





'''simple docstring'''
from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

a__							:							Tuple =logging.get_logger(__name__)

@add_end_docstrings(__lowerCamelCase       )
class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Optional[Any]							,	**__A       :							Dict	):
        super().__init__(**__A	)
        requires_backends(self							,	'vision'	)
        requires_backends(self							,	'torch'	)

        if self.framework != "pt":
               raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''	)

        self.check_model_type(__A	)






 def        _lowerCamelCase					(       self       :							int							,	**__A       :							Dict	):
        __UpperCamelCase       =  {}
        __UpperCamelCase       =  {}
        __UpperCamelCase       =  {}
        # preprocess args
        if "points_per_batch" in kwargs:
               __UpperCamelCase       =  kwargs['points_per_batch']
        if "points_per_crop" in kwargs:
               __UpperCamelCase       =  kwargs['points_per_crop']
        if "crops_n_layers" in kwargs:
               __UpperCamelCase       =  kwargs['crops_n_layers']
        if "crop_overlap_ratio" in kwargs:
               __UpperCamelCase       =  kwargs['crop_overlap_ratio']
        if "crop_n_points_downscale_factor" in kwargs:
               __UpperCamelCase       =  kwargs['crop_n_points_downscale_factor']
        # postprocess args
        if "pred_iou_thresh" in kwargs:
               __UpperCamelCase       =  kwargs['pred_iou_thresh']
        if "stability_score_offset" in kwargs:
               __UpperCamelCase       =  kwargs['stability_score_offset']
        if "mask_threshold" in kwargs:
               __UpperCamelCase       =  kwargs['mask_threshold']
        if "stability_score_thresh" in kwargs:
               __UpperCamelCase       =  kwargs['stability_score_thresh']
        if "crops_nms_thresh" in kwargs:
               __UpperCamelCase       =  kwargs['crops_nms_thresh']
        if "output_rle_mask" in kwargs:
               __UpperCamelCase       =  kwargs['output_rle_mask']
        if "output_bboxes_mask" in kwargs:
               __UpperCamelCase       =  kwargs['output_bboxes_mask']
        return preprocess_kwargs, forward_params, postprocess_kwargs






 def __call__(       self       :							Union[str, Any]							,	__A       :							List[str]							,	*__A       :							List[Any]							,	__A       :							int=None							,	__A       :							Optional[int]=None							,	**__A       :							Any	):
        return super().__call__(__A							,	*__A							,	num_workers=__A							,	batch_size=__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							Optional[int]							,	__A       :							Union[str, Any]=6_4							,	__A       :							int = 0							,	__A       :							float = 5_1_2 / 1_5_0_0							,	__A       :							Optional[int] = 3_2							,	__A       :							Optional[int] = 1							,	):
        __UpperCamelCase       =  load_image(__A	)
        __UpperCamelCase       =  self.image_processor.size['longest_edge']
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  self.image_processor.generate_crop_boxes(
            __A							,	__A							,	__A							,	__A							,	__A							,	__A	)
        __UpperCamelCase       =  self.image_processor(images=__A							,	return_tensors='pt'	)

        with self.device_placement():
               if self.framework == "pt":
                      __UpperCamelCase       =  self.get_inference_context()
                      with inference_context():
                             __UpperCamelCase       =  self._ensure_tensor_on_device(__A							,	device=self.device	)
                             __UpperCamelCase       =  self.model.get_image_embeddings(model_inputs.pop('pixel_values'	)	)
                             __UpperCamelCase       =  image_embeddings

        __UpperCamelCase       =  grid_points.shape[1]
        __UpperCamelCase       =  points_per_batch if points_per_batch is not None else n_points

        if points_per_batch <= 0:
               raise ValueError(
                   'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
                   'To return all points at once, set points_per_batch to None'	)

        for i in range(0							,	__A							,	__A	):
               __UpperCamelCase       =  grid_points[:, i : i + points_per_batch, :, :]
               __UpperCamelCase       =  input_labels[:, i : i + points_per_batch]
               __UpperCamelCase       =  i == n_points - points_per_batch
               yield {
                   "input_points": batched_points,
                   "input_labels": labels,
                   "input_boxes": crop_boxes,
                   "is_last": is_last,
                   **model_inputs,
               }






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							Union[str, Any]							,	__A       :							Any=0.88							,	__A       :							Any=0.95							,	__A       :							Tuple=0							,	__A       :							str=1							,	):
        __UpperCamelCase       =  model_inputs.pop('input_boxes'	)
        __UpperCamelCase       =  model_inputs.pop('is_last'	)
        __UpperCamelCase       =  model_inputs.pop('original_sizes'	).tolist()
        __UpperCamelCase       =  model_inputs.pop('reshaped_input_sizes'	).tolist()

        __UpperCamelCase       =  self.model(**__A	)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        __UpperCamelCase       =  model_outputs['pred_masks']
        __UpperCamelCase       =  self.image_processor.post_process_masks(
            __A							,	__A							,	__A							,	__A							,	binarize=__A	)
        __UpperCamelCase       =  model_outputs['iou_scores']
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  self.image_processor.filter_masks(
            masks[0]							,	iou_scores[0]							,	original_sizes[0]							,	input_boxes[0]							,	__A							,	__A							,	__A							,	__A							,	)
        return {
            "masks": masks,
            "is_last": is_last,
            "boxes": boxes,
            "iou_scores": iou_scores,
        }






