





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





def  __magic_name__  (				lowercase				,       lowercase				)				-> bool:

							"""simple docstring"""



							lowercase_	:   str  				=     len(lowercase				)
							lowercase_	:   Dict  				=     len(lowercase				)
							if p_len > t_len:
														return False

							lowercase_	:   int  				=     0
							lowercase_	:   int  				=     0
							lowercase_	:   Union[str, Any]  				=     1

							# Calculating the hash of pattern and substring of text
							for i in range(lowercase				):
														lowercase_	:   Optional[Any]  				=     (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
														lowercase_	:   Optional[int]  				=     (ord(text[i]				) + text_hash * alphabet_size) % modulus
														if i == p_len - 1:
																					continue
														lowercase_	:   List[Any]  				=     (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
														lowercase_	:   str  				=     (
														    (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
														    + ord(text[i + p_len]				)
														) % modulus
							return False





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   List[Any]  				=     """abc1abc12"""
							lowercase_	:   Optional[Any]  				=     """alskfjaldsabc1abc1abc12k23adsfabcabc"""
							lowercase_	:   Union[str, Any]  				=     """alskfjaldsk23adsfabcabc"""
							assert rabin_karp(lowercase				,       lowercase				) and not rabin_karp(lowercase				,       lowercase				)

							# Test 2)
							lowercase_	:   Tuple  				=     """ABABX"""
							lowercase_	:   str  				=     """ABABZABABYABABX"""
							assert rabin_karp(lowercase				,       lowercase				)

							# Test 3)
							lowercase_	:   Optional[Any]  				=     """AAAB"""
							lowercase_	:   Optional[Any]  				=     """ABAAAAAB"""
							assert rabin_karp(lowercase				,       lowercase				)

							# Test 4)
							lowercase_	:   Union[str, Any]  				=     """abcdabcy"""
							lowercase_	:   List[Any]  				=     """abcxabcdabxabcdabcdabcy"""
							assert rabin_karp(lowercase				,       lowercase				)

							# Test 5)
							lowercase_	:   Dict  				=     """Lü"""
							lowercase_	:   Dict  				=     """Lüsai"""
							assert rabin_karp(lowercase				,       lowercase				)
							lowercase_	:   Optional[int]  				=     """Lue"""
							assert not rabin_karp(lowercase				,       lowercase				)
							print("""Success."""				)


if __name__ == "__main__":
							test_rabin_karp()






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask