

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,
)


SCREAMING_SNAKE_CASE		:List[Any]				  =    logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE		:Optional[int]				  =    """cuda""" if torch.cuda.is_available() else """cpu"""




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=1_0_0 ,	SCREAMING_SNAKE_CASE_=" " )->	List[str]:



    """simple docstring"""






    UpperCamelCase_      =	text.split(SCREAMING_SNAKE_CASE_ )
    return [character.join(text[i : i + n] ).strip() for i in range(0 ,	len(SCREAMING_SNAKE_CASE_ ) ,	SCREAMING_SNAKE_CASE_ )]




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	dict:



    """simple docstring"""






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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	dict:



    """simple docstring"""






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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	)->	Union[str, Any]:



    """simple docstring"""






    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(SCREAMING_SNAKE_CASE_ ,	batched=SCREAMING_SNAKE_CASE_ ,	num_proc=processing_args.num_proc )

    # And compute the embeddings
    UpperCamelCase_      =	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=SCREAMING_SNAKE_CASE_ )
    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(SCREAMING_SNAKE_CASE_ ,	ctx_encoder=SCREAMING_SNAKE_CASE_ ,	ctx_tokenizer=SCREAMING_SNAKE_CASE_ ) ,	batched=SCREAMING_SNAKE_CASE_ ,	batch_size=processing_args.batch_size ,	features=SCREAMING_SNAKE_CASE_ ,	)

    # And finally save your dataset
    UpperCamelCase_      =	os.path.join(rag_example_args.output_dir ,	"my_knowledge_dataset" )
    dataset.save_to_disk(SCREAMING_SNAKE_CASE_ )
    # 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=SCREAMING_SNAKE_CASE_ )

    # And save the index
    UpperCamelCase_      =	os.path.join(rag_example_args.output_dir ,	"my_knowledge_dataset_hnsw_index.faiss" )
    dataset.get_index("embeddings" ).save(SCREAMING_SNAKE_CASE_ )
    # 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=SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,	retriever=SCREAMING_SNAKE_CASE_ )
    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(SCREAMING_SNAKE_CASE_ ,	return_tensors="pt" )["input_ids"]
    UpperCamelCase_      =	model.generate(SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ ,	skip_special_tokens=SCREAMING_SNAKE_CASE_ )[0]
    logger.info("Q: " + question )
    logger.info("A: " + generated_string )






@dataclass
class 	__magic_name__				:
     UpperCamelCase_  :str								=			field(
         default=str(Path(snake_case							).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=snake_case ,			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=snake_case ,			metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""} ,			)






@dataclass
class 	__magic_name__				:
     UpperCamelCase_  :Optional[int]								=			field(
         default=snake_case ,			metadata={
             """help""": """The number of processes to use to split the documents into passages. Default is single process."""
         } ,			)
     UpperCamelCase_  :int								=			field(
         default=1_6 ,			metadata={
             """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
         } ,			)






@dataclass
class 	__magic_name__				:
     UpperCamelCase_  :int								=			field(
         default=7_6_8 ,			metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""} ,			)
     UpperCamelCase_  :int								=			field(
         default=1_2_8 ,			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		:int				  =    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 SCREAMING_SNAKE_CASE  ,       SCREAMING_SNAKE_CASE  ,       SCREAMING_SNAKE_CASE		:Any				  =    parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  SCREAMING_SNAKE_CASE		:str				  =    rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)



from __future__ import annotations




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	str:



    """simple docstring"""






    global graph, reversed_graph, scc, component, visit, stack
    if visit[u]:
        return
    UpperCamelCase_      =	True
    for v in graph[u]:
        dfs(SCREAMING_SNAKE_CASE_ )
    stack.append(SCREAMING_SNAKE_CASE_ )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	Dict:



    """simple docstring"""






    global graph, reversed_graph, scc, component, visit, stack
    if visit[u]:
        return
    UpperCamelCase_      =	True
    component.append(SCREAMING_SNAKE_CASE_ )
    for v in reversed_graph[u]:
        dfsa(SCREAMING_SNAKE_CASE_ )




def  lowerCAmelCase(			)->	Dict:



    """simple docstring"""






    global graph, reversed_graph, scc, component, visit, stack
    for i in range(SCREAMING_SNAKE_CASE_ ):
        dfs(SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	[False] * n
    for i in stack[::-1]:
        if visit[i]:
            continue
        UpperCamelCase_      =	[]
        dfsa(SCREAMING_SNAKE_CASE_ )
        scc.append(SCREAMING_SNAKE_CASE_ )
    return scc


if __name__ == "__main__":
 # n - no of nodes, m - no of edges
 SCREAMING_SNAKE_CASE  ,       SCREAMING_SNAKE_CASE		:str				  =    list(map(int, input().strip().split()))

 SCREAMING_SNAKE_CASE		:list[list[int]]				  =    [[] for _ in range(n)]  # graph
 SCREAMING_SNAKE_CASE		:list[list[int]]				  =    [[] for i in range(n)]  # reversed graph
 # input graph data (edges)
 for _ in range(m):
  SCREAMING_SNAKE_CASE  ,       SCREAMING_SNAKE_CASE		:str				  =    list(map(int, input().strip().split()))
  graph[u].append(v)
  reversed_graph[v].append(u)

 SCREAMING_SNAKE_CASE		:list[int]				  =    []
 SCREAMING_SNAKE_CASE		:list[bool]				  =    [False] * n
 SCREAMING_SNAKE_CASE		:list[int]				  =    []
 SCREAMING_SNAKE_CASE		:list[int]				  =    []
 print(kosaraju())