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Dict							,	__A       :							Dict=False							,	__A       :							Optional[Any]=False							,	__A       :							Union[str, Any]=0.7							,	):
        __UpperCamelCase       =  []
        __UpperCamelCase       =  []
        __UpperCamelCase       =  []
        for model_output in model_outputs:
               all_scores.append(model_output.pop('iou_scores'	)	)
               all_masks.extend(model_output.pop('masks'	)	)
               all_boxes.append(model_output.pop('boxes'	)	)

        __UpperCamelCase       =  torch.cat(__A	)
        __UpperCamelCase       =  torch.cat(__A	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  self.image_processor.post_process_for_mask_generation(
            __A							,	__A							,	__A							,	__A	)

        __UpperCamelCase       =  defaultdict(__A	)
        for output in model_outputs:
               for k, v in output.items():
                      extra[k].append(__A	)

        __UpperCamelCase       =  {}
        if output_rle_mask:
               __UpperCamelCase       =  rle_mask

        if output_bboxes_mask:
               __UpperCamelCase       =  bounding_boxes

        return {"masks": output_masks, "scores": iou_scores, **optional, **extra}







'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  tempfile.mkdtemp()
        __UpperCamelCase       =  8

        # DPR tok
        __UpperCamelCase       =  [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)
        os.makedirs(__A							,	exist_ok=__A	)
        __UpperCamelCase       =  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
        __UpperCamelCase       =  [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        __UpperCamelCase       =  dict(zip(__A							,	range(len(__A	)	)	)	)
        __UpperCamelCase       =  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        __UpperCamelCase       =  {'unk_token': '<unk>'}

        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'bart_tokenizer'	)
        os.makedirs(__A							,	exist_ok=__A	)
        __UpperCamelCase       =  os.path.join(__A							,	BART_VOCAB_FILES_NAMES['vocab_file']	)
        __UpperCamelCase       =  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	):
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'dpr_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,	'bart_tokenizer'	)	)






 def        _lowerCamelCase					(       self       :							str	):
        shutil.rmtree(self.tmpdirname	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  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       :							Tuple	):
        __UpperCamelCase       =  self.get_dummy_dataset()
        __UpperCamelCase       =  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       =  dataset
               __UpperCamelCase       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        return retriever






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							bool	):
        __UpperCamelCase       =  self.get_dummy_dataset()
        __UpperCamelCase       =  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size							,	question_encoder=DPRConfig().to_dict()							,	generator=BartConfig().to_dict()							,	index_name='custom'							,	)
        if from_disk:
               __UpperCamelCase       =  os.path.join(self.tmpdirname							,	'dataset'	)
               __UpperCamelCase       =  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       =  RagRetriever(
                   __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()							,	)
        else:
               __UpperCamelCase       =  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       :							int	):
        __UpperCamelCase       =  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       =  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       =  os.path.join(self.tmpdirname							,	'psgs_w100.tsv.pkl'	)
        __UpperCamelCase       =  {sample['id']: [sample['text'], sample['title']] for sample in dataset}
        pickle.dump(__A							,	open(__A							,	'wb'	)	)

        __UpperCamelCase       =  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       =  RagRetriever(
            __A							,	question_encoder_tokenizer=self.get_dpr_tokenizer()							,	generator_tokenizer=self.get_bart_tokenizer()	)
        return retriever






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  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       :							Optional[Any]	):
        __UpperCamelCase       =  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       =  self.get_dummy_dataset()
                      retriever.save_pretrained(__A	)
                      __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
                      self.assertIsInstance(__A							,	__A	)
                      __UpperCamelCase       =  np.array(
                          [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
                      __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
                      self.assertTrue(out is not None	)






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






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






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_legacy_index_retriever()
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  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       :							str	):
        __UpperCamelCase       =  self.get_dummy_legacy_index_retriever()
        with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(__A	)
               __UpperCamelCase       =  RagRetriever.from_pretrained(__A	)
               self.assertIsInstance(__A							,	__A	)
               __UpperCamelCase       =  np.array(
                   [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
               __UpperCamelCase       =  retriever.retrieve(__A							,	n_docs=1	)
               self.assertTrue(out is not None	)






 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        import torch

        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_canonical_hf_index_retriever()
        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  retriever(__A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  (
            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	)

        __UpperCamelCase       =  retriever(
            __A							,	__A							,	prefix=retriever.config.generator.prefix							,	n_docs=__A							,	return_tensors='pt'							,	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  (  # 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	):
        __UpperCamelCase       =  self.get_dpr_ctx_encoder_tokenizer()
        __UpperCamelCase       =  1
        __UpperCamelCase       =  self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
        retriever.set_ctx_encoder_tokenizer(__A	)

        __UpperCamelCase       =  [[5, 7], [1_0, 1_1]]
        __UpperCamelCase       =  np.array(
            [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]							,	dtype=np.floataa	)
        __UpperCamelCase       =  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.
