

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   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	XLMRobertaModel.from_pretrained("xlm-roberta-base")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
									# The dog is cute and lives in the garden house

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor(
									    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])
									#  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():
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	model(_a)["last_hidden_state"].detach()
									self.assertEqual(output.shape       ,						_a)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]       ,						_a       ,						atol=1E-3))




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	XLMRobertaModel.from_pretrained("xlm-roberta-large")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
									# The dog is cute and lives in the garden house

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor(
									    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]])
									#  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():
															SCREAMING_SNAKE_CASE_:			Any  		=	model(_a)["last_hidden_state"].detach()
									self.assertEqual(output.shape       ,						_a)
									# compare the actual values for a slice of last dim
									self.assertTrue(torch.allclose(output[:, :, -1]       ,						_a       ,						atol=1E-3))








import os

# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase							:  Optional[int]            =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	os.path.dirname(os.path.realpath(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	os.path.join(_UpperCAmelCase    ,     "words.txt"   )

						SCREAMING_SNAKE_CASE_:			Dict  		=	""
						with open(_UpperCAmelCase   ) as f:
												SCREAMING_SNAKE_CASE_:			int  		=	f.readline()

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[word.strip("\""   ) for word in words.strip("\r\n"   ).split(","   )]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	[
						    word
						    for word in [sum(ord(_UpperCAmelCase   ) - 64 for x in word   ) for word in words]
						    if word in TRIANGULAR_NUMBERS
						]
						return len(_UpperCAmelCase   )


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






