






'''simple docstring'''





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM





@require_torch
class 		SCREAMING_SNAKE_CASE__ :



		def __init__(     self:     List[Any]					, a:     int					, a:     List[str]=99					, a:     Optional[int]=13					, a:     Dict=16					, a:     Dict=7					, a:     List[Any]=True					, a:     str=True					, a:     Any=True					, a:     List[str]=False					, a:     Union[str, Any]=True					, a:     Any=2					, a:     Any=32					, a:     Dict=4					, a:     Tuple=4					, a:     Any=30					, a:     Optional[Any]=0					, a:     Dict=1					, a:     str=2					, a:     Optional[int]=None					, )	->Optional[int]:






								'''simple docstring'''


								a_					  = parent
								a_					  = batch_size
								a_					  = decoder_seq_length
								# For common tests
								a_					  = self.decoder_seq_length
								a_					  = is_training
								a_					  = use_attention_mask
								a_					  = use_labels

								a_					  = vocab_size
								a_					  = d_model
								a_					  = d_model
								a_					  = decoder_layers
								a_					  = decoder_layers
								a_					  = decoder_ffn_dim
								a_					  = decoder_attention_heads
								a_					  = decoder_attention_heads
								a_					  = eos_token_id
								a_					  = bos_token_id
								a_					  = pad_token_id
								a_					  = decoder_start_token_id
								a_					  = use_cache
								a_					  = max_position_embeddings

								a_					  = None
								a_					  = decoder_seq_length
								a_					  = 2
								a_					  = 1



		def _lowerCAmelCase  (     self:     Dict)	->Tuple:






								'''simple docstring'''


								a_					  = ids_tensor([self.batch_size, self.decoder_seq_length]					, self.vocab_size)

								a_					  = None
								if self.use_attention_mask:
														a_					  = ids_tensor([self.batch_size, self.decoder_seq_length]					, vocab_size=2)

								a_					  = None
								if self.use_labels:
														a_					  = ids_tensor([self.batch_size, self.decoder_seq_length]					, self.vocab_size)

								a_					  = TrOCRConfig(
								    vocab_size=self.vocab_size					, d_model=self.d_model					, decoder_layers=self.decoder_layers					, decoder_ffn_dim=self.decoder_ffn_dim					, decoder_attention_heads=self.decoder_attention_heads					, eos_token_id=self.eos_token_id					, bos_token_id=self.bos_token_id					, use_cache=self.use_cache					, pad_token_id=self.pad_token_id					, decoder_start_token_id=self.decoder_start_token_id					, max_position_embeddings=self.max_position_embeddings					, )

								return (config, input_ids, attention_mask, lm_labels)



		def _lowerCAmelCase  (     self:     str					, a:     Union[str, Any]					, a:     str					, a:     int					, a:     List[str]					, )	->Optional[Any]:






								'''simple docstring'''


								a_					  = True
								a_					  = TrOCRDecoder(config=a).to(a).eval()
								a_					  = input_ids[:2]

								input_ids[input_ids == 0] += 1
								# first forward pass
								a_					  = model(a					, use_cache=a)
								a_					  = model(a)
								a_					  = model(a					, use_cache=a)

								self.parent.assertTrue(len(a) == len(a))
								self.parent.assertTrue(len(a) == len(a) + 1)

								a_					  = outputs["past_key_values"]

								# create hypothetical next token and extent to next_input_ids
								a_					  = ids_tensor((2, 1)					, config.vocab_size - 1) + 1

								# append to next input_ids and
								a_					  = torch.cat([input_ids, next_tokens]					, dim=-1)

								a_					  = model(a)["last_hidden_state"]
								a_					  = model(a					, past_key_values=a)["last_hidden_state"]

								# select random slice
								a_					  = ids_tensor((1,)					, output_from_past.shape[-1]).item()
								a_					  = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
								a_					  = output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								assert torch.allclose(a					, a					, atol=1e-3)




		def _lowerCAmelCase  (     self:     Any)	->Dict:






								'''simple docstring'''


								a_					  = self.prepare_config_and_inputs()
								a_					  = config_and_inputs

								a_					  = {"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict





@require_torch
class 		SCREAMING_SNAKE_CASE__ (  lowercase_		,		lowercase_		,		lowercase_		,		unittest.TestCase     ):
		_UpperCAmelCase								=(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
		_UpperCAmelCase								=(TrOCRForCausalLM,) if is_torch_available() else ()
		_UpperCAmelCase								={'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
		_UpperCAmelCase								=True
		_UpperCAmelCase								=False



		def _lowerCAmelCase  (     self:     Optional[int])	->Union[str, Any]:






								'''simple docstring'''


								a_					  = TrOCRStandaloneDecoderModelTester(self					, is_training=a)
								a_					  = ConfigTester(self					, config_class=a)



		def _lowerCAmelCase  (     self:     List[Any])	->List[str]:






								'''simple docstring'''


								pass



		def _lowerCAmelCase  (     self:     Optional[int])	->Dict:






								'''simple docstring'''


								pass



		def _lowerCAmelCase  (     self:     str)	->int:






								'''simple docstring'''


								pass



		def _lowerCAmelCase  (     self:     Dict)	->Optional[int]:






								'''simple docstring'''


								self.config_tester.run_common_tests()



		def _lowerCAmelCase  (     self:     Optional[Any])	->Union[str, Any]:






								'''simple docstring'''


								a_					  = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past(*a)



		def _lowerCAmelCase  (     self:     Dict)	->Optional[Any]:






								'''simple docstring'''


								return




		@unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
		def _lowerCAmelCase  (     self:     Any)	->Optional[int]:






								'''simple docstring'''


								pass











'''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 							_UpperCAmelCase    (		snake_case      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  tempfile.mkdtemp()
       lowercase_						:     str							     =  8

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

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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       shutil.rmtree(self.tmpdirname      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  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	:							List[Any]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							bool      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  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      )

       lowercase_						:     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"      )      )

       lowercase_						:     Dict							     =  os.path.join(self.tmpdirname	,  "psgs_w100.tsv.pkl"      )
       lowercase_						:     List[str]							     =  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
       pickle.dump(a	,  open(a	,  "wb"      )      )

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




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       import torch

       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     str							     =  self.get_dummy_canonical_hf_index_retriever()
       lowercase_						:     Optional[int]							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     str							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     List[str]							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Union[str, 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(a	,  a      )
       self.assertIsInstance(a	,  a      )
       self.assertIsInstance(a	,  np.ndarray      )

       lowercase_						:     Tuple							     =  retriever(
           a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a	,  return_tensors="pt"	,  )
       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  (  # 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(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.get_dpr_ctx_encoder_tokenizer()
       lowercase_						:     Tuple							     =  1
       lowercase_						:     int							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       retriever.set_ctx_encoder_tokenizer(a      )

       lowercase_						:     Tuple							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     Optional[Any]							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     Dict							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )

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




