


'''simple docstring'''

import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.framework.errors_impl import ResourceExhaustedError

							from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
							import pyanvml.pyanvml as nvml

lowerCAmelCase      :Any    	=			logging.get_logger(__name__)





def lowerCamelCase			(			lowerCAmelCase				:	bool		,		lowerCAmelCase				:	bool		):





	"""simple docstring"""







	def run_func(lowerCAmelCase				:	List[Any]		):
		@wraps(UpperCamelCase__		)
		def run_in_eager_mode(*lowerCAmelCase				:	List[str]		,		**lowerCAmelCase				:	Any		):
			return func(*UpperCamelCase__		,		**UpperCamelCase__		)

		@wraps(UpperCamelCase__		)
		@tf.function(experimental_compile=UpperCamelCase__		)
		def run_in_graph_mode(*lowerCAmelCase				:	Any		,		**lowerCAmelCase				:	Dict		):
			return func(*UpperCamelCase__		,		**UpperCamelCase__		)

		if do_eager_mode is True:
			if use_xla is not False:
				raise ValueError(
				    'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'		)
			return run_in_eager_mode
		else:
			return run_in_graph_mode

	return run_func





def lowerCamelCase			(			lowerCAmelCase				:	int		,		lowerCAmelCase				:	int		,		lowerCAmelCase				:	int		):





	"""simple docstring"""







	__magic_name__       :    Tuple     	=     random.Random()
	__magic_name__       :    int     	=     [rng.randint(0		,		vocab_size - 1		) for i in range(batch_size * sequence_length		)]
	return tf.constant(UpperCamelCase__		,		shape=(batch_size, sequence_length)		,		dtype=tf.intaa		)





class _lowerCamelCase       (						lowercase__							):



		'''simple docstring'''







		A_	:			str          =				42
		A_	:			Optional[int]          =				42
		A_	:			Optional[Any]          =				"""TensorFlow"""







		@property
		def    __lowerCAmelCase		(					self		:						str			)  -> List[str]:
			return tf.__version__







		def    __lowerCAmelCase		(					self		:						List[Any]  ,     _A		:						Optional[int]  ,     _A		:						Dict  ,     _A		:						int			)  -> float:
			# initialize GPU on separate process
			__magic_name__       :    str     	=     self.args.strategy
			if strategy is None:
				raise ValueError('A device strategy has to be initialized before using TensorFlow.'			)
			__magic_name__       :    str     	=     self._prepare_inference_func(_A  ,     _A  ,     _A			)
			return self._measure_speed(_inference			)







		def    __lowerCAmelCase		(					self		:						List[Any]  ,     _A		:						Optional[int]  ,     _A		:						Dict  ,     _A		:						Optional[int]			)  -> float:
			__magic_name__       :    Optional[Any]     	=     self.args.strategy
			if strategy is None:
				raise ValueError('A device strategy has to be initialized before using TensorFlow.'			)
			__magic_name__       :    str     	=     self._prepare_train_func(_A  ,     _A  ,     _A			)
			return self._measure_speed(_train			)







		def    __lowerCAmelCase		(					self		:						List[str]  ,     _A		:						Any  ,     _A		:						str  ,     _A		:						Dict			)  -> [Memory, Optional[MemorySummary]]:
			# initialize GPU on separate process
			if self.args.is_gpu:
				tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,     _A			)
			__magic_name__       :    List[str]     	=     self.args.strategy
			if strategy is None:
				raise ValueError('A device strategy has to be initialized before using TensorFlow.'			)
			__magic_name__       :    Tuple     	=     self._prepare_inference_func(_A  ,     _A  ,     _A			)
			return self._measure_memory(_inference			)







		def    __lowerCAmelCase		(					self		:						List[Any]  ,     _A		:						Optional[Any]  ,     _A		:						str  ,     _A		:						Optional[int]			)  -> [Memory, Optional[MemorySummary]]:
			if self.args.is_gpu:
				tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]  ,     _A			)
			__magic_name__       :    Dict     	=     self.args.strategy
			if strategy is None:
				raise ValueError('A device strategy has to be initialized before using TensorFlow.'			)

			__magic_name__       :    Optional[int]     	=     self._prepare_train_func(_A  ,     _A  ,     _A			)
			return self._measure_memory(_train			)







