
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


SCREAMING_SNAKE_CASE							:Any										=    logging.getLogger(__name__)


def 						UpperCAmelCase      (     a_=2		,       a_=3		,       a_=1_6		,       a_ = 1_0		,       a_ = 2						)		->     Tuple:







				"""simple docstring"""



				def get_dataset(a_						):
								__A             =			torch.randn(batch_size * n_batches		,       1						)
								return TensorDataset(a_		,       a * x + b + 0.1 * torch.randn(batch_size * n_batches		,       1						)						)

				__A             =			get_dataset(a_						)
				__A             =			get_dataset(a_						)
				__A             =			DataLoader(a_		,       shuffle=a_		,       batch_size=a_		,       num_workers=4						)
				__A             =			DataLoader(a_		,       shuffle=a_		,       batch_size=a_		,       num_workers=4						)
				return (train_dataloader, valid_dataloader)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_		,       a_=None						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			[]
				for epoch in range(a_						):
								# Train quickly
								model.train()
								for batch in dataloader:
												__A      ,							__A             =			batch
												__A             =			model(a_						)
												__A             =			torch.nn.functional.mse_loss(a_		,       a_						)
												accelerator.backward(a_						)
												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[str]			):
					super().__init__()
					__A             =			nn.Parameter(torch.randn(1			)			)
					__A             =			nn.Parameter(torch.randn(1			)			)







	def     UpperCamelCase_  (	self :			str							,A :			Union[str, Any]			):
					return x * self.a + self.b





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





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

									# Train partially
									set_seed(42			)
									__A             =			DummyModel()
									__A             =			torch.optim.Adam(params=model.parameters()							,lr=1E-3			)
									__A      ,							__A             =			dummy_dataloaders()
									__A             =			Accelerator()
									__A      ,							__A      ,							__A      ,							__A             =			accelerator.prepare(
									    A							,A							,A							,A			)
									accelerator.load_state(A			)
									((__A)      ,							(__A))             =			model.a.item(), model.b.item()
									__A             =			optimizer.state_dict()
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)

									__A             =			train(2							,A							,A							,A							,A			)
									# Save everything
									__A             =			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))             =			model.a.item(), model.b.item()
									__A             =			optimizer.state_dict()
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)






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

									# Train baseline
									__A             =			Accelerator(project_dir=A							,project_config=A			)
									__A      ,							__A      ,							__A      ,							__A             =			accelerator.prepare(
									    A							,A							,A							,A			)
									# Save initial
									accelerator.save_state()
									((__A)      ,							(__A))             =			model.a.item(), model.b.item()
									__A             =			optimizer.state_dict()
									__A             =			train(3							,A							,A							,A							,A			)
									((__A)      ,							(__A))             =			model.a.item(), model.b.item()
									__A             =			optimizer.state_dict()

									# Train partially
									set_seed(42			)
									__A             =			DummyModel()
									__A             =			torch.optim.Adam(params=model.parameters()							,lr=1E-3			)
									__A      ,							__A             =			dummy_dataloaders()
									__A             =			ProjectConfiguration(iteration=1							,automatic_checkpoint_naming=A			)
									__A             =			Accelerator(project_dir=A							,project_config=A			)
									__A      ,							__A      ,							__A      ,							__A             =			accelerator.prepare(
									    A							,A							,A							,A			)
									accelerator.load_state(os.path.join(A							,"checkpoints"							,"checkpoint_0"			)			)
									((__A)      ,							(__A))             =			model.a.item(), model.b.item()
									__A             =			optimizer.state_dict()
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)

									__A             =			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))             =			model.a.item(), model.b.item()
									__A             =			optimizer.state_dict()
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)
									self.assertEqual(A							,A			)






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			torch.tensor([1, 2, 3]			)
					__A             =			torch.tensor([2, 3, 4]			)
					__A             =			DummyModel()
					__A             =			torch.optim.Adam(net.parameters()			)
					__A             =			Accelerator()
					with self.assertRaises(A			) as ve:
									accelerator.register_for_checkpointing(A							,A							,A							,A			)
					__A             =			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     UpperCamelCase_  (	self :			Tuple			):
					with tempfile.TemporaryDirectory() as tmpdir:
									set_seed(42			)
									__A             =			DummyModel()
									__A             =			torch.optim.Adam(params=model.parameters()							,lr=1E-3			)
									__A             =			torch.optim.lr_scheduler.StepLR(A							,step_size=1							,gamma=0.99			)
									__A      ,							__A             =			dummy_dataloaders()
									__A             =			ProjectConfiguration(automatic_checkpoint_naming=A			)
									# Train baseline
									__A             =			Accelerator(project_dir=A							,project_config=A			)
									__A      ,							__A      ,							__A      ,							__A      ,							__A             =			accelerator.prepare(
									    A							,A							,A							,A							,A			)
									# Save initial
									accelerator.save_state()
									__A             =			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     UpperCamelCase_  (	self :			Optional[int]			):
					with tempfile.TemporaryDirectory() as tmpdir:
									set_seed(42			)
									__A             =			DummyModel()
									__A             =			ProjectConfiguration(automatic_checkpoint_naming=A							,total_limit=2			)
									# Train baseline
									__A             =			Accelerator(project_dir=A							,project_config=A			)
									__A             =			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     UpperCamelCase_  (	self :			str			):
					__A             =			["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__			)]
					execute_subprocess_async(A							,env=os.environ.copy()			)


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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
