



"""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


UpperCAmelCase  :						Tuple	=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCAmelCase  :						Dict	=       "cuda" if torch.cuda.is_available() else "cpu"


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase=1_00       ,     __lowerCAmelCase=" "							)       ->  List[str]:







  '''simple docstring'''


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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  dict:







  '''simple docstring'''


  lowercase_    ,			lowercase_	    =       [], []
  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 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase							)       ->  dict:







  '''simple docstring'''


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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     )       ->  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
  lowercase_	    =       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
  lowercase_	    =       dataset.map(UpperCamelCase__       ,     batched=UpperCamelCase__       ,     num_proc=processing_args.num_proc							)

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


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







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







@dataclass
class       SCREAMING_SNAKE_CASE__       :
    lowercase__          =      field(
        default=768					,     metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}					,     )
    lowercase__          =      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  :						Dict	=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  :						str	=       parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              UpperCAmelCase  :						List[Any]	=       rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
							assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
							assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.width, self.height]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.arange(self.height * self.width							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='trunc'							),
							    ]			,						axis=1			,						)
							return coords


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

							__lowerCamelCase		       =    self.get_image_coords()
							__lowerCamelCase		       =    torch.broadcast_to(coords.unsqueeze(0							)			,						[batch_size * inner_batch_size, *coords.shape]							)
							__lowerCamelCase		       =    self.get_camera_rays(lowerCamelCase__							)

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase       ,						__lowerCamelCase		       =    coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

							__lowerCamelCase		       =    (flat.float() / (res - 1)) * 2 - 1
							__lowerCamelCase		       =    fracs * torch.tan(fov / 2							)

							__lowerCamelCase		       =    fracs.view(lowerCamelCase__			,						-1			,						2							)
							__lowerCamelCase		       =    (
							    self.z.view(lowerCamelCase__			,						1			,						3							)
							    + self.x.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, 1:]
							)
							__lowerCamelCase		       =    directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3							)			,						[batch_size, directions.shape[1], 3]							),
							        directions,
							    ]			,						dim=2			,						)
							return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



							'''simple docstring'''
							assert width * self.height == height * self.width, "The aspect ratio should not change."
							return DifferentiableProjectiveCamera(
							    origin=self.origin			,						x=self.x			,						y=self.y			,						z=self.z			,						width=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				for theta in np.linspace(0 ,     2 * np.pi ,     num=20      ):
								__lowerCamelCase		       =    np.array([np.sin(UpperCamelCase__      ), np.cos(UpperCamelCase__      ), -0.5]      )
								z /= np.sqrt(np.sum(z**2      )      )
								__lowerCamelCase		       =    -z * 4
								__lowerCamelCase		       =    np.array([np.cos(UpperCamelCase__      ), -np.sin(UpperCamelCase__      ), 0.0]      )
								__lowerCamelCase		       =    np.cross(UpperCamelCase__ ,     UpperCamelCase__      )
								origins.append(UpperCamelCase__      )
								xs.append(UpperCamelCase__      )
								ys.append(UpperCamelCase__      )
								zs.append(UpperCamelCase__      )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     x=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     y=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     z=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     width=UpperCamelCase__ ,     height=UpperCamelCase__ ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(UpperCamelCase__      )) ,     )


