


'''simple docstring'''







SCREAMING_SNAKE_CASE_      =			2_56
# Modulus to hash a string
SCREAMING_SNAKE_CASE_      =			1_00_00_03




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					bool:
     """simple docstring"""




     __a       =   len(__SCREAMING_SNAKE_CASE		)
     __a       =   len(__SCREAMING_SNAKE_CASE		)
     if p_len > t_len:
          return False

     __a       =   0
     __a       =   0
     __a       =   1

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




def        __lowercase						(      )				->					None:
     """simple docstring"""




     __a       =   """abc1abc12"""
     __a       =   """alskfjaldsabc1abc1abc12k23adsfabcabc"""
     __a       =   """alskfjaldsk23adsfabcabc"""
     assert rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) and not rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     # Test 2)
     __a       =   """ABABX"""
     __a       =   """ABABZABABYABABX"""
     assert rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     # Test 3)
     __a       =   """AAAB"""
     __a       =   """ABAAAAAB"""
     assert rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     # Test 4)
     __a       =   """abcdabcy"""
     __a       =   """abcxabcdabxabcdabcdabcy"""
     assert rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     # Test 5)
     __a       =   """Lü"""
     __a       =   """Lüsai"""
     assert rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     __a       =   """Lue"""
     assert not rabin_karp(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   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

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   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():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   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

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   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():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






