




'''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


lowerCamelCase_			=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_			=						'cuda' if torch.cuda.is_available() else 'cpu'
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			int=1_00	,					__A    :			Tuple=" "  )			->			List[str]:
   _SCREAMING_SNAKE_CASE	=				text.split(__A  )
   return [character.join(text[i : i + n]  ).strip() for i in range(0	,					len(__A  )	,					__A  )]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			dict  )			->			dict:
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				[], []
   for title, text in zip(documents["title"]	,					documents["text"]  ):
      if text is not None:
         for passage in split_text(__A  ):
            titles.append(title if title is not None else ""  )
            texts.append(__A  )
   return {"title": titles, "text": texts}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			dict	,					__A    :			DPRContextEncoder	,					__A    :			DPRContextEncoderTokenizerFast  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				ctx_tokenizer(
       documents["title"]	,					documents["text"]	,					truncation=__A	,					padding="longest"	,					return_tensors="pt"  )["input_ids"]
   _SCREAMING_SNAKE_CASE	=				ctx_encoder(input_ids.to(device=__A  )	,					return_dict=__A  ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			"RagExampleArguments"	,					__A    :			"ProcessingArguments"	,					__A    :			"IndexHnswArguments"	,					)			->			int:
   ######################################
   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
   _SCREAMING_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
   _SCREAMING_SNAKE_CASE	=				dataset.map(__A	,					batched=__A	,					num_proc=processing_args.num_proc  )

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

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

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







@dataclass
class 		lowercase_   :


   """simple docstring"""

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







@dataclass
class 		lowercase_   :


   """simple docstring"""

   lowerCamelCase_      =						field(
       default=A					,  metadata={
           '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
       }					,  )
   lowerCamelCase_      =						field(
       default=16					,  metadata={
           '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
       }					,  )







@dataclass
class 		lowercase_   :


   """simple docstring"""

   lowerCamelCase_      =						field(
       default=768					,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}					,  )
   lowerCamelCase_      =						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)

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







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_			=						{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CTRLForSequenceClassification',
     'CTRLLMHeadModel',
     'CTRLModel',
     'CTRLPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCTRLForSequenceClassification',
     'TFCTRLLMHeadModel',
     'TFCTRLModel',
     'TFCTRLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .tokenization_ctrl import CTRLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ctrl import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
      CTRLPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
      TFCTRLPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

