




'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      if isinstance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
            snake_case__       :   Optional[Any]	      =    np.full((len(__SCREAMING_SNAKE_CASE		), sequence_length, 2)				,      __SCREAMING_SNAKE_CASE		)
      else:
            snake_case__       :   Dict	      =    np.full((len(__SCREAMING_SNAKE_CASE		), sequence_length)				,      __SCREAMING_SNAKE_CASE		)

      for i, tensor in enumerate(__SCREAMING_SNAKE_CASE		):
            if padding_side == "right":
                  if isinstance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
                        snake_case__       :   int	      =    tensor[:sequence_length]
                  else:
                        snake_case__       :   Union[str, Any]	      =    tensor[:sequence_length]
            else:
                  if isinstance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
                        snake_case__       :   int	      =    tensor[:sequence_length]
                  else:
                        snake_case__       :   Optional[Any]	      =    tensor[:sequence_length]

      return out_tensor.tolist()






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[Any]:
      snake_case__       :   List[str]	      =    ord(__SCREAMING_SNAKE_CASE		)
      if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
            return True
      snake_case__       :   Any	      =    unicodedata.category(__SCREAMING_SNAKE_CASE		)
      if cat.startswith('P'		):
            return True
      return False






@dataclass
class        lowercase_      (						lowerCAmelCase_							):
     A_											=			42
     A_											=			True
     A_											=			None
     A_											=			None
     A_											=			-1_0_0
     A_											=			"pt"





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Union[str, Any]			):
           import torch

           snake_case__       :   List[str]	      =    'label' if 'label' in features[0].keys() else 'labels'
           snake_case__       :   Any	      =    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
           snake_case__       :   Tuple	      =    self.tokenizer.pad(
               __lowerCamelCase     ,							padding=self.padding     ,							max_length=self.max_length     ,							pad_to_multiple_of=self.pad_to_multiple_of     ,							return_tensors='pt' if labels is None else None     ,							)

           if labels is None:
                 return batch

           snake_case__       :   Optional[int]	      =    torch.tensor(batch['entity_ids']			).shape[1]
           snake_case__       :   List[str]	      =    self.tokenizer.padding_side
           if padding_side == "right":
                 snake_case__       :   List[str]	      =    [
                     list(__lowerCamelCase			) + [self.label_pad_token_id] * (sequence_length - len(__lowerCamelCase			)) for label in labels
                 ]
           else:
                 snake_case__       :   int	      =    [
                     [self.label_pad_token_id] * (sequence_length - len(__lowerCamelCase			)) + list(__lowerCamelCase			) for label in labels
                 ]

           snake_case__       :   Optional[Any]	      =    [feature['ner_tags'] for feature in features]
           snake_case__       :   List[str]	      =    padding_tensor(__lowerCamelCase     ,							-1     ,							__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    [feature['original_entity_spans'] for feature in features]
           snake_case__       :   Optional[Any]	      =    padding_tensor(__lowerCamelCase     ,							(-1, -1)     ,							__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Dict	      =    {k: torch.tensor(__lowerCamelCase     ,							dtype=torch.intaa			) for k, v in batch.items()}

           return batch






'''simple docstring'''






import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
      import faiss






@require_faiss
class        lowercase_      (						lowerCAmelCase_							):





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Tuple	      =    tempfile.mkdtemp()
           snake_case__       :   Dict	      =    8

           # DPR tok
           snake_case__       :   Any	      =    [
               '[UNK]',
               '[CLS]',
               '[SEP]',
               '[PAD]',
               '[MASK]',
               'want',
               '##want',
               '##ed',
               'wa',
               'un',
               'runn',
               '##ing',
               ',',
               'low',
               'lowest',
           ]
           snake_case__       :   Optional[int]	      =    os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)
           os.makedirs(__lowerCamelCase     ,							exist_ok=__lowerCamelCase			)
           snake_case__       :   Tuple	      =    os.path.join(__lowerCamelCase     ,							DPR_VOCAB_FILES_NAMES['vocab_file']			)
           with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'			) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)

