




'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.layoutlm.modeling_tf_layoutlm import (
       TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFLayoutLMForMaskedLM,
       TFLayoutLMForQuestionAnswering,
       TFLayoutLMForSequenceClassification,
       TFLayoutLMForTokenClassification,
       TFLayoutLMModel,
   )

class        _a    :






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=13 ,_SCREAMING_SNAKE_CASE=7 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=99 ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=37 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=512 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=1_000 ,)							->      Optional[Any]:
          _snake_case							=				parent
          _snake_case							=				batch_size
          _snake_case							=				seq_length
          _snake_case							=				is_training
          _snake_case							=				use_input_mask
          _snake_case							=				use_token_type_ids
          _snake_case							=				use_labels
          _snake_case							=				vocab_size
          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				max_position_embeddings
          _snake_case							=				type_vocab_size
          _snake_case							=				type_sequence_label_size
          _snake_case							=				initializer_range
          _snake_case							=				num_labels
          _snake_case							=				num_choices
          _snake_case							=				scope
          _snake_case							=				range_bbox






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size   )

          # convert bbox to numpy since TF does not support item assignment
          _snake_case							=				ids_tensor([self.batch_size, self.seq_length, 4] ,self.range_bbox   ).numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]   ):
               for j in range(bbox.shape[1]   ):
                    if bbox[i, j, 3] < bbox[i, j, 1]:
                         _snake_case							=				bbox[i, j, 3]
                         _snake_case							=				bbox[i, j, 1]
                         _snake_case							=				t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         _snake_case							=				bbox[i, j, 2]
                         _snake_case							=				bbox[i, j, 0]
                         _snake_case							=				t
          _snake_case							=				tf.convert_to_tensor(_SCREAMING_SNAKE_CASE   )

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

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

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

          _snake_case							=				LayoutLMConfig(
              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 ,initializer_range=self.initializer_range ,)

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				TFLayoutLMModel(config=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)   )
          self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				TFLayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE ,labels=_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          _snake_case							=				self.num_labels
          _snake_case							=				TFLayoutLMForSequenceClassification(config=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Any:
          _snake_case							=				self.num_labels
          _snake_case							=				TFLayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE ,labels=_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          _snake_case							=				TFLayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				model(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length)   )
          self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length)   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.prepare_config_and_inputs()
          (
              (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, 
          )							=				config_and_inputs
          _snake_case							=				{
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict







@require_tf
class        _a    (						__lowerCAmelCase , __lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						(
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:   List[str]       =						(
         {
             """feature-extraction""": TFLayoutLMModel,
             """fill-mask""": TFLayoutLMForMaskedLM,
             """text-classification""": TFLayoutLMForSequenceClassification,
             """token-classification""": TFLayoutLMForTokenClassification,
             """zero-shot""": TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						False
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						True
     SCREAMING_SNAKE_CASE_							:   List[str]       =						10






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				TFLayoutLMModelTester(self   )
          _snake_case							=				ConfigTester(self ,config_class=_SCREAMING_SNAKE_CASE ,hidden_size=37   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.config_tester.run_common_tests()






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      int:
          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case							=				TFLayoutLMModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )






     @unittest.skip("Onnx compliancy broke with TF 2.10"   )
     def     _lowercase     (			self   )							->      int:
          pass







def      __a     (       )						->			str:

     """simple docstring"""




     _snake_case							=				tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]   )  # noqa: E231
     _snake_case							=				tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]   )  # noqa: E231
     _snake_case							=				tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]   )  # noqa: E231
     _snake_case							=				tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]   )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     _snake_case							=				tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]   )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels





@require_tf
class        _a    (						unittest.TestCase							):






     @slow
     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"   )

          _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				prepare_layoutlm_batch_inputs()

          # forward pass
          _snake_case							=				model(input_ids=_SCREAMING_SNAKE_CASE ,bbox=_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE   )

          # test the sequence output on [0, :3, :3]
          _snake_case							=				tf.convert_to_tensor(
              [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]] ,)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )   )

          # test the pooled output on [1, :3]
          _snake_case							=				tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]   )

          self.assertTrue(np.allclose(outputs.pooler_output[1, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )   )






     @slow
     def     _lowercase     (			self   )							->      Dict:
          # initialize model with randomly initialized sequence classification head
          _snake_case							=				TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,num_labels=2   )

          _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				prepare_layoutlm_batch_inputs()

          # forward pass
          _snake_case							=				model(
              input_ids=_SCREAMING_SNAKE_CASE ,bbox=_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE ,labels=tf.convert_to_tensor([1, 1]   ) ,)

