

"""simple docstring"""






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
						import torch






class       _UpperCAmelCase     :
			def __init__(						self ,						_A ,						_A=14 ,						_A=7 ,						_A=True ,						_A=True ,						_A=False ,						_A=True ,						_A=99 ,						_A=32 ,						_A=5 ,						_A=4 ,						_A=37 ,						_A="gelu" ,						_A=0.1 ,						_A=0.1 ,						_A=5_12 ,						_A=0.02 ,						)      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   parent
				_UpperCAmelCase      :							str       =   batch_size
				_UpperCAmelCase      :							Any       =   seq_length
				_UpperCAmelCase      :							List[str]       =   is_training
				_UpperCAmelCase      :							Any       =   use_input_mask
				_UpperCAmelCase      :							int       =   use_token_type_ids
				_UpperCAmelCase      :							Dict       =   use_labels
				_UpperCAmelCase      :							str       =   vocab_size
				_UpperCAmelCase      :							Tuple       =   hidden_size
				_UpperCAmelCase      :							Union[str, Any]       =   num_hidden_layers
				_UpperCAmelCase      :							int       =   num_attention_heads
				_UpperCAmelCase      :							List[str]       =   intermediate_size
				_UpperCAmelCase      :							int       =   hidden_act
				_UpperCAmelCase      :							str       =   hidden_dropout_prob
				_UpperCAmelCase      :							List[Any]       =   attention_probs_dropout_prob
				_UpperCAmelCase      :							str       =   max_position_embeddings
				_UpperCAmelCase      :							Union[str, Any]       =   initializer_range
				_UpperCAmelCase      :							str       =   None
				_UpperCAmelCase      :							Any       =   vocab_size - 1
				_UpperCAmelCase      :							Optional[Any]       =   vocab_size - 1
				_UpperCAmelCase      :							Union[str, Any]       =   vocab_size - 1
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       )

				_UpperCAmelCase      :							Tuple       =   None
				if self.use_input_mask:
					_UpperCAmelCase      :							Optional[int]       =   random_attention_mask([self.batch_size, self.seq_length]       )

				_UpperCAmelCase      :							List[Any]       =   GPTaConfig(
				    vocab_size=self.vocab_size ,						n_embd=self.hidden_size ,						n_layer=self.num_hidden_layers ,						n_head=self.num_attention_heads ,						n_positions=self.max_position_embeddings ,						use_cache=_A ,						bos_token_id=self.bos_token_id ,						eos_token_id=self.eos_token_id ,						pad_token_id=self.pad_token_id ,						)

				return (config, input_ids, input_mask)
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   self.prepare_config_and_inputs()
				_UpperCAmelCase      :							List[Any]       =   config_and_inputs
				_UpperCAmelCase      :							Optional[int]       =   {"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   self.prepare_config_and_inputs()

				_UpperCAmelCase      :							List[Any]       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
				_UpperCAmelCase      :							List[Any]       =   ids_tensor([self.batch_size, self.seq_length] ,						vocab_size=2       )

				return (
				    config,
				    input_ids,
				    attention_mask,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   20
				_UpperCAmelCase      :							Optional[int]       =   model_class_name(_A       )

				_UpperCAmelCase      :							str       =   model.init_cache(input_ids.shape[0] ,						_A       )
				_UpperCAmelCase      :							int       =   jnp.ones((input_ids.shape[0], max_decoder_length) ,						dtype="""i4"""       )

				_UpperCAmelCase      :							List[str]       =   jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1       )[None, :] ,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )
				_UpperCAmelCase      :							List[str]       =   model(
				    input_ids[:, :-1] ,						attention_mask=_A ,						past_key_values=_A ,						position_ids=_A ,						)

				_UpperCAmelCase      :							Tuple       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,						dtype="""i4"""       )
				_UpperCAmelCase      :							Union[str, Any]       =   model(
				    input_ids[:, -1:] ,						attention_mask=_A ,						past_key_values=outputs_cache.past_key_values ,						position_ids=_A ,						)

				_UpperCAmelCase      :							Optional[int]       =   model(_A       )

