

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A_ :				List[Any]						 =    {
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[Any]													= '''albert'''






					def __init__(				self					, A__=3_0000					, A__=128					, A__=4096					, A__=12					, A__=1					, A__=64					, A__=1_6384					, A__=1					, A__="gelu_new"					, A__=0					, A__=0					, A__=512					, A__=2					, A__=0.0_2					, A__=1e-12					, A__=0.1					, A__="absolute"					, A__=0					, A__=2					, A__=3					, **A__					, ):
						super().__init__(pad_token_id=A__					, bos_token_id=A__					, eos_token_id=A__					, **A__    )

						A__							:					Tuple       =	vocab_size
						A__							:					str       =	embedding_size
						A__							:					List[Any]       =	hidden_size
						A__							:					Union[str, Any]       =	num_hidden_layers
						A__							:					Dict       =	num_hidden_groups
						A__							:					List[str]       =	num_attention_heads
						A__							:					List[Any]       =	inner_group_num
						A__							:					Optional[int]       =	hidden_act
						A__							:					Any       =	intermediate_size
						A__							:					Tuple       =	hidden_dropout_prob
						A__							:					Dict       =	attention_probs_dropout_prob
						A__							:					str       =	max_position_embeddings
						A__							:					Union[str, Any]       =	type_vocab_size
						A__							:					str       =	initializer_range
						A__							:					Tuple       =	layer_norm_eps
						A__							:					Optional[Any]       =	classifier_dropout_prob
						A__							:					Any       =	position_embedding_type



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					@property
					def 						__A    (				self    ):
						if self.task == "multiple-choice":
							A__							:					Optional[Any]       =	{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
							A__							:					int       =	{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						        ("""token_type_ids""", dynamic_axis),
						    ]    )






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()



