





'''simple docstring'''

import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


a   	=    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase = 16000   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							int(round(sample_rate * max_length   )   )
   if len(__UpperCAmelCase   ) <= sample_length:
      return wav
   __SCREAMING_SNAKE_CASE   				=							randint(0				,						len(__UpperCAmelCase   ) - sample_length - 1   )
   return wav[random_offset : random_offset + sample_length]







@dataclass
class __a			:
   __UpperCamelCase :			Optional[str]						   =						field(default=_snake_case, metadata={'help': 'Name of a dataset from the datasets package'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'A file containing the training audio paths and labels.'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'A file containing the validation audio paths and labels.'}      )
   __UpperCamelCase :			str						   =						field(
       default='train', metadata={
           'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
       }, )
   __UpperCamelCase :			str						   =						field(
       default='validation', metadata={
           'help': (
               'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
           )
       }, )
   __UpperCamelCase :			str						   =						field(
       default='audio', metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''}, )
   __UpperCamelCase :			str						   =						field(
       default='label', metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''}      )
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       }, )
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       }, )
   __UpperCamelCase :			float						   =						field(
       default=20, metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'}, )







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						field(
       default='facebook/wav2vec2-base', metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'}      )
   __UpperCamelCase :			str						   =						field(
       default='main', metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Name or path of preprocessor config.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to freeze the feature encoder layers of the model.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to generate an attention mask in the feature extractor.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       }, )
   __UpperCamelCase :			Optional[bool]						   =						field(
       default=_snake_case, metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}, )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      if not self.freeze_feature_extractor and self.freeze_feature_encoder:
         warnings.warn(
             """The argument `--freeze_feature_extractor` is deprecated and """
             """will be removed in a future version. Use `--freeze_feature_encoder`"""
             """instead. Setting `freeze_feature_encoder==True`."""					,lowerCamelCase					,)
      if self.freeze_feature_extractor and not self.freeze_feature_encoder:
         raise ValueError(
             """The argument `--freeze_feature_extractor` is deprecated and """
             """should not be used in combination with `--freeze_feature_encoder`."""
             """Only make use of `--freeze_feature_encoder`."""     )





def     __magic_name__							(				)    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
   if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("""run_audio_classification"""				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,						datefmt="""%m/%d/%Y %H:%M:%S"""				,						handlers=[logging.StreamHandler(sys.stdout   )]				,						)

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   __SCREAMING_SNAKE_CASE   				=							training_args.get_process_log_level()
   logger.setLevel(__UpperCAmelCase   )
   transformers.utils.logging.set_verbosity(__UpperCAmelCase   )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
       + f"""distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"""   )
   logger.info(f"""Training/evaluation parameters {training_args}"""   )

   # Set seed before initializing model.
   set_seed(training_args.seed   )

   # Detecting last checkpoint.
   __SCREAMING_SNAKE_CASE   				=							None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      __SCREAMING_SNAKE_CASE   				=							get_last_checkpoint(training_args.output_dir   )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
         raise ValueError(
             f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             """Use --overwrite_output_dir to train from scratch."""   )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""   )

    # Initialize our dataset and prepare it for the audio classification task.
   __SCREAMING_SNAKE_CASE   				=							DatasetDict()
   __SCREAMING_SNAKE_CASE   				=							load_dataset(
       data_args.dataset_name				,						data_args.dataset_config_name				,						split=data_args.train_split_name				,						use_auth_token=True if model_args.use_auth_token else None				,						)
   __SCREAMING_SNAKE_CASE   				=							load_dataset(
       data_args.dataset_name				,						data_args.dataset_config_name				,						split=data_args.eval_split_name				,						use_auth_token=True if model_args.use_auth_token else None				,						)

   if data_args.audio_column_name not in raw_datasets["train"].column_names:
      raise ValueError(
          f"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
          """Make sure to set `--audio_column_name` to the correct audio column - one of """
          f"""{', '.join(raw_datasets['train'].column_names   )}."""   )

   if data_args.label_column_name not in raw_datasets["train"].column_names:
      raise ValueError(
          f"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
          """Make sure to set `--label_column_name` to the correct text column - one of """
          f"""{', '.join(raw_datasets['train'].column_names   )}."""   )

   # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
   # transformer outputs in the classifier, but it doesn't always lead to better accuracy
   __SCREAMING_SNAKE_CASE   				=							AutoFeatureExtractor.from_pretrained(
       model_args.feature_extractor_name or model_args.model_name_or_path				,						return_attention_mask=model_args.attention_mask				,						cache_dir=model_args.cache_dir				,						revision=model_args.model_revision				,						use_auth_token=True if model_args.use_auth_token else None				,						)

   # `datasets` takes care of automatically loading and resampling the audio,
   # so we just need to set the correct target sampling rate.
   __SCREAMING_SNAKE_CASE   				=							raw_datasets.cast_column(
       data_args.audio_column_name				,						datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate   )   )

