
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowercase		:						int =					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''







					if "token" in model_name_or_path:
										return "rag_token"
					if "sequence" in model_name_or_path:
										return "rag_sequence"
					if "bart" in model_name_or_path:
										return "bart"
					return None


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Union[str, Any]:


					'''simple docstring'''
					return max(metric_fn(_A	,				_A ) for gt in ground_truths )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Tuple:


					'''simple docstring'''
					__a:						str		    =					[line.strip() for line in open(_A	,				"""r""" ).readlines()]
					__a:						Any		    =					[]

					if args.gold_data_mode == "qa":
										__a:						Any		    =					pd.read_csv(_A	,				sep="""\t"""	,				header=_A )
										for answer_list in data[1]:
															__a:						List[str]		    =					ast.literal_eval(_A )
															answers.append(_A )
					else:
										__a:						Any		    =					[line.strip() for line in open(_A	,				"""r""" ).readlines()]
										__a:						Optional[int]		    =					[[reference] for reference in references]

					__a:						Any		    =					0
					for prediction, ground_truths in zip(_A	,				_A ):
										total += 1
										em += metric_max_over_ground_truths(_A	,				_A	,				_A )
										fa += metric_max_over_ground_truths(_A	,				_A	,				_A )

					__a:						str		    =					100.0 * em / total
					__a:						Optional[int]		    =					100.0 * fa / total

					logger.info(f"""F1: {fa:.2f}""" )
					logger.info(f"""EM: {em:.2f}""" )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Tuple:


					'''simple docstring'''
					__a:						Dict		    =					args.k
					__a:						Dict		    =					[line.strip() for line in open(_A	,				"""r""" ).readlines()]
					__a:						int		    =					[line.strip() for line in open(_A	,				"""r""" ).readlines()]

					__a:						Optional[int]		    =					0
					for hypo, reference in zip(_A	,				_A ):
										__a:						Optional[int]		    =					set(hypo.split("""\t""" )[:k] )
										__a:						List[str]		    =					set(reference.split("""\t""" ) )
										total += 1
										em += len(hypo_provenance & ref_provenance ) / k

					__a:						Any		    =					100.0 * em / total
					logger.info(f"""Precision@{k}: {em: .2f}""" )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Dict:


					'''simple docstring'''







					def strip_title(lowerCamelCase_ ):
										if title.startswith("""\"""" ):
															__a:						Any		    =					title[1:]
										if title.endswith("""\"""" ):
															__a:						Tuple		    =					title[:-1]
										return title

					__a:						Tuple		    =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    _A	,				return_tensors="""pt"""	,				padding=_A	,				truncation=_A	,				)["""input_ids"""].to(args.device )

					__a:						List[Any]		    =					rag_model.rag.question_encoder(_A )
					__a:						List[Any]		    =					question_enc_outputs[0]

					__a:						Optional[Any]		    =					rag_model.retriever(
					    _A	,				question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy()	,				prefix=rag_model.rag.generator.config.prefix	,				n_docs=rag_model.config.n_docs	,				return_tensors="""pt"""	,				)
					__a:						List[str]		    =					rag_model.retriever.index.get_doc_dicts(result.doc_ids )
					__a:						str		    =					[]
					for docs in all_docs:
										__a:						Union[str, Any]		    =					[strip_title(_A ) for title in docs["""title"""]]
										provenance_strings.append("""\t""".join(_A ) )
					return provenance_strings


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''







					with torch.no_grad():
										__a:						Tuple		    =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    _A	,				return_tensors="""pt"""	,				padding=_A	,				truncation=_A )

										__a:						Optional[Any]		    =					inputs_dict.input_ids.to(args.device )
										__a:						str		    =					inputs_dict.attention_mask.to(args.device )
										__a:						List[str]		    =					rag_model.generate(  # rag_model overwrites generate
										    _A	,				attention_mask=_A	,				num_beams=args.num_beams	,				min_length=args.min_length	,				max_length=args.max_length	,				early_stopping=_A	,				num_return_sequences=1	,				bad_words_ids=[[0, 0]]	,				)
										__a:						Dict		    =					rag_model.retriever.generator_tokenizer.batch_decode(_A	,				skip_special_tokens=_A )

										if args.print_predictions:
															for q, a in zip(_A	,				_A ):
																				logger.info("""Q: {} - A: {}""".format(_A	,				_A ) )

