



import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
    )
    from transformers.models.efficientformer.modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )


if is_vision_available():
    from PIL import Image

    from transformers import EfficientFormerImageProcessor



class 						_snake_case					:





 """simple docstring"""



 def __init__(  self		,			a		,			a = 1_3		,			a = 6_4		,			a = 2		,			a = 3		,			a = 3		,			a = True		,			a = True		,			a = 1_2_8		,			a=[1_6, 3_2, 6_4, 1_2_8]		,			a = 7		,			a = 4		,			a = 3_7		,			a = "gelu"		,			a = 0.1		,			a = 0.1		,			a = 1_0		,			a = 0.02		,			a = 2		,			a = 1		,			a = 1_2_8		,			a = [2, 2, 2, 2]		,			a = 2		,			a = 2		,			)		->							Dict:




   """simple docstring"""







   _A							=			parent
   _A							=			batch_size
   _A							=			image_size
   _A							=			patch_size
   _A							=			num_channels
   _A							=			is_training
   _A							=			use_labels
   _A							=			hidden_size
   _A							=			num_hidden_layers
   _A							=			num_attention_heads
   _A							=			intermediate_size
   _A							=			hidden_act
   _A							=			hidden_dropout_prob
   _A							=			attention_probs_dropout_prob
   _A							=			type_sequence_label_size
   _A							=			initializer_range
   _A							=			encoder_stride
   _A							=			num_attention_outputs
   _A							=			embed_dim
   _A							=			embed_dim + 1
   _A							=			resolution
   _A							=			depths
   _A							=			hidden_sizes
   _A							=			dim
   _A							=			mlp_expansion_ratio
 def lowercase_						(  self    )		->							Union[str, Any]:




   """simple docstring"""







   _A							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

   _A							=			None
   if self.use_labels:
     _A							=			ids_tensor([self.batch_size]		,			self.type_sequence_label_size    )

   _A							=			self.get_config()
   return config, pixel_values, labels
 def lowercase_						(  self    )		->							Any:




   """simple docstring"""







   return EfficientFormerConfig(
       image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			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		,			is_decoder=a		,			initializer_range=self.initializer_range		,			encoder_stride=self.encoder_stride		,			resolution=self.resolution		,			depths=self.depths		,			hidden_sizes=self.hidden_sizes		,			dim=self.dim		,			mlp_expansion_ratio=self.mlp_expansion_ratio		,			)
 def lowercase_						(  self		,			a		,			a		,			a    )		->							int:




   """simple docstring"""







   _A							=			EfficientFormerModel(config=a    )
   model.to(a    )
   model.eval()
   _A							=			model(a    )
   self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)    )
 def lowercase_						(  self		,			a		,			a		,			a    )		->							Optional[int]:




   """simple docstring"""







   _A							=			self.type_sequence_label_size
   _A							=			EfficientFormerForImageClassification(a    )
   model.to(a    )
   model.eval()
   _A							=			model(a		,			labels=a    )
   self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)    )

   # test greyscale images
   _A							=			1
   _A							=			EfficientFormerForImageClassification(a    )
   model.to(a    )
   model.eval()

   _A							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   _A							=			model(a    )
   self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)    )



 def lowercase_						(  self    )		->							Optional[Any]:




   """simple docstring"""







   _A							=			self.prepare_config_and_inputs()
   (
       (
       _A      
   )			,      (
       _A      
   )			,      (
       _A      
   )			,            
   )							=			config_and_inputs
   _A							=			{'''pixel_values''': pixel_values}
   return config, inputs_dict







@require_torch
class 						_snake_case					(	__snake_case    ,__snake_case    ,unittest.TestCase ):





 """simple docstring"""



 lowerCamelCase_								=	(
     (
         EfficientFormerModel,
         EfficientFormerForImageClassificationWithTeacher,
         EfficientFormerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 lowerCamelCase_								=	(
     {
         'feature-extraction': EfficientFormerModel,
         'image-classification': (
             EfficientFormerForImageClassification,
             EfficientFormerForImageClassificationWithTeacher,
         ),
     }
     if is_torch_available()
     else {}
 )
 lowerCamelCase_								=	False

 lowerCamelCase_								=	False
 lowerCamelCase_								=	False
 lowerCamelCase_								=	False
 def lowercase_						(  self    )		->							Optional[Any]:




   """simple docstring"""







   _A							=			EfficientFormerModelTester(self    )
   _A							=			ConfigTester(
       self		,			config_class=a		,			has_text_modality=a		,			hidden_size=3_7    )
 def lowercase_						(  self    )		->							Tuple:




   """simple docstring"""







   self.config_tester.run_common_tests()
 @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''    )
 def lowercase_						(  self    )		->							str:




   """simple docstring"""







   pass
 @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''    )
 def lowercase_						(  self    )		->							List[str]:




   """simple docstring"""







   pass
 def lowercase_						(  self    )		->							List[Any]:




   """simple docstring"""