          # test whether we get a loss as a scalar
          _snake_case							=				outputs.loss
          _snake_case							=				(2,)
          self.assertEqual(loss.shape ,_SCREAMING_SNAKE_CASE   )

          # test the shape of the logits
          _snake_case							=				outputs.logits
          _snake_case							=				(2, 2)
          self.assertEqual(logits.shape ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      List[str]:
          # initialize model with randomly initialized token classification head
          _snake_case							=				TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased" ,num_labels=13   )

          _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				prepare_layoutlm_batch_inputs()

          # forward pass
          _snake_case							=				model(
              input_ids=_SCREAMING_SNAKE_CASE ,bbox=_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE ,labels=_SCREAMING_SNAKE_CASE   )

          # test the shape of the logits
          _snake_case							=				outputs.logits
          _snake_case							=				tf.convert_to_tensor((2, 25, 13)   )
          self.assertEqual(logits.shape ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      Optional[int]:
          # initialize model with randomly initialized token classification head
          _snake_case							=				TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"   )

          _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case							=				prepare_layoutlm_batch_inputs()

          # forward pass
          _snake_case							=				model(input_ids=_SCREAMING_SNAKE_CASE ,bbox=_SCREAMING_SNAKE_CASE ,attention_mask=_SCREAMING_SNAKE_CASE ,token_type_ids=_SCREAMING_SNAKE_CASE   )

          # test the shape of the logits
          _snake_case							=				tf.convert_to_tensor((2, 25)   )
          self.assertEqual(outputs.start_logits.shape ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(outputs.end_logits.shape ,_SCREAMING_SNAKE_CASE   )








'''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_						:      Any       =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_						:      Optional[Any]       =						'''cuda''' if torch.cuda.is_available() else '''cpu'''





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Union[str, Any]=100 ,						_UpperCamelCase:   List[str]=" "   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				text.split(_UpperCamelCase   )
     return [character.join(text[i : i + n]   ).strip() for i in range(0 ,						len(_UpperCamelCase   ) ,						_UpperCamelCase   )]





def      __a     (       _UpperCamelCase:   dict   )						->			dict:

     """simple docstring"""




     _snake_case							, _snake_case							=				[], []
     for title, text in zip(documents["title"] ,						documents["text"]   ):
          if text is not None:
               for passage in split_text(_UpperCamelCase   ):
                    titles.append(title if title is not None else ""   )
                    texts.append(_UpperCamelCase   )
     return {"title": titles, "text": texts}





def      __a     (       _UpperCamelCase:   dict ,						_UpperCamelCase:   DPRContextEncoder ,						_UpperCamelCase:   DPRContextEncoderTokenizerFast   )						->			dict:

     """simple docstring"""




     _snake_case							=				ctx_tokenizer(
         documents["title"] ,						documents["text"] ,						truncation=_UpperCamelCase ,						padding="longest" ,						return_tensors="pt"   )["input_ids"]
     _snake_case							=				ctx_encoder(input_ids.to(device=_UpperCamelCase   ) ,						return_dict=_UpperCamelCase   ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}





def      __a     (       _UpperCamelCase:   "RagExampleArguments" ,						_UpperCamelCase:   "ProcessingArguments" ,						_UpperCamelCase:   "IndexHnswArguments" ,						)						->			Dict:

     """simple docstring"""




     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
     _snake_case							=				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
     _snake_case							=				dataset.map(_UpperCamelCase ,						batched=_UpperCamelCase ,						num_proc=processing_args.num_proc   )

     # And compute the embeddings
     _snake_case							=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=_UpperCamelCase   )
     _snake_case							=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
     _snake_case							=				Features(
         {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space
     _snake_case							=				dataset.map(
         partial(_UpperCamelCase ,						ctx_encoder=_UpperCamelCase ,						ctx_tokenizer=_UpperCamelCase   ) ,						batched=_UpperCamelCase ,						batch_size=processing_args.batch_size ,						features=_UpperCamelCase ,						)

     # And finally save your dataset
     _snake_case							=				os.path.join(rag_example_args.output_dir ,						"my_knowledge_dataset"   )
     dataset.save_to_disk(_UpperCamelCase   )
     # 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
     _snake_case							=				faiss.IndexHNSWFlat(index_hnsw_args.d ,						index_hnsw_args.m ,						faiss.METRIC_INNER_PRODUCT   )
     dataset.add_faiss_index("embeddings" ,						custom_index=_UpperCamelCase   )

     # And save the index
     _snake_case							=				os.path.join(rag_example_args.output_dir ,						"my_knowledge_dataset_hnsw_index.faiss"   )
     dataset.get_index("embeddings"   ).save(_UpperCamelCase   )
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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







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







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

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