           # BART tok
           snake_case__       :   Dict	      =    [
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           snake_case__       :   str	      =    dict(zip(__lowerCamelCase     ,							range(len(__lowerCamelCase			)			)			)			)
           snake_case__       :   Union[str, Any]	      =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           snake_case__       :   Any	      =    {'unk_token': '<unk>'}

           snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'bart_tokenizer'			)
           os.makedirs(__lowerCamelCase     ,							exist_ok=__lowerCamelCase			)
           snake_case__       :   Dict	      =    os.path.join(__lowerCamelCase     ,							BART_VOCAB_FILES_NAMES['vocab_file']			)
           snake_case__       :   int	      =    os.path.join(__lowerCamelCase     ,							BART_VOCAB_FILES_NAMES['merges_file']			)
           with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'			) as fp:
                 fp.write(json.dumps(__lowerCamelCase			) + '\n'			)
           with open(self.merges_file     ,							'w'     ,							encoding='utf-8'			) as fp:
                 fp.write('\n'.join(__lowerCamelCase			)			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'bart_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					int			):
           shutil.rmtree(self.tmpdirname			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   int	      =    Dataset.from_dict(
               {
                   'id': ['0', '1'],
                   'text': ['foo', 'bar'],
                   'title': ['Foo', 'Bar'],
                   'embeddings': [np.ones(self.retrieval_vector_size			), 2 * np.ones(self.retrieval_vector_size			)],
               }			)
           dataset.add_faiss_index('embeddings'     ,							string_factory='Flat'     ,							metric_type=faiss.METRIC_INNER_PRODUCT			)
           return dataset





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[str]	      =    self.get_dummy_dataset()
           snake_case__       :   Dict	      =    RagConfig(
               retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							)
           with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
                 snake_case__       :   Optional[Any]	      =    dataset
                 snake_case__       :   List[Any]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
           return retriever





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					bool			):
           snake_case__       :   Union[str, Any]	      =    self.get_dummy_dataset()
           snake_case__       :   Dict	      =    RagConfig(
               retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							index_name='custom'     ,							)
           if from_disk:
                 snake_case__       :   List[Any]	      =    os.path.join(self.tmpdirname     ,							'dataset'			)
                 snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'index.faiss'			)
                 dataset.get_index('embeddings'			).save(os.path.join(self.tmpdirname     ,							'index.faiss'			)			)
                 dataset.drop_index('embeddings'			)
                 dataset.save_to_disk(os.path.join(self.tmpdirname     ,							'dataset'			)			)
                 del dataset
                 snake_case__       :   Optional[int]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
           else:
                 snake_case__       :   Union[str, Any]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							index=CustomHFIndex(config.retrieval_vector_size     ,							__lowerCamelCase			)     ,							)
           return retriever





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Dict	      =    Dataset.from_dict(
               {
                   'id': ['0', '1'],
                   'text': ['foo', 'bar'],
                   'title': ['Foo', 'Bar'],
                   'embeddings': [np.ones(self.retrieval_vector_size + 1			), 2 * np.ones(self.retrieval_vector_size + 1			)],
               }			)
           dataset.add_faiss_index('embeddings'     ,							string_factory='Flat'     ,							metric_type=faiss.METRIC_INNER_PRODUCT			)

           snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'hf_bert_base.hnswSQ8_correct_phi_128.c_index'			)
           dataset.save_faiss_index('embeddings'     ,							index_file_name + '.index.dpr'			)
           pickle.dump(dataset['id']     ,							open(index_file_name + '.index_meta.dpr'     ,							'wb'			)			)

           snake_case__       :   Optional[int]	      =    os.path.join(self.tmpdirname     ,							'psgs_w100.tsv.pkl'			)
           snake_case__       :   Optional[Any]	      =    {sample['id']: [sample['text'], sample['title']] for sample in dataset}
           pickle.dump(__lowerCamelCase     ,							open(__lowerCamelCase     ,							'wb'			)			)

           snake_case__       :   Union[str, Any]	      =    RagConfig(
               retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							index_name='legacy'     ,							index_path=self.tmpdirname     ,							)
           snake_case__       :   List[str]	      =    RagRetriever(
               __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()			)
           return retriever





