

"""simple docstring"""






import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
lowerCamelCase__			:					Optional[int]							  =					logging.get_logger(__name__)

lowerCamelCase__			:					Tuple							  =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase__			:					Dict							  =					[
    '''lm_head''',
]
def  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Dict	)			->     List[Any]:
	for attribute in key.split("""."""	):
		_UpperCAmelCase      :							List[str]       =   getattr(_lowerCAmelCase,					_lowerCAmelCase	)

	if weight_type is not None:
		_UpperCAmelCase      :							Optional[int]       =   getattr(_lowerCAmelCase,					_lowerCAmelCase	).shape
	else:
		_UpperCAmelCase      :							List[Any]       =   hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
		    f''' {value.shape} for {full_name}'''	)

	if weight_type == "weight":
		_UpperCAmelCase      :							Optional[Any]       =   value
	elif weight_type == "weight_g":
		_UpperCAmelCase      :							int       =   value
	elif weight_type == "weight_v":
		_UpperCAmelCase      :							List[str]       =   value
	elif weight_type == "bias":
		_UpperCAmelCase      :							List[Any]       =   value
	else:
		_UpperCAmelCase      :							Union[str, Any]       =   value

	logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  List[Any],					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Dict	)			->     Optional[int]:
	_UpperCAmelCase      :							Optional[int]       =   []
	_UpperCAmelCase      :							Any       =   fairseq_model.state_dict()

	if not is_headless:
		_UpperCAmelCase      :							int       =   hf_model.dataavec_audio.feature_extractor
		_UpperCAmelCase      :							int       =   hf_model.dataavec_audio.encoder.pos_conv_embed

	else:
		_UpperCAmelCase      :							str       =   hf_model.feature_extractor
		_UpperCAmelCase      :							List[str]       =   hf_model.encoder.pos_conv_embed

	for name, value in fairseq_dict.items():
		_UpperCAmelCase      :							int       =   False
		if "conv_layers" in name:
			load_conv_layer(
			    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					)
			_UpperCAmelCase      :							Tuple       =   True
		elif "pos_conv" in name:
			load_pos_conv_layer(
			    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					)
			_UpperCAmelCase      :							Dict       =   True
		else:
			for key, mapped_key in MAPPING.items():
				if not is_headless:
					_UpperCAmelCase      :							List[Any]       =   """data2vec_audio.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
					_UpperCAmelCase      :							List[Any]       =   True
					if "*" in mapped_key:
						_UpperCAmelCase      :							str       =   name.split(_lowerCAmelCase	)[0].split("""."""	)[-2]
						_UpperCAmelCase      :							Optional[int]       =   mapped_key.replace("""*""",					_lowerCAmelCase	)
					if "weight_g" in name:
						_UpperCAmelCase      :							Union[str, Any]       =   """weight_g"""
					elif "weight_v" in name:
						_UpperCAmelCase      :							Tuple       =   """weight_v"""
					elif "bias" in name:
						_UpperCAmelCase      :							Optional[int]       =   """bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						_UpperCAmelCase      :							str       =   """weight"""
					else:
						_UpperCAmelCase      :							Optional[Any]       =   None
					set_recursively(_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase	)
				continue
		if not is_used:
			unused_weights.append(_lowerCAmelCase	)

	logger.warning(f'''Unused weights: {unused_weights}'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  int,					_lowerCAmelCase       :  int	)			->     Optional[Any]:
	_UpperCAmelCase      :							int       =   path.split("""."""	)
	return reduce(_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  Optional[int],					_lowerCAmelCase       :  int	)			->     int:
	_UpperCAmelCase      :							str       =   access_by_string(_lowerCAmelCase,					_lowerCAmelCase	)
	_UpperCAmelCase      :							Optional[Any]       =   hf_weight.data

	if fsq_value.shape != hf_value.shape:
		raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''	)
	_UpperCAmelCase      :							int       =   fsq_value
	logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int],					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  List[str]	)			->     List[str]:
	_UpperCAmelCase      :							int       =   full_name.split("""conv_layers."""	)[-1]
	_UpperCAmelCase      :							Any       =   name.split("""."""	)
	_UpperCAmelCase      :							int       =   int(items[0]	)
	_UpperCAmelCase      :							Any       =   int(items[1]	)

