
'''simple docstring'''







import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
				import faiss


@require_faiss
class 		__UpperCAmelCase   (      UpperCamelCase__		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	tempfile.mkdtemp()
								_snake_case     		=	8

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

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





								"""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"""






								_snake_case     		=	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"""






								_snake_case     		=	self.get_dummy_dataset()
								_snake_case     		=	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     		=	dataset
									_snake_case     		=	RagRetriever(
									    __A					,     question_encoder_tokenizer=self.get_dpr_tokenizer()					,     generator_tokenizer=self.get_bart_tokenizer()					,     )
								return retriever


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.get_dummy_dataset()
								_snake_case     		=	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     		=	os.path.join(self.tmpdirname					,     'dataset'						)
									_snake_case     		=	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     		=	RagRetriever(
									    __A					,     question_encoder_tokenizer=self.get_dpr_tokenizer()					,     generator_tokenizer=self.get_bart_tokenizer()					,     )
								else:
									_snake_case     		=	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						):





								"""simple docstring"""






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

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	1
								_snake_case     		=	self.get_dummy_canonical_hf_index_retriever()
								_snake_case     		=	np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]					,     dtype=np.floataa						)
								_snake_case		,				_snake_case		,				_snake_case     		=	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						):





								"""simple docstring"""






								_snake_case     		=	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     		=	self.get_dummy_dataset()
										retriever.save_pretrained(__A						)
										_snake_case     		=	RagRetriever.from_pretrained(__A						)
										self.assertIsInstance(__A					,     __A						)
										_snake_case     		=	np.array(
										    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]					,     dtype=np.floataa						)
										_snake_case     		=	retriever.retrieve(__A					,     n_docs=1						)
										self.assertTrue(out is not None						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	1
								_snake_case     		=	self.get_dummy_custom_hf_index_retriever(from_disk=__A						)
								_snake_case     		=	np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]					,     dtype=np.floataa						)
								_snake_case		,				_snake_case		,				_snake_case     		=	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						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	1
								_snake_case     		=	self.get_dummy_custom_hf_index_retriever(from_disk=__A						)
								_snake_case     		=	np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]					,     dtype=np.floataa						)
								_snake_case		,				_snake_case		,				_snake_case     		=	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						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	1
								_snake_case     		=	self.get_dummy_legacy_index_retriever()
								_snake_case     		=	np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]					,     dtype=np.floataa						)
								_snake_case		,				_snake_case		,				_snake_case     		=	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						):





								"""simple docstring"""






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


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





								"""simple docstring"""






								import torch

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

								_snake_case     		=	retriever(
								    __A					,     __A					,     prefix=retriever.config.generator.prefix					,     n_docs=__A					,     return_tensors='pt'					,     )
								_snake_case		,				_snake_case		,				_snake_case		,				_snake_case     		=	(  # 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						):





								"""simple docstring"""






								_snake_case     		=	self.get_dpr_ctx_encoder_tokenizer()
								_snake_case     		=	1
								_snake_case     		=	self.get_dummy_custom_hf_index_retriever(from_disk=__A						)
								retriever.set_ctx_encoder_tokenizer(__A						)

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



'''simple docstring'''







from typing import Any

import numpy as np







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bool:
	return np.array_equal(__A    ,       matrix.conjugate().T			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Any:
	_snake_case     		=	v.conjugate().T
	_snake_case     		=	v_star.dot(__A			)
	assert isinstance(__A    ,       np.ndarray			)
	return (v_star_dot.dot(__A			)) / (v_star.dot(__A			))







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case     		=	np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]			)
	_snake_case     		=	np.array([[1], [2], [3]]			)
	assert is_hermitian(__A			), F'{a} is not hermitian.'
	print(rayleigh_quotient(__A    ,       __A			)			)

	_snake_case     		=	np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
	assert is_hermitian(__A			), F'{a} is not hermitian.'
	assert rayleigh_quotient(__A    ,       __A			) == float(3			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					tests()

