
"""simple docstring"""

_A						=			2_5_6
# Modulus to hash a string
_A						=			1_0_0_0_0_0_3
def 		UpperCAmelCase (		a_,						a_      ):
		lowerCamelCase					:      str          =   len(__SCREAMING_SNAKE_CASE      )
		lowerCamelCase					:      List[Any]          =   len(__SCREAMING_SNAKE_CASE      )
		if p_len > t_len:
				return False

		lowerCamelCase					:      Any          =   0
		lowerCamelCase					:      str          =   0
		lowerCamelCase					:      Dict          =   1

		# Calculating the hash of pattern and substring of text
		for i in range(__SCREAMING_SNAKE_CASE      ):
				lowerCamelCase					:      Optional[Any]          =   (ord(pattern[i]      ) + p_hash * alphabet_size) % modulus
				lowerCamelCase					:      Union[str, Any]          =   (ord(text[i]      ) + text_hash * alphabet_size) % modulus
				if i == p_len - 1:
						continue
				lowerCamelCase					:      str          =   (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					:      Optional[Any]          =   (
				    (text_hash - ord(text[i]      ) * modulus_power) * alphabet_size
				    + ord(text[i + p_len]      )
				) % modulus
		return False
def 		UpperCAmelCase (		):
		lowerCamelCase					:      Optional[Any]          =   """abc1abc12"""
		lowerCamelCase					:      Tuple          =   """alskfjaldsabc1abc1abc12k23adsfabcabc"""
		lowerCamelCase					:      Optional[int]          =   """alskfjaldsk23adsfabcabc"""
		assert rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      ) and not rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      )

		# Test 2)
		lowerCamelCase					:      Optional[int]          =   """ABABX"""
		lowerCamelCase					:      List[Any]          =   """ABABZABABYABABX"""
		assert rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      )

		# Test 3)
		lowerCamelCase					:      List[str]          =   """AAAB"""
		lowerCamelCase					:      Dict          =   """ABAAAAAB"""
		assert rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      )

		# Test 4)
		lowerCamelCase					:      Optional[Any]          =   """abcdabcy"""
		lowerCamelCase					:      str          =   """abcxabcdabxabcdabcdabcy"""
		assert rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      )

		# Test 5)
		lowerCamelCase					:      Any          =   """Lü"""
		lowerCamelCase					:      Optional[Any]          =   """Lüsai"""
		assert rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      )
		lowerCamelCase					:      Optional[Any]          =   """Lue"""
		assert not rabin_karp(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE      )
		print('Success.'      )


if __name__ == "__main__":
					test_rabin_karp()








"""simple docstring"""

import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A						=			logging.getLogger(__name__)






class 	_lowercase       :




	def __init__(			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			if not self.initialized:
					lowerCamelCase					:      Optional[int]          =   RagRetriever(
					    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
					lowerCamelCase					:      Union[str, Any]          =   True




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.retriever.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   self.retriever._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return doc_ids, retrieved_doc_embeds






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Dict:
			if index is not None and index.is_initialized() and len(UpperCAmelCase_ ) > 0:
					raise ValueError(
					    'When using Ray for distributed fine-tuning, '
					    'you\'ll need to provide the paths instead, '
					    'as the dataset and the index are loaded '
					    'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
			super().__init__(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
			lowerCamelCase					:      List[str]          =   retrieval_workers
			if len(self.retrieval_workers ) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
					        for worker in self.retrieval_workers
					    ] )




	def       _UpperCamelCase   (			self )		->							Dict:
			logger.info('initializing retrieval' )

			if len(self.retrieval_workers ) > 0:
					ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
			else:
					# Non-distributed training. Load index into this same process.
					self.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if len(self.retrieval_workers ) > 0:
					# Select a random retrieval actor.
					lowerCamelCase					:      Tuple          =   self.retrieval_workers[random.randint(0		,			len(self.retrieval_workers ) - 1 )]
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   ray.get(random_worker.retrieve.remote(UpperCAmelCase_		,			UpperCAmelCase_ ) )
			else:
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			return super(UpperCAmelCase_		,			cls ).get_tokenizers(UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   kwargs.pop('config'		,			UpperCAmelCase_ ) or RagConfig.from_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   RagTokenizer.from_pretrained(UpperCAmelCase_		,			config=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   rag_tokenizer.question_encoder
			lowerCamelCase					:      Union[str, Any]          =   rag_tokenizer.generator
			if indexed_dataset is not None:
					lowerCamelCase					:      int          =   'custom'
					lowerCamelCase					:      int          =   CustomHFIndex(config.retrieval_vector_size		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   cls._build_index(UpperCAmelCase_ )
			return cls(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			retrieval_workers=UpperCAmelCase_		,			index=UpperCAmelCase_		,			)






