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


__SCREAMING_SNAKE_CASE								=logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE								="cuda" if torch.cuda.is_available() else "cpu"



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  str=1_00  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=" "   ):
     lowercase_			:       Tuple									=			text.split(__UpperCAmelCase   )
     return [character.join(text[i : i + n]   ).strip() for i in range(0  ,    len(__UpperCAmelCase   )  ,    __UpperCAmelCase   )]



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any   ):
     lowercase_							,						lowercase_			:       List[str]									=			[], []
     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     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Any   ):
     lowercase_			:       str									=			ctx_tokenizer(
         documents['title']  ,    documents['text']  ,    truncation=__UpperCAmelCase  ,    padding='longest'  ,    return_tensors='pt'   )['input_ids']
     lowercase_			:       Union[str, Any]									=			ctx_encoder(input_ids.to(device=__UpperCAmelCase   )  ,    return_dict=__UpperCAmelCase   ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  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
     lowercase_			:       List[Any]									=			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_			:       Any									=			dataset.map(__UpperCAmelCase  ,    batched=__UpperCAmelCase  ,    num_proc=processing_args.num_proc   )

     # And compute the embeddings
     lowercase_			:       Any									=			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__UpperCAmelCase   )
     lowercase_			:       str									=			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
     lowercase_			:       Union[str, Any]									=			Features(
         {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space
     lowercase_			:       List[str]									=			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_			:       int									=			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_			:       str									=			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_			:       Union[str, Any]									=			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  UpperCamelCase	:
   lowercase							=						field(
       default=str(Path(_snake_case						).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=_snake_case		,   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(_snake_case						).parent / 'test_run' / 'dummy-kb'						)		,   metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}		,   )







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







@dataclass
class  UpperCamelCase	:
   lowercase							=						field(
       default=7_6_8		,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}		,   )
   lowercase							=						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								=HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       __SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE								=parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              __SCREAMING_SNAKE_CASE								=rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)


"""simple docstring"""







from random import randint, random



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  int = 5  ,    ):
     lowercase_			:       Union[str, Any]									=			[[-1] * number_of_cells]  # Create a highway without any car
     lowercase_			:       str									=			0
     lowercase_			:       List[Any]									=			max(__SCREAMING_SNAKE_CASE  ,    0   )
     while i < number_of_cells:
          lowercase_			:       Union[str, Any]									=			(
              randint(0  ,    __SCREAMING_SNAKE_CASE   ) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1  ,    max_speed * 2   ) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			0
     lowercase_			:       Dict									=			highway_now[car_index + 1 :]
     for cell in range(len(__SCREAMING_SNAKE_CASE   )   ):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(__SCREAMING_SNAKE_CASE  ,    -1   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   )
     # Beforce calculations, the highway is empty
     lowercase_			:       Dict									=			[-1] * number_of_cells

     for car_index in range(__SCREAMING_SNAKE_CASE   ):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               lowercase_			:       Any									=			min(highway_now[car_index] + 1  ,    __SCREAMING_SNAKE_CASE   )
               # Number of empty cell before the next car
               lowercase_			:       List[Any]									=			get_distance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) - 1
               # We can't have the car causing an accident
               lowercase_			:       Dict									=			min(next_highway[car_index]  ,    __SCREAMING_SNAKE_CASE   )
               if random() < probability:
                    # Randomly, a driver will slow down
                    lowercase_			:       Optional[Any]									=			max(next_highway[car_index] - 1  ,    0   )
     return next_highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			len(highway[0]   )

     for i in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			update(highway[i]  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[str]									=			[-1] * number_of_cells

          for car_index in range(__SCREAMING_SNAKE_CASE   ):
               lowercase_			:       Optional[Any]									=			next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    lowercase_			:       Any									=			(car_index + speed) % number_of_cells
                    # Commit the change of position
                    lowercase_			:       Optional[Any]									=			speed
          highway.append(__SCREAMING_SNAKE_CASE   )

     return highway


if __name__ == "__main__":
       import doctest

       doctest.testmod()

