


'''simple docstring'''




__snake_case    						=256
# Modulus to hash a string
__snake_case    						=1_000_003





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     str				):
   lowerCAmelCase     =			len(lowerCamelCase				)
   lowerCAmelCase     =			len(lowerCamelCase				)
   if p_len > t_len:
      return False

   lowerCAmelCase     =			0
   lowerCAmelCase     =			0
   lowerCAmelCase     =			1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			(ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      lowerCAmelCase     =			(ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      lowerCAmelCase     =			(modulus_power * alphabet_size) % modulus

   for i in range(0	, t_len - p_len + 1				):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      lowerCAmelCase     =			(
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False





def 	a_					( ):
   lowerCAmelCase     =			'abc1abc12'
   lowerCAmelCase     =			'alskfjaldsabc1abc1abc12k23adsfabcabc'
   lowerCAmelCase     =			'alskfjaldsk23adsfabcabc'
   assert rabin_karp(lowerCamelCase	, lowerCamelCase				) and not rabin_karp(lowerCamelCase	, lowerCamelCase				)

   # Test 2)
   lowerCAmelCase     =			'ABABX'
   lowerCAmelCase     =			'ABABZABABYABABX'
   assert rabin_karp(lowerCamelCase	, lowerCamelCase				)

   # Test 3)
   lowerCAmelCase     =			'AAAB'
   lowerCAmelCase     =			'ABAAAAAB'
   assert rabin_karp(lowerCamelCase	, lowerCamelCase				)

   # Test 4)
   lowerCAmelCase     =			'abcdabcy'
   lowerCAmelCase     =			'abcxabcdabxabcdabcdabcy'
   assert rabin_karp(lowerCamelCase	, lowerCamelCase				)

   # Test 5)
   lowerCAmelCase     =			'Lü'
   lowerCAmelCase     =			'Lüsai'
   assert rabin_karp(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			'Lue'
   assert not rabin_karp(lowerCamelCase	, lowerCamelCase				)
   print('Success.'				)


if __name__ == "__main__":
       test_rabin_karp()









'''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 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			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():
           lowerCAmelCase     =			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       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			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():
           lowerCAmelCase     =			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    )    )