				_UpperCAmelCase      :							Dict       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
				self.parent.assertTrue(diff < 1e-3 ,						msg=f'''Max diff is {diff}'''       )
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   20
				_UpperCAmelCase      :							int       =   model_class_name(_A       )

				_UpperCAmelCase      :							int       =   jnp.concatenate(
				    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )] ,						axis=-1 ,						)

				_UpperCAmelCase      :							List[str]       =   model.init_cache(input_ids.shape[0] ,						_A       )
				_UpperCAmelCase      :							Tuple       =   jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1       )[None, :] ,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )

				_UpperCAmelCase      :							List[str]       =   model(
				    input_ids[:, :-1] ,						attention_mask=_A ,						past_key_values=_A ,						position_ids=_A ,						)
				_UpperCAmelCase      :							Union[str, Any]       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,						dtype="""i4"""       )
				_UpperCAmelCase      :							Optional[Any]       =   model(
				    input_ids[:, -1:] ,						past_key_values=outputs_cache.past_key_values ,						attention_mask=_A ,						position_ids=_A ,						)

				_UpperCAmelCase      :							str       =   model(_A ,						attention_mask=_A       )

				_UpperCAmelCase      :							List[str]       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
				self.parent.assertTrue(diff < 1e-3 ,						msg=f'''Max diff is {diff}'''       )






@require_flax
class       _UpperCAmelCase     (						snake_case__      ,       snake_case__      ,       unittest.TestCase):
			__a	:    int			   =							(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
			__a	:    Dict			   =							(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   FlaxGPTaModelTester(self       )
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
					_UpperCAmelCase      :							Any       =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward(_A ,						_A ,						_A ,						_A       )
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
					_UpperCAmelCase      :							str       =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    _A ,						_A ,						_A ,						_A       )
			@slow
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							List[Any]       =   GPTaTokenizer.from_pretrained("""gpt2""" ,						pad_token="""</s>""" ,						padding_side="""left"""       )
				_UpperCAmelCase      :							Optional[Any]       =   tokenizer(["""Hello this is a long string""", """Hey"""] ,						return_tensors="""np""" ,						padding=_A ,						truncation=_A       )

				_UpperCAmelCase      :							Any       =   FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""       )
				_UpperCAmelCase      :							str       =   False
				_UpperCAmelCase      :							str       =   model.config.eos_token_id

				_UpperCAmelCase      :							Any       =   jax.jit(model.generate       )

				_UpperCAmelCase      :							Any       =   jit_generate(inputs["""input_ids"""] ,						attention_mask=inputs["""attention_mask"""]       ).sequences

				_UpperCAmelCase      :							List[Any]       =   tokenizer.batch_decode(_A ,						skip_special_tokens=_A       )

				_UpperCAmelCase      :							str       =   [
				    "Hello this is a long string of words. I'm going to start with the first one.\n",
				    "Hey, I'm not sure if I'm going to be able to do",
				]

				self.assertListEqual(_A ,						_A       )
			@is_pt_flax_cross_test
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__       ):
						# prepare inputs
						_UpperCAmelCase      :							Dict       =   self._prepare_for_class(_A ,						_A       )
						_UpperCAmelCase      :							Tuple       =   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

						# load corresponding PyTorch class
						_UpperCAmelCase      :							Optional[Any]       =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
						_UpperCAmelCase      :							List[str]       =   getattr(_A ,						_A       )

						_UpperCAmelCase      :							Union[str, Any]       =   pt_inputs["input_ids"].shape
						_UpperCAmelCase      :							Union[str, Any]       =   np.random.randint(0 ,						seq_length - 1 ,						size=(batch_size,)       )
						for batch_idx, start_index in enumerate(_A       ):
							_UpperCAmelCase      :							Dict       =   0
							_UpperCAmelCase      :							Union[str, Any]       =   1
							_UpperCAmelCase      :							List[Any]       =   0
							_UpperCAmelCase      :							int       =   1
						_UpperCAmelCase      :							Tuple       =   pt_model_class(_A       ).eval()
						_UpperCAmelCase      :							str       =   model_class(_A ,						dtype=jnp.floataa       )

