
import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     DeiTForImageClassification,
     DeiTForImageClassificationWithTeacher,
     DeiTForMaskedImageModeling,
     DeiTModel,
 )
 from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Tuple=13					, SCREAMING_SNAKE_CASE__			:			Optional[int]=30					, SCREAMING_SNAKE_CASE__			:			Tuple=2					, SCREAMING_SNAKE_CASE__			:			List[Any]=3					, SCREAMING_SNAKE_CASE__			:			Optional[int]=True					, SCREAMING_SNAKE_CASE__			:			int=True					, SCREAMING_SNAKE_CASE__			:			Any=32					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=5					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=4					, SCREAMING_SNAKE_CASE__			:			str=37					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]="gelu"					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			List[str]=0.1					, SCREAMING_SNAKE_CASE__			:			int=10					, SCREAMING_SNAKE_CASE__			:			Tuple=0.02					, SCREAMING_SNAKE_CASE__			:			Tuple=3					, SCREAMING_SNAKE_CASE__			:			List[str]=None					, SCREAMING_SNAKE_CASE__			:			List[str]=2					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					patch_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					use_labels
      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						    =					type_sequence_label_size
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					scope
      UpperCamelCase						    =					encoder_stride

      # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
      UpperCamelCase						    =					(image_size // patch_size) ** 2
      UpperCamelCase						    =					num_patches + 2

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






      """simple docstring"""



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

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

      UpperCamelCase						    =					self.get_config()

      return config, pixel_values, labels

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return DeiTConfig(
          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=UpperCAmelCase__					, initializer_range=self.initializer_range					, encoder_stride=self.encoder_stride					, )

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






      """simple docstring"""



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

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






      """simple docstring"""



      UpperCamelCase						    =					DeiTForMaskedImageModeling(config=UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__   )
      self.parent.assertEqual(
          result.reconstruction.shape					, (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

      # test greyscale images
      UpperCamelCase						    =					1
      UpperCamelCase						    =					DeiTForMaskedImageModeling(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()

      UpperCamelCase						    =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      UpperCamelCase						    =					model(UpperCAmelCase__   )
      self.parent.assertEqual(result.reconstruction.shape					, (self.batch_size, 1, self.image_size, self.image_size)   )

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






      """simple docstring"""



      UpperCamelCase						    =					self.type_sequence_label_size
      UpperCamelCase						    =					DeiTForImageClassification(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()
      UpperCamelCase						    =					model(UpperCAmelCase__					, labels=UpperCAmelCase__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)   )

      # test greyscale images
      UpperCamelCase						    =					1
      UpperCamelCase						    =					DeiTForImageClassification(UpperCAmelCase__   )
      model.to(UpperCAmelCase__   )
      model.eval()

      UpperCamelCase						    =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
      UpperCamelCase						    =					model(UpperCAmelCase__					, labels=UpperCAmelCase__   )
      self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)   )






 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



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




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

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_				:					int       						=(
     {
         "feature-extraction": DeiTModel,
         "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=False
 SCREAMING_SNAKE_CASE_				:					str       						=False
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=False

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					DeiTModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=UpperCAmelCase__					, has_text_modality=UpperCAmelCase__					, hidden_size=37   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      self.config_tester.run_common_tests()

 @unittest.skip(reason='DeiT does not use inputs_embeds'   )
 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      pass

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






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(UpperCAmelCase__   )
           self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)   )
           UpperCamelCase						    =					model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCAmelCase__					, nn.Linear   )   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase						    =					['''pixel_values''']
           self.assertListEqual(arg_names[:1]					, UpperCAmelCase__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Any=False   ):






      """simple docstring"""



      UpperCamelCase						    =					super()._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__					, return_labels=UpperCAmelCase__   )

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

      return inputs_dict

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      if not self.model_tester.is_training:
           return

      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					True

      for model_class in self.all_model_classes:
           # DeiTForImageClassificationWithTeacher supports inference-only
           if (
               model_class in get_values(UpperCAmelCase__   )
               or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
           ):
                continue
           UpperCamelCase						    =					model_class(UpperCAmelCase__   )
           model.to(UpperCAmelCase__   )
           model.train()
           UpperCamelCase						    =					self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__					, return_labels=UpperCAmelCase__   )
           UpperCamelCase						    =					model(**UpperCAmelCase__   ).loss
           loss.backward()

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
           return

      UpperCamelCase						    =					False
      UpperCamelCase						    =					True

      for model_class in self.all_model_classes:
           if model_class in get_values(UpperCAmelCase__   ) or not model_class.supports_gradient_checkpointing:
                continue
           # DeiTForImageClassificationWithTeacher supports inference-only
           if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                continue
           UpperCamelCase						    =					model_class(UpperCAmelCase__   )
           model.gradient_checkpointing_enable()
           model.to(UpperCAmelCase__   )
           model.train()
           UpperCamelCase						    =					self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__					, return_labels=UpperCAmelCase__   )
           UpperCamelCase						    =					model(**UpperCAmelCase__   ).loss
           loss.backward()

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      UpperCamelCase						    =					[
          {'''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(UpperCAmelCase__   ),
                   *get_values(UpperCAmelCase__   ),
               ]
               or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
           ):
                continue

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

                     UpperCamelCase						    =					model_class(UpperCAmelCase__   )
                     model.to(UpperCAmelCase__   )
                     model.train()

                     UpperCamelCase						    =					self._prepare_for_class(UpperCAmelCase__					, UpperCAmelCase__					, return_labels=UpperCAmelCase__   )

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

                     UpperCamelCase						    =					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=UpperCAmelCase__   ) as warning_list:
                          UpperCamelCase						    =					model(**UpperCAmelCase__   ).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      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase						    =					DeiTModel.from_pretrained(UpperCAmelCase__   )
           self.assertIsNotNone(UpperCAmelCase__   )




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







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

 """simple docstring"""

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






      """simple docstring"""



      return (
          DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'   )
          if is_vision_available()
          else None
      )

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






      """simple docstring"""



      UpperCamelCase						    =					DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'   ).to(
          UpperCAmelCase__   )

      UpperCamelCase						    =					self.default_image_processor
      UpperCamelCase						    =					prepare_img()
      UpperCamelCase						    =					image_processor(images=UpperCAmelCase__					, return_tensors='pt'   ).to(UpperCAmelCase__   )

      # forward pass
      with torch.no_grad():
           UpperCamelCase						    =					model(**UpperCAmelCase__   )

      # verify the logits
      UpperCamelCase						    =					torch.Size((1, 10_00)   )
      self.assertEqual(outputs.logits.shape					, UpperCAmelCase__   )

      UpperCamelCase						    =					torch.tensor([-1.0266, 0.1912, -1.2861]   ).to(UpperCAmelCase__   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]					, UpperCAmelCase__					, atol=1e-4   )   )






 @slow
 @require_accelerate
 @require_torch_gpu
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DeiTModel.from_pretrained(
          'facebook/deit-base-distilled-patch16-224'					, torch_dtype=torch.floataa					, device_map='auto'   )
      UpperCamelCase						    =					self.default_image_processor

      UpperCamelCase						    =					prepare_img()
      UpperCamelCase						    =					image_processor(images=UpperCAmelCase__					, return_tensors='pt'   )
      UpperCamelCase						    =					inputs.pixel_values.to(UpperCAmelCase__   )

      # forward pass to make sure inference works in fp16
      with torch.no_grad():
           UpperCamelCase						    =					model(UpperCAmelCase__   )





'''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)





