     =	0
			_UpperCamelCase					:Union[str, Any]					     =	0

			for i, name in enumerate(snake_case__		):
						for letter in name:
									name_score += ord(snake_case__		) - 64

						total_score += (i + 1) * name_score
						_UpperCamelCase					:List[Any]					     =	0
			return total_score


if __name__ == "__main__":
					print(solution())

