


"""simple docstring"""



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__snake_case         =					{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__snake_case         =					logging.get_logger(__name__)







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: str													=						'''maskformer'''
   __UpperCAmelCase				: str													=						{'''hidden_size''': '''mask_feature_size'''}
   __UpperCAmelCase				: Optional[int]													=						['''resnet''', '''swin''']
   __UpperCAmelCase				: Union[str, Any]													=						['''detr''']







   def __init__(			self		,			UpperCamelCase__ = 256		,			UpperCamelCase__ = 256		,			UpperCamelCase__ = 0.1		,			UpperCamelCase__ = False		,			UpperCamelCase__ = None		,			UpperCamelCase__ = None		,			UpperCamelCase__ = 0.02		,			UpperCamelCase__ = 1.0		,			UpperCamelCase__ = 1.0		,			UpperCamelCase__ = 1.0		,			UpperCamelCase__ = 20.0		,			UpperCamelCase__ = None		,			**UpperCamelCase__		,			)			->	List[Any]:

       '''simple docstring'''




       if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           snake_case					:   Optional[Any]         =					SwinConfig(
               image_size=384		,			in_channels=3		,			patch_size=4		,			embed_dim=128		,			depths=[2, 2, 18, 2]		,			num_heads=[4, 8, 16, 32]		,			window_size=12		,			drop_path_rate=0.3		,			out_features=["stage1", "stage2", "stage3", "stage4"]		,			)

       if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Optional[int]         =					backbone_config.pop("model_type"		)
           snake_case					:   Dict         =					CONFIG_MAPPING[backbone_model_type]
           snake_case					:   Tuple         =					config_class.from_dict(UpperCamelCase__		)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported		)}'		)

       if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           snake_case					:   Dict         =					DetrConfig()
       else:
           # verify that the decoder is supported
           snake_case					:   Optional[Any]         =					(
               decoder_config.pop("model_type"		) if isinstance(UpperCamelCase__		,			UpperCamelCase__		) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
               raise ValueError(
                   F'Transformer Decoder {decoder_type} not supported, please use one of'
                   F' {",".join(self.decoders_supported		)}'		)
           if isinstance(UpperCamelCase__		,			UpperCamelCase__		):
               snake_case					:   Tuple         =					CONFIG_MAPPING[decoder_type]
               snake_case					:   Optional[Any]         =					config_class.from_dict(UpperCamelCase__		)

       snake_case					:   str         =					backbone_config
       snake_case					:   Tuple         =					decoder_config
       # main feature dimension for the model
       snake_case					:   Optional[Any]         =					fpn_feature_size
       snake_case					:   Dict         =					mask_feature_size
       # initializer
       snake_case					:   List[str]         =					init_std
       snake_case					:   Any         =					init_xavier_std
       # Hungarian matcher && loss
       snake_case					:   Any         =					cross_entropy_weight
       snake_case					:   Optional[int]         =					dice_weight
       snake_case					:   Union[str, Any]         =					mask_weight
       snake_case					:   Optional[Any]         =					use_auxiliary_loss
       snake_case					:   Optional[int]         =					no_object_weight
       snake_case					:   Union[str, Any]         =					output_auxiliary_logits

       snake_case					:   List[str]         =					self.decoder_config.encoder_attention_heads
       snake_case					:   Dict         =					self.decoder_config.num_hidden_layers
       super().__init__(**UpperCamelCase__		)







   @classmethod
   def 				lowerCamelCase	(			cls		,			UpperCamelCase__		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	List[str]:

       '''simple docstring'''




       return cls(
           backbone_config=UpperCamelCase__		,			decoder_config=UpperCamelCase__		,			**UpperCamelCase__		,			)







   def 				lowerCamelCase	(			self		)			->	Dict[str, any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					copy.deepcopy(self.__dict__		)
       snake_case					:   List[Any]         =					self.backbone_config.to_dict()
       snake_case					:   Any         =					self.decoder_config.to_dict()
       snake_case					:   List[str]         =					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 _lowerCAmelCase       (   snake_case_			):







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					tempfile.mkdtemp()
       snake_case					:   Union[str, Any]         =					8

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

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







   def 				lowerCamelCase	(			self		)			->	DPRQuestionEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	DPRContextEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	BartTokenizer:

       '''simple docstring'''




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







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

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					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		)			->	Union[str, Any]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_dataset()
       snake_case					:   Any         =					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					:   str         =					os.path.join(self.tmpdirname		,			"dataset"		)
           snake_case					:   Any         =					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					:   Any         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
       else:
           snake_case					:   str         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			index=CustomHFIndex(config.retrieval_vector_size		,			UpperCamelCase__		)		,			)
       return retriever







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[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		)

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

       snake_case					:   List[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					:   Dict         =					RagRetriever(
           UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		)
       return retriever







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   str         =					1
       snake_case					:   Any         =					self.get_dummy_canonical_hf_index_retriever()
       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					:   Any         =					retriever.retrieve(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["embeddings", "id", "text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["id"]		)		,			UpperCamelCase__		)
       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]:

       '''simple docstring'''




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







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

       '''simple docstring'''




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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					1
       snake_case					:   int         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       snake_case					:   int         =					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(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["embeddings", "id", "text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["id"]		)		,			UpperCamelCase__		)
       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		)			->	Tuple:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					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					:   Any         =					retriever.retrieve(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["text"]		)		,			UpperCamelCase__		)
       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		)			->	List[str]:

       '''simple docstring'''




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







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       import torch

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

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







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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

       snake_case					:   str         =					[[5, 7], [10, 11]]
       snake_case					:   str         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case					:   Dict         =					retriever(UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		)

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





