



from __future__ import annotations

from collections import deque




class 							lowerCAmelCase__  :







					def __init__(       self      :     Optional[Any]			,		_A      :     list[str]):
											A__	:							list[dict]						    = []
											self.adlist.append(
											    {"value": "", "next_states": [], "fail_state": 0, "output": []})

											for keyword in keywords:
																	self.add_keyword(_A)
											self.set_fail_transitions()







					def  _lowercase			(       self      :     Dict			,		_A      :     int			,		_A      :     str):
											for state in self.adlist[current_state]["next_states"]:
																	if char == self.adlist[state]["value"]:
																							return state
											return None







					def  _lowercase			(       self      :     int			,		_A      :     str):
											A__	:							Optional[int]						    = 0
											for character in keyword:
																	A__	:							Optional[int]						    = self.find_next_state(_A			,		_A)
																	if next_state is None:
																							self.adlist.append(
																							    {
																							        "value": character,
																							        "next_states": [],
																							        "fail_state": 0,
																							        "output": [],
																							    })
																							self.adlist[current_state]["next_states"].append(len(self.adlist) - 1)
																							A__	:							Tuple						    = len(self.adlist) - 1
																	else:
																							A__	:							int						    = next_state
											self.adlist[current_state]["output"].append(_A)







					def  _lowercase			(       self      :     int):
											A__	:							deque						    = deque()
											for node in self.adlist[0]["next_states"]:
																	q.append(_A)
																	A__	:							Optional[int]						    = 0
											while q:
																	A__	:							Dict						    = q.popleft()
																	for child in self.adlist[r]["next_states"]:
																							q.append(_A)
																							A__	:							str						    = self.adlist[r]["fail_state"]
																							while (
																							    self.find_next_state(_A			,		self.adlist[child]["value"]) is None
																							    and state != 0
																							):
																													A__	:							int						    = self.adlist[state]["fail_state"]
																							A__	:							List[Any]						    = self.find_next_state(
																							    _A			,		self.adlist[child]["value"])
																							if self.adlist[child]["fail_state"] is None:
																													A__	:							List[Any]						    = 0
																							A__	:							Tuple						    = (
																							    self.adlist[child]["output"]
																							    + self.adlist[self.adlist[child]["fail_state"]]["output"]
																							)







					def  _lowercase			(       self      :     Any			,		_A      :     str):
											A__	:							dict						    = {}  # returns a dict with keywords and list of its occurrences
											A__	:							Optional[Any]						    = 0
											for i in range(len(_A)):
																	while (
																	    self.find_next_state(_A			,		string[i]) is None
																	    and current_state != 0
																	):
																							A__	:							Optional[Any]						    = self.adlist[current_state]["fail_state"]
																	A__	:							str						    = self.find_next_state(_A			,		string[i])
																	if next_state is None:
																							A__	:							List[str]						    = 0
																	else:
																							A__	:							int						    = next_state
																							for key in self.adlist[current_state]["output"]:
																													if key not in result:
																																			A__	:							Any						    = []
																													result[key].append(i - len(_A) + 1)
											return result


if __name__ == "__main__":
		import doctest

		doctest.testmod()




import logging
import random

import ray

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


snake_case    :						Any  =						logging.getLogger(__name__)




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]):
											A__	:							Dict						    = False







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if not self.initialized:
																	A__	:							List[Any]						    = RagRetriever(
																	    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
																	A__	:							Dict						    = True







					def  _lowercase			(       self      :     Union[str, Any]):
											self.retriever.index.init_index()







					def  _lowercase			(       self      :     List[str]			,		_A      :     Optional[int]			,		_A      :     List[str]):
											A__    ,       A__	:							str						    = self.retriever._main_retrieve(_A			,		_A)
											return doc_ids, retrieved_doc_embeds




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Union[str, Any]			,		_A      :     Tuple			,		_A      :     int			,		_A      :     Tuple			,		_A      :     Union[str, Any]=None):
											if index is not None and index.is_initialized() and len(_A) > 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__(
											    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
											A__	:							str						    = retrieval_workers
											if len(self.retrieval_workers) > 0:
																	ray.get(
																	    [
																	        worker.create_rag_retriever.remote(_A			,		_A			,		_A			,		_A)
																	        for worker in self.retrieval_workers
																	    ])







					def  _lowercase			(       self      :     Optional[Any]):
											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  _lowercase			(       self      :     Union[str, Any]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if len(self.retrieval_workers) > 0:
																	# Select a random retrieval actor.
																	A__	:							Optional[Any]						    = self.retrieval_workers[random.randint(0			,		len(self.retrieval_workers) - 1)]
																	A__    ,       A__	:							List[Any]						    = ray.get(random_worker.retrieve.remote(_A			,		_A))
											else:
																	A__    ,       A__	:							Union[str, Any]						    = self._main_retrieve(_A			,		_A)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_A)







					@classmethod
					def  _lowercase			(       cls      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											return super(_A			,		cls).get_tokenizers(_A			,		_A			,		**_A)







					@classmethod
					def  _lowercase			(       cls      :     Any			,		_A      :     Union[str, Any]			,		_A      :     Optional[Any]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											A__	:							str						    = kwargs.pop("config"			,		_A) or RagConfig.from_pretrained(_A			,		**_A)
											A__	:							int						    = RagTokenizer.from_pretrained(_A			,		config=_A)
											A__	:							List[str]						    = rag_tokenizer.question_encoder
											A__	:							str						    = rag_tokenizer.generator
											if indexed_dataset is not None:
																	A__	:							Tuple						    = "custom"
																	A__	:							Tuple						    = CustomHFIndex(config.retrieval_vector_size			,		_A)
											else:
																	A__	:							Union[str, Any]						    = cls._build_index(_A)
											return cls(
											    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		retrieval_workers=_A			,		index=_A			,		)