


'''simple docstring'''



import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

A__						:   Any				=    random.Random()



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Dict=1.0 ,       UpperCAmelCase_       :							str=None ,       UpperCAmelCase_       :							List[Any]=None				)	->						Union[str, Any]:
     if rng is None:
          __lowerCamelCase  :      Any														=						global_rng

     __lowerCamelCase  :      List[Any]														=						[]
     for batch_idx in range(shape[0]				):
          values.append([]				)
          for _ in range(shape[1]				):
               values[-1].append(rng.random() * scale				)

     return values





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=20_00     ,	SCREAMING_SNAKE_CASE_=20_48     ,	SCREAMING_SNAKE_CASE_=1_28     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=4_41_00     ,	)							->	str:
         __lowerCamelCase  :      Dict														=						parent
         __lowerCamelCase  :      Union[str, Any]														=						batch_size
         __lowerCamelCase  :      Any														=						min_seq_length
         __lowerCamelCase  :      List[Any]														=						max_seq_length
         __lowerCamelCase  :      List[Any]														=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __lowerCamelCase  :      Any														=						spectrogram_length
         __lowerCamelCase  :      Any														=						feature_size
         __lowerCamelCase  :      List[str]														=						num_audio_channels
         __lowerCamelCase  :      Dict														=						hop_length
         __lowerCamelCase  :      Optional[Any]														=						chunk_length
         __lowerCamelCase  :      Optional[Any]														=						sampling_rate







    def  lowercase_							(				self      )							->	List[str]:
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         def _flatten(SCREAMING_SNAKE_CASE_      ):
              return list(itertools.chain(*SCREAMING_SNAKE_CASE_      )      )

         if equal_length:
              __lowerCamelCase  :      Tuple														=						[floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
         else:
              # make sure that inputs increase in size
              __lowerCamelCase  :      Dict														=						[
                  floats_list((x, self.feature_size)      )
                  for x in range(self.min_seq_length     ,	self.max_seq_length     ,	self.seq_length_diff      )
              ]
         if numpify:
              __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Dict					=     TvltFeatureExtractor







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						TvltFeatureExtractionTester(self      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Tuple														=						self.feature_extraction_class(**self.feat_extract_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'spectrogram_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'feature_size'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'num_audio_channels'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'hop_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'chunk_length'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'sampling_rate'      )      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      List[str]														=						feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE_      )[0]
              check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[int]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      List[Any]														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						self.feature_extraction_class(**self.feat_extract_dict      )

         with tempfile.TemporaryDirectory() as tmpdirname:
              __lowerCamelCase  :      int														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	'feat_extract.json'      )
              feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Any														=						self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						feat_extract_first.to_dict()
         __lowerCamelCase  :      Optional[Any]														=						feat_extract_second.to_dict()
         __lowerCamelCase  :      Union[str, Any]														=						dict_first.pop('mel_filters'      )
         __lowerCamelCase  :      str														=						dict_second.pop('mel_filters'      )
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )      )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[str]:
         # Initialize feature_extractor
         __lowerCamelCase  :      Dict														=						self.feature_extraction_class(**self.feat_extract_dict      )

         # create three inputs of length 800, 1000, and 1200
         __lowerCamelCase  :      int														=						[floats_list((1, x)      )[0] for x in range(8_00     ,	14_00     ,	2_00      )]
         __lowerCamelCase  :      Union[str, Any]														=						[np.asarray(SCREAMING_SNAKE_CASE_      ) for speech_input in speech_inputs]

         # Test not batched input
         __lowerCamelCase  :      Union[str, Any]														=						feature_extractor(np_speech_inputs[0]     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test batched
         __lowerCamelCase  :      Any														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test audio masking
         __lowerCamelCase  :      Dict														=						feature_extractor(
             SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00     ,	mask_audio=SCREAMING_SNAKE_CASE_      ).audio_values

         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

         # Test 2-D numpy arrays are batched.
         __lowerCamelCase  :      Optional[Any]														=						[floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
         __lowerCamelCase  :      int														=						np.asarray(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'     ,	sampling_rate=4_41_00      ).audio_values
         self.assertTrue(encoded_audios.ndim == 4      )
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,	'clean'     ,	split='validation'      )
         # automatic decoding with librispeech
         __lowerCamelCase  :      Union[str, Any]														=						ds.sort('id'      ).select(range(SCREAMING_SNAKE_CASE_      )      )[:num_samples]['audio']

         return [x["array"] for x in speech_samples]







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						self._load_datasamples(1      )
         __lowerCamelCase  :      List[Any]														=						TvltFeatureExtractor()
         __lowerCamelCase  :      List[str]														=						feature_extractor(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).audio_values

         self.assertEquals(audio_values.shape     ,	(1, 1, 1_92, 1_28)      )

         __lowerCamelCase  :      str														=						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]      )
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )












'''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.
