





'''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 		lowerCamelCase	(   unittest.TestCase							):



	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= XLMRobertaModel.from_pretrained("xlm-roberta-base"						)
			lowerCAmelCase  :     Union[str, Any]       						= torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]						)
			# The dog is cute and lives in the garden house

			lowerCAmelCase  :     str       						= torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
			lowerCAmelCase  :     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():
					lowerCAmelCase  :     List[Any]       						= model(lowerCAmelCase_						)['last_hidden_state'].detach()
			self.assertEqual(output.shape		,   lowerCAmelCase_						)
			# compare the actual values for a slice of last dim
			self.assertTrue(torch.allclose(output[:, :, -1]		,   lowerCAmelCase_		,   atol=1e-3						)						)





	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[Any]       						= XLMRobertaModel.from_pretrained("xlm-roberta-large"						)
			lowerCAmelCase  :     int       						= torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]						)
			# The dog is cute and lives in the garden house

			lowerCAmelCase  :     Optional[Any]       						= torch.Size((1, 12, 1_024)						)  # batch_size, sequence_length, embedding_vector_dim
			lowerCAmelCase  :     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():
					lowerCAmelCase  :     str       						= model(lowerCAmelCase_						)['last_hidden_state'].detach()
			self.assertEqual(output.shape		,   lowerCAmelCase_						)
			# compare the actual values for a slice of last dim
			self.assertTrue(torch.allclose(output[:, :, -1]		,   lowerCAmelCase_		,   atol=1e-3						)						)











"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
			lowerCAmelCase__    						=			input('Enter image url: ').strip()
			print(F'Downloading image from {url} ...')
			lowerCAmelCase__    						=			BeautifulSoup(requests.get(url).content, 'html.parser')
			# The image URL is in the content field of the first meta tag with property og:image
			lowerCAmelCase__    						=			soup.find('meta', {'property': 'og:image'})['content']
			lowerCAmelCase__    						=			requests.get(image_url).content
			lowerCAmelCase__    						=			F'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
			with open(file_name, 'wb') as fp:
						fp.write(image_data)
			print(F'Done. Image saved to disk as {file_name}.')