						_UpperCAmelCase      :							Any       =   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,						_A       )
						_UpperCAmelCase      :							List[Any]       =   fx_state

						with torch.no_grad():
							_UpperCAmelCase      :							Optional[int]       =   pt_model(**_A       ).to_tuple()

						_UpperCAmelCase      :							str       =   fx_model(**_A       ).to_tuple()
						self.assertEqual(len(_A       ) ,						len(_A       ) ,						"""Output lengths differ between Flax and PyTorch"""       )
						for fx_output, pt_output in zip(_A ,						_A       ):
							self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2       )

						with tempfile.TemporaryDirectory() as tmpdirname:
							pt_model.save_pretrained(_A       )
							_UpperCAmelCase      :							List[Any]       =   model_class.from_pretrained(_A ,						from_pt=_A       )

						_UpperCAmelCase      :							List[Any]       =   fx_model_loaded(**_A       ).to_tuple()
						self.assertEqual(
						    len(_A       ) ,						len(_A       ) ,						"""Output lengths differ between Flax and PyTorch"""       )
						for fx_output_loaded, pt_output in zip(_A ,						_A       ):
							self.assert_almost_equals(fx_output_loaded[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2       )
			@is_pt_flax_cross_test
			def 							__snake_case	(						self       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__       ):
						# prepare inputs
						_UpperCAmelCase      :							Tuple       =   self._prepare_for_class(_A ,						_A       )
						_UpperCAmelCase      :							Dict       =   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

						# load corresponding PyTorch class
						_UpperCAmelCase      :							List[Any]       =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
						_UpperCAmelCase      :							List[Any]       =   getattr(_A ,						_A       )

						_UpperCAmelCase      :							List[Any]       =   pt_model_class(_A       ).eval()
						_UpperCAmelCase      :							Tuple       =   model_class(_A ,						dtype=jnp.floataa       )

						_UpperCAmelCase      :							Tuple       =   load_flax_weights_in_pytorch_model(_A ,						fx_model.params       )
						_UpperCAmelCase      :							List[Any]       =   pt_inputs["input_ids"].shape
						_UpperCAmelCase      :							int       =   np.random.randint(0 ,						seq_length - 1 ,						size=(batch_size,)       )
						for batch_idx, start_index in enumerate(_A       ):
							_UpperCAmelCase      :							List[str]       =   0
							_UpperCAmelCase      :							Optional[Any]       =   1
							_UpperCAmelCase      :							str       =   0
							_UpperCAmelCase      :							Optional[Any]       =   1

						# make sure weights are tied in PyTorch
						pt_model.tie_weights()

						with torch.no_grad():
							_UpperCAmelCase      :							Dict       =   pt_model(**_A       ).to_tuple()

						_UpperCAmelCase      :							Optional[Any]       =   fx_model(**_A       ).to_tuple()
						self.assertEqual(len(_A       ) ,						len(_A       ) ,						"""Output lengths differ between Flax and PyTorch"""       )
						for fx_output, pt_output in zip(_A ,						_A       ):
							self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2       )

						with tempfile.TemporaryDirectory() as tmpdirname:
							fx_model.save_pretrained(_A       )
							_UpperCAmelCase      :							Union[str, Any]       =   pt_model_class.from_pretrained(_A ,						from_flax=_A       )

						with torch.no_grad():
							_UpperCAmelCase      :							List[str]       =   pt_model_loaded(**_A       ).to_tuple()

						self.assertEqual(
						    len(_A       ) ,						len(_A       ) ,						"""Output lengths differ between Flax and PyTorch"""       )
						for fx_output, pt_output in zip(_A ,						_A       ):
							self.assert_almost_equals(fx_output[:, -1] ,						pt_output[:, -1].numpy() ,						4e-2       )
			@slow
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
					_UpperCAmelCase      :							List[str]       =   model_class_name.from_pretrained("""gpt2""" ,						from_pt=_A       )
					_UpperCAmelCase      :							Any       =   model(np.ones((1, 1)       )       )
					self.assertIsNotNone(_A       )








"""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)





