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


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







def  __UpperCamelCase    (						A ,							A=100 ,							A=" "  ):
     UpperCamelCase__										=      text.split(_lowerCamelCase  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0 ,							len(_lowerCamelCase  ) ,							_lowerCamelCase  )]







def  __UpperCamelCase    (						A  ):
     UpperCamelCase__										=      [], []
     for title, text in zip(documents['''title'''] ,							documents['''text''']  ):
          if text is not None:
               for passage in split_text(_lowerCamelCase  ):
                    titles.append(title if title is not None else ''''''  )
                    texts.append(_lowerCamelCase  )
     return {"title": titles, "text": texts}







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







def  __UpperCamelCase    (						A ,							A ,							A ,							):
     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
     UpperCamelCase__										=      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
     UpperCamelCase__										=      dataset.map(_lowerCamelCase ,							batched=_lowerCamelCase ,							num_proc=processing_args.num_proc  )

     # And compute the embeddings
     UpperCamelCase__										=      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=_lowerCamelCase  )
     UpperCamelCase__										=      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
     UpperCamelCase__										=      Features(
         {'''text''': Value('''string'''  ), '''title''': Value('''string'''  ), '''embeddings''': Sequence(Value('''float32'''  )  )}  )  # optional, save as float32 instead of float64 to save space
     UpperCamelCase__										=      dataset.map(
         partial(_lowerCamelCase ,							ctx_encoder=_lowerCamelCase ,							ctx_tokenizer=_lowerCamelCase  ) ,							batched=_lowerCamelCase ,							batch_size=processing_args.batch_size ,							features=_lowerCamelCase ,							)

     # And finally save your dataset
     UpperCamelCase__										=      os.path.join(rag_example_args.output_dir ,							'''my_knowledge_dataset'''  )
     dataset.save_to_disk(_lowerCamelCase  )
     # 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
     UpperCamelCase__										=      faiss.IndexHNSWFlat(index_hnsw_args.d ,							index_hnsw_args.m ,							faiss.METRIC_INNER_PRODUCT  )
     dataset.add_faiss_index('''embeddings''' ,							custom_index=_lowerCamelCase  )

     # And save the index
     UpperCamelCase__										=      os.path.join(rag_example_args.output_dir ,							'''my_knowledge_dataset_hnsw_index.faiss'''  )
     dataset.get_index('''embeddings'''  ).save(_lowerCamelCase  )
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index



@dataclass
class     _A   :
  SCREAMING_SNAKE_CASE_	:	int						=field(
      default=str(Path(lowercase__							).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"							)	, metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}	, )
  SCREAMING_SNAKE_CASE_	:	str						=field(
      default=lowercase__	, metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}	, )
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=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_	:	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'"
          )
      }	, )
  SCREAMING_SNAKE_CASE_	:	List[Any]						=field(
      default=str(Path(lowercase__							).parent / "test_run" / "dummy-kb"							)	, metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}	, )



@dataclass
class     _A   :
  SCREAMING_SNAKE_CASE_	:	int						=field(
      default=lowercase__	, metadata={
          "help": "The number of processes to use to split the documents into passages. Default is single process."
      }	, )
  SCREAMING_SNAKE_CASE_	:	Any						=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_	:	List[str]						=field(
      default=7_68	, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}	, )
  SCREAMING_SNAKE_CASE_	:	List[Any]						=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)

     __magic_name__								=HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     __magic_name__								=parser.parse_args_into_dataclasses()
     with TemporaryDirectory() as tmp_dir:
          __magic_name__								=rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__magic_name__								='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
     raise ImportWarning(
         '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
     )

if version.parse(pyarrow.__version__).major < 8:
     raise ImportWarning(
         '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
         '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
     )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__magic_name__								=concatenate_datasets
__magic_name__								=DownloadConfig
__magic_name__								=DownloadManager
__magic_name__								=DownloadMode
__magic_name__								=DownloadConfig
__magic_name__								=DownloadMode
__magic_name__								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