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   List[str]	      =    1
           snake_case__       :   int	      =    self.get_dummy_canonical_hf_index_retriever()
           snake_case__       :   Tuple	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   int	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['embeddings', 'id', 'text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['id']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['id'][0]     ,							'1'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['id'][0]     ,							'0'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   str	      =    self.get_dummy_canonical_hf_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
                       snake_case__       :   Union[str, Any]	      =    self.get_dummy_dataset()
                       retriever.save_pretrained(__lowerCamelCase			)
                       snake_case__       :   List[Any]	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                       self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                       snake_case__       :   str	      =    np.array(
                           [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                       snake_case__       :   Tuple	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                       self.assertTrue(out is not None			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Any	      =    1
           snake_case__       :   Tuple	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           snake_case__       :   Dict	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   List[str]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['embeddings', 'id', 'text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['id']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['id'][0]     ,							'1'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['id'][0]     ,							'0'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   str	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase			)
                 snake_case__       :   int	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                 self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Tuple	      =    np.array(
                     [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                 snake_case__       :   List[str]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                 self.assertTrue(out is not None			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Any	      =    1
           snake_case__       :   Optional[int]	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           snake_case__       :   str	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   List[Any]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['embeddings', 'id', 'text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['id']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['id'][0]     ,							'1'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['id'][0]     ,							'0'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Optional[int]	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase			)
                 snake_case__       :   int	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                 self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Optional[int]	      =    np.array(
                     [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                 snake_case__       :   List[Any]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                 self.assertTrue(out is not None			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Tuple	      =    1
           snake_case__       :   Tuple	      =    self.get_dummy_legacy_index_retriever()
           snake_case__       :   str	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   List[str]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['text']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['text'][0]     ,							'bar'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['text'][0]     ,							'foo'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Any	      =    self.get_dummy_legacy_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase			)
                 snake_case__       :   Dict	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                 self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Any	      =    np.array(
                     [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                 snake_case__       :   List[Any]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                 self.assertTrue(out is not None			)





     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 			_lowerCAmelCase      (     self     :					Any			):
           import torch

           snake_case__       :   Optional[Any]	      =    1
           snake_case__       :   int	      =    self.get_dummy_canonical_hf_index_retriever()
           snake_case__       :   List[Any]	      =    [[5, 7], [10, 11]]
           snake_case__       :   List[Any]	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__       :   Any	      =    retriever(__lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   Any	      =    (
               out['context_input_ids'],
               out['context_attention_mask'],
               out['retrieved_doc_embeds'],
           )
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
           self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
           self.assertIsInstance(__lowerCamelCase     ,							np.ndarray			)

           snake_case__       :   Dict	      =    retriever(
               __lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase     ,							return_tensors='pt'     ,							)
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[Any]	      =    (  # noqa: F841
               out['context_input_ids'],
               out['context_attention_mask'],
               out['retrieved_doc_embeds'],
               out['doc_ids'],
           )
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)
           self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)
           self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)





     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   List[Any]	      =    self.get_dpr_ctx_encoder_tokenizer()
           snake_case__       :   List[Any]	      =    1
           snake_case__       :   Any	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           retriever.set_ctx_encoder_tokenizer(__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    [[5, 7], [10, 11]]
           snake_case__       :   Tuple	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__       :   Tuple	      =    retriever(__lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase			)

           self.assertEqual(
               len(__lowerCamelCase			)     ,							6			)  # check whether the retriever output consist of 6 attributes including tokenized docs
           self.assertEqual(
               all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')			)     ,							__lowerCamelCase			)  # check for doc token related keys in dictionary.
