



'''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:
						UpperCamelCase__:       int							     =      None

if is_ray_available():
						import ray  # noqa: E402 # isort:skip
						from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
						UpperCamelCase__:       List[Any]							     =      None
						UpperCamelCase__:       Tuple							     =      None
						UpperCamelCase__:       Any							     =      None


def 			snake_case_					( _lowerCAmelCase  :						Tuple  )				->       List[Any]:





							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														UpperCAmelCase			:	Optional[Any]										=		unittest.skip('''test requires Datasets, Faiss, psutil'''  )(SCREAMING_SNAKE_CASE_  )
							return test_case




@require_distributed_retrieval
class     SCREAMING_SNAKE_CASE(							A__      ):







						"""simple docstring"""






						def 					A     (						self      :					int		)      ->		List[Any]:
													UpperCAmelCase			:	Optional[Any]										=		tempfile.mkdtemp()
													UpperCAmelCase			:	Union[str, Any]										=		8

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

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







						def 					A     (						self      :					int		)      ->		DPRQuestionEncoderTokenizer:
													return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,      '''dpr_tokenizer'''		)		)







						def 					A     (						self      :					Any		)      ->		BartTokenizer:
													return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,      '''bart_tokenizer'''		)		)







						def 					A     (						self      :					Any		)      ->		Optional[Any]:
													shutil.rmtree(self.tmpdirname		)







						def 					A     (						self      :					Optional[Any]		)      ->		Union[str, Any]:
													UpperCAmelCase			:	Optional[Any]										=		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 					A     (						self      :					Tuple		,      __snake_case      :					int		,      __snake_case      :					Tuple=12345		)      ->		RagPyTorchDistributedRetriever:
													UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_dataset()
													UpperCAmelCase			:	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:
																				UpperCAmelCase			:	Optional[Any]										=		dataset
																				UpperCAmelCase			:	str										=		RagPyTorchDistributedRetriever(
																				    __snake_case		,      question_encoder_tokenizer=self.get_dpr_tokenizer()		,      generator_tokenizer=self.get_bart_tokenizer()		,      )
																				if init_retrieval:
																											retriever.init_retrieval(__snake_case		)
													return retriever







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







						def 					A     (						self      :					List[str]		,      __snake_case      :					Optional[int]		,      __snake_case      :					List[Any]		,      __snake_case      :					str=12345		)      ->		int:
													UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_dataset()
													UpperCAmelCase			:	Union[str, Any]										=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size		,      question_encoder=DPRConfig().to_dict()		,      generator=BartConfig().to_dict()		,      index_name='''custom'''		,      )
													if from_disk:
																				UpperCAmelCase			:	Tuple										=		os.path.join(self.tmpdirname		,      '''dataset'''		)
																				UpperCAmelCase			:	Any										=		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			:	Dict										=		RagPyTorchDistributedRetriever(
																				    __snake_case		,      question_encoder_tokenizer=self.get_dpr_tokenizer()		,      generator_tokenizer=self.get_bart_tokenizer()		,      )
													else:
																				UpperCAmelCase			:	List[str]										=		RagPyTorchDistributedRetriever(
																				    __snake_case		,      question_encoder_tokenizer=self.get_dpr_tokenizer()		,      generator_tokenizer=self.get_bart_tokenizer()		,      index=CustomHFIndex(config.retrieval_vector_size		,      __snake_case		)		,      )
													if init_retrieval:
																				retriever.init_retrieval(__snake_case		)
													return retriever







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







						def 					A     (						self      :					Optional[int]		,      __snake_case      :					Tuple		,      __snake_case      :					int		,      __snake_case      :					str		)      ->		None:
													UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		retriever.retrieve(__snake_case		,      n_docs=__snake_case		)
													self.assertEqual(retrieved_doc_embeds.shape		,      (2, n_docs, self.retrieval_vector_size)		)
													self.assertEqual(len(__snake_case		)		,      2		)
													self.assertEqual(sorted(doc_dicts[0]		)		,      ['''embeddings''', '''id''', '''text''', '''title''']		)
													self.assertEqual(len(doc_dicts[0]['''id''']		)		,      __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 					A     (						self      :					Optional[Any]		)      ->		Tuple:
													UpperCAmelCase			:	Tuple										=		1
													UpperCAmelCase			:	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_pytorch_distributed_retriever(init_retrieval=__snake_case		)		,      __snake_case		,      __snake_case		)







						def 					A     (						self      :					Optional[Any]		)      ->		Optional[Any]:
													UpperCAmelCase			:	List[Any]										=		1
													UpperCAmelCase			:	Union[str, 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_pytorch_retriever(init_retrieval=__snake_case		,      from_disk=__snake_case		)		,      __snake_case		,      __snake_case		,      )







						def 					A     (						self      :					Union[str, Any]		)      ->		Optional[Any]:
													UpperCAmelCase			:	int										=		1
													UpperCAmelCase			:	Union[str, 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_pytorch_retriever(init_retrieval=__snake_case		,      from_disk=__snake_case		)		,      __snake_case		,      __snake_case		,      )







						@require_ray
						def 					A     (						self      :					Optional[int]		)      ->		Optional[Any]:
													UpperCAmelCase			:	Optional[int]										=		1
													UpperCAmelCase			:	Tuple										=		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=__snake_case		)		,      __snake_case		,      __snake_case		)
													ray.shutdown()







						@require_ray
						def 					A     (						self      :					List[str]		)      ->		Dict:
													UpperCAmelCase			:	Tuple										=		1
													UpperCAmelCase			:	Any										=		np.array(
													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,      dtype=np.floataa		)
													with self.assertRaises(__snake_case		):
																				self.distributed_retriever_check(
																				    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__snake_case		,      from_disk=__snake_case		)		,      __snake_case		,      __snake_case		,      )
													ray.shutdown()







						@require_ray
						def 					A     (						self      :					Dict		)      ->		int:
													UpperCAmelCase			:	List[Any]										=		1
													UpperCAmelCase			:	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_custom_hf_index_ray_retriever(init_retrieval=__snake_case		,      from_disk=__snake_case		)		,      __snake_case		,      __snake_case		)
													ray.shutdown()










'''simple docstring'''






UpperCamelCase__:       str							     =      [0.485, 0.456, 0.406]
UpperCamelCase__:       List[str]							     =      [0.229, 0.224, 0.225]
UpperCamelCase__:       List[str]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       Optional[Any]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       List[str]							     =      [0.48145466, 0.4578275, 0.40821073]
UpperCamelCase__:       List[str]							     =      [0.26862954, 0.26130258, 0.27577711]





