






"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCAmelCase_		:			List[Any]				   =					1_6
lowerCAmelCase_		:			Dict				   =					3_2






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase = 16						):







       '''simple docstring'''
       UpperCAmelCase					=	AutoTokenizer.from_pretrained("""bert-base-cased"""						)
       UpperCAmelCase					=	load_dataset("""glue"""		,					"""mrpc"""						)

       def tokenize_function(lowerCAmelCase						):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase					=	tokenizer(examples["""sentence1"""]		,					examples["""sentence2"""]		,					truncation=lowerCAmelCase		,					max_length=lowerCAmelCase						)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCAmelCase					=	datasets.map(
                  lowerCAmelCase		,					batched=lowerCAmelCase		,					remove_columns=["""idx""", """sentence1""", """sentence2"""]		,					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCAmelCase					=	tokenized_datasets.rename_column("""label"""		,					"""labels"""						)

       def collate_fn(lowerCAmelCase						):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase					=	128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase					=	16
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase					=	8
              else:
                     UpperCAmelCase					=	None

              return tokenizer.pad(
                  lowerCAmelCase		,					padding="""longest"""		,					max_length=lowerCAmelCase		,					pad_to_multiple_of=lowerCAmelCase		,					return_tensors="""pt"""		,					)

       # Instantiate dataloaders.
       UpperCAmelCase					=	DataLoader(
           tokenized_datasets["""train"""]		,					shuffle=lowerCAmelCase		,					collate_fn=lowerCAmelCase		,					batch_size=lowerCAmelCase						)
       UpperCAmelCase					=	DataLoader(
           tokenized_datasets["""validation"""]		,					shuffle=lowerCAmelCase		,					collate_fn=lowerCAmelCase		,					batch_size=lowerCAmelCase						)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 lowerCAmelCase_		:			List[Any]				   =					mocked_dataloaders  # noqa: F811






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,					lowerCAmelCase						) == "1":
              UpperCAmelCase					=	2
       # Initialize accelerator
       UpperCAmelCase					=	Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision						)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase					=	config["""lr"""]
       UpperCAmelCase					=	int(config["""num_epochs"""]						)
       UpperCAmelCase					=	int(config["""seed"""]						)
       UpperCAmelCase					=	int(config["""batch_size"""]						)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps		,					"""isdigit"""						):
              if args.checkpointing_steps == "epoch":
                     UpperCAmelCase					=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCAmelCase					=	int(args.checkpointing_steps						)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
       else:
              UpperCAmelCase					=	None

       set_seed(lowerCAmelCase						)

       UpperCAmelCase			,				UpperCAmelCase					=	get_dataloaders(lowerCAmelCase		,					lowerCAmelCase						)
       UpperCAmelCase					=	evaluate.load("""glue"""		,					"""mrpc"""						)

       # If the batch size is too big we use gradient accumulation
       UpperCAmelCase					=	1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCAmelCase					=	batch_size // MAX_GPU_BATCH_SIZE
              UpperCAmelCase					=	MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase					=	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,					return_dict=lowerCAmelCase						)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCAmelCase					=	model.to(accelerator.device						)

       # Instantiate optimizer
       UpperCAmelCase					=	AdamW(params=model.parameters()		,					lr=lowerCAmelCase						)

       # Instantiate scheduler
       UpperCAmelCase					=	get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase		,					num_warmup_steps=100		,					num_training_steps=(len(lowerCAmelCase						) * num_epochs) // gradient_accumulation_steps		,					)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	accelerator.prepare(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCAmelCase					=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCAmelCase					=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''						)
                     accelerator.load_state(args.resume_from_checkpoint						)
                     UpperCAmelCase					=	os.path.basename(args.resume_from_checkpoint						)
              else:
                     # Get the most recent checkpoint
                     UpperCAmelCase					=	[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
                     dirs.sort(key=os.path.getctime						)
                     UpperCAmelCase					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCAmelCase					=	os.path.splitext(lowerCAmelCase						)[0]

              if "epoch" in training_difference:
                     UpperCAmelCase					=	int(training_difference.replace("""epoch_"""		,					""""""						)						) + 1
                     UpperCAmelCase					=	None
              else:
                     UpperCAmelCase					=	int(training_difference.replace("""step_"""		,					""""""						)						)
                     UpperCAmelCase					=	resume_step // len(lowerCAmelCase						)
                     resume_step -= starting_epoch * len(lowerCAmelCase						)

