



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class  __lowerCAmelCase     (	SCREAMING_SNAKE_CASE__   ):
 lowerCAmelCase__       					=  ["""image_processor""", """tokenizer"""]
 lowerCAmelCase__       					=  """FlavaImageProcessor"""
 lowerCAmelCase__       					=  ("""BertTokenizer""", """BertTokenizerFast""")




 def __init__(						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''	,				snake_case__	,				)
       __lowerCamelCase													=    kwargs.pop('''feature_extractor'''				)

    __lowerCamelCase													=    image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''				)
    if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''				)

    super().__init__(snake_case__	,				snake_case__				)
    __lowerCamelCase													=    self.image_processor




 def __call__(						self	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = True	,				__UpperCAmelCase = False	,				__UpperCAmelCase = False	,				__UpperCAmelCase = None	,				__UpperCAmelCase = 0	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = False	,				__UpperCAmelCase = False	,				__UpperCAmelCase = False	,				__UpperCAmelCase = False	,				__UpperCAmelCase = True	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''

    if text is None and images is None:
       raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

    if text is not None:
       __lowerCamelCase													=    self.tokenizer(
           text=snake_case__	,				add_special_tokens=snake_case__	,				padding=snake_case__	,				truncation=snake_case__	,				max_length=snake_case__	,				stride=snake_case__	,				pad_to_multiple_of=snake_case__	,				return_token_type_ids=snake_case__	,				return_attention_mask=snake_case__	,				return_overflowing_tokens=snake_case__	,				return_special_tokens_mask=snake_case__	,				return_offsets_mapping=snake_case__	,				return_length=snake_case__	,				verbose=snake_case__	,				return_tensors=snake_case__	,				**snake_case__	,				)
    if images is not None:
       __lowerCamelCase													=    self.image_processor(
           snake_case__	,				return_image_mask=snake_case__	,				return_codebook_pixels=snake_case__	,				return_tensors=snake_case__	,				**snake_case__	,				)

    if text is not None and images is not None:
       encoding.update(snake_case__				)
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**snake_case__				)	,				tensor_type=snake_case__				)




 def        lowerCamelCase      (						self	,				*__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.tokenizer.batch_decode(*snake_case__	,				**snake_case__				)




 def        lowerCamelCase      (						self	,				*__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.tokenizer.decode(*snake_case__	,				**snake_case__				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.tokenizer.model_input_names
    __lowerCamelCase													=    self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,				snake_case__	,				)
    return self.image_processor_class




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,				snake_case__	,				)
    return self.image_processor











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     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    tempfile.mkdtemp()
    __lowerCamelCase													=    8

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

    __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''bart_tokenizer'''				)
    os.makedirs(__UpperCAmelCase	,				exist_ok=__UpperCAmelCase				)
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				BART_VOCAB_FILES_NAMES['''vocab_file''']				)
    __lowerCamelCase													=    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				):



    '''simple docstring'''
    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''dpr_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''dpr_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				'''bart_tokenizer'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    shutil.rmtree(self.tmpdirname				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_dataset()
    __lowerCamelCase													=    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:
       __lowerCamelCase													=    dataset
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				)
    return retriever




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_dataset()
    __lowerCamelCase													=    RagConfig(
        retrieval_vector_size=self.retrieval_vector_size	,				question_encoder=DPRConfig().to_dict()	,				generator=BartConfig().to_dict()	,				index_name='''custom'''	,				)
    if from_disk:
       __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''dataset'''				)
       __lowerCamelCase													=    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
       __lowerCamelCase													=    RagRetriever(
           __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()	,				)
    else:
       __lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    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				)

    __lowerCamelCase													=    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'''				)				)

    __lowerCamelCase													=    os.path.join(self.tmpdirname	,				'''psgs_w100.tsv.pkl'''				)
    __lowerCamelCase													=    {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
    pickle.dump(__UpperCAmelCase	,				open(__UpperCAmelCase	,				'''wb'''				)				)

    __lowerCamelCase													=    RagConfig(
        retrieval_vector_size=self.retrieval_vector_size	,				question_encoder=DPRConfig().to_dict()	,				generator=BartConfig().to_dict()	,				index_name='''legacy'''	,				index_path=self.tmpdirname	,				)
    __lowerCamelCase													=    RagRetriever(
        __UpperCAmelCase	,				question_encoder_tokenizer=self.get_dpr_tokenizer()	,				generator_tokenizer=self.get_bart_tokenizer()				)
    return retriever




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_canonical_hf_index_retriever()
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    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:
          __lowerCamelCase													=    self.get_dummy_dataset()
          retriever.save_pretrained(__UpperCAmelCase				)
          __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
          self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
          __lowerCamelCase													=    np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
          __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
          self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_legacy_index_retriever()
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
       retriever.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    RagRetriever.from_pretrained(__UpperCAmelCase				)
       self.assertIsInstance(__UpperCAmelCase	,				__UpperCAmelCase				)
       __lowerCamelCase													=    np.array(
           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
       __lowerCamelCase													=    retriever.retrieve(__UpperCAmelCase	,				n_docs=1				)
       self.assertTrue(out is not None				)




 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_canonical_hf_index_retriever()
    __lowerCamelCase													=    [[5, 7], [10, 11]]
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase													=    retriever(__UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    (
        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				)

    __lowerCamelCase													=    retriever(
        __UpperCAmelCase	,				__UpperCAmelCase	,				prefix=retriever.config.generator.prefix	,				n_docs=__UpperCAmelCase	,				return_tensors='''pt'''	,				)
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    (  # 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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dpr_ctx_encoder_tokenizer()
    __lowerCamelCase													=    1
    __lowerCamelCase													=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase				)
    retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase				)

    __lowerCamelCase													=    [[5, 7], [10, 11]]
    __lowerCamelCase													=    np.array(
        [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]	,				dtype=np.floataa				)
    __lowerCamelCase													=    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.






