





"""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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase_					:						List[str]      = logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_					:						List[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=100			, _UpperCAmelCase=" " ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_						,	A_  :	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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, ):






  """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
  A_  :	Dict    						=  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
  A_  :	List[Any]    						=  dataset.map(__UpperCamelCase			, batched=__UpperCamelCase			, num_proc=processing_args.num_proc )

  # And compute the embeddings
  A_  :	Union[str, Any]    						=  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__UpperCamelCase )
  A_  :	Any    						=  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
  A_  :	List[str]    						=  Features(
      {'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} )  # optional, save as float32 instead of float64 to save space
  A_  :	Dict    						=  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
  A_  :	str    						=  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
  A_  :	Optional[int]    						=  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
  A_  :	Tuple    						=  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

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

  # Easy way to load the model
  A_  :	Optional[Any]    						=  RagRetriever.from_pretrained(
      rag_example_args.rag_model_name			, index_name='custom'			, indexed_dataset=__UpperCamelCase )
  A_  :	Optional[int]    						=  RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			, retriever=__UpperCamelCase )
  A_  :	Dict    						=  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' )
  ######################################

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

@dataclass
class 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			str        =       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'"""}      ,       )
      lowercase_				:			Optional[str]        =       field(
          default=_lowerCAmelCase      ,       metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}      ,       )
      lowercase_				:			str        =       field(
          default="""facebook/rag-sequence-nq"""      ,       metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}      ,       )
      lowercase_				:			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'"""
              )
          }      ,       )
      lowercase_				:			Optional[str]        =       field(
          default=_lowerCAmelCase      ,       metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}      ,       )


@dataclass
class 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			Optional[int]        =       field(
          default=_lowerCAmelCase      ,       metadata={
              """help""": """The number of processes to use to split the documents into passages. Default is single process."""
          }      ,       )
      lowercase_				:			int        =       field(
          default=16      ,       metadata={
              """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
          }      ,       )


@dataclass
class 			_UpperCAmelCase  :



      '''simple docstring'''
      lowercase_				:			int        =       field(
          default=768      ,       metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      ,       )
      lowercase_				:			int        =       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_					:						str      = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_					:						Tuple      = parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      lowerCamelCase_					:						Any      = rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)






"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_					:						Any      = {
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						Union[str, Any]      = [
       'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TvltModel',
       'TvltForPreTraining',
       'TvltForAudioVisualClassification',
       'TvltPreTrainedModel',
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						List[Any]      = ['TvltImageProcessor']


if TYPE_CHECKING:
   from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
   from .processing_tvlt import TvltProcessor
   from .feature_extraction_tvlt import TvltFeatureExtractor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tvlt import (
          TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TvltForAudioVisualClassification,
          TvltForPreTraining,
          TvltModel,
          TvltPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_tvlt import TvltImageProcessor


else:
   import sys

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