										return answers


def     lowerCamelCase_    (	)     ->			Union[str, Any]:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					argparse.ArgumentParser()
					parser.add_argument(
					    """--model_type"""	,				choices=["""rag_sequence""", """rag_token""", """bart"""]	,				type=_A	,				help=(
					        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
					        """ model_name_or_path"""
					    )	,				)
					parser.add_argument(
					    """--index_name"""	,				default=_A	,				choices=["""exact""", """compressed""", """legacy"""]	,				type=_A	,				help="""RAG model retriever type"""	,				)
					parser.add_argument(
					    """--index_path"""	,				default=_A	,				type=_A	,				help="""Path to the retrieval index"""	,				)
					parser.add_argument("""--n_docs"""	,				default=5	,				type=_A	,				help="""Number of retrieved docs""" )
					parser.add_argument(
					    """--model_name_or_path"""	,				default=_A	,				type=_A	,				required=_A	,				help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""	,				)
					parser.add_argument(
					    """--eval_mode"""	,				choices=["""e2e""", """retrieval"""]	,				default="""e2e"""	,				type=_A	,				help=(
					        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
					        """ precision@k."""
					    )	,				)
					parser.add_argument("""--k"""	,				default=1	,				type=_A	,				help="""k for the precision@k calculation""" )
					parser.add_argument(
					    """--evaluation_set"""	,				default=_A	,				type=_A	,				required=_A	,				help="""Path to a file containing evaluation samples"""	,				)
					parser.add_argument(
					    """--gold_data_path"""	,				default=_A	,				type=_A	,				required=_A	,				help="""Path to a tab-separated file with gold samples"""	,				)
					parser.add_argument(
					    """--gold_data_mode"""	,				default="""qa"""	,				type=_A	,				choices=["""qa""", """ans"""]	,				help=(
					        """Format of the gold data file"""
					        """qa - a single line in the following format: question [tab] answer_list"""
					        """ans - a single line of the gold file contains the expected answer string"""
					    )	,				)
					parser.add_argument(
					    """--predictions_path"""	,				type=_A	,				default="""predictions.txt"""	,				help="""Name of the predictions file, to be stored in the checkpoints directory"""	,				)
					parser.add_argument(
					    """--eval_all_checkpoints"""	,				action="""store_true"""	,				help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""	,				)
					parser.add_argument(
					    """--eval_batch_size"""	,				default=8	,				type=_A	,				help="""Batch size per GPU/CPU for evaluation."""	,				)
					parser.add_argument(
					    """--recalculate"""	,				help="""Recalculate predictions even if the prediction file exists"""	,				action="""store_true"""	,				)
					parser.add_argument(
					    """--num_beams"""	,				default=4	,				type=_A	,				help="""Number of beams to be used when generating answers"""	,				)
					parser.add_argument("""--min_length"""	,				default=1	,				type=_A	,				help="""Min length of the generated answers""" )
					parser.add_argument("""--max_length"""	,				default=50	,				type=_A	,				help="""Max length of the generated answers""" )

					parser.add_argument(
					    """--print_predictions"""	,				action="""store_true"""	,				help="""If True, prints predictions while evaluating."""	,				)
					parser.add_argument(
					    """--print_docs"""	,				action="""store_true"""	,				help="""If True, prints docs retried while generating."""	,				)
					__a:						Optional[int]		    =					parser.parse_args()
					__a:						List[Any]		    =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
					return args


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Tuple:


					'''simple docstring'''
					__a:						int		    =					{}
					if args.model_type is None:
										__a:						List[str]		    =					infer_model_type(args.model_name_or_path )
										assert args.model_type is not None
					if args.model_type.startswith("""rag""" ):
										__a:						Union[str, Any]		    =					RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
										__a:						str		    =					args.n_docs
										if args.index_name is not None:
															__a:						int		    =					args.index_name
										if args.index_path is not None:
															__a:						Union[str, Any]		    =					args.index_path
					else:
										__a:						List[Any]		    =					BartForConditionalGeneration

					__a:						List[str]		    =					(
					    [f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
					    if args.eval_all_checkpoints
					    else [args.model_name_or_path]
					)

					logger.info("""Evaluate the following checkpoints: %s"""	,				_A )

