
import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			str					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]=13					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=7					, SCREAMING_SNAKE_CASE__			:			Dict=True					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			Tuple=False					, SCREAMING_SNAKE_CASE__			:			Tuple=True					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=99					, SCREAMING_SNAKE_CASE__			:			Any=32					, SCREAMING_SNAKE_CASE__			:			Optional[int]=5					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=4					, SCREAMING_SNAKE_CASE__			:			Dict=37					, SCREAMING_SNAKE_CASE__			:			str="gelu"					, SCREAMING_SNAKE_CASE__			:			Tuple=0.1					, SCREAMING_SNAKE_CASE__			:			Dict=0.1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=5_12					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=16					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			List[str]=0.02					, SCREAMING_SNAKE_CASE__			:			List[Any]=3					, SCREAMING_SNAKE_CASE__			:			Tuple=4					, SCREAMING_SNAKE_CASE__			:			List[str]=None					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					seq_length
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					use_input_mask
      UpperCamelCase						    =					use_token_type_ids
      UpperCamelCase						    =					use_labels
      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					intermediate_size
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					max_position_embeddings
      UpperCamelCase						    =					type_vocab_size
      UpperCamelCase						    =					type_sequence_label_size
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					num_labels
      UpperCamelCase						    =					num_choices
      UpperCamelCase						    =					scope

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size   )

      UpperCamelCase						    =					None
      if self.use_input_mask:
           UpperCamelCase						    =					random_attention_mask([self.batch_size, self.seq_length]   )

      UpperCamelCase						    =					None
      if self.use_token_type_ids:
           UpperCamelCase						    =					ids_tensor([self.batch_size, self.seq_length]					, self.type_vocab_size   )

      UpperCamelCase						    =					None
      UpperCamelCase						    =					None
      UpperCamelCase						    =					None
      if self.use_labels:
           UpperCamelCase						    =					ids_tensor([self.batch_size]					, self.type_sequence_label_size   )
           UpperCamelCase						    =					ids_tensor([self.batch_size, self.seq_length]					, self.num_labels   )
           UpperCamelCase						    =					ids_tensor([self.batch_size]					, self.num_choices   )

      UpperCamelCase						    =					self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return OpenLlamaConfig(
          vocab_size=self.vocab_size					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, num_attention_heads=self.num_attention_heads					, intermediate_size=self.intermediate_size					, hidden_act=self.hidden_act					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, max_position_embeddings=self.max_position_embeddings					, type_vocab_size=self.type_vocab_size					, is_decoder=UpperCAmelCase__					, initializer_range=self.initializer_range					, use_stable_embedding=UpperCAmelCase__					, )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					OpenLlamaModel(config=UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__   )
      UpperCamelCase						    =					model(UpperCAmelCase__   )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					True
      UpperCamelCase						    =					OpenLlamaModel(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(
          UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, encoder_hidden_states=UpperCAmelCase__					, encoder_attention_mask=UpperCAmelCase__					, )
      UpperCamelCase						    =					model(
          UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, encoder_hidden_states=UpperCAmelCase__					, )
      UpperCamelCase						    =					model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__   )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)   )

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Any					, ):






      """simple docstring"""



      UpperCamelCase						    =					OpenLlamaForCausalLM(config=UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, labels=UpperCAmelCase__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)   )

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			List[Any]					, ):






      """simple docstring"""



      UpperCamelCase						    =					True
      UpperCamelCase						    =					True
      UpperCamelCase						    =					OpenLlamaForCausalLM(config=UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()

      # first forward pass
      UpperCamelCase						    =					model(
          UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, encoder_hidden_states=UpperCAmelCase__					, encoder_attention_mask=UpperCAmelCase__					, use_cache=UpperCAmelCase__					, )
      UpperCamelCase						    =					outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      UpperCamelCase						    =					ids_tensor((self.batch_size, 3)					, config.vocab_size   )
      UpperCamelCase						    =					ids_tensor((self.batch_size, 3)					, vocab_size=2   )

      # append to next input_ids and
      UpperCamelCase						    =					torch.cat([input_ids, next_tokens]					, dim=-1   )
      UpperCamelCase						    =					torch.cat([input_mask, next_mask]					, dim=-1   )

      UpperCamelCase						    =					model(
          UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, encoder_hidden_states=UpperCAmelCase__					, encoder_attention_mask=UpperCAmelCase__					, output_hidden_states=UpperCAmelCase__					, )['''hidden_states'''][0]
      UpperCamelCase						    =					model(
          UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, encoder_hidden_states=UpperCAmelCase__					, encoder_attention_mask=UpperCAmelCase__					, past_key_values=UpperCAmelCase__					, output_hidden_states=UpperCAmelCase__					, )['''hidden_states'''][0]

      # select random slice
      UpperCamelCase						    =					ids_tensor((1,)					, output_from_past.shape[-1]   ).item()
      UpperCamelCase						    =					output_from_no_past[:, -3:, random_slice_idx].detach()
      UpperCamelCase						    =					output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-3   )   )






 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					self.prepare_config_and_inputs()
      (
          UpperCamelCase	
      )						    =					config_and_inputs
      UpperCamelCase						    =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict




@require_torch
class       _lowerCAmelCase  (		lowercase__			,	lowercase__			,	lowercase__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					int       						=(
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 SCREAMING_SNAKE_CASE_				:					int       						=(OpenLlamaForCausalLM,) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE_				:					Dict       						=(
     {
         "feature-extraction": OpenLlamaModel,
         "text-classification": OpenLlamaForSequenceClassification,
         "text-generation": OpenLlamaForCausalLM,
         "zero-shot": OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 SCREAMING_SNAKE_CASE_				:					Tuple       						=False
 SCREAMING_SNAKE_CASE_				:					str       						=False

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					OpenLlamaModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=UpperCAmelCase__					, hidden_size=37   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      self.config_tester.run_common_tests()

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCamelCase						    =					type
           self.model_tester.create_and_check_model(*UpperCAmelCase__   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3
      UpperCamelCase						    =					input_dict['''input_ids''']
      UpperCamelCase						    =					input_ids.ne(1   ).to(UpperCAmelCase__   )
      UpperCamelCase						    =					ids_tensor([self.model_tester.batch_size]					, self.model_tester.type_sequence_label_size   )
      UpperCamelCase						    =					OpenLlamaForSequenceClassification(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, labels=UpperCAmelCase__   )
      self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3
      UpperCamelCase						    =					'''single_label_classification'''
      UpperCamelCase						    =					input_dict['''input_ids''']
      UpperCamelCase						    =					input_ids.ne(1   ).to(UpperCAmelCase__   )
      UpperCamelCase						    =					ids_tensor([self.model_tester.batch_size]					, self.model_tester.type_sequence_label_size   )
      UpperCamelCase						    =					OpenLlamaForSequenceClassification(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, labels=UpperCAmelCase__   )
      self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3
      UpperCamelCase						    =					'''multi_label_classification'''
      UpperCamelCase						    =					input_dict['''input_ids''']
      UpperCamelCase						    =					input_ids.ne(1   ).to(UpperCAmelCase__   )
      UpperCamelCase						    =					ids_tensor(
          [self.model_tester.batch_size, config.num_labels]					, self.model_tester.type_sequence_label_size   ).to(torch.float   )
      UpperCamelCase						    =					OpenLlamaForSequenceClassification(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__					, attention_mask=UpperCAmelCase__					, labels=UpperCAmelCase__   )
      self.assertEqual(result.logits.shape					, (self.model_tester.batch_size, self.model_tester.num_labels)   )

 @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'   )
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      pass






 @parameterized.expand([('linear',), ('dynamic',)]   )
 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					ids_tensor([1, 10]					, config.vocab_size   )
      UpperCamelCase						    =					ids_tensor([1, int(config.max_position_embeddings * 1.5   )]					, config.vocab_size   )

      set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
      UpperCamelCase						    =					OpenLlamaModel(UpperCAmelCase__   )
      original_model.to(UpperCAmelCase__   )
      original_model.eval()
      UpperCamelCase						    =					original_model(UpperCAmelCase__   ).last_hidden_state
      UpperCamelCase						    =					original_model(UpperCAmelCase__   ).last_hidden_state

      set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
      UpperCamelCase						    =					{'''type''': scaling_type, '''factor''': 10.0}
      UpperCamelCase						    =					OpenLlamaModel(UpperCAmelCase__   )
      scaled_model.to(UpperCAmelCase__   )
      scaled_model.eval()
      UpperCamelCase						    =					scaled_model(UpperCAmelCase__   ).last_hidden_state
      UpperCamelCase						    =					scaled_model(UpperCAmelCase__   ).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
           self.assertTrue(torch.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-5   )   )
      else:
           self.assertFalse(torch.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-5   )   )

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(UpperCAmelCase__					, UpperCAmelCase__					, atol=1e-5   )   )





'''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


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






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






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






def 				_lowerCAmelCase			(   __magic_name__						:     dict   , __magic_name__						:     DPRContextEncoder   , __magic_name__						:     DPRContextEncoderTokenizerFast						)				->  dict:
						lowercase :							Dict    		=ctx_tokenizer(
						    documents['''title''']   , documents['''text''']   , truncation=__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)['''input_ids''']
						lowercase :							Optional[int]    		=ctx_encoder(input_ids.to(device=__magic_name__						)   , return_dict=__magic_name__						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						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 :							Tuple    		=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 :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

						# And compute the embeddings
						lowercase :							Any    		=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__magic_name__						)
						lowercase :							Any    		=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
						lowercase :							Optional[int]    		=Features(
						    {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
						lowercase :							Optional[Any]    		=dataset.map(
						    partial(__magic_name__   , ctx_encoder=__magic_name__   , ctx_tokenizer=__magic_name__						)   , batched=__magic_name__   , batch_size=processing_args.batch_size   , features=__magic_name__   , )

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# 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 :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

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





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





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





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    field(
				    default=1_28						,							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)





