




'''simple docstring'''






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
      pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_			      =		logging.get_logger(__name__)

A_			      =		{
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"align_text_model"





     def __init__(     self     :					Tuple     ,							__lowerCamelCase     :					Dict=30522     ,							__lowerCamelCase     :					List[str]=768     ,							__lowerCamelCase     :					Tuple=12     ,							__lowerCamelCase     :					List[str]=12     ,							__lowerCamelCase     :					Tuple=3072     ,							__lowerCamelCase     :					Optional[Any]="gelu"     ,							__lowerCamelCase     :					List[Any]=0.1     ,							__lowerCamelCase     :					Dict=0.1     ,							__lowerCamelCase     :					Any=512     ,							__lowerCamelCase     :					Any=2     ,							__lowerCamelCase     :					Optional[int]=0.0_2     ,							__lowerCamelCase     :					int=1E-12     ,							__lowerCamelCase     :					Dict=0     ,							__lowerCamelCase     :					List[str]="absolute"     ,							__lowerCamelCase     :					List[str]=True     ,							**__lowerCamelCase     :					Optional[Any]     ,							):
           super().__init__(**__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    vocab_size
           snake_case__       :   List[Any]	      =    hidden_size
           snake_case__       :   str	      =    num_hidden_layers
           snake_case__       :   List[str]	      =    num_attention_heads
           snake_case__       :   Any	      =    hidden_act
           snake_case__       :   Optional[Any]	      =    intermediate_size
           snake_case__       :   Tuple	      =    hidden_dropout_prob
           snake_case__       :   Union[str, Any]	      =    attention_probs_dropout_prob
           snake_case__       :   Union[str, Any]	      =    max_position_embeddings
           snake_case__       :   Tuple	      =    type_vocab_size
           snake_case__       :   List[Any]	      =    initializer_range
           snake_case__       :   Optional[int]	      =    layer_norm_eps
           snake_case__       :   List[Any]	      =    position_embedding_type
           snake_case__       :   Optional[int]	      =    use_cache
           snake_case__       :   Tuple	      =    pad_token_id





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					str     ,							__lowerCamelCase     :					Union[str, os.PathLike]     ,							**__lowerCamelCase     :					int			):
           cls._set_token_in_kwargs(__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   str	      =    cls.get_config_dict(__lowerCamelCase     ,							**__lowerCamelCase			)

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get('model_type'			) == "align":
                 snake_case__       :   Tuple	      =    config_dict['text_config']

           if "model_type" in config_dict and hasattr(cls     ,							'model_type'			) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                     F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."			)

           return cls.from_dict(__lowerCamelCase     ,							**__lowerCamelCase			)






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"align_vision_model"





     def __init__(     self     :					Optional[Any]     ,							__lowerCamelCase     :					int = 3     ,							__lowerCamelCase     :					int = 600     ,							__lowerCamelCase     :					float = 2.0     ,							__lowerCamelCase     :					float = 3.1     ,							__lowerCamelCase     :					int = 8     ,							__lowerCamelCase     :					List[int] = [3, 3, 5, 3, 5, 5, 3]     ,							__lowerCamelCase     :					List[int] = [32, 16, 24, 40, 80, 112, 192]     ,							__lowerCamelCase     :					List[int] = [16, 24, 40, 80, 112, 192, 320]     ,							__lowerCamelCase     :					List[int] = []     ,							__lowerCamelCase     :					List[int] = [1, 2, 2, 2, 1, 2, 1]     ,							__lowerCamelCase     :					List[int] = [1, 2, 2, 3, 3, 4, 1]     ,							__lowerCamelCase     :					List[int] = [1, 6, 6, 6, 6, 6, 6]     ,							__lowerCamelCase     :					float = 0.2_5     ,							__lowerCamelCase     :					str = "swish"     ,							__lowerCamelCase     :					int = 2560     ,							__lowerCamelCase     :					str = "mean"     ,							__lowerCamelCase     :					float = 0.0_2     ,							__lowerCamelCase     :					float = 0.0_0_1     ,							__lowerCamelCase     :					float = 0.9_9     ,							__lowerCamelCase     :					float = 0.2     ,							**__lowerCamelCase     :					Optional[Any]     ,							):
           super().__init__(**__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    num_channels
           snake_case__       :   List[Any]	      =    image_size
           snake_case__       :   Union[str, Any]	      =    width_coefficient
           snake_case__       :   Optional[Any]	      =    depth_coefficient
           snake_case__       :   Dict	      =    depth_divisor
           snake_case__       :   List[Any]	      =    kernel_sizes
           snake_case__       :   int	      =    in_channels
           snake_case__       :   List[Any]	      =    out_channels
           snake_case__       :   List[Any]	      =    depthwise_padding
           snake_case__       :   Tuple	      =    strides
           snake_case__       :   List[Any]	      =    num_block_repeats
           snake_case__       :   Optional[Any]	      =    expand_ratios
           snake_case__       :   str	      =    squeeze_expansion_ratio
           snake_case__       :   List[Any]	      =    hidden_act
           snake_case__       :   Dict	      =    hidden_dim
           snake_case__       :   Optional[Any]	      =    pooling_type
           snake_case__       :   List[str]	      =    initializer_range
           snake_case__       :   Optional[Any]	      =    batch_norm_eps
           snake_case__       :   Tuple	      =    batch_norm_momentum
           snake_case__       :   Dict	      =    drop_connect_rate
           snake_case__       :   Union[str, Any]	      =    sum(__lowerCamelCase			) * 4





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[str]     ,							__lowerCamelCase     :					Union[str, os.PathLike]     ,							**__lowerCamelCase     :					Any			):
           cls._set_token_in_kwargs(__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Union[str, Any]	      =    cls.get_config_dict(__lowerCamelCase     ,							**__lowerCamelCase			)

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get('model_type'			) == "align":
                 snake_case__       :   int	      =    config_dict['vision_config']

           if "model_type" in config_dict and hasattr(cls     ,							'model_type'			) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                     F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."			)

           return cls.from_dict(__lowerCamelCase     ,							**__lowerCamelCase			)






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"align"
     A_											=			True





     def __init__(     self     :					Dict     ,							__lowerCamelCase     :					Any=None     ,							__lowerCamelCase     :					int=None     ,							__lowerCamelCase     :					int=640     ,							__lowerCamelCase     :					int=1.0     ,							__lowerCamelCase     :					Optional[int]=0.0_2     ,							**__lowerCamelCase     :					List[Any]     ,							):
           super().__init__(**__lowerCamelCase			)

           if text_config is None:
                 snake_case__       :   Optional[Any]	      =    {}
                 logger.info('text_config is None. Initializing the AlignTextConfig with default values.'			)

           if vision_config is None:
                 snake_case__       :   str	      =    {}
                 logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'			)

           snake_case__       :   Any	      =    AlignTextConfig(**__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    AlignVisionConfig(**__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    projection_dim
           snake_case__       :   Optional[Any]	      =    temperature_init_value
           snake_case__       :   Optional[int]	      =    initializer_range





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[int]     ,							__lowerCamelCase     :					AlignTextConfig     ,							__lowerCamelCase     :					AlignVisionConfig     ,							**__lowerCamelCase     :					str			):
           return cls(text_config=text_config.to_dict()     ,							vision_config=vision_config.to_dict()     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   List[Any]	      =    copy.deepcopy(self.__dict__			)
           snake_case__       :   int	      =    self.text_config.to_dict()
           snake_case__       :   Any	      =    self.vision_config.to_dict()
           snake_case__       :   Optional[Any]	      =    self.__class__.model_type
           return output






'''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.