   _A			,      _A							=			self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _A							=			model_class(a    )
     _A							=			inspect.signature(model.forward    )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _A							=			[*signature.parameters.keys()]

     _A							=			['''pixel_values''']
     self.assertListEqual(arg_names[:1]		,			a    )
 def lowercase_						(  self    )		->							Any:




   """simple docstring"""







   def check_hidden_states_output(a		,			a		,			a    ):
     _A							=			model_class(a    )
     model.to(a    )
     model.eval()

     with torch.no_grad():
       _A							=			model(**self._prepare_for_class(a		,			a    )    )

     _A							=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     _A							=			getattr(
         self.model_tester		,			'''expected_num_hidden_layers'''		,			self.model_tester.num_hidden_layers + 1    )
     self.assertEqual(len(a    )		,			a    )

     if hasattr(self.model_tester		,			'''encoder_seq_length'''    ):
       _A							=			self.model_tester.encoder_seq_length
       if hasattr(self.model_tester		,			'''chunk_length'''    ) and self.model_tester.chunk_length > 1:
         _A							=			seq_length * self.model_tester.chunk_length
     else:
       _A							=			self.model_tester.seq_length

     self.assertListEqual(
         list(hidden_states[-1].shape[-2:]    )		,			[seq_length, self.model_tester.hidden_size]		,			)

     if config.is_encoder_decoder:
       _A							=			outputs.decoder_hidden_states

       self.assertIsInstance(a		,			(list, tuple)    )
       self.assertEqual(len(a    )		,			a    )
       _A							=			getattr(self.model_tester		,			'''seq_length'''		,			a    )
       _A							=			getattr(self.model_tester		,			'''decoder_seq_length'''		,			a    )

       self.assertListEqual(
           list(hidden_states[-1].shape[-2:]    )		,			[decoder_seq_length, self.model_tester.hidden_size]		,			)

   _A			,      _A							=			self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _A							=			True
     check_hidden_states_output(a		,			a		,			a    )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _A							=			True

     check_hidden_states_output(a		,			a		,			a    )
 def lowercase_						(  self		,			a		,			a		,			a=False    )		->							Any:




   """simple docstring"""







   _A							=			super()._prepare_for_class(a		,			a		,			return_labels=a    )

   if return_labels:
     if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
       del inputs_dict["labels"]

   return inputs_dict
 def lowercase_						(  self    )		->							List[Any]:




   """simple docstring"""







   _A							=			self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a    )
 @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''    )
 def lowercase_						(  self    )		->							List[str]:




   """simple docstring"""







   _A							=			self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*a    )
 def lowercase_						(  self    )		->							Optional[Any]:




   """simple docstring"""







   _A							=			self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a    )
 def lowercase_						(  self    )		->							Optional[Any]:




   """simple docstring"""







   if not self.model_tester.is_training:
     return

   _A			,      _A							=			self.model_tester.prepare_config_and_inputs_for_common()
   _A							=			True

   for model_class in self.all_model_classes:
     # EfficientFormerForImageClassificationWithTeacher supports inference-only
     if (
         model_class in get_values(a    )
         or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
     ):
       continue
     _A							=			model_class(a    )
     model.to(a    )
     model.train()
     _A							=			self._prepare_for_class(a		,			a		,			return_labels=a    )
     _A							=			model(**a    ).loss
     loss.backward()
 def lowercase_						(  self    )		->							Tuple:




   """simple docstring"""







   _A			,      _A							=			self.model_tester.prepare_config_and_inputs_for_common()

   _A							=			[
       {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
       {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
       {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
   ]

   for model_class in self.all_model_classes:
     if (
         model_class
         not in [
             *get_values(a    ),
         ]
         or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
     ):
       continue

     for problem_type in problem_types:
       with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''    ):
         _A							=			problem_type['''title''']
         _A							=			problem_type['''num_labels''']

         _A							=			model_class(a    )
         model.to(a    )
         model.train()

         _A							=			self._prepare_for_class(a		,			a		,			return_labels=a    )

         if problem_type["num_labels"] > 1:
           _A							=			inputs['''labels'''].unsqueeze(1    ).repeat(1		,			problem_type['''num_labels''']    )

         _A							=			inputs['''labels'''].to(problem_type['''dtype''']    )

         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
         # they have the same size." which is a symptom something in wrong for the regression problem.
         # See https://github.com/huggingface/transformers/issues/11780
         with warnings.catch_warnings(record=a    ) as warning_list:
           _A							=			model(**a    ).loss
         for w in warning_list:
           if "Using a target size that is different to the input size" in str(w.message    ):
             raise ValueError(
                 f'''Something is going wrong in the regression problem: intercepted {w.message}'''    )

         loss.backward()
 @slow
 def lowercase_						(  self    )		->							Dict:




   """simple docstring"""







   for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _A							=			EfficientFormerModel.from_pretrained(a    )
     self.assertIsNotNone(a    )



 def lowercase_						(  self    )		->							Dict:




   """simple docstring"""







