



'''simple docstring'''





def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					0

    # if input_string is "aba" than new_input_string become "a|b|a"
    lowerCAmelCase__ :  Union[str, Any]							      =					""""""
    lowerCAmelCase__ :  Tuple							      =					""""""

    # append each character + "|" in new_string for range(0, length-1)
    for i in input_string[: len(UpperCamelCase			) - 1]:
        new_input_string += i + "|"
    # append last character
    new_input_string += input_string[-1]

    # we will store the starting and ending of previous furthest ending palindromic
    # substring
    lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					0, 0

    # length[i] shows the length of palindromic substring with center i
    lowerCAmelCase__ :  str							      =					[1 for i in range(len(UpperCamelCase			)			)]

    # for each character in new_string find corresponding palindromic string
    lowerCAmelCase__ :  Optional[int]							      =					0
    for j in range(len(UpperCamelCase			)			):
        lowerCAmelCase__ :  Tuple							      =					1 if j > r else min(length[l + r - j] // 2							,    r - j + 1			)
        while (
            j - k >= 0
            and j + k < len(UpperCamelCase			)
            and new_input_string[k + j] == new_input_string[j - k]
        ):
            k += 1

        lowerCAmelCase__ :  str							      =					2 * k - 1

        # does this string is ending after the previously explored end (that is r) ?
        # if yes the update the new r to the last index of this
        if j + k - 1 > r:
            lowerCAmelCase__ :  Tuple							      =					j - k + 1  # noqa: E741
            lowerCAmelCase__ :  Dict							      =					j + k - 1

        # update max_length and start position
        if max_length < length[j]:
            lowerCAmelCase__ :  List[str]							      =					length[j]
            lowerCAmelCase__ :  Union[str, Any]							      =					j

    # create that string
    lowerCAmelCase__ :  List[Any]							      =					new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
    for i in s:
        if i != "|":
            output_string += i

    return output_string


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''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     ):
  '''simple docstring'''




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
      lowerCAmelCase__ :  Optional[int]							      =					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

      lowerCAmelCase__ :  str							      =					torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Dict							      =					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():
          lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
      lowerCAmelCase__ :  Optional[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

      lowerCAmelCase__ :  Dict							      =					torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Union[str, Any]							      =					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():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )


