

import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


A_ :				List[Any]						 =    logging.getLogger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__					, A__					, A__=None    ):
						super().__init__(
						    A__					, question_encoder_tokenizer=A__					, generator_tokenizer=A__					, index=A__					, init_retrieval=A__					, )
						A__							:					List[str]       =	None






					def 						__A    (				self					, A__    ):
						logger.info("""initializing retrieval"""    )

						# initializing a separate process group for retrieval as the default
						# nccl backend doesn't support gather/scatter operations while gloo
						# is too slow to replace nccl for the core gpu communication
						if dist.is_initialized():
							logger.info("""dist initialized"""    )
							# needs to be set manually
							A__							:					List[Any]       =	self._infer_socket_ifname()
							# avoid clash with the NCCL port
							A__							:					Tuple       =	str(distributed_port + 1    )
							A__							:					Dict       =	dist.new_group(ranks=A__					, backend="""gloo"""    )

						# initialize retriever only on the main worker
						if not dist.is_initialized() or self._is_main():
							logger.info("""dist not initialized / main"""    )
							self.index.init_index()

						# all processes wait untill the retriever is initialized by the main process
						if dist.is_initialized():
							torch.distributed.barrier(group=self.process_group    )






					def 						__A    (				self    ):
						return dist.get_rank(group=self.process_group    ) == 0






					def 						__A    (				self					, A__					, A__					, A__=torch.floataa    ):
						A__							:					Tuple       =	torch.empty(A__					, dtype=A__    )
						dist.scatter(A__					, src=0					, scatter_list=A__					, group=self.process_group    )
						return target_tensor






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	psutil.net_if_addrs()
						# a hacky way to deal with varying network interface names
						A__							:					Optional[int]       =	next((addr for addr in addrs if addr.startswith("""e"""    ))					, A__    )
						return ifname






					def 						__A    (				self					, A__					, A__    ):

						# single GPU training
						if not dist.is_initialized():
							A__     ,     A__							:					str       =	self._main_retrieve(A__					, A__    )
							return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A__    )

						# distributed training
						A__							:					str       =	dist.get_world_size(group=self.process_group    )

						# gather logic
						A__							:					Dict       =	None
						if self._is_main():
							A__							:					Any       =	[torch.empty(question_hidden_states.shape					, dtype=torch.floataa    ) for _ in range(A__    )]
						dist.gather(torch.tensor(A__    )					, dst=0					, gather_list=A__					, group=self.process_group    )

						# scatter logic
						A__							:					Union[str, Any]       =	question_hidden_states.shape[0]
						A__							:					List[str]       =	[]
						A__							:					Union[str, Any]       =	[]
						if self._is_main():
							assert len(A__    ) == world_size
							A__     ,     A__							:					Any       =	self._main_retrieve(torch.cat(A__    ).numpy()					, A__    )
							A__     ,     A__							:					int       =	torch.tensor(A__    ), torch.tensor(A__    )
							A__							:					int       =	self._chunk_tensor(A__					, A__    )
							A__							:					Dict       =	self._chunk_tensor(A__					, A__    )
						A__							:					Optional[int]       =	self._scattered(A__					, [n_queries, n_docs]					, target_type=torch.intaa    )
						A__							:					Optional[int]       =	self._scattered(A__					, [n_queries, n_docs, question_hidden_states.shape[1]]    )

						return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A__    )






import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


A_ :				Any						 =    logging.getLogger(__name__)

def 		UpperCamelCase      (lowercase_:				Optional[Any]=2					,   lowercase_:				Union[str, Any]=3					,   lowercase_:				int=16					,   lowercase_:				int = 10					,   lowercase_:				int = 2      )  ->   int:

	def get_dataset(lowercase_:				Optional[int]      ):
		A__							:					Optional[Any]       =	torch.randn(batch_size * n_batches					,   1      )
		return TensorDataset(lowercase_					,   a * x + b + 0.1 * torch.randn(batch_size * n_batches					,   1      )      )

	A__							:					Dict       =	get_dataset(lowercase_      )
	A__							:					Any       =	get_dataset(lowercase_      )
	A__							:					Dict       =	DataLoader(lowercase_					,   shuffle=lowercase_					,   batch_size=lowercase_					,   num_workers=4      )
	A__							:					Optional[Any]       =	DataLoader(lowercase_					,   shuffle=lowercase_					,   batch_size=lowercase_					,   num_workers=4      )
	return (train_dataloader, valid_dataloader)

def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				List[str]					,   lowercase_:				int					,   lowercase_:				int					,   lowercase_:				List[str]					,   lowercase_:				Dict=None      )  ->   List[Any]:
	A__							:					List[Any]       =	[]
	for epoch in range(lowercase_      ):
		# Train quickly
		model.train()
		for batch in dataloader:
			A__     ,     A__							:					Any       =	batch
			A__							:					Any       =	model(lowercase_      )
			A__							:					Any       =	torch.nn.functional.mse_loss(lowercase_					,   lowercase_      )
			accelerator.backward(lowercase_      )
			optimizer.step()
			optimizer.zero_grad()
		rands.append(random.random()      )  # Introduce some randomness
		if scheduler is not None:
			scheduler.step()
	return rands



class 					_a		(nn.Module       ):


					'''simple docstring'''






					def __init__(				self    ):
						super().__init__()
						A__							:					str       =	nn.Parameter(torch.randn(1    )    )
						A__							:					Any       =	nn.Parameter(torch.randn(1    )    )






					def 						__A    (				self					, A__    ):
						return x * self.a + self.b



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					Optional[Any]       =	DummyModel()
							A__							:					Optional[Any]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					str       =	dummy_dataloaders()
							A__							:					Dict       =	ProjectConfiguration(total_limit=1					, project_dir=A__					, automatic_checkpoint_naming=A__    )
							# Train baseline
							A__							:					List[str]       =	Accelerator(project_config=A__    )
							A__     ,     A__     ,     A__     ,     A__							:					Any       =	accelerator.prepare(
							    A__					, A__					, A__					, A__    )
							# Save initial
							accelerator.save_state()

							# Save second state
							accelerator.save_state()
							self.assertEqual(len(os.listdir(accelerator.project_dir    )    )					, 1    )






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					str       =	DummyModel()
							A__							:					Optional[int]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					int       =	dummy_dataloaders()
							# Train baseline
							A__							:					str       =	Accelerator()
							A__     ,     A__     ,     A__     ,     A__							:					List[str]       =	accelerator.prepare(
							    A__					, A__					, A__					, A__    )
							# Save initial
							A__							:					List[Any]       =	os.path.join(A__					, """initial"""    )
							accelerator.save_state(A__    )
							((A__)     ,     (A__))							:					str       =	model.a.item(), model.b.item()
							A__							:					Dict       =	optimizer.state_dict()
							A__							:					List[str]       =	train(3					, A__					, A__					, A__					, A__    )
							((A__)     ,     (A__))							:					str       =	model.a.item(), model.b.item()
							A__							:					Any       =	optimizer.state_dict()

							# Train partially
							set_seed(42    )
							A__							:					Optional[int]       =	DummyModel()
							A__							:					Dict       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					Dict       =	dummy_dataloaders()
							A__							:					List[str]       =	Accelerator()
							A__     ,     A__     ,     A__     ,     A__							:					Optional[Any]       =	accelerator.prepare(
							    A__					, A__					, A__					, A__    )
							accelerator.load_state(A__    )
							((A__)     ,     (A__))							:					Tuple       =	model.a.item(), model.b.item()
							A__							:					Union[str, Any]       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )

							A__							:					List[str]       =	train(2					, A__					, A__					, A__					, A__    )
							# Save everything
							A__							:					Optional[int]       =	os.path.join(A__					, """checkpoint"""    )
							accelerator.save_state(A__    )

							# Load everything back in and make sure all states work
							accelerator.load_state(A__    )
							test_rands += train(1					, A__					, A__					, A__					, A__    )
							((A__)     ,     (A__))							:					Union[str, Any]       =	model.a.item(), model.b.item()
							A__							:					Optional[int]       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					int       =	DummyModel()
							A__							:					Optional[int]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					List[str]       =	dummy_dataloaders()
							A__							:					str       =	ProjectConfiguration(automatic_checkpoint_naming=A__    )

							# Train baseline
							A__							:					Any       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__     ,     A__     ,     A__     ,     A__							:					str       =	accelerator.prepare(
							    A__					, A__					, A__					, A__    )
							# Save initial
							accelerator.save_state()
							((A__)     ,     (A__))							:					Tuple       =	model.a.item(), model.b.item()
							A__							:					int       =	optimizer.state_dict()
							A__							:					int       =	train(3					, A__					, A__					, A__					, A__    )
							((A__)     ,     (A__))							:					Optional[Any]       =	model.a.item(), model.b.item()
							A__							:					Any       =	optimizer.state_dict()

							# Train partially
							set_seed(42    )
							A__							:					Dict       =	DummyModel()
							A__							:					List[Any]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__     ,     A__							:					Union[str, Any]       =	dummy_dataloaders()
							A__							:					List[Any]       =	ProjectConfiguration(iteration=1					, automatic_checkpoint_naming=A__    )
							A__							:					Dict       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__     ,     A__     ,     A__     ,     A__							:					Union[str, Any]       =	accelerator.prepare(
							    A__					, A__					, A__					, A__    )
							accelerator.load_state(os.path.join(A__					, """checkpoints"""					, """checkpoint_0"""    )    )
							((A__)     ,     (A__))							:					Optional[int]       =	model.a.item(), model.b.item()
							A__							:					Tuple       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )

							A__							:					str       =	train(2					, A__					, A__					, A__					, A__    )
							# Save everything
							accelerator.save_state()