   _A			,      _A							=			self.model_tester.prepare_config_and_inputs_for_common()
   _A							=			True

   _A							=			getattr(self.model_tester		,			'''seq_length'''		,			a    )
   _A							=			getattr(self.model_tester		,			'''encoder_seq_length'''		,			a    )
   _A							=			getattr(self.model_tester		,			'''key_length'''		,			a    )
   _A							=			getattr(self.model_tester		,			'''chunk_length'''		,			a    )

   if chunk_length is not None and hasattr(self.model_tester		,			'''num_hashes'''    ):
     _A							=			encoder_seq_length * self.model_tester.num_hashes

   for model_class in self.all_model_classes:
     _A							=			True
     _A							=			False
     _A							=			True
     _A							=			model_class(a    )
     model.to(a    )
     model.eval()
     with torch.no_grad():
       _A							=			model(**self._prepare_for_class(a		,			a    )    )
     _A							=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
     self.assertEqual(len(a    )		,			self.model_tester.num_attention_outputs    )

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     _A							=			True
     _A							=			model_class(a    )
     model.to(a    )
     model.eval()
     with torch.no_grad():
       _A							=			model(**self._prepare_for_class(a		,			a    )    )
     _A							=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
     self.assertEqual(len(a    )		,			self.model_tester.num_attention_outputs    )

     if chunk_length is not None:
       self.assertListEqual(
           list(attentions[0].shape[-4:]    )		,			[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]		,			)
     else:
       self.assertListEqual(
           list(attentions[0].shape[-3:]    )		,			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]		,			)







def 			UpperCAmelCase__       (							)      ->       List[str]:
  _A							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
  return image



@require_torch
@require_vision
class 						_snake_case					(	unittest.TestCase ):





 """simple docstring"""



 @cached_property
 def lowercase_						(  self    )		->							List[str]:




   """simple docstring"""







   return (
       EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''    )
       if is_vision_available()
       else None
   )
 @slow
 def lowercase_						(  self    )		->							Union[str, Any]:




   """simple docstring"""







   _A							=			EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''    ).to(
       a    )

   _A							=			self.default_image_processor
   _A							=			prepare_img()
   _A							=			image_processor(images=a		,			return_tensors='''pt'''    ).to(a    )

   # forward pass
   with torch.no_grad():
     _A							=			model(**a    )

   # verify the logits
   _A							=			(1, 1_0_0_0)
   self.assertEqual(outputs.logits.shape		,			a    )

   _A							=			torch.tensor([-0.0_555, 0.4_825, -0.0_852]    ).to(a    )
   self.assertTrue(torch.allclose(outputs.logits[0][:3]		,			a		,			atol=1e-4    )    )



 @slow
 def lowercase_						(  self    )		->							str:




   """simple docstring"""







   _A							=			EfficientFormerForImageClassificationWithTeacher.from_pretrained(
       '''snap-research/efficientformer-l1-300'''    ).to(a    )

   _A							=			self.default_image_processor
   _A							=			prepare_img()
   _A							=			image_processor(images=a		,			return_tensors='''pt'''    ).to(a    )

   # forward pass
   with torch.no_grad():
     _A							=			model(**a    )

   # verify the logits
   _A							=			(1, 1_0_0_0)
   self.assertEqual(outputs.logits.shape		,			a    )

   _A							=			torch.tensor([-0.1_312, 0.4_353, -1.0_499]    ).to(a    )
   self.assertTrue(torch.allclose(outputs.logits[0][:3]		,			a		,			atol=1e-4    )    )
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_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					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 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					logger.info('Step 1 - Create the dataset'						)
					######################################

					# The dataset needed for RAG must have three columns:
					# - title (string): title of the document
					# - text (string): text of a passage of the document
					# - embeddings (array of dimension d): DPR representation of the passage

					# Let's say you have documents in tab-separated csv files with columns "title" and "text"
					assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

					# You can load a Dataset object this way
					__a							=   load_dataset(
					    'csv'      ,				data_files=[rag_example_args.csv_path]      ,				split='train'      ,				delimiter='\t'      ,				column_names=['title', 'text']						)

					# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

					# Then split the documents into passages of 100 words
					__a							=   dataset.map(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

					# And compute the embeddings
					__a							=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowerCamelCase						)
					__a							=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
					__a							=   Features(
					    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
					__a							=   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
					__a							=   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
					__a							=   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
					__a							=   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
					__a							=   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,				index_name='custom'      ,				indexed_dataset=__lowerCamelCase						)
					__a							=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,				retriever=__lowerCamelCase						)
					__a							=   RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

					# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
					# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

					######################################
					logger.info('Step 4 - Have fun'						)
					######################################

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


@dataclass
class 						a__							:
			A__				:   str								=			field(
			    default=str(Path(__snake_case		).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=__snake_case      ,						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=__snake_case      ,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}      ,						)






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






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

	lowerCamelCase_				:		Tuple       						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Union[str, Any]       						=       parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCamelCase_				:		Union[str, Any]       						=       rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)




