


'''simple docstring'''
from __future__ import annotations

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		list[tuple[int, int]]

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		[
    [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],
]

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													A_			    =		pos_x
													A_			    =		pos_y
													A_			    =		(pos_y, pos_x)
													A_			    =		goal_x
													A_			    =		goal_y
													A_			    =		g_cost
													A_			    =		parent
													A_			    =		self.calculate_heuristic()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													A_			    =		Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase	)
													A_			    =		Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_99_99 ,  lowerCAmelCase	)

													A_			    =		[self.start]
													A_			    =		[]

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str	):
													while self.open_nodes:
																				# Open Nodes are sorted using __lt__
																				self.open_nodes.sort()
																				A_			    =		self.open_nodes.pop(0	)

																				if current_node.pos == self.target.pos:
																											A_			    =		True
																											return self.retrace_path(lowerCAmelCase	)

																				self.closed_nodes.append(lowerCAmelCase	)
																				A_			    =		self.get_successors(lowerCAmelCase	)

																				for child_node in successors:
																											if child_node in self.closed_nodes:
																																		continue

																											if child_node not in self.open_nodes:
																																		self.open_nodes.append(lowerCAmelCase	)
																											else:
																																		# retrieve the best current path
																																		A_			    =		self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

																																		if child_node.g_cost < better_node.g_cost:
																																									self.open_nodes.append(lowerCAmelCase	)
																																		else:
																																									self.open_nodes.append(lowerCAmelCase	)

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






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													A_			    =		[]
													for action in delta:
																				A_			    =		parent.pos_x + action[1]
																				A_			    =		parent.pos_y + action[0]

																				if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCAmelCase	) - 1):
																											continue

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

																				successors.append(
																				    Node(
																				        lowerCAmelCase ,  lowerCAmelCase ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase ,  )	)
													return successors






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													A_			    =		node
													A_			    =		[]
													while current_node is not None:
																				path.append((current_node.pos_y, current_node.pos_x)	)
																				A_			    =		current_node.parent
													path.reverse()
													return path


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		(0, 0)
							__SCREAMING_SNAKE_CASE					:				Dict									=		(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)









'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE					:				Dict									=		'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







def       a_				(	UpperCamelCase_ ):
							A_    ,					A_			    =		[], []
							for title, text in zip(documents["title"]						,  documents["text"] ):
														if text is not None:
																					for passage in split_text(UpperCamelCase_ ):
																												titles.append(title if title is not None else "" )
																												texts.append(UpperCamelCase_ )
							return {"title": titles, "text": texts}







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		ctx_tokenizer(
							    documents["title"]						,  documents["text"]						,  truncation=UpperCamelCase_						,  padding="longest"						,  return_tensors="pt" )["input_ids"]
							A_			    =		ctx_encoder(input_ids.to(device=UpperCamelCase_ )						,  return_dict=UpperCamelCase_ ).pooler_output
							return {"embeddings": embeddings.detach().cpu().numpy()}







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							logger.info("Step 1 - Create the dataset" )
							######################################

							# The dataset needed for RAG must have three columns:
							# - title (string): title of the document
							# - text (string): text of a passage of the document
							# - embeddings (array of dimension d): DPR representation of the passage
							# Let's say you have documents in tab-separated csv files with columns "title" and "text"
							assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"

							# You can load a Dataset object this way
							A_			    =		load_dataset(
							    "csv"						,  data_files=[rag_example_args.csv_path]						,  split="train"						,  delimiter="\t"						,  column_names=["title", "text"] )

							# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

							# Then split the documents into passages of 100 words
							A_			    =		dataset.map(UpperCamelCase_						,  batched=UpperCamelCase_						,  num_proc=processing_args.num_proc )

							# And compute the embeddings
							A_			    =		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCamelCase_ )
							A_			    =		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
							A_			    =		Features(
							    {"text": Value("string" ), "title": Value("string" ), "embeddings": Sequence(Value("float32" ) )} )  # optional, save as float32 instead of float64 to save space
							A_			    =		dataset.map(
							    partial(UpperCamelCase_						,  ctx_encoder=UpperCamelCase_						,  ctx_tokenizer=UpperCamelCase_ )						,  batched=UpperCamelCase_						,  batch_size=processing_args.batch_size						,  features=UpperCamelCase_						,  )

							# And finally save your dataset
							A_			    =		os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset" )
							dataset.save_to_disk(UpperCamelCase_ )
							# from datasets import load_from_disk
							# dataset = load_from_disk(passages_path)  # to reload the dataset

							######################################
							logger.info("Step 2 - Index the dataset" )
							######################################

							# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
							A_			    =		faiss.IndexHNSWFlat(index_hnsw_args.d						,  index_hnsw_args.m						,  faiss.METRIC_INNER_PRODUCT )
							dataset.add_faiss_index("embeddings"						,  custom_index=UpperCamelCase_ )

							# And save the index
							A_			    =		os.path.join(rag_example_args.output_dir						,  "my_knowledge_dataset_hnsw_index.faiss" )
							dataset.get_index("embeddings" ).save(UpperCamelCase_ )
							# dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       str   						=field(
						    default=str(Path(lowercase						).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"						)				,			metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}				,			)
						_UpperCAmelCase		:       Optional[str]   						=field(
						    default=lowercase				,			metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default="facebook/rag-sequence-nq"				,			metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default="facebook/dpr-ctx_encoder-multiset-base"				,			metadata={
						        "help": (
						            "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
						            " 'facebook/dpr-ctx_encoder-multiset-base'"
						        )
						    }				,			)
						_UpperCAmelCase		:       Optional[str]   						=field(
						    default=str(Path(lowercase						).parent / "test_run" / "dummy-kb"						)				,			metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}				,			)






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			metadata={
						        "help": "The number of processes to use to split the documents into passages. Default is single process."
						    }				,			)
						_UpperCAmelCase		:       int   						=field(
						    default=16				,			metadata={
						        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
						    }				,			)






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       int   						=field(
						    default=768				,			metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}				,			)
						_UpperCAmelCase		:       int   						=field(
						    default=128				,			metadata={
						        "help": (
						            "The number of bi-directional links created for every new element during the HNSW index construction."
						        )
						    }				,			)


if __name__ == "__main__":
							logging.basicConfig(level=logging.WARNING)
							logger.setLevel(logging.INFO)

							__SCREAMING_SNAKE_CASE					:				List[str]									=		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Dict									=		parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														__SCREAMING_SNAKE_CASE					:				List[str]									=		rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





