

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,
)


__a						:List[Any]            = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__a						:Dict            = 'cuda' if torch.cuda.is_available() else 'cpu'

def 			__snake_case	(   __UpperCamelCase  :   str							,__UpperCamelCase  :   List[Any]=100							,__UpperCamelCase  :   Optional[int]=" "						):



      """simple docstring"""

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

def 			__snake_case	(   __UpperCamelCase  :   dict						):



      """simple docstring"""

      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 			__snake_case	(   __UpperCamelCase  :   dict							,__UpperCamelCase  :   DPRContextEncoder							,__UpperCamelCase  :   DPRContextEncoderTokenizerFast						):



      """simple docstring"""

      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 			__snake_case	(   __UpperCamelCase  :   "RagExampleArguments"							,__UpperCamelCase  :   "ProcessingArguments"							,__UpperCamelCase  :   "IndexHnswArguments"							,):



      """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
      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

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

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

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

@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: 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\''}    ,							)
      _lowerCamelCase					: Optional[str]					=					field(
          default=snake_case_    ,							metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}    ,							)
      _lowerCamelCase					: str					=					field(
          default='facebook/rag-sequence-nq'    ,							metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}    ,							)
      _lowerCamelCase					: 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\''
              )
          }    ,							)
      _lowerCamelCase					: Optional[str]					=					field(
          default=snake_case_    ,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}    ,							)




@dataclass
class      _a				:




      """simple docstring"""

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




@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: int					=					field(
          default=7_6_8    ,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}    ,							)
      _lowerCamelCase					: 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)

      __a						:Optional[Any]            = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __a      ,			__a      ,			__a						:Optional[int]            = parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            __a						:List[Any]            = rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)


import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with open(__UpperCamelCase							,"wb"						) as f:
            f.write(json.dumps(__UpperCamelCase						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()