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(A__					, """checkpoints"""					, """checkpoint_1"""    )    )
							test_rands += train(1					, A__					, A__					, A__					, A__    )
							((A__)     ,     (A__))							:					Optional[int]       =	model.a.item(), model.b.item()
							A__							:					List[Any]       =	optimizer.state_dict()
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )
							self.assertEqual(A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	torch.tensor([1, 2, 3]    )
						A__							:					int       =	torch.tensor([2, 3, 4]    )
						A__							:					List[Any]       =	DummyModel()
						A__							:					List[Any]       =	torch.optim.Adam(net.parameters()    )
						A__							:					Tuple       =	Accelerator()
						with self.assertRaises(A__    ) as ve:
							accelerator.register_for_checkpointing(A__					, A__					, A__					, A__    )
						A__							:					Any       =	str(ve.exception    )
						self.assertTrue("""Item at index 0""" in message    )
						self.assertTrue("""Item at index 1""" in message    )
						self.assertFalse("""Item at index 2""" in message    )
						self.assertFalse("""Item at index 3""" in message    )






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					Any       =	DummyModel()
							A__							:					Union[str, Any]       =	torch.optim.Adam(params=model.parameters()					, lr=1e-3    )
							A__							:					Dict       =	torch.optim.lr_scheduler.StepLR(A__					, step_size=1					, gamma=0.9_9    )
							A__     ,     A__							:					List[Any]       =	dummy_dataloaders()
							A__							:					Tuple       =	ProjectConfiguration(automatic_checkpoint_naming=A__    )
							# Train baseline
							A__							:					Optional[Any]       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__     ,     A__     ,     A__     ,     A__     ,     A__							:					Union[str, Any]       =	accelerator.prepare(
							    A__					, A__					, A__					, A__					, A__    )
							# Save initial
							accelerator.save_state()
							A__							:					Tuple       =	scheduler.state_dict()
							train(3					, A__					, A__					, A__					, A__					, A__    )
							self.assertNotEqual(A__					, scheduler.state_dict()    )

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(A__					, """checkpoints"""					, """checkpoint_0"""    )    )
							self.assertEqual(A__					, scheduler.state_dict()    )






					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42    )
							A__							:					Optional[Any]       =	DummyModel()
							A__							:					int       =	ProjectConfiguration(automatic_checkpoint_naming=A__					, total_limit=2    )
							# Train baseline
							A__							:					List[str]       =	Accelerator(project_dir=A__					, project_config=A__    )
							A__							:					Union[str, Any]       =	accelerator.prepare(A__    )
							# Save 3 states:
							for _ in range(11    ):
								accelerator.save_state()
							self.assertTrue(not os.path.exists(os.path.join(A__					, """checkpoints"""					, """checkpoint_0"""    )    )    )
							self.assertTrue(os.path.exists(os.path.join(A__					, """checkpoints"""					, """checkpoint_9"""    )    )    )
							self.assertTrue(os.path.exists(os.path.join(A__					, """checkpoints"""					, """checkpoint_10"""    )    )    )






					@require_cuda
					def 						__A    (				self    ):
						A__							:					Dict       =	["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
						execute_subprocess_async(A__					, env=os.environ.copy()    )


if __name__ == "__main__":
	A_ :				List[str]						 =    '/tmp/accelerate/state_checkpointing'
	A_ :				Optional[Any]						 =    DummyModel()
	A_ :				Union[str, Any]						 =    torch.optim.Adam(params=model.parameters(), lr=1E-3)
	A_ :				str						 =    torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
	A_ ,    A_ :				List[Any]						 =    dummy_dataloaders()
	A_ :				int						 =    ProjectConfiguration(automatic_checkpoint_naming=True)
	# Train baseline
	A_ :				List[str]						 =    Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
	if accelerator.process_index == 0:
		if os.path.exists(savedir):
			shutil.rmtree(savedir)
		os.makedirs(savedir)
	A_ ,    A_ ,    A_ ,    A_ ,    A_ :				List[Any]						 =    accelerator.prepare(
	    model, optimizer, train_dataloader, valid_dataloader, scheduler
	)
	A_ ,    A_ :				Dict						 =    accelerator.prepare(model, optimizer)
	train(3, model, train_dataloader, optimizer, accelerator, scheduler)
	# Check that the intial optimizer is loaded on the GPU
	for group in optimizer.param_groups:
		A_ :				str						 =    group['params'][0].device
		break
	assert param_device.type == accelerator.device.type
	A_ :				Optional[Any]						 =    model.cpu()
	accelerator.wait_for_everyone()
	accelerator.save_state()
	accelerator.wait_for_everyone()

	# Check CPU state
	accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
	for group in optimizer.param_groups:
		A_ :				str						 =    group['params'][0].device
		break
	assert (
	    param_device.type == torch.device('cpu').type
	), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

	# Check device state
	model.to(accelerator.device)
	accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
	for group in optimizer.param_groups:
		A_ :				Tuple						 =    group['params'][0].device
		break
	assert (
	    param_device.type == accelerator.device.type
	), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

	# Check error
	with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
		accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
	accelerator.wait_for_everyone()
	if accelerator.process_index == 0:
		shutil.rmtree(savedir)
	accelerator.wait_for_everyone()



