SCREAMING_SNAKE_CASE :	List[Any]							     =		256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		100_0003



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  len(_A					)
 __a       =  len(_A					)
 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(_A					):
  __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   __A		(			):



 """simple docstring"""


 __a       =  '''abc1abc12'''
 __a       =  '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __a       =  '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(_A							,				_A					) and not rabin_karp(_A							,				_A					)

 # Test 2)
 __a       =  '''ABABX'''
 __a       =  '''ABABZABABYABABX'''
 assert rabin_karp(_A							,				_A					)

 # Test 3)
 __a       =  '''AAAB'''
 __a       =  '''ABAAAAAB'''
 assert rabin_karp(_A							,				_A					)

 # Test 4)
 __a       =  '''abcdabcy'''
 __a       =  '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(_A							,				_A					)

 # Test 5)
 __a       =  '''Lü'''
 __a       =  '''Lüsai'''
 assert rabin_karp(_A							,				_A					)
 __a       =  '''Lue'''
 assert not rabin_karp(_A							,				_A					)
 print("Success."					)


if __name__ == "__main__":
  test_rabin_karp()



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


