


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,
)


UpperCAmelCase__            =							logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCAmelCase__            =							'''cuda''' if torch.cuda.is_available() else '''cpu'''
def     UpperCAmelCase_ (							__snake_case					,						__snake_case=100					,						__snake_case=" "						)					->      List[str]:







 """simple docstring"""


 _lowercase											=text.split(__snake_case						)
 return [character.join(text[i : i + n]						).strip() for i in range(0					,						len(__snake_case						)					,						__snake_case						)]
def     UpperCAmelCase_ (							__snake_case						)					->      dict:







 """simple docstring"""


 _lowercase				,   _lowercase											=[], []
 for title, text in zip(documents['''title''']					,						documents['''text''']						):
  if text is not None:
   for passage in split_text(__snake_case						):
    titles.append(title if title is not None else ''''''						)
    texts.append(__snake_case						)
 return {"title": titles, "text": texts}
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      dict:







 """simple docstring"""


 _lowercase											=ctx_tokenizer(
     documents['''title''']					,						documents['''text''']					,						truncation=__snake_case					,						padding='''longest'''					,						return_tensors='''pt'''						)['''input_ids''']
 _lowercase											=ctx_encoder(input_ids.to(device=__snake_case						)					,						return_dict=__snake_case						).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						)					->      List[str]:







 """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(__snake_case					,						batched=__snake_case					,						num_proc=processing_args.num_proc						)

 # And compute the embeddings
 _lowercase											=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__snake_case						)
 _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(__snake_case					,						ctx_encoder=__snake_case					,						ctx_tokenizer=__snake_case						)					,						batched=__snake_case					,						batch_size=processing_args.batch_size					,						features=__snake_case					,						)

 # And finally save your dataset
 _lowercase											=os.path.join(rag_example_args.output_dir					,						'''my_knowledge_dataset'''						)
 dataset.save_to_disk(__snake_case						)
 # 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=__snake_case						)

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

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

 # Easy way to load the model
 _lowercase											=RagRetriever.from_pretrained(
     rag_example_args.rag_model_name					,						index_name='''custom'''					,						indexed_dataset=__snake_case						)
 _lowercase											=RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name					,						retriever=__snake_case						)
 _lowercase											=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'''						)
 ######################################

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







@dataclass
class 				lowerCamelCase__       :
     SCREAMING_SNAKE_CASE__				      =      field(
         default=str(Path(lowerCAmelCase).parent / '''test_data''' / '''my_knowledge_dataset.csv''')       ,							metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}       ,							)
     SCREAMING_SNAKE_CASE__				      =      field(
         default=lowerCAmelCase       ,							metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}       ,							)
     SCREAMING_SNAKE_CASE__				      =      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__				      =      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__				      =      field(
         default=lowerCAmelCase       ,							metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}       ,							)







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







@dataclass
class 				lowerCamelCase__       :
     SCREAMING_SNAKE_CASE__				      =      field(
         default=768       ,							metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}       ,							)
     SCREAMING_SNAKE_CASE__				      =      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__            =							HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__            =							parser.parse_args_into_dataclasses()
  with TemporaryDirectory() as tmp_dir:
    UpperCAmelCase__            =							rag_example_args.output_dir or tmp_dir
    main(rag_example_args, processing_args, index_hnsw_args)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__            =							{'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__            =							[
      '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VisualBertForMultipleChoice''',
      '''VisualBertForPreTraining''',
      '''VisualBertForQuestionAnswering''',
      '''VisualBertForRegionToPhraseAlignment''',
      '''VisualBertForVisualReasoning''',
      '''VisualBertLayer''',
      '''VisualBertModel''',
      '''VisualBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_visual_bert import (
        VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VisualBertForMultipleChoice,
        VisualBertForPreTraining,
        VisualBertForQuestionAnswering,
        VisualBertForRegionToPhraseAlignment,
        VisualBertForVisualReasoning,
        VisualBertLayer,
        VisualBertModel,
        VisualBertPreTrainedModel,
    )


else:
  import sys

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

