


import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=13       ,  lowercase_=7       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=99       ,  lowercase_=32       ,  lowercase_=5       ,  lowercase_=4       ,  lowercase_=37       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=16       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=4       ,  )  -> Dict:
											a__      =parent
											a__      =batch_size
											a__      =seq_length
											a__      =is_training
											a__      =use_attention_mask
											a__      =use_token_type_ids
											a__      =use_labels
											a__      =vocab_size
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =max_position_embeddings
											a__      =type_vocab_size
											a__      =type_sequence_label_size
											a__      =initializer_range
											a__      =num_choices





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size)

											a__      =None
											if self.use_attention_mask:
																		a__      =random_attention_mask([self.batch_size, self.seq_length])

											a__      =None
											if self.use_token_type_ids:
																		a__      =ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size)

											a__      =RobertaConfig(
											    vocab_size=self.vocab_size       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  type_vocab_size=self.type_vocab_size       ,  is_decoder=lowercase_       ,  initializer_range=self.initializer_range       ,  )

											return config, input_ids, token_type_ids, attention_mask





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__    ,     a__    ,     a__      =config_and_inputs
											a__      ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__    ,     a__    ,     a__      =config_and_inputs

											a__      =True
											a__      =floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
											a__      =ids_tensor([self.batch_size, self.seq_length]       ,  vocab_size=2)

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)







@require_flax
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =True

				snake_case	    =(
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =FlaxRobertaModelTester(self)





				@slow
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											for model_class_name in self.all_model_classes:
																		a__      =model_class_name.from_pretrained('roberta-base'       ,  from_pt=lowercase_)
																		a__      =model(np.ones((1, 1)))
																		self.assertIsNotNone(lowercase_)





__a	 =      256
# Modulus to hash a string
__a	 =      100_0003
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->bool:
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   UpperCAmelCase      					=  len(lowerCAmelCase_ )
   if p_len > t_len:
      return False

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  1

   # Calculating the hash of pattern and substring of text
   for i in range(lowerCAmelCase_ ):
      UpperCAmelCase      					=  (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
      UpperCAmelCase      					=  (ord(text[i] ) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      UpperCAmelCase      					=  (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
      UpperCAmelCase      					=  (
          (text_hash - ord(text[i] ) * modulus_power) * alphabet_size
          + ord(text[i + p_len] )
      ) % modulus
   return False
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  """abc1abc12"""
   UpperCAmelCase      					=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
   UpperCAmelCase      					=  """alskfjaldsk23adsfabcabc"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ ) and not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 2)
   UpperCAmelCase      					=  """ABABX"""
   UpperCAmelCase      					=  """ABABZABABYABABX"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 3)
   UpperCAmelCase      					=  """AAAB"""
   UpperCAmelCase      					=  """ABAAAAAB"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 4)
   UpperCAmelCase      					=  """abcdabcy"""
   UpperCAmelCase      					=  """abcxabcdabxabcdabcdabcy"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Test 5)
   UpperCAmelCase      					=  """Lü"""
   UpperCAmelCase      					=  """Lüsai"""
   assert rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  """Lue"""
   assert not rabin_karp(lowerCAmelCase_	,    lowerCAmelCase_ )
   print("""Success.""" )


if __name__ == "__main__":
 test_rabin_karp()