   __SCREAMING_SNAKE_CASE   				=							feature_extractor.model_input_names[0]

   def train_transforms(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							[]
      for audio in batch[data_args.audio_column_name]:
         __SCREAMING_SNAKE_CASE   				=							random_subsample(
             audio["""array"""]				,						max_length=data_args.max_length_seconds				,						sample_rate=feature_extractor.sampling_rate   )
         subsampled_wavs.append(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							feature_extractor(__UpperCAmelCase				,						sampling_rate=feature_extractor.sampling_rate   )
      __SCREAMING_SNAKE_CASE   				=							{model_input_name: inputs.get(__UpperCAmelCase   )}
      __SCREAMING_SNAKE_CASE   				=							list(batch[data_args.label_column_name]   )

      return output_batch

   def val_transforms(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							[audio["""array"""] for audio in batch[data_args.audio_column_name]]
      __SCREAMING_SNAKE_CASE   				=							feature_extractor(__UpperCAmelCase				,						sampling_rate=feature_extractor.sampling_rate   )
      __SCREAMING_SNAKE_CASE   				=							{model_input_name: inputs.get(__UpperCAmelCase   )}
      __SCREAMING_SNAKE_CASE   				=							list(batch[data_args.label_column_name]   )

      return output_batch

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   __SCREAMING_SNAKE_CASE   				=							raw_datasets["""train"""].features[data_args.label_column_name].names
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							{}, {}
   for i, label in enumerate(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							str(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							label

   # Load the accuracy metric from the datasets package
   __SCREAMING_SNAKE_CASE   				=							evaluate.load("""accuracy"""   )

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
   # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
   def compute_metrics(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							np.argmax(eval_pred.predictions				,						axis=1   )
      return metric.compute(predictions=__UpperCAmelCase				,						references=eval_pred.label_ids   )

   __SCREAMING_SNAKE_CASE   				=							AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path				,						num_labels=len(__UpperCAmelCase   )				,						labelaid=__UpperCAmelCase				,						idalabel=__UpperCAmelCase				,						finetuning_task="""audio-classification"""				,						cache_dir=model_args.cache_dir				,						revision=model_args.model_revision				,						use_auth_token=True if model_args.use_auth_token else None				,						)
   __SCREAMING_SNAKE_CASE   				=							AutoModelForAudioClassification.from_pretrained(
       model_args.model_name_or_path				,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )				,						config=__UpperCAmelCase				,						cache_dir=model_args.cache_dir				,						revision=model_args.model_revision				,						use_auth_token=True if model_args.use_auth_token else None				,						ignore_mismatched_sizes=model_args.ignore_mismatched_sizes				,						)

   # freeze the convolutional waveform encoder
   if model_args.freeze_feature_encoder:
      model.freeze_feature_encoder()

   if training_args.do_train:
      if data_args.max_train_samples is not None:
         __SCREAMING_SNAKE_CASE   				=							(
             raw_datasets["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
         )
      # Set the training transforms
      raw_datasets["train"].set_transform(__UpperCAmelCase				,						output_all_columns=__UpperCAmelCase   )

   if training_args.do_eval:
      if data_args.max_eval_samples is not None:
         __SCREAMING_SNAKE_CASE   				=							(
             raw_datasets["""eval"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
         )
      # Set the validation transforms
      raw_datasets["eval"].set_transform(__UpperCAmelCase				,						output_all_columns=__UpperCAmelCase   )

   # Initialize our trainer
   __SCREAMING_SNAKE_CASE   				=							Trainer(
       model=__UpperCAmelCase				,						args=__UpperCAmelCase				,						train_dataset=raw_datasets["""train"""] if training_args.do_train else None				,						eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None				,						compute_metrics=__UpperCAmelCase				,						tokenizer=__UpperCAmelCase				,						)

   # Training
   if training_args.do_train:
      __SCREAMING_SNAKE_CASE   				=							None
      if training_args.resume_from_checkpoint is not None:
         __SCREAMING_SNAKE_CASE   				=							training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         __SCREAMING_SNAKE_CASE   				=							last_checkpoint
      __SCREAMING_SNAKE_CASE   				=							trainer.train(resume_from_checkpoint=__UpperCAmelCase   )
      trainer.save_model()
      trainer.log_metrics("""train"""				,						train_result.metrics   )
      trainer.save_metrics("""train"""				,						train_result.metrics   )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      __SCREAMING_SNAKE_CASE   				=							trainer.evaluate()
      trainer.log_metrics("""eval"""				,						__UpperCAmelCase   )
      trainer.save_metrics("""eval"""				,						__UpperCAmelCase   )

   # Write model card and (optionally) push to hub
   __SCREAMING_SNAKE_CASE   				=							{
       """finetuned_from""": model_args.model_name_or_path,
       """tasks""": """audio-classification""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""audio-classification"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__UpperCAmelCase   )
   else:
      trainer.create_model_card(**__UpperCAmelCase   )


if __name__ == "__main__":
    main()












'''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


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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __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(__UpperCAmelCase   ):
            titles.append(title if title is not None else """"""   )
            texts.append(__UpperCAmelCase   )
   return {"title": titles, "text": texts}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''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
   __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(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						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=__UpperCAmelCase   )
   __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(__UpperCAmelCase				,						ctx_encoder=__UpperCAmelCase				,						ctx_tokenizer=__UpperCAmelCase   )				,						batched=__UpperCAmelCase				,						batch_size=processing_args.batch_size				,						features=__UpperCAmelCase				,						)

   # And finally save your dataset
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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 __a			:
   __UpperCamelCase :			str						   =						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\''}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/rag-sequence-nq', metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}, )
   __UpperCamelCase :			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\''
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						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 __a			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						field(
       default=768, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}, )
   __UpperCamelCase :			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)

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