    # Now we train the model
       for epoch in range(lowerCAmelCase		,					lowerCAmelCase						):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCAmelCase					=	accelerator.skip_first_batches(lowerCAmelCase		,					lowerCAmelCase						)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCAmelCase					=	train_dataloader
              for step, batch in enumerate(lowerCAmelCase						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device						)
                     UpperCAmelCase					=	model(**lowerCAmelCase						)
                     UpperCAmelCase					=	outputs.loss
                     UpperCAmelCase					=	loss / gradient_accumulation_steps
                     accelerator.backward(lowerCAmelCase						)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(lowerCAmelCase		,					lowerCAmelCase						):
                            UpperCAmelCase					=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCAmelCase					=	os.path.join(args.output_dir		,					lowerCAmelCase						)
                                   accelerator.save_state(lowerCAmelCase						)

              model.eval()
              for step, batch in enumerate(lowerCAmelCase						):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device						)
                     with torch.no_grad():
                            UpperCAmelCase					=	model(**lowerCAmelCase						)
                     UpperCAmelCase					=	outputs.logits.argmax(dim=-1						)
                     UpperCAmelCase			,				UpperCAmelCase					=	accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
                     metric.add_batch(
                         predictions=lowerCAmelCase		,					references=lowerCAmelCase		,					)

              UpperCAmelCase					=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''		,					lowerCAmelCase						)

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCAmelCase					=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCAmelCase					=	os.path.join(args.output_dir		,					lowerCAmelCase						)
                     accelerator.save_state(lowerCAmelCase						)






def 					_lowerCAmelCase				(					):







       '''simple docstring'''
       UpperCAmelCase					=	argparse.ArgumentParser(description="""Simple example of training script."""						)
       parser.add_argument(
           """--mixed_precision"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					choices=["""no""", """fp16""", """bf16""", """fp8"""]		,					help="""Whether to use mixed precision. Choose"""
           """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
           """and an Nvidia Ampere GPU."""		,					)
       parser.add_argument("""--cpu"""		,					action="""store_true"""		,					help="""If passed, will train on the CPU."""						)
       parser.add_argument(
           """--checkpointing_steps"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""		,					)
       parser.add_argument(
           """--output_dir"""		,					type=lowerCAmelCase		,					default="""."""		,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,					)
       parser.add_argument(
           """--resume_from_checkpoint"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					help="""If the training should continue from a checkpoint folder."""		,					)
       UpperCAmelCase					=	parser.parse_args()
       UpperCAmelCase					=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(lowerCAmelCase		,					lowerCAmelCase						)


if __name__ == "__main__":
 main()




'''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 					_snake_case   (     A       )						->    Any:
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														lowerCAmelCase__          =	unittest.skip('''test requires Datasets, Faiss, psutil'''       )(A       )
							return test_case






@require_distributed_retrieval
class 		a__				(							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	tempfile.mkdtemp()
														lowerCAmelCase__          =	8

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

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







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







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







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														shutil.rmtree(self.tmpdirname   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														lowerCAmelCase__          =	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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=1_23_45   )       ->							RagPyTorchDistributedRetriever:
														lowerCAmelCase__          =	self.get_dummy_dataset()
														lowerCAmelCase__          =	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:
																					lowerCAmelCase__          =	dataset
																					lowerCAmelCase__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
																					if init_retrieval:
																												retriever.init_retrieval(lowerCamelCase_   )
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							RagRayDistributedRetriever:
														# 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=lowerCamelCase_   )
														lowerCAmelCase__          =	RagConfig(
														    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    )
														lowerCAmelCase__          =	ray.remote(lowerCamelCase_   )
														lowerCAmelCase__          =	[remote_cls.remote() for _ in range(1   )]
														with patch('''transformers.models.rag.retrieval_rag.load_dataset'''   ) as mock_load_dataset:
																					lowerCAmelCase__          =	self.get_dummy_dataset()
																					lowerCAmelCase__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    )
																					if init_retrieval:
																												retriever.init_retrieval()
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_=1_23_45   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.get_dummy_dataset()
														lowerCAmelCase__          =	RagConfig(
														    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    index_name='''custom'''					,    )
														if from_disk:
																					lowerCAmelCase__          =	os.path.join(self.tmpdirname					,    '''dataset'''   )
																					lowerCAmelCase__          =	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
																					lowerCAmelCase__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
														else:
																					lowerCAmelCase__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCamelCase_   )					,    )
														if init_retrieval:
																					retriever.init_retrieval(lowerCamelCase_   )
														return retriever







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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	retriever.retrieve(lowerCamelCase_					,    n_docs=lowerCamelCase_   )
														self.assertEqual(retrieved_doc_embeds.shape					,    (2, n_docs, self.retrieval_vector_size)   )
														self.assertEqual(len(lowerCamelCase_   )					,    2   )
														self.assertEqual(sorted(doc_dicts[0]   )					,    ['''embeddings''', '''id''', '''text''', '''title''']   )
														self.assertEqual(len(doc_dicts[0]['''id''']   )					,    lowerCamelCase_   )
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )
														ray.shutdown()







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )
														with self.assertRaises(lowerCamelCase_   ):
																					self.distributed_retriever_check(
																					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )
														ray.shutdown()







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )
														ray.shutdown()