	_UpperCAmelCase      :							Dict       =   name.split("""."""	)[-1]
	if type_id == 0:
		_UpperCAmelCase      :							List[str]       =   """conv"""
	elif type_id == 2:
		_UpperCAmelCase      :							List[str]       =   """layer_norm"""
	else:
		unused_weights.append(_lowerCAmelCase	)
		return

	set_weights(_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Optional[Any]	)			->     Union[str, Any]:
	_UpperCAmelCase      :							List[str]       =   full_name.split("""pos_conv."""	)[-1]
	_UpperCAmelCase      :							str       =   name.split("""."""	)
	_UpperCAmelCase      :							Any       =   int(items[0]	)
	_UpperCAmelCase      :							Dict       =   int(items[1]	)

	_UpperCAmelCase      :							List[str]       =   name.split("""."""	)[-1]
	if type_id != 0:
		unused_weights.append(_lowerCAmelCase	)
		return
	else:
		_UpperCAmelCase      :							Union[str, Any]       =   """conv"""

	set_weights(_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					f'''layers.{layer_id}.{layer_type}.{weight_type}'''	)
@torch.no_grad()
def  UpperCamelCase   (     _lowerCAmelCase       :  int,					_lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Union[str, Any]=None,					_lowerCAmelCase       :  List[Any]=None,					_lowerCAmelCase       :  Optional[Any]=True	)			->     Tuple:
	if config_path is not None:
		_UpperCAmelCase      :							int       =   DataaVecAudioConfig.from_pretrained(_lowerCAmelCase	)
	else:
		_UpperCAmelCase      :							Dict       =   DataaVecAudioConfig()

	if not is_finetuned:
		# Modify final_proj layer name
		_UpperCAmelCase      :							Any       =   DataaVecAudioModel(_lowerCAmelCase	)
		_UpperCAmelCase      :							List[str]       =   os.path.dirname(_lowerCAmelCase	)

		_UpperCAmelCase      :							Optional[Any]       =   torch.load(_lowerCAmelCase	)
		_UpperCAmelCase      :							Dict       =   state_dict["""model"""].pop("""final_proj.0.weight"""	)
		_UpperCAmelCase      :							Any       =   state_dict["""model"""].pop("""final_proj.0.bias"""	)
		_UpperCAmelCase      :							Union[str, Any]       =   os.path.join(_lowerCAmelCase,					"""converted.pt"""	)
		torch.save(_lowerCAmelCase,					_lowerCAmelCase	)
	else:
		_UpperCAmelCase      :							Tuple       =   DataaVecAudioForCTC(_lowerCAmelCase	)
		_UpperCAmelCase      :							str       =   checkpoint_path

	def load_dataavec(_lowerCAmelCase       :  Optional[int]	):
		_UpperCAmelCase  ,				_UpperCAmelCase  ,				_UpperCAmelCase      :							Dict       =   fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
		return model[0].eval()

	_UpperCAmelCase      :							str       =   load_dataavec(_lowerCAmelCase	)

	recursively_load_weights(_lowerCAmelCase,					_lowerCAmelCase,					not is_finetuned	)

	_UpperCAmelCase      :							Optional[int]       =   WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""	)

	_UpperCAmelCase      :							Tuple       =   load_dataset("""patrickvonplaten/librispeech_asr_dummy""",					"""clean""",					split="""validation"""	)
	_UpperCAmelCase      :							Tuple       =   [x["""array"""] for x in ds[:4]["""audio"""]]

	_UpperCAmelCase      :							Dict       =   processor(_lowerCAmelCase,					return_tensors="""pt""",					padding=_lowerCAmelCase	)

	_UpperCAmelCase      :							int       =   inputs.input_values
	_UpperCAmelCase      :							List[Any]       =   inputs.attention_mask
	#    input_values = inputs.input_values[:, :-1]
	#    attention_mask = inputs.attention_mask[:, :-1]

	hf_wavavec.eval()
	model.eval()
	if is_finetuned:
		_UpperCAmelCase      :							List[Any]       =   model(source=_lowerCAmelCase,					padding_mask=(1 - attention_mask),					mask=_lowerCAmelCase,					features_only=_lowerCAmelCase	)[
		    """encoder_out"""
		].transpose(0,					1	)
		_UpperCAmelCase      :							Dict       =   hf_wavavec(_lowerCAmelCase,					attention_mask=_lowerCAmelCase	)["""logits"""]

