



"""simple docstring"""

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

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
							from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
							A_	:						Optional[Any]				=			None

if is_ray_available():
							import ray  # noqa: E402 # isort:skip
							from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
							A_	:						Optional[Any]				=			None
							A_	:						Tuple				=			None
							A_	:						List[Any]				=			None
def    __snake_case       (  __A			:	Optional[Any]    )    ->					str:






				'''simple docstring'''
				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    unittest.skip('test requires Datasets, Faiss, psutil'    )(__A    )
				return test_case







@require_distributed_retrieval
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE	:       Tuple 							=    8

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

					SCREAMING_SNAKE_CASE	:       Any 							=    os.path.join(self.tmpdirname    ,					'bart_tokenizer'						)
					os.makedirs(_SCREAMING_SNAKE_CASE    ,					exist_ok=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       str 							=    os.path.join(_SCREAMING_SNAKE_CASE    ,					BART_VOCAB_FILES_NAMES['vocab_file']						)
					SCREAMING_SNAKE_CASE	:       Any 							=    os.path.join(_SCREAMING_SNAKE_CASE    ,					BART_VOCAB_FILES_NAMES['merges_file']						)
					with open(self.vocab_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									fp.write(json.dumps(_SCREAMING_SNAKE_CASE						) + '\n'						)
					with open(self.merges_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									fp.write('\n'.join(_SCREAMING_SNAKE_CASE						)						)





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  DPRQuestionEncoderTokenizer:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  BartTokenizer:





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  str:





					"""simple docstring"""


					shutil.rmtree(self.tmpdirname						)





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    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    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=12_345						)	->  RagPyTorchDistributedRetriever:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.get_dummy_dataset()
					SCREAMING_SNAKE_CASE	:       Tuple 							=    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:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    dataset
									SCREAMING_SNAKE_CASE	:       Any 							=    RagPyTorchDistributedRetriever(
									    _SCREAMING_SNAKE_CASE    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					)
									if init_retrieval:
													retriever.init_retrieval(_SCREAMING_SNAKE_CASE						)
					return retriever





	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					bool						)	->  RagRayDistributedRetriever:





					"""simple docstring"""


					ray.init(local_mode=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size    ,					question_encoder=DPRConfig().to_dict()    ,					generator=BartConfig().to_dict()    ,					)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    ray.remote(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    [remote_cls.remote() for _ in range(1						)]
					with patch('transformers.models.rag.retrieval_rag.load_dataset'						) as mock_load_dataset:
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    RagRayDistributedRetriever(
									    _SCREAMING_SNAKE_CASE    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					retrieval_workers=_SCREAMING_SNAKE_CASE    ,					)
									if init_retrieval:
													retriever.init_retrieval()
					return retriever





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					Any=12_345						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.get_dummy_dataset()
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size    ,					question_encoder=DPRConfig().to_dict()    ,					generator=BartConfig().to_dict()    ,					index_name='custom'    ,					)
					if from_disk:
									SCREAMING_SNAKE_CASE	:       Any 							=    os.path.join(self.tmpdirname    ,					'dataset'						)
									SCREAMING_SNAKE_CASE	:       int 							=    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
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    RagPyTorchDistributedRetriever(
									    _SCREAMING_SNAKE_CASE    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					)
					else:
									SCREAMING_SNAKE_CASE	:       List[str] 							=    RagPyTorchDistributedRetriever(
									    _SCREAMING_SNAKE_CASE    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					index=CustomHFIndex(config.retrieval_vector_size    ,					_SCREAMING_SNAKE_CASE						)    ,					)
					if init_retrieval:
									retriever.init_retrieval(_SCREAMING_SNAKE_CASE						)
					return retriever





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					bool    ,					_SCREAMING_SNAKE_CASE							:					bool						)	->  Dict:





					"""simple docstring"""


					ray.init(local_mode=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.get_dummy_dataset()
					SCREAMING_SNAKE_CASE	:       str 							=    RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size    ,					question_encoder=DPRConfig().to_dict()    ,					generator=BartConfig().to_dict()    ,					index_name='custom'    ,					)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    ray.remote(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    [remote_cls.remote() for _ in range(1						)]
					if from_disk:
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    os.path.join(self.tmpdirname    ,					'dataset'						)
									SCREAMING_SNAKE_CASE	:       str 							=    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
									SCREAMING_SNAKE_CASE	:       Tuple 							=    RagRayDistributedRetriever(
									    _SCREAMING_SNAKE_CASE    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					retrieval_workers=_SCREAMING_SNAKE_CASE    ,					index=CustomHFIndex.load_from_disk(
									        vector_size=config.retrieval_vector_size    ,					dataset_path=config.passages_path    ,					index_path=config.index_path    ,					)    ,					)
					else:
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    RagRayDistributedRetriever(
									    _SCREAMING_SNAKE_CASE    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					retrieval_workers=_SCREAMING_SNAKE_CASE    ,					index=CustomHFIndex(config.retrieval_vector_size    ,					_SCREAMING_SNAKE_CASE						)    ,					)
					if init_retrieval:
									retriever.init_retrieval()
					return retriever





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					RagRetriever    ,					_SCREAMING_SNAKE_CASE							:					np.array    ,					_SCREAMING_SNAKE_CASE							:					int						)	->  None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    retriever.retrieve(_SCREAMING_SNAKE_CASE    ,					n_docs=_SCREAMING_SNAKE_CASE						)
					self.assertEqual(retrieved_doc_embeds.shape    ,					(2, n_docs, self.retrieval_vector_size)						)
					self.assertEqual(len(_SCREAMING_SNAKE_CASE						)    ,					2						)
					self.assertEqual(sorted(doc_dicts[0]						)    ,					['embeddings', 'id', 'text', 'title']						)
					self.assertEqual(len(doc_dicts[0]['id']						)    ,					_SCREAMING_SNAKE_CASE						)
					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[int]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    1
					SCREAMING_SNAKE_CASE	:       List[str] 							=    np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]    ,					dtype=np.floataa						)

					self.distributed_retriever_check(
					    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_SCREAMING_SNAKE_CASE						)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					str						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    1
					SCREAMING_SNAKE_CASE	:       Dict 							=    np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]    ,					dtype=np.floataa						)

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_SCREAMING_SNAKE_CASE    ,					from_disk=_SCREAMING_SNAKE_CASE						)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					)





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    1
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]    ,					dtype=np.floataa						)

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_SCREAMING_SNAKE_CASE    ,					from_disk=_SCREAMING_SNAKE_CASE						)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					)





	@require_ray
	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    1
					SCREAMING_SNAKE_CASE	:       int 							=    np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]    ,					dtype=np.floataa						)

					self.distributed_retriever_check(
					    self.get_dummy_ray_distributed_retriever(init_retrieval=_SCREAMING_SNAKE_CASE						)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					ray.shutdown()





	@require_ray
	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    1
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]    ,					dtype=np.floataa						)
					with self.assertRaises(_SCREAMING_SNAKE_CASE						):
									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_SCREAMING_SNAKE_CASE    ,					from_disk=_SCREAMING_SNAKE_CASE						)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					)
					ray.shutdown()







	@require_ray
	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    1
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    np.array(
					    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]    ,					dtype=np.floataa						)

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_SCREAMING_SNAKE_CASE    ,					from_disk=_SCREAMING_SNAKE_CASE						)    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					ray.shutdown()










"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  None:





					"""simple docstring"""


					warnings.warn(
					    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use ConvNextImageProcessor instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
					super().__init__(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





