






'''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			):





   @slow
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       XLMRobertaModel.from_pretrained("""xlm-roberta-base"""    )
      __SCREAMING_SNAKE_CASE      							=       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      							=       torch.Size((1, 12, 768)    )  # batch_size, sequence_length, embedding_vector_dim
      __SCREAMING_SNAKE_CASE      							=       torch.tensor(
          [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
      #  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      							=       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      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       XLMRobertaModel.from_pretrained("""xlm-roberta-large"""    )
      __SCREAMING_SNAKE_CASE      							=       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      							=       torch.Size((1, 12, 1024)    )  # batch_size, sequence_length, embedding_vector_dim
      __SCREAMING_SNAKE_CASE      							=       torch.tensor(
          [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
      #  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      							=       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    )    )












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase = "AAPL"      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase      ).text							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       """My(6px) Pos(r) smartphone_Mt(6px)"""
   return soup.find("""div"""							,	class_=class_      ).find("""span"""      ).text


if __name__ == "__main__":
    for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
        print(F'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')





