


from __future__ import annotations

import unittest
from collections.abc import Iterator







class     lowerCamelCase__		:






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    label
						UpperCamelCase__   =    parent
						UpperCamelCase__   =    None
						UpperCamelCase__   =    None







class     lowerCamelCase__		:






			"""simple docstring"""






			def __init__(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    None




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    None




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return self.root is None




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._put(self.root ,  snake_case       )




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case = None       ):

						'''simple docstring'''


						if node is None:
									UpperCamelCase__   =    Node(snake_case ,  snake_case       )
						else:
									if label < node.label:
												UpperCamelCase__   =    self._put(node.left ,  snake_case ,  snake_case       )
									elif label > node.label:
												UpperCamelCase__   =    self._put(node.right ,  snake_case ,  snake_case       )
									else:
												UpperCamelCase__   =    F'''Node with label {label} already exists'''
												raise Exception(snake_case       )

						return node




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						return self._search(self.root ,  snake_case       )




			def 			snake_case__			(							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						if node is None:
									UpperCamelCase__   =    F'''Node with label {label} does not exist'''
									raise Exception(snake_case       )
						else:
									if label < node.label:
												UpperCamelCase__   =    self._search(node.left ,  snake_case       )
									elif label > node.label:
												UpperCamelCase__   =    self._search(node.right ,  snake_case       )

						return node




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.search(snake_case       )
						if node.right and node.left:
									UpperCamelCase__   =    self._get_lowest_node(node.right       )
									UpperCamelCase__   =    node.left
									UpperCamelCase__   =    node.right
									UpperCamelCase__   =    lowest_node
									if node.right:
												UpperCamelCase__   =    lowest_node
									self._reassign_nodes(snake_case ,  snake_case       )
						elif not node.right and node.left:
									self._reassign_nodes(snake_case ,  node.left       )
						elif node.right and not node.left:
									self._reassign_nodes(snake_case ,  node.right       )
						else:
									self._reassign_nodes(snake_case ,  snake_case       )




			def 			snake_case__			(							self ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						if new_children:
									UpperCamelCase__   =    node.parent

						if node.parent:
									if node.parent.right == node:
												UpperCamelCase__   =    new_children
									else:
												UpperCamelCase__   =    new_children
						else:
									UpperCamelCase__   =    new_children




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						if node.left:
									UpperCamelCase__   =    self._get_lowest_node(node.left       )
						else:
									UpperCamelCase__   =    node
									self._reassign_nodes(snake_case ,  node.right       )

						return lowest_node




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						try:
									self.search(snake_case       )
									return True
						except Exception:
									return False




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						if self.root is None:
									raise Exception("Binary search tree is empty"       )

						UpperCamelCase__   =    self.root
						while node.right is not None:
									UpperCamelCase__   =    node.right

						return node.label




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						if self.root is None:
									raise Exception("Binary search tree is empty"       )

						UpperCamelCase__   =    self.root
						while node.left is not None:
									UpperCamelCase__   =    node.left

						return node.label




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return self._inorder_traversal(self.root       )




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						if node is not None:
									yield from self._inorder_traversal(node.left       )
									yield node
									yield from self._inorder_traversal(node.right       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return self._preorder_traversal(self.root       )







			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						if node is not None:
									yield node
									yield from self._preorder_traversal(node.left       )
									yield from self._preorder_traversal(node.right       )







class     lowerCamelCase__		(							unittest.TestCase ):






			"""simple docstring"""






			@staticmethod
			def 			snake_case__			(							):

						'''simple docstring'''


						UpperCamelCase__   =    BinarySearchTree()
						t.put(8       )
						t.put(3       )
						t.put(6       )
						t.put(1       )
						t.put(10       )
						t.put(14       )
						t.put(13       )
						t.put(4       )
						t.put(7       )
						t.put(5       )

						return t




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    BinarySearchTree()
						assert t.is_empty()

						t.put(8       )
						assert t.root is not None
						assert t.root.parent is None
						assert t.root.label == 8

						t.put(10       )
						assert t.root.right is not None
						assert t.root.right.parent == t.root
						assert t.root.right.label == 10

						t.put(3       )
						assert t.root.left is not None
						assert t.root.left.parent == t.root
						assert t.root.left.label == 3

						t.put(6       )
						assert t.root.left.right is not None
						assert t.root.left.right.parent == t.root.left
						assert t.root.left.right.label == 6

						t.put(1       )
						assert t.root.left.left is not None
						assert t.root.left.left.parent == t.root.left
						assert t.root.left.left.label == 1

						with self.assertRaises(snake_case       ):  # noqa: B017
									t.put(1       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						UpperCamelCase__   =    t.search(6       )
						assert node.label == 6