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     _A		:						List[str]  ,     _A		:						List[str]  ,     _A		:						int			)  -> Callable[[], None]:
			__magic_name__       :    Dict     	=     self.config_dict[model_name]

			if self.args.fpaa:
				raise NotImplementedError('Mixed precision is currently not supported.'			)

			__magic_name__       :    Dict     	=     (
			    hasattr(_A  ,     'architectures'			)
			    and isinstance(config.architectures  ,     _A			)
			    and len(config.architectures			) > 0
			)
			if not self.args.only_pretrain_model and has_model_class_in_config:
				try:
					__magic_name__       :    Tuple     	=     '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
					__magic_name__       :    Optional[Any]     	=     __import__('transformers'  ,     fromlist=[model_class]			)
					__magic_name__       :    Any     	=     getattr(_A  ,     _A			)
					__magic_name__       :    Any     	=     model_cls(_A			)
				except ImportError:
					raise ImportError(
					    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
					    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'			)
			else:
				__magic_name__       :    str     	=     TF_MODEL_MAPPING[config.__class__](_A			)

			# encoder-decoder has vocab size saved differently
			__magic_name__       :    Optional[Any]     	=     config.vocab_size if hasattr(_A  ,     'vocab_size'			) else config.encoder.vocab_size
			__magic_name__       :    List[Any]     	=     random_input_ids(_A  ,     _A  ,     _A			)

			@run_with_tf_optimizations(self.args.eager_mode  ,     self.args.use_xla			)
			def encoder_decoder_forward():
				return model(_A  ,     decoder_input_ids=_A  ,     training=_A			)

			@run_with_tf_optimizations(self.args.eager_mode  ,     self.args.use_xla			)
			def encoder_forward():
				return model(_A  ,     training=_A			)

			__magic_name__       :    Dict     	=     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

			return _inference







		def    __lowerCAmelCase		(					self		:						List[Any]  ,     _A		:						Tuple  ,     _A		:						List[str]  ,     _A		:						Union[str, Any]			)  -> Callable[[], None]:
			__magic_name__       :    Union[str, Any]     	=     self.config_dict[model_name]

			if self.args.eager_mode is not False:
				raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'			)

			if self.args.fpaa:
				raise NotImplementedError('Mixed precision is currently not supported.'			)

			__magic_name__       :    List[Any]     	=     (
			    hasattr(_A  ,     'architectures'			)
			    and isinstance(config.architectures  ,     _A			)
			    and len(config.architectures			) > 0
			)
			if not self.args.only_pretrain_model and has_model_class_in_config:
				try:
					__magic_name__       :    Optional[int]     	=     '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
					__magic_name__       :    Any     	=     __import__('transformers'  ,     fromlist=[model_class]			)
					__magic_name__       :    List[str]     	=     getattr(_A  ,     _A			)
					__magic_name__       :    Union[str, Any]     	=     model_cls(_A			)
				except ImportError:
					raise ImportError(
					    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
					    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'			)
			else:
				__magic_name__       :    Union[str, Any]     	=     TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_A			)

			# encoder-decoder has vocab size saved differently
			__magic_name__       :    str     	=     config.vocab_size if hasattr(_A  ,     'vocab_size'			) else config.encoder.vocab_size
			__magic_name__       :    int     	=     random_input_ids(_A  ,     _A  ,     _A			)

			@run_with_tf_optimizations(self.args.eager_mode  ,     self.args.use_xla			)
			def encoder_decoder_train():
				__magic_name__       :    Tuple     	=     model(_A  ,     decoder_input_ids=_A  ,     labels=_A  ,     training=_A			)[0]
				__magic_name__       :    Union[str, Any]     	=     tf.gradients(_A  ,     model.trainable_variables			)
				return gradients

			@run_with_tf_optimizations(self.args.eager_mode  ,     self.args.use_xla			)
			def encoder_train():
				__magic_name__       :    Dict     	=     model(_A  ,     labels=_A  ,     training=_A			)[0]
				__magic_name__       :    List[Any]     	=     tf.gradients(_A  ,     model.trainable_variables			)
				return gradients