		_UpperCAmelCase      :							Tuple       =   torch.argmax(_lowerCAmelCase,					dim=-1	)
		_UpperCAmelCase      :							List[Any]       =   processor.batch_decode(_lowerCAmelCase	)

		print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''	)
	else:
		_UpperCAmelCase      :							List[str]       =   model(source=_lowerCAmelCase,					padding_mask=(1 - attention_mask),					mask=_lowerCAmelCase,					features_only=_lowerCAmelCase	)[
		    """layer_results"""
		][-1][0].transpose(0,					1	)
		_UpperCAmelCase      :							List[str]       =   hf_wavavec(_lowerCAmelCase,					attention_mask=_lowerCAmelCase	)["""last_hidden_state"""]

	print(our_output.shape,					their_output.shape	)
	_UpperCAmelCase      :							List[Any]       =   torch.max(torch.abs(our_output - their_output	)	).item()
	print(f'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
	_UpperCAmelCase      :							Dict       =   torch.allclose(_lowerCAmelCase,					_lowerCAmelCase,					atol=1E-3	)
	print("""Do both models output the same tensors?""",					"""🔥""" if success else """💩"""	)
	if not success:
		raise Exception("""Something went wRoNg"""	)

	hf_wavavec.save_pretrained(_lowerCAmelCase	)

	if is_finetuned:
		processor.save_pretrained(_lowerCAmelCase	)
	else:
		processor.feature_extractor.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
						lowerCamelCase__			:					int							  =					argparse.ArgumentParser()
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						parser.add_argument(
						    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
						)
						lowerCamelCase__			:					List[str]							  =					parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)








"""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__			:					int							  =					logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__			:					int							  =					'''cuda''' if torch.cuda.is_available() else '''cpu'''
def  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  int=100,					_lowerCAmelCase       :  List[str]=" "	)			->     List[str]:
	_UpperCAmelCase      :							Tuple       =   text.split(_lowerCAmelCase	)
	return [character.join(text[i : i + n]	).strip() for i in range(0,					len(_lowerCAmelCase	),					_lowerCAmelCase	)]
def  UpperCamelCase   (     _lowerCAmelCase       :  dict	)			->     dict:
	_UpperCAmelCase  ,				_UpperCAmelCase      :							List[str]       =   [], []
	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   (     _lowerCAmelCase       :  dict,					_lowerCAmelCase       :  DPRContextEncoder,					_lowerCAmelCase       :  DPRContextEncoderTokenizerFast	)			->     dict:
	_UpperCAmelCase      :							int       =   ctx_tokenizer(
	    documents["""title"""],					documents["""text"""],					truncation=_lowerCAmelCase,					padding="""longest""",					return_tensors="""pt"""	)["""input_ids"""]
	_UpperCAmelCase      :							Optional[Any]       =   ctx_encoder(input_ids.to(device=_lowerCAmelCase	),					return_dict=_lowerCAmelCase	).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}
def  UpperCamelCase   (     _lowerCAmelCase       :  "RagExampleArguments",					_lowerCAmelCase       :  "ProcessingArguments",					_lowerCAmelCase       :  "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
	_UpperCAmelCase      :							Optional[int]       =   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      :							List[str]       =   dataset.map(_lowerCAmelCase,					batched=_lowerCAmelCase,					num_proc=processing_args.num_proc	)

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

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

	# Easy way to load the model
	_UpperCAmelCase      :							List[Any]       =   RagRetriever.from_pretrained(
	    rag_example_args.rag_model_name,					index_name="""custom""",					indexed_dataset=_lowerCAmelCase	)
	_UpperCAmelCase      :							Dict       =   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,					retriever=_lowerCAmelCase	)
	_UpperCAmelCase      :							Tuple       =   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"""	)
	######################################

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






@dataclass
class       _UpperCAmelCase     :
			__a	:    str			   =							field(
			    default=str(Path(__a).parent / """test_data""" / """my_knowledge_dataset.csv""")      ,       metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}      ,       )
			__a	:    Optional[str]			   =							field(
			    default=__a      ,       metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}      ,       )
			__a	:    str			   =							field(
			    default="""facebook/rag-sequence-nq"""      ,       metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}      ,       )
			__a	:    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'"""
			        )
			    }      ,       )
			__a	:    Optional[str]			   =							field(
			    default=__a      ,       metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}      ,       )






@dataclass
class       _UpperCAmelCase     :
			__a	:    Optional[int]			   =							field(
			    default=__a      ,       metadata={
			        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
			    }      ,       )
			__a	:    int			   =							field(
			    default=1_6      ,       metadata={
			        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
			    }      ,       )






@dataclass
class       _UpperCAmelCase     :
			__a	:    int			   =							field(
			    default=7_6_8      ,       metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      ,       )
			__a	:    int			   =							field(
			    default=1_2_8      ,       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__			:					Tuple							  =					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			:					Dict							  =					parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												lowerCamelCase__			:					List[str]							  =					rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)