						UpperCamelCase__   =    t.search(13       )
						assert node.label == 13

						with self.assertRaises(snake_case       ):  # noqa: B017
									t.search(2       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						t.remove(13       )
						assert t.root is not None
						assert t.root.right is not None
						assert t.root.right.right is not None
						assert t.root.right.right.right is None
						assert t.root.right.right.left is None

						t.remove(7       )
						assert t.root.left is not None
						assert t.root.left.right is not None
						assert t.root.left.right.left is not None
						assert t.root.left.right.right is None
						assert t.root.left.right.left.label == 4

						t.remove(6       )
						assert t.root.left.left is not None
						assert t.root.left.right.right is not None
						assert t.root.left.left.label == 1
						assert t.root.left.right.label == 4
						assert t.root.left.right.right.label == 5
						assert t.root.left.right.left is None
						assert t.root.left.left.parent == t.root.left
						assert t.root.left.right.parent == t.root.left

						t.remove(3       )
						assert t.root is not None
						assert t.root.left.label == 4
						assert t.root.left.right.label == 5
						assert t.root.left.left.label == 1
						assert t.root.left.parent == t.root
						assert t.root.left.left.parent == t.root.left
						assert t.root.left.right.parent == t.root.left

						t.remove(4       )
						assert t.root.left is not None
						assert t.root.left.left is not None
						assert t.root.left.label == 5
						assert t.root.left.right is None
						assert t.root.left.left.label == 1
						assert t.root.left.parent == t.root
						assert t.root.left.left.parent == t.root.left




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						t.remove(3       )
						assert t.root is not None
						assert t.root.left is not None
						assert t.root.left.left is not None
						assert t.root.left.right is not None
						assert t.root.left.right.left is not None
						assert t.root.left.right.right is not None
						assert t.root.left.label == 4
						assert t.root.left.right.label == 6
						assert t.root.left.left.label == 1
						assert t.root.left.right.right.label == 7
						assert t.root.left.right.left.label == 5
						assert t.root.left.parent == t.root
						assert t.root.left.right.parent == t.root.left
						assert t.root.left.left.parent == t.root.left
						assert t.root.left.right.left.parent == t.root.left.right




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()
						t.empty()
						assert t.root is None




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()
						assert not t.is_empty()

						t.empty()
						assert t.is_empty()




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						assert t.exists(6       )
						assert not t.exists(-1       )




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						assert t.get_max_label() == 14

						t.empty()
						with self.assertRaises(snake_case       ):  # noqa: B017
									t.get_max_label()




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						assert t.get_min_label() == 1

						t.empty()
						with self.assertRaises(snake_case       ):  # noqa: B017
									t.get_min_label()




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						UpperCamelCase__   =    [i.label for i in t.inorder_traversal()]
						assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]







			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    self._get_binary_search_tree()

						UpperCamelCase__   =    [i.label for i in t.preorder_traversal()]
						assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]



def     UpperCamelCase_(			)->						None:
			UpperCamelCase__   =    BinarySearchTree()
			t.put(8					)
			t.put(3					)
			t.put(6					)
			t.put(1					)
			t.put(10					)
			t.put(14					)
			t.put(13					)
			t.put(4					)
			t.put(7					)
			t.put(5					)

			print(
			    "\n            8\n           / \\\n          3   10\n         / \\    \\\n        1   6    14\n           / \\   /\n          4   7 13\n           \\\n            5\n        "					)

			print("Label 6 exists:"			,       t.exists(6					)					)
			print("Label 13 exists:"			,       t.exists(13					)					)
			print("Label -1 exists:"			,       t.exists(-1					)					)
			print("Label 12 exists:"			,       t.exists(12					)					)

			# Prints all the elements of the list in inorder traversal
			UpperCamelCase__   =    [i.label for i in t.inorder_traversal()]
			print("Inorder traversal:"			,       _A					)

			# Prints all the elements of the list in preorder traversal
			UpperCamelCase__   =    [i.label for i in t.preorder_traversal()]
			print("Preorder traversal:"			,       _A					)

			print("Max. label:"			,       t.get_max_label()					)
			print("Min. label:"			,       t.get_min_label()					)