			__magic_name__       :    Any     	=     encoder_decoder_train if config.is_encoder_decoder else encoder_train

			return _train







		def    __lowerCAmelCase		(					self		:						List[str]  ,     _A		:						Optional[Any]			)  -> float:
			with self.args.strategy.scope():
				try:
					if self.args.is_tpu or self.args.use_xla:
						# run additional 10 times to stabilize compilation for tpu
						logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'			)
						timeit.repeat(_A  ,     repeat=1  ,     number=5			)

					# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
					__magic_name__       :    List[str]     	=     timeit.repeat(
					    _A  ,     repeat=self.args.repeat  ,     number=10  ,     )

					return min(_A			) / 10.0
				except ResourceExhaustedError as e:
					self.print_fn(F'Doesn\'t fit on GPU. {e}'			)







		def    __lowerCAmelCase		(					self		:						Tuple  ,     _A		:						Union[str, Any]			)  -> [Memory, MemorySummary]:
			logger.info(
			    'Note that TensorFlow allocates more memory than '
			    'it might need to speed up computation. '
			    'The memory reported here corresponds to the memory '
			    'reported by `nvidia-smi`, which can vary depending '
			    'on total available memory on the GPU that is used.'			)
			with self.args.strategy.scope():
				try:
					if self.args.trace_memory_line_by_line:
						if not self.args.eager_mode:
							raise ValueError(
							    '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
							    ' consumption line by line.'			)
						__magic_name__       :    Optional[Any]     	=     start_memory_tracing('transformers'			)

					if self.args.is_tpu:
						# tpu
						raise NotImplementedError(
						    'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
						    ' with `args.memory=False`'			)
					elif self.args.is_gpu:
						# gpu
						if not is_pyanvml_available():
							logger.warning(
							    'py3nvml not installed, we won\'t log GPU memory usage. '
							    'Install py3nvml (pip install py3nvml) to log information about GPU.'			)
							__magic_name__       :    Optional[Any]     	=     '''N/A'''
						else:
							logger.info(
							    'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
							    ' running on the same GPU.'			)
							# init nvml
							nvml.nvmlInit()
							func()
							__magic_name__       :    int     	=     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
							__magic_name__       :    int     	=     nvml.nvmlDeviceGetMemoryInfo(_A			)
							__magic_name__       :    List[Any]     	=     meminfo.used
							__magic_name__       :    Any     	=     Memory(_A			)
							# shutdown nvml
							nvml.nvmlShutdown()
					else:
						# cpu
						if self.args.trace_memory_line_by_line:
							logger.info(
							    'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
							    ' TensorFlow.'			)
							__magic_name__       :    Union[str, Any]     	=     None
						else:
							__magic_name__       :    Optional[int]     	=     measure_peak_memory_cpu(_A			)
							__magic_name__       :    int     	=     Memory(_A			) if isinstance(_A  ,     _A			) else memory_bytes
					if self.args.trace_memory_line_by_line:
						__magic_name__       :    List[Any]     	=     stop_memory_tracing(_A			)
						if memory is None:
							__magic_name__       :    Tuple     	=     summary.total
					else:
						__magic_name__       :    int     	=     None

					return memory, summary
				except ResourceExhaustedError as e:
					self.print_fn(F'Doesn\'t fit on GPU. {e}'			)
					return "N/A", None




"""simple docstring"""
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


_lowerCAmelCase    :Tuple     =					logging.getLogger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]=2     ,       UpperCamelCase__		:		List[Any]=3     ,       UpperCamelCase__		:		List[Any]=16     ,       UpperCamelCase__		:		int = 10     ,       UpperCamelCase__		:		int = 2							):

    def get_dataset(UpperCamelCase__		:		List[str]							):
        _UpperCAmelCase							:  Optional[Any]											=    torch.randn(batch_size * n_batches     ,       1							)
        return TensorDataset(UpperCamelCase__     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1							)							)

    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    return (train_dataloader, valid_dataloader)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Tuple=None							):
    _UpperCAmelCase							:  Tuple											=    []
    for epoch in range(UpperCamelCase__							):
        # Train quickly
        model.train()
        for batch in dataloader:
            _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    batch
            _UpperCAmelCase							:  int											=    model(UpperCamelCase__							)
            _UpperCAmelCase							:  Dict											=    torch.nn.functional.mse_loss(UpperCamelCase__     ,       UpperCamelCase__							)
            accelerator.backward(UpperCamelCase__							)
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()							)  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands
class 							_UpperCAmelCase							( nn.Module      ):





