




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





def 	__a	(   _UpperCamelCase:    Optional[int]					,						_UpperCamelCase:    int=100					,						_UpperCamelCase:    int=" "    )						-> List[str]:

     """simple docstring"""




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





def 	__a	(   _UpperCamelCase:    Any    )						-> dict:

     """simple docstring"""




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





def 	__a	(   _UpperCamelCase:    str					,						_UpperCamelCase:    str					,						_UpperCamelCase:    Optional[int]    )						-> dict:

     """simple docstring"""




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





def 	__a	(   _UpperCamelCase:    Tuple					,						_UpperCamelCase:    Dict					,						_UpperCamelCase:    Tuple					,						)						-> 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
     _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(__lowercase					,						batched=__lowercase					,						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=__lowercase    )
     _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(__lowercase					,						ctx_encoder=__lowercase					,						ctx_tokenizer=__lowercase    )					,						batched=__lowercase					,						batch_size=processing_args.batch_size					,						features=__lowercase					,						)

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

     # 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(__lowercase    )
     # 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=__lowercase    )
     _snake_case							=      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name					,						retriever=__lowercase    )
     _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(__lowercase					,						return_tensors="pt"    )["input_ids"]
     _snake_case							=      model.generate(__lowercase    )
     _snake_case							=      tokenizer.batch_decode(__lowercase					,						skip_special_tokens=__lowercase    )[0]
     logger.info("Q: " + question    )
     logger.info("A: " + generated_string    )

@dataclass
class    _a				:
     SCREAMING_SNAKE_CASE_							:    int    				=						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\'"""}					,					)
     SCREAMING_SNAKE_CASE_							:    Any    				=						field(
         default=_UpperCamelCase					,					metadata={"""help""": """Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'."""}					,					)
     SCREAMING_SNAKE_CASE_							:    List[Any]    				=						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_							:    Optional[int]    				=						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_							:    Union[str, Any]    				=						field(
         default=_UpperCamelCase					,					metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}					,					)







@dataclass
class    _a				:
     SCREAMING_SNAKE_CASE_							:    Dict    				=						field(
         default=_UpperCamelCase					,					metadata={
             """help""": """The number of processes to use to split the documents into passages. Default is single process."""
         }					,					)
     SCREAMING_SNAKE_CASE_							:    int    				=						field(
         default=16					,					metadata={
             """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
         }					,					)







@dataclass
class    _a				:
     SCREAMING_SNAKE_CASE_							:    Optional[Any]    				=						field(
         default=7_68					,					metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}					,					)
     SCREAMING_SNAKE_CASE_							:    str    				=						field(
         default=1_28					,					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_						:			Union[str, Any]  	=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_						:			List[str]  	=						parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      UpperCamelCase_						:			Dict  	=						rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)






"""simple docstring"""


import math
from decimal import Decimal, getcontext






def    a__       ( __lowercase = 400_0000      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	100
				_A         =	(Decimal(5      ) ** Decimal(0.5      ) + 1) / Decimal(2      )

				_A         =	(math.floor(math.log(n * (phi + 2)   ,	__lowercase      ) - 1      ) // 3) * 3 + 2
				_A         =	Decimal(round(phi ** Decimal(index + 1      )      )      ) / (phi + 2)
				_A         =	num // 2
				return int(__lowercase      )


if __name__ == "__main__":
		print(f'''{solution() = }''')