"""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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


__UpperCamelCase								=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase								=    '''cuda''' if torch.cuda.is_available() else '''cpu'''







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase=100		,      UpperCAmelCase=" "      )       ->   List[str]:
     snake_case_       =      text.split(_UpperCAmelCase      )
     return [character.join(text[i : i + n]      ).strip() for i in range(0		,      len(_UpperCAmelCase      )		,      _UpperCAmelCase      )]







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   dict:
     snake_case_ ,			snake_case_       =      [], []
     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       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   dict:
     snake_case_       =      ctx_tokenizer(
         documents['title']		,      documents['text']		,      truncation=_UpperCAmelCase		,      padding='longest'		,      return_tensors='pt'      )['input_ids']
     snake_case_       =      ctx_encoder(input_ids.to(device=_UpperCAmelCase      )		,      return_dict=_UpperCAmelCase      ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      )       ->   Union[str, Any]:
     ######################################
     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
     snake_case_       =      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
     snake_case_       =      dataset.map(_UpperCAmelCase		,      batched=_UpperCAmelCase		,      num_proc=processing_args.num_proc      )

     # And compute the embeddings
     snake_case_       =      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      ).to(device=_UpperCAmelCase      )
     snake_case_       =      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      )
     snake_case_       =      Features(
         {'text': Value('string'      ), 'title': Value('string'      ), 'embeddings': Sequence(Value('float32'      )      )}      )  # optional, save as float32 instead of float64 to save space
     snake_case_       =      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
     snake_case_       =      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
     snake_case_       =      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
     snake_case_       =      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

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

     # Easy way to load the model
     snake_case_       =      RagRetriever.from_pretrained(
         rag_example_args.rag_model_name		,      index_name='custom'		,      indexed_dataset=_UpperCAmelCase      )
     snake_case_       =      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name		,      retriever=_UpperCAmelCase      )
     snake_case_       =      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'      )
     ######################################

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




@dataclass
class 				UpperCamelCase      :
       SCREAMING_SNAKE_CASE_				=   field(
           default=str(Path(lowerCamelCase__     ).parent / "test_data" / "my_knowledge_dataset.csv"     )					,	metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}					,	)
       SCREAMING_SNAKE_CASE_				=   field(
           default=lowerCamelCase__					,	metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}					,	)
       SCREAMING_SNAKE_CASE_				=   field(
           default="facebook/rag-sequence-nq"					,	metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}					,	)
       SCREAMING_SNAKE_CASE_				=   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'"
               )
           }					,	)
       SCREAMING_SNAKE_CASE_				=   field(
           default=lowerCamelCase__					,	metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}					,	)




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




@dataclass
class 				UpperCamelCase      :
       SCREAMING_SNAKE_CASE_				=   field(
           default=7_6_8					,	metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}					,	)
       SCREAMING_SNAKE_CASE_				=   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)

    __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)

"""simple docstring"""


from __future__ import annotations

from queue import Queue







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   tuple[int, int]:
     a ^= b
     b ^= a
     a ^= b
     return a, b







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   list[list[int]]:
     snake_case_       =      1
     while (1 << j) < max_node:
          for i in range(1		,      max_node + 1      ):
               snake_case_       =      parent[j - 1][parent[j - 1][i]]
          j += 1
     return parent







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   int:
     # u must be deeper in the tree than v
     if level[u] < level[v]:
          snake_case_ ,			snake_case_       =      swap(UpperCAmelCase		,      UpperCAmelCase      )
     # making depth of u same as depth of v
     for i in range(18		,      -1		,      -1      ):
          if level[u] - (1 << i) >= level[v]:
               snake_case_       =      parent[i][u]
    # at the same depth if u==v that mean lca is found
     if u == v:
          return u
     # moving both nodes upwards till lca in found
     for i in range(18		,      -1		,      -1      ):
          if parent[i][u] not in [0, parent[i][v]]:
               snake_case_ ,			snake_case_       =      parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
     return parent[0][u]







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase = 1		,      )       ->   tuple[list[int], list[list[int]]]:
     snake_case_       =      0
     snake_case_       =      Queue(maxsize=UpperCAmelCase      )
     q.put(UpperCAmelCase      )
     while q.qsize() != 0:
          snake_case_       =      q.get()
          for v in graph[u]:
               if level[v] == -1:
                    snake_case_       =      level[u] + 1
                    q.put(UpperCAmelCase      )
                    snake_case_       =      u
     return level, parent







def       UpperCAmelCase					(	)       ->   None:
     snake_case_       =      13
     # initializing with 0
     snake_case_       =      [[0 for _ in range(max_node + 10      )] for _ in range(20      )]
     # initializing with -1 which means every node is unvisited
     snake_case_       =      [-1 for _ in range(max_node + 10      )]
     snake_case_       =      {
         1: [2, 3, 4],
         2: [5],
         3: [6, 7],
         4: [8],
         5: [9, 10],
         6: [11],
         7: [],
         8: [12, 13],
         9: [],
         10: [],
         11: [],
         12: [],
         13: [],
     }
     snake_case_ ,			snake_case_       =      breadth_first_search(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      1      )
     snake_case_       =      create_sparse(UpperCAmelCase		,      UpperCAmelCase      )
     print('LCA of node 1 and 3 is: '		,      lowest_common_ancestor(1		,      3		,      UpperCAmelCase		,      UpperCAmelCase      )      )
     print('LCA of node 5 and 6 is: '		,      lowest_common_ancestor(5		,      6		,      UpperCAmelCase		,      UpperCAmelCase      )      )
     print('LCA of node 7 and 11 is: '		,      lowest_common_ancestor(7		,      11		,      UpperCAmelCase		,      UpperCAmelCase      )      )
     print('LCA of node 6 and 7 is: '		,      lowest_common_ancestor(6		,      7		,      UpperCAmelCase		,      UpperCAmelCase      )      )
     print('LCA of node 4 and 12 is: '		,      lowest_common_ancestor(4		,      12		,      UpperCAmelCase		,      UpperCAmelCase      )      )
     print('LCA of node 8 and 8 is: '		,      lowest_common_ancestor(8		,      8		,      UpperCAmelCase		,      UpperCAmelCase      )      )


if __name__ == "__main__":
    main()

