





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]=2       ,			UpperCamelCase__   :   int=3       ,			UpperCamelCase__   :   List[Any]=16       ,			UpperCamelCase__   :   Union[str, Any] = 10       ,			UpperCamelCase__   :   Dict = 2					)      ->    List[Any]:

						"""simple docstring"""





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

						__SCREAMING_SNAKE_CASE:				Tuple           =   get_dataset(_SCREAMING_SNAKE_CASE					)
						__SCREAMING_SNAKE_CASE:				Dict           =   get_dataset(_SCREAMING_SNAKE_CASE					)
						__SCREAMING_SNAKE_CASE:				Any           =   DataLoader(_SCREAMING_SNAKE_CASE       ,			shuffle=_SCREAMING_SNAKE_CASE       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			num_workers=4					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   DataLoader(_SCREAMING_SNAKE_CASE       ,			shuffle=_SCREAMING_SNAKE_CASE       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			num_workers=4					)
						return (train_dataloader, valid_dataloader)






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   str=None					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   []
						for epoch in range(_SCREAMING_SNAKE_CASE					):
												# Train quickly
												model.train()
												for batch in dataloader:
																		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   batch
																		__SCREAMING_SNAKE_CASE:				Dict           =   model(_SCREAMING_SNAKE_CASE					)
																		__SCREAMING_SNAKE_CASE:				Any           =   torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE					)
																		accelerator.backward(_SCREAMING_SNAKE_CASE					)
																		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			):






				def __init__(  self					):

										"""simple docstring"""




										super().__init__()
										__SCREAMING_SNAKE_CASE:				Any           =   nn.Parameter(torch.randn(1					)					)
										__SCREAMING_SNAKE_CASE:				Dict           =   nn.Parameter(torch.randn(1					)					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return x * self.a + self.b





class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

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

																__SCREAMING_SNAKE_CASE:				Dict           =   train(2			, snake_case_			, snake_case_			, snake_case_			, snake_case_					)
																# Save everything
																__SCREAMING_SNAKE_CASE:				List[Any]           =   os.path.join(snake_case_			, '''checkpoint'''					)
																accelerator.save_state(snake_case_					)

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42					)
																__SCREAMING_SNAKE_CASE:				str           =   DummyModel()
																__SCREAMING_SNAKE_CASE:				Dict           =   torch.optim.Adam(params=model.parameters()			, lr=1e-3					)
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   dummy_dataloaders()
																__SCREAMING_SNAKE_CASE:				List[Any]           =   ProjectConfiguration(automatic_checkpoint_naming=snake_case_					)

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

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

																__SCREAMING_SNAKE_CASE:				List[str]           =   train(2			, snake_case_			, snake_case_			, snake_case_			, snake_case_					)
																# Save everything
																accelerator.save_state()

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.tensor([1, 2, 3]					)
										__SCREAMING_SNAKE_CASE:				str           =   torch.tensor([2, 3, 4]					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   DummyModel()
										__SCREAMING_SNAKE_CASE:				Tuple           =   torch.optim.Adam(net.parameters()					)
										__SCREAMING_SNAKE_CASE:				Any           =   Accelerator()
										with self.assertRaises(snake_case_					) as ve:
																accelerator.register_for_checkpointing(snake_case_			, snake_case_			, snake_case_			, snake_case_					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   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 			snake_case_    (  self					):

										"""simple docstring"""




										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   DummyModel()
																__SCREAMING_SNAKE_CASE:				Any           =   torch.optim.Adam(params=model.parameters()			, lr=1e-3					)
																__SCREAMING_SNAKE_CASE:				Tuple           =   torch.optim.lr_scheduler.StepLR(snake_case_			, step_size=1			, gamma=0.99					)
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Any           =   dummy_dataloaders()
																__SCREAMING_SNAKE_CASE:				List[str]           =   ProjectConfiguration(automatic_checkpoint_naming=snake_case_					)
																# Train baseline
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   Accelerator(project_dir=snake_case_			, project_config=snake_case_					)
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   accelerator.prepare(
																    snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_					)
																# Save initial
																accelerator.save_state()
																__SCREAMING_SNAKE_CASE:				List[Any]           =   scheduler.state_dict()
																train(3			, snake_case_			, snake_case_			, snake_case_			, snake_case_			, snake_case_					)
																self.assertNotEqual(snake_case_			, scheduler.state_dict()					)

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				@require_cuda
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   ['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__					)]
										execute_subprocess_async(snake_case_			, env=os.environ.copy()					)


if __name__ == "__main__":
							lowerCAmelCase							:       Tuple					 =	"/tmp/accelerate/state_checkpointing"
							lowerCAmelCase							:       List[str]					 =	DummyModel()
							lowerCAmelCase							:       Union[str, Any]					 =	torch.optim.Adam(params=model.parameters(), lr=1e-3)
							lowerCAmelCase							:       Optional[Any]					 =	torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
							lowerCAmelCase							:       Dict					 =	dummy_dataloaders()
							lowerCAmelCase							:       Any					 =	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							:       int					 =	accelerator.prepare(
							    model, optimizer, train_dataloader, valid_dataloader, scheduler
							)
							lowerCAmelCase							:       Optional[int]					 =	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							:       Optional[int]					 =	group["params"][0].device
														break
							assert param_device.type == accelerator.device.type
							lowerCAmelCase							:       str					 =	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							:       int					 =	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							:       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()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





