






'''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_					=				None

if is_ray_available():
  import ray  # noqa: E402 # isort:skip
  from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
  UpperCamelCase_					=				None
  UpperCamelCase_					=				None
  UpperCamelCase_					=				None






def  lowerCAmelCase__		(					a_  :     Optional[int]			)   -> Optional[int]:
       if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
              UpperCAmelCase__	:   str												=	unittest.skip('''test requires Datasets, Faiss, psutil'''			)(a_			)
       return test_case




@require_distributed_retrieval
class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	tempfile.mkdtemp()
             UpperCAmelCase__	:   Union[str, Any]												=	8

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

             UpperCAmelCase__	:   Optional[int]												=	os.path.join(self.tmpdirname  ,		'''bart_tokenizer'''						)
             os.makedirs(_UpperCAmelCase  ,		exist_ok=_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	os.path.join(_UpperCAmelCase  ,		BART_VOCAB_FILES_NAMES['''vocab_file''']						)
             UpperCAmelCase__	:   Optional[Any]												=	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						):
             return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		'''dpr_tokenizer'''						)						)




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




      def 							lowerCamelCase     (					self						):
             shutil.rmtree(self.tmpdirname						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[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 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=1_2345						):
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_dataset()
             UpperCAmelCase__	:   Any												=	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__	:   Tuple												=	dataset
                    UpperCAmelCase__	:   int												=	RagPyTorchDistributedRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		)
                    if init_retrieval:
                           retriever.init_retrieval(_UpperCAmelCase						)
             return retriever




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             # Have to run in local mode because sys.path modifications at top of
             # file are not propogated to remote workers.
             # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
             ray.init(local_mode=_UpperCAmelCase						)
             UpperCAmelCase__	:   int												=	RagConfig(
                 retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		)
             UpperCAmelCase__	:   List[Any]												=	ray.remote(_UpperCAmelCase						)
             UpperCAmelCase__	:   Any												=	[remote_cls.remote() for _ in range(1						)]
             with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
                    UpperCAmelCase__	:   List[Any]												=	self.get_dummy_dataset()
                    UpperCAmelCase__	:   Dict												=	RagRayDistributedRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		retrieval_workers=_UpperCAmelCase  ,		)
                    if init_retrieval:
                           retriever.init_retrieval()
             return retriever




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase=1_2345						):
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_dataset()
             UpperCAmelCase__	:   Tuple												=	RagConfig(
                 retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		index_name='''custom'''  ,		)
             if from_disk:
                    UpperCAmelCase__	:   List[Any]												=	os.path.join(self.tmpdirname  ,		'''dataset'''						)
                    UpperCAmelCase__	:   List[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
                    UpperCAmelCase__	:   int												=	RagPyTorchDistributedRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		)
             else:
                    UpperCAmelCase__	:   Tuple												=	RagPyTorchDistributedRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		index=CustomHFIndex(config.retrieval_vector_size  ,		_UpperCAmelCase						)  ,		)
             if init_retrieval:
                    retriever.init_retrieval(_UpperCAmelCase						)
             return retriever




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             # Have to run in local mode because sys.path modifications at top of
             # file are not propogated to remote workers.
             # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
             ray.init(local_mode=_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	self.get_dummy_dataset()
             UpperCAmelCase__	:   Tuple												=	RagConfig(
                 retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		index_name='''custom'''  ,		)
             UpperCAmelCase__	:   Tuple												=	ray.remote(_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	[remote_cls.remote() for _ in range(1						)]
             if from_disk:
                    UpperCAmelCase__	:   int												=	os.path.join(self.tmpdirname  ,		'''dataset'''						)
                    UpperCAmelCase__	:   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
                    UpperCAmelCase__	:   str												=	RagRayDistributedRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		retrieval_workers=_UpperCAmelCase  ,		index=CustomHFIndex.load_from_disk(
                            vector_size=config.retrieval_vector_size  ,		dataset_path=config.passages_path  ,		index_path=config.index_path  ,		)  ,		)
             else:
                    UpperCAmelCase__	:   List[Any]												=	RagRayDistributedRetriever(
                        _UpperCAmelCase  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		retrieval_workers=_UpperCAmelCase  ,		index=CustomHFIndex(config.retrieval_vector_size  ,		_UpperCAmelCase						)  ,		)
             if init_retrieval:
                    retriever.init_retrieval()
             return retriever




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	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						):
             UpperCAmelCase__	:   Any												=	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_pytorch_distributed_retriever(init_retrieval=_UpperCAmelCase						)  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Union[str, 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_pytorch_retriever(init_retrieval=_UpperCAmelCase  ,		from_disk=_UpperCAmelCase						)  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[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=_UpperCAmelCase  ,		from_disk=_UpperCAmelCase						)  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		)




      @require_ray
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	1
             UpperCAmelCase__	:   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_ray_distributed_retriever(init_retrieval=_UpperCAmelCase						)  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
             ray.shutdown()




      @require_ray
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[Any]												=	1
             UpperCAmelCase__	:   List[Any]												=	np.array(
                 [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]  ,		dtype=np.floataa						)
             with self.assertRaises(_UpperCAmelCase						):
                    self.distributed_retriever_check(
                        self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase  ,		from_disk=_UpperCAmelCase						)  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		)
             ray.shutdown()




      @require_ray
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	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_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase  ,		from_disk=_UpperCAmelCase						)  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
             ray.shutdown()







'''simple docstring'''







import re






def  lowerCAmelCase__		(					a_  :     Optional[int]			)   -> Optional[Any]:
       def docstring_decorator(a_  :     Dict			):
              UpperCAmelCase__	:   Optional[int]												=	fn.__doc__
              UpperCAmelCase__	:   Dict												=	func_doc.split('''\n'''			)
              UpperCAmelCase__	:   Any												=	0
              while i < len(a_			) and re.search(r'''^\s*Examples?:\s*$'''			,						lines[i]			) is None:
                     i += 1
              if i < len(a_			):
                     UpperCAmelCase__	:   List[Any]												=	example_docstring
                     UpperCAmelCase__	:   int												=	'''\n'''.join(a_			)
              else:
                     raise ValueError(
                         f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
                         f"""current docstring is:\n{func_doc}"""			)
              UpperCAmelCase__	:   Any												=	func_doc
              return fn

       return docstring_decorator