			# Delete elements
			print("\nDeleting elements 13, 10, 8, 3, 6, 14"					)
			print(
			    "\n          4\n         / \\\n        1   7\n             \\\n              5\n        "					)
			t.remove(13					)
			t.remove(10					)
			t.remove(8					)
			t.remove(3					)
			t.remove(6					)
			t.remove(14					)

			# Prints all the elements of the list in inorder traversal after delete
			UpperCamelCase__   =    [i.label for i in t.inorder_traversal()]
			print("Inorder traversal after delete:"			,       _A					)

			# Prints all the elements of the list in preorder traversal after delete
			UpperCamelCase__   =    [i.label for i in t.preorder_traversal()]
			print("Preorder traversal after delete:"			,       _A					)

			print("Max. label:"			,       t.get_max_label()					)
			print("Min. label:"			,       t.get_min_label()					)


if __name__ == "__main__":
						binary_search_tree_example()






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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


__UpperCamelCase      						=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase      						=    'cuda' if torch.cuda.is_available() else 'cpu'
def     UpperCamelCase_(			_A				:str			,       _A				:Tuple=1_00			,       _A				:str=" "					)->						List[str]:
			UpperCamelCase__   =    text.split(_A					)
			return [character.join(text[i : i + n]					).strip() for i in range(0			,       len(_A					)			,       _A					)]
def     UpperCamelCase_(			_A				:dict					)->						dict:
			UpperCamelCase__, UpperCamelCase__   =    [], []
			for title, text in zip(documents["title"]			,       documents["text"]					):
						if text is not None:
									for passage in split_text(_A					):
												titles.append(title if title is not None else ""					)
												texts.append(_A					)
			return {"title": titles, "text": texts}
def     UpperCamelCase_(			_A				:dict			,       _A				:DPRContextEncoder			,       _A				:DPRContextEncoderTokenizerFast					)->						dict:
			UpperCamelCase__   =    ctx_tokenizer(
			    documents["title"]			,       documents["text"]			,       truncation=_A			,       padding="longest"			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    ctx_encoder(input_ids.to(device=_A					)			,       return_dict=_A					).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}
def     UpperCamelCase_(			_A				:"RagExampleArguments"			,       _A				:"ProcessingArguments"			,       _A				:"IndexHnswArguments"			,       )->						List[str]:
			######################################
			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
			UpperCamelCase__   =    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
			UpperCamelCase__   =    dataset.map(_A			,       batched=_A			,       num_proc=processing_args.num_proc					)

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

			# And finally save your dataset
			UpperCamelCase__   =    os.path.join(rag_example_args.output_dir			,       "my_knowledge_dataset"					)
			dataset.save_to_disk(_A					)
			# 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
			UpperCamelCase__   =    faiss.IndexHNSWFlat(index_hnsw_args.d			,       index_hnsw_args.m			,       faiss.METRIC_INNER_PRODUCT					)
			dataset.add_faiss_index("embeddings"			,       custom_index=_A					)

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

			######################################
			logger.info("Step 3 - Load RAG"					)
			######################################

			# Easy way to load the model
			UpperCamelCase__   =    RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name			,       index_name="custom"			,       indexed_dataset=_A					)
			UpperCamelCase__   =    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_A					)
			UpperCamelCase__   =    RagTokenizer.from_pretrained(rag_example_args.rag_model_name					)

			# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
			# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

			######################################
			logger.info("Step 4 - Have fun"					)
			######################################

			UpperCamelCase__   =    rag_example_args.question or "What does Moses' rod turn into ?"
			UpperCamelCase__   =    tokenizer.question_encoder(_A			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    model.generate(_A					)
			UpperCamelCase__   =    tokenizer.batch_decode(_A			,       skip_special_tokens=_A					)[0]
			logger.info("Q: " + question					)
			logger.info("A: " + generated_string					)







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   str					=						field(
			    default=str(Path(UpperCAmelCase ).parent / 'test_data' / 'my_knowledge_dataset.csv' )   ,		metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}   ,		)
			_UpperCamelCase						:   Optional[str]					=						field(
			    default=UpperCAmelCase   ,		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=UpperCAmelCase   ,		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=UpperCAmelCase   ,		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)

						__UpperCamelCase      						=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						__UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase      						=    parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												__UpperCamelCase      						=    rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)