					__a:						str		    =					get_scores if args.eval_mode == """e2e""" else get_precision_at_k
					__a:						Dict		    =					evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

					for checkpoint in checkpoints:
										if os.path.exists(args.predictions_path ) and (not args.recalculate):
															logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
															score_fn(_A	,				args.predictions_path	,				args.gold_data_path )
															continue

										logger.info("""***** Running evaluation for {} *****""".format(_A ) )
										logger.info("""  Batch size = %d"""	,				args.eval_batch_size )
										logger.info("""  Predictions will be stored under {}""".format(args.predictions_path ) )

										if args.model_type.startswith("""rag""" ):
															__a:						Tuple		    =					RagRetriever.from_pretrained(_A	,				**_A )
															__a:						Union[str, Any]		    =					model_class.from_pretrained(_A	,				retriever=_A	,				**_A )
															model.retriever.init_retrieval()
										else:
															__a:						Optional[int]		    =					model_class.from_pretrained(_A	,				**_A )
										model.to(args.device )

										with open(args.evaluation_set	,				"""r""" ) as eval_file, open(args.predictions_path	,				"""w""" ) as preds_file:
															__a:						Union[str, Any]		    =					[]
															for line in tqdm(_A ):
																				questions.append(line.strip() )
																				if len(_A ) == args.eval_batch_size:
																									__a:						Optional[Any]		    =					evaluate_batch_fn(_A	,				_A	,				_A )
																									preds_file.write("""\n""".join(_A ) + """\n""" )
																									preds_file.flush()
																									__a:						Dict		    =					[]
															if len(_A ) > 0:
																				__a:						str		    =					evaluate_batch_fn(_A	,				_A	,				_A )
																				preds_file.write("""\n""".join(_A ) )
																				preds_file.flush()

															score_fn(_A	,				args.predictions_path	,				args.gold_data_path )


if __name__ == "__main__":
			lowercase		:						Optional[int] =					get_args()
			main(args)


"""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:
			lowercase		:						str =					None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			lowercase		:						Optional[int] =					None
			lowercase		:						Union[str, Any] =					None
			lowercase		:						List[str] =					None


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Union[str, Any]:


					'''simple docstring'''
					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										__a:						int		    =					unittest.skip("""test requires Datasets, Faiss, psutil""" )(lowerCamelCase_ )
					return test_case




@require_distributed_retrieval
class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					tempfile.mkdtemp()
												__a:						int		    =					8

												# DPR tok
												__a:						Any		    =					[
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												__a:						str		    =					os.path.join(self.tmpdirname						, """dpr_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					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
												__a:						Any		    =					[
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__a:						List[str]		    =					dict(zip(snake_case__						, range(len(snake_case__    )    )    )    )
												__a:						List[str]		    =					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__a:						List[str]		    =					{"""unk_token""": """<unk>"""}

												__a:						Optional[int]		    =					os.path.join(self.tmpdirname						, """bart_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					os.path.join(snake_case__						, BART_VOCAB_FILES_NAMES["""vocab_file"""]    )
												__a:						str		    =					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 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												shutil.rmtree(self.tmpdirname    )






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						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 							__lowercase  (					self						:			Any						, snake_case__						:			bool						, snake_case__						:			Dict=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						str		    =					self.get_dummy_dataset()
												__a:						int		    =					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:
																	__a:						List[str]		    =					dataset
																	__a:						List[Any]		    =					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 							__lowercase  (					self						:			Dict						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						int		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, )
												__a:						List[str]		    =					ray.remote(snake_case__    )
												__a:						Tuple		    =					[remote_cls.remote() for _ in range(1    )]
												with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
																	__a:						int		    =					self.get_dummy_dataset()
																	__a:						List[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 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool						, snake_case__						:			Union[str, Any]=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.get_dummy_dataset()
												__a:						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:
																	__a:						int		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						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
																	__a:						str		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
												else:
																	__a:						List[Any]		    =					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 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						Tuple		    =					self.get_dummy_dataset()
												__a:						Dict		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												__a:						Optional[Any]		    =					ray.remote(snake_case__    )
												__a:						Any		    =					[remote_cls.remote() for _ in range(1    )]
												if from_disk:
																	__a:						List[Any]		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						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
																	__a:						Any		    =					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:
																	__a:						Any		    =					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 							__lowercase  (					self						:			str						, snake_case__						:			RagRetriever						, snake_case__						:			np.array						, snake_case__						:			int    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Optional[Any]		    =					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 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						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=snake_case__    )						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						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_pytorch_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							@require_ray
							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						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_ray_distributed_retriever(init_retrieval=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						List[str]		    =					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 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						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()
