






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 			__a  (    tf.keras.layers.Layer       ):







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int = None							,   SCREAMING_SNAKE_CASE					:   int = None						):


      '''simple docstring'''






      super().__init__()
      UpperCamelCase__			:   Optional[Any]       				=  pad_token_id
      UpperCamelCase__			:   Union[str, Any]       				=  max_length
      UpperCamelCase__			:   Union[str, Any]       				=  vocab
      UpperCamelCase__			:   int       				=  merges
      UpperCamelCase__			:   Union[str, Any]       				=  BytePairTokenizer(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   sequence_length=SCREAMING_SNAKE_CASE						)







 @classmethod
 def 					__lowercase					(		cls					:   Tuple							,   SCREAMING_SNAKE_CASE					:   GPTaTokenizer							,   *SCREAMING_SNAKE_CASE					:   Optional[Any]							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  [" ".join(SCREAMING_SNAKE_CASE						) for m in tokenizer.bpe_ranks.keys()]
      UpperCamelCase__			:   List[Any]       				=  tokenizer.get_vocab()
      return cls(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   *SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 @classmethod
 def 					__lowercase					(		cls					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[str, os.PathLike]							,   *SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   **SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							,   *SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
      return cls.from_tokenizer(SCREAMING_SNAKE_CASE							,   *SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 @classmethod
 def 					__lowercase					(		cls					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      return cls(**SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      return {
          "vocab": self.vocab,
          "merges": self.merges,
          "max_length": self.max_length,
          "pad_token_id": self.pad_token_id,
      }







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   int = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self.tf_tokenizer(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  tf.ones_like(SCREAMING_SNAKE_CASE						)

      if self.pad_token_id is not None:
           # pad the tokens up to max length
           UpperCamelCase__			:   Union[str, Any]       				=  max_length if max_length is not None else self.max_length

           if max_length is not None:
                UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  pad_model_inputs(
                    SCREAMING_SNAKE_CASE							,   max_seq_length=SCREAMING_SNAKE_CASE							,   pad_value=self.pad_token_id						)

      return {"attention_mask": attention_mask, "input_ids": input_ids}







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 			__a  (    A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  tempfile.mkdtemp()
      UpperCamelCase__			:   Any       				=  8

      # DPR tok
      UpperCamelCase__			:   List[Any]       				=  [
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      UpperCamelCase__			:   Any       				=  os.path.join(self.tmpdirname							,   "dpr_tokenizer"						)
      os.makedirs(SCREAMING_SNAKE_CASE							,   exist_ok=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  os.path.join(SCREAMING_SNAKE_CASE							,   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
      UpperCamelCase__			:   str       				=  [
          "l",
          "o",
          "w",
          "e",
          "r",
          "s",
          "t",
          "i",
          "d",
          "n",
          "\u0120",
          "\u0120l",
          "\u0120n",
          "\u0120lo",
          "\u0120low",
          "er",
          "\u0120lowest",
          "\u0120newer",
          "\u0120wider",
          "<unk>",
      ]
      UpperCamelCase__			:   int       				=  dict(zip(SCREAMING_SNAKE_CASE							,   range(len(SCREAMING_SNAKE_CASE						)						)						)						)
      UpperCamelCase__			:   Optional[Any]       				=  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
      UpperCamelCase__			:   Union[str, Any]       				=  {"unk_token": "<unk>"}

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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,   "dpr_tokenizer"						)						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,   "dpr_tokenizer"						)						)







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,   "bart_tokenizer"						)						)







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      shutil.rmtree(self.tmpdirname						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  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 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.get_dummy_dataset()
      UpperCamelCase__			:   List[Any]       				=  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:
           UpperCamelCase__			:   str       				=  dataset
           UpperCamelCase__			:   Optional[int]       				=  RagRetriever(
               SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()							,   )
      return retriever







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   bool						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self.get_dummy_dataset()
      UpperCamelCase__			:   Tuple       				=  RagConfig(
          retrieval_vector_size=self.retrieval_vector_size							,   question_encoder=DPRConfig().to_dict()							,   generator=BartConfig().to_dict()							,   index_name="custom"							,   )
      if from_disk:
           UpperCamelCase__			:   Optional[int]       				=  os.path.join(self.tmpdirname							,   "dataset"						)
           UpperCamelCase__			:   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
           UpperCamelCase__			:   str       				=  RagRetriever(
               SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()							,   )
      else:
           UpperCamelCase__			:   List[Any]       				=  RagRetriever(
               SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()							,   index=CustomHFIndex(config.retrieval_vector_size							,   SCREAMING_SNAKE_CASE						)							,   )
      return retriever







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  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						)

      UpperCamelCase__			:   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"						)						)

      UpperCamelCase__			:   Optional[int]       				=  os.path.join(self.tmpdirname							,   "psgs_w100.tsv.pkl"						)
      UpperCamelCase__			:   Tuple       				=  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
      pickle.dump(SCREAMING_SNAKE_CASE							,   open(SCREAMING_SNAKE_CASE							,   "wb"						)						)

      UpperCamelCase__			:   List[str]       				=  RagConfig(
          retrieval_vector_size=self.retrieval_vector_size							,   question_encoder=DPRConfig().to_dict()							,   generator=BartConfig().to_dict()							,   index_name="legacy"							,   index_path=self.tmpdirname							,   )
      UpperCamelCase__			:   List[str]       				=  RagRetriever(
          SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()						)
      return retriever







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  1
      UpperCamelCase__			:   Tuple       				=  self.get_dummy_canonical_hf_index_retriever()
      UpperCamelCase__			:   str       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["embeddings", "id", "text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["id"]						)							,   SCREAMING_SNAKE_CASE						)
      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 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  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:
                UpperCamelCase__			:   Optional[int]       				=  self.get_dummy_dataset()
                retriever.save_pretrained(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Any       				=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE						)
                self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   str       				=  np.array(
                    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
                UpperCamelCase__			:   Any       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=1						)
                self.assertTrue(out is not None						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   Dict       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["embeddings", "id", "text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["id"]						)							,   SCREAMING_SNAKE_CASE						)
      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 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   List[str]       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["embeddings", "id", "text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["id"]						)							,   SCREAMING_SNAKE_CASE						)
      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 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  1
      UpperCamelCase__			:   Any       				=  self.get_dummy_legacy_index_retriever()
      UpperCamelCase__			:   Optional[int]       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["text"]						)							,   SCREAMING_SNAKE_CASE						)
      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 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






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







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      import torch

      UpperCamelCase__			:   Optional[Any]       				=  1
      UpperCamelCase__			:   Optional[int]       				=  self.get_dummy_canonical_hf_index_retriever()
      UpperCamelCase__			:   Optional[Any]       				=  [[5, 7], [10, 11]]
      UpperCamelCase__			:   List[str]       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__			:   int       				=  retriever(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   prefix=retriever.config.generator.prefix							,   n_docs=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  (
          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(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   np.ndarray						)

      UpperCamelCase__			:   List[Any]       				=  retriever(
          SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   prefix=retriever.config.generator.prefix							,   n_docs=SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   )
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[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(SCREAMING_SNAKE_CASE							,   torch.Tensor						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.get_dpr_ctx_encoder_tokenizer()
      UpperCamelCase__			:   Union[str, Any]       				=  1
      UpperCamelCase__			:   Tuple       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE						)

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

      self.assertEqual(
          len(SCREAMING_SNAKE_CASE						)							,   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")						)							,   SCREAMING_SNAKE_CASE						)  # check for doc token related keys in dictionary.