     '''simple docstring'''



     def __init__(					self		)							->					List[Any]:
         super().__init__()
         _UpperCAmelCase							:  List[Any]											=    nn.Parameter(torch.randn(1		)		)
         _UpperCAmelCase							:  int											=    nn.Parameter(torch.randn(1		)		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Tuple:
         return x * self.a + self.b



class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Any:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Any											=    ProjectConfiguration(total_limit=1       ,	project_dir=A       ,	automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    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  __lowerCAmelCase	(					self		)							->					List[str]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Optional[Any]											=    DummyModel()
             _UpperCAmelCase							:  int											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    dummy_dataloaders()
             # Train baseline
             _UpperCAmelCase							:  Optional[int]											=    Accelerator()
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             _UpperCAmelCase							:  Union[str, Any]											=    os.path.join(A       ,	'''initial'''		)
             accelerator.save_state(A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Optional[Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  str											=    optimizer.state_dict()
             _UpperCAmelCase							:  Tuple											=    train(3       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  List[Any]											=    optimizer.state_dict()

             # Train partially
             set_seed(4_2		)
             _UpperCAmelCase							:  Dict											=    DummyModel()
             _UpperCAmelCase							:  Optional[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Tuple											=    Accelerator()
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             accelerator.load_state(A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  List[str]											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)

             _UpperCAmelCase							:  Union[str, Any]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             _UpperCAmelCase							:  List[str]											=    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		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Dict											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					int:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  List[str]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)

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

             # Train partially
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  Union[str, Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(iteration=1       ,	automatic_checkpoint_naming=A		)
             _UpperCAmelCase							:  Tuple											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_0'''		)		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  str											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)

             _UpperCAmelCase							:  List[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		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  List[str]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Tuple											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[Any]											=    torch.tensor([1, 2, 3]		)
         _UpperCAmelCase							:  List[str]											=    torch.tensor([2, 3, 4]		)
         _UpperCAmelCase							:  Optional[int]											=    DummyModel()
         _UpperCAmelCase							:  Dict											=    torch.optim.Adam(net.parameters()		)
         _UpperCAmelCase							:  Optional[int]											=    Accelerator()
         with self.assertRaises(A		) as ve:
             accelerator.register_for_checkpointing(A       ,	A       ,	A       ,	A		)
         _UpperCAmelCase							:  Dict											=    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  __lowerCAmelCase	(					self		)							->					Tuple:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Tuple											=    DummyModel()
             _UpperCAmelCase							:  List[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							:  Optional[int]											=    torch.optim.lr_scheduler.StepLR(A       ,	step_size=1       ,	gamma=0.99		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  int											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()
             _UpperCAmelCase							:  List[str]											=    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  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    ProjectConfiguration(automatic_checkpoint_naming=A       ,	total_limit=2		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							:  Optional[Any]											=    accelerator.prepare(A		)
             # Save 3 states:
             for _ in range(1_1		):
                 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  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  str											=    ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
         execute_subprocess_async(A       ,	env=os.environ.copy()		)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					'/tmp/accelerate/state_checkpointing'
  _lowerCAmelCase    :Any     =					DummyModel()
  _lowerCAmelCase    :Tuple     =					torch.optim.Adam(params=model.parameters(), lr=1E-3)
  _lowerCAmelCase    :Dict     =					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
  _lowerCAmelCase,_lowerCAmelCase    :Any     =					dummy_dataloaders()
  _lowerCAmelCase    :Tuple     =					ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  _lowerCAmelCase    :Optional[Any]     =					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)
  _lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase    :str     =					accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  _lowerCAmelCase,_lowerCAmelCase    :List[Any]     =					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:
    _lowerCAmelCase    :int     =					group['params'][0].device
    break
  assert param_device.type == accelerator.device.type
  _lowerCAmelCase    :Dict     =					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:
    _lowerCAmelCase    :List[Any]     =					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:
    _lowerCAmelCase    :Union[str, Any]     =					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()


