



from __future__ import annotations

import time

snake_case    :						Union[str, Any]  =						list[tuple[int, int]]

snake_case    :						List[str]  =						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

snake_case    :						Optional[Any]  =						[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right




class 							lowerCAmelCase__  :







					def __init__(       self      :     str			,		_A      :     int			,		_A      :     int			,		_A      :     int			,		_A      :     int			,		_A      :     Node | None):
											A__	:							int						    = pos_x
											A__	:							str						    = pos_y
											A__	:							Optional[int]						    = (pos_y, pos_x)
											A__	:							Dict						    = goal_x
											A__	:							Optional[int]						    = goal_y
											A__	:							Optional[Any]						    = parent




class 							lowerCAmelCase__  :







					def __init__(       self      :     List[str]			,		_A      :     tuple[int, int]			,		_A      :     tuple[int, int]):
											A__	:							int						    = Node(start[1]			,		start[0]			,		goal[1]			,		goal[0]			,		_A)
											A__	:							int						    = Node(goal[1]			,		goal[0]			,		goal[1]			,		goal[0]			,		_A)

											A__	:							List[Any]						    = [self.start]
											A__	:							List[Any]						    = False







					def  _lowercase			(       self      :     Optional[Any]):
											while self.node_queue:
																	A__	:							Optional[int]						    = self.node_queue.pop(0)

																	if current_node.pos == self.target.pos:
																							A__	:							str						    = True
																							return self.retrace_path(_A)

																	A__	:							List[str]						    = self.get_successors(_A)

																	for node in successors:
																							self.node_queue.append(_A)

											if not self.reached:
																	return [self.start.pos]
											return None







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     Node):
											A__	:							str						    = []
											for action in delta:
																	A__	:							str						    = parent.pos_x + action[1]
																	A__	:							Tuple						    = parent.pos_y + action[0]
																	if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(_A) - 1):
																							continue

																	if grid[pos_y][pos_x] != 0:
																							continue

																	successors.append(
																	    Node(_A			,		_A			,		self.target.pos_y			,		self.target.pos_x			,		_A))
											return successors







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     Node | None):
											A__	:							int						    = node
											A__	:							List[str]						    = []
											while current_node is not None:
																	path.append((current_node.pos_y, current_node.pos_x))
																	A__	:							List[str]						    = current_node.parent
											path.reverse()
											return path




class 							lowerCAmelCase__  :







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







					def  _lowercase			(       self      :     List[str]):
											while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																	A__	:							str						    = self.fwd_bfs.node_queue.pop(0)
																	A__	:							str						    = self.bwd_bfs.node_queue.pop(0)

																	if current_bwd_node.pos == current_fwd_node.pos:
																							A__	:							int						    = True
																							return self.retrace_bidirectional_path(
																							    _A			,		_A)

																	A__	:							List[str]						    = current_bwd_node
																	A__	:							Dict						    = current_fwd_node

																	A__	:							Dict						    = {
																	    self.fwd_bfs: self.fwd_bfs.get_successors(_A),
																	    self.bwd_bfs: self.bwd_bfs.get_successors(_A),
																	}

																	for bfs in [self.fwd_bfs, self.bwd_bfs]:
																							for node in successors[bfs]:
																													bfs.node_queue.append(_A)

											if not self.reached:
																	return [self.fwd_bfs.start.pos]
											return None







					def  _lowercase			(       self      :     List[str]			,		_A      :     Node			,		_A      :     Node):
											A__	:							Dict						    = self.fwd_bfs.retrace_path(_A)
											A__	:							Tuple						    = self.bwd_bfs.retrace_path(_A)
											bwd_path.pop()
											bwd_path.reverse()
											A__	:							Optional[int]						    = fwd_path + bwd_path
											return path


if __name__ == "__main__":
		# all coordinates are given in format [y,x]
		import doctest

		doctest.testmod()
		snake_case    :						Union[str, Any]  =						(0, 0)
		snake_case    :						Optional[int]  =						(len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		snake_case    :						Tuple  =						time.time()
		snake_case    :						Union[str, Any]  =						BreadthFirstSearch(init, goal)
		snake_case    :						Optional[Any]  =						bfs.search()
		snake_case    :						Dict  =						time.time() - start_bfs_time

		print('Unidirectional BFS computation time : ', bfs_time)

		snake_case    :						int  =						time.time()
		snake_case    :						str  =						BidirectionalBreadthFirstSearch(init, goal)
		snake_case    :						int  =						bd_bfs.search()
		snake_case    :						Union[str, Any]  =						time.time() - start_bd_bfs_time

		print('Bidirectional BFS computation time : ', bd_bfs_time)




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			,		)