





'''simple docstring'''
from __future__ import annotations

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Optional[int]							,	__A       :							list[list[int]]	):
        __UpperCamelCase       =  TypeError(
            'Matrices must be formed from a list of zero or more lists containing at '
            'least one and the same number of values, each of which must be of type '
            'int or float.'	)
        if len(__A	) != 0:
               __UpperCamelCase       =  len(rows[0]	)
               if cols == 0:
                      raise error
               for row in rows:
                      if len(__A	) != cols:
                             raise error
                      for value in row:
                             if not isinstance(__A							,	(int, float)	):
                                    raise error
               __UpperCamelCase       =  rows
        else:
               __UpperCamelCase       =  []






 def        _lowerCamelCase					(       self       :							int	):
        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]	)	)]






 @property
 def        _lowerCamelCase					(       self       :							str	):
        return len(self.rows	)






 @property
 def        _lowerCamelCase					(       self       :							Any	):
        return len(self.rows[0]	)






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return (self.num_rows, self.num_columns)






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        return self.order[0] == self.order[1]






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  [
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows	)]
            for row_num in range(self.num_rows	)
        ]
        return Matrix(__A	)






 def        _lowerCamelCase					(       self       :							Any	):
        if not self.is_square:
               return 0
        if self.order == (0, 0):
               return 1
        if self.order == (1, 1):
               return int(self.rows[0][0]	)
        if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])	)
        else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns	)	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        return bool(self.determinant()	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int							,	__A       :							int	):
        __UpperCamelCase       =  [
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns	)
                if other_column != column
            ]
            for other_row in range(self.num_rows	)
            if other_row != row
        ]
        return Matrix(__A	).determinant()






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int							,	__A       :							int	):
        if (row + column) % 2 == 0:
               return self.get_minor(__A							,	__A	)
        return -1 * self.get_minor(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        return Matrix(
            [
                [self.get_minor(__A							,	__A	) for column in range(self.num_columns	)]
                for row in range(self.num_rows	)
            ]	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns	)
                ]
                for row in range(self.minors().num_rows	)
            ]	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  [
            [self.cofactors().rows[column][row] for column in range(self.num_columns	)]
            for row in range(self.num_rows	)
        ]
        return Matrix(__A	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.determinant()
        if not determinant:
               raise TypeError('Only matrices with a non-zero determinant have an inverse'	)
        return self.adjugate() * (1 / determinant)






 def __repr__(       self       :							Optional[Any]	):
        return str(self.rows	)






 def __str__(       self       :							Union[str, Any]	):
        if self.num_rows == 0:
               return "[]"
        if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]	)	) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    '[' + '. '.join([str(__A	) for value in row]	) + '.]'
                    for row in self.rows
                ]	)
            + "]"
        )






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							list[int]							,	__A       :							int | None = None	):
        __UpperCamelCase       =  TypeError('Row must be a list containing all ints and/or floats'	)
        if not isinstance(__A							,	__A	):
               raise type_error
        for value in row:
               if not isinstance(__A							,	(int, float)	):
                      raise type_error
        if len(__A	) != self.num_columns:
               raise ValueError(
                   'Row must be equal in length to the other rows in the matrix'	)
        if position is None:
               self.rows.append(__A	)
        else:
               __UpperCamelCase       =  self.rows[0:position] + [row] + self.rows[position:]






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							list[int]							,	__A       :							int | None = None	):
        __UpperCamelCase       =  TypeError(
            'Column must be a list containing all ints and/or floats'	)
        if not isinstance(__A							,	__A	):
               raise type_error
        for value in column:
               if not isinstance(__A							,	(int, float)	):
                      raise type_error
        if len(__A	) != self.num_rows:
               raise ValueError(
                   'Column must be equal in length to the other columns in the matrix'	)
        if position is None:
               __UpperCamelCase       =  [self.rows[i] + [column[i]] for i in range(self.num_rows	)]
        else:
               __UpperCamelCase       =  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows	)
               ]






 def __eq__(       self       :							Tuple							,	__A       :							object	):
        if not isinstance(__A							,	__A	):
               return NotImplemented
        return self.rows == other.rows






 def __ne__(       self       :							Any							,	__A       :							object	):
        return not self == other






 def __neg__(       self       :							List[Any]	):
        return self * -1






 def __add__(       self       :							List[str]							,	__A       :							Matrix	):
        if self.order != other.order:
               raise ValueError('Addition requires matrices of the same order'	)
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns	)]
                for i in range(self.num_rows	)
            ]	)






 def __sub__(       self       :							str							,	__A       :							Matrix	):
        if self.order != other.order:
               raise ValueError('Subtraction requires matrices of the same order'	)
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns	)]
                for i in range(self.num_rows	)
            ]	)






 def __mul__(       self       :							str							,	__A       :							Matrix | int | float	):
        if isinstance(__A							,	(int, float)	):
               return Matrix(
                   [[int(element * other	) for element in row] for row in self.rows]	)
        elif isinstance(__A							,	__A	):
               if self.num_columns != other.num_rows:
                      raise ValueError(
                          'The number of columns in the first matrix must '
                          'be equal to the number of rows in the second'	)
               return Matrix(
                   [
                       [Matrix.dot_product(__A							,	__A	) for column in other.columns()]
                       for row in self.rows
                   ]	)
        else:
               raise TypeError(
                   'A Matrix can only be multiplied by an int, float, or another matrix'	)






 def __pow__(       self       :							Union[str, Any]							,	__A       :							int	):
        if not isinstance(__A							,	__A	):
               raise TypeError('A Matrix can only be raised to the power of an int'	)
        if not self.is_square:
               raise ValueError('Only square matrices can be raised to a power'	)
        if other == 0:
               return self.identity()
        if other < 0:
               if self.is_invertable():
                      return self.inverse() ** (-other)
               raise ValueError(
                   'Only invertable matrices can be raised to a negative power'	)
        __UpperCamelCase       =  self
        for _ in range(other - 1	):
               result *= self
        return result






 @classmethod
 def        _lowerCamelCase					(       cls       :							Tuple							,	__A       :							list[int]							,	__A       :							list[int]	):
        return sum(row[i] * column[i] for i in range(len(__A	)	)	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()







'''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.
