
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


_snake_case      :							List[str]     	=    logging.getLogger(__name__)

def  __snake_case							(	__magic_name__=2		,    __magic_name__=3		,    __magic_name__=16		,    __magic_name__ = 10		,    __magic_name__ = 2						):







				'''simple docstring'''


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

				lowercase         =      get_dataset(__magic_name__						)
				lowercase         =      get_dataset(__magic_name__						)
				lowercase         =      DataLoader(__magic_name__		,    shuffle=__magic_name__		,    batch_size=__magic_name__		,    num_workers=4						)
				lowercase         =      DataLoader(__magic_name__		,    shuffle=__magic_name__		,    batch_size=__magic_name__		,    num_workers=4						)
				return (train_dataloader, valid_dataloader)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__=None						):







				'''simple docstring'''


				lowercase         =      []
				for epoch in range(__magic_name__						):
								# Train quickly
								model.train()
								for batch in dataloader:
												lowercase     ,	lowercase         =      batch
												lowercase         =      model(__magic_name__						)
												lowercase         =      torch.nn.functional.mse_loss(__magic_name__		,    __magic_name__						)
												accelerator.backward(__magic_name__						)
												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							:Optional[Any]     )  ->List[str]:
										super().__init__()
										lowercase         =      nn.Parameter(torch.randn(1     )     )
										lowercase         =      nn.Parameter(torch.randn(1     )     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:str     )  ->List[str]:
										return x * self.a + self.b





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Dict:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42     )
														lowercase         =      DummyModel()
														lowercase         =      torch.optim.Adam(params=model.parameters()		,    lr=1E-3     )
														lowercase     ,	lowercase         =      dummy_dataloaders()
														lowercase         =      ProjectConfiguration(total_limit=1		,    project_dir=lowerCAmelCase__		,    automatic_checkpoint_naming=lowerCAmelCase__     )
														# Train baseline
														lowercase         =      Accelerator(project_config=lowerCAmelCase__     )
														lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      accelerator.prepare(
														    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														# Save initial
														accelerator.save_state()

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





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Dict:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42     )
														lowercase         =      DummyModel()
														lowercase         =      torch.optim.Adam(params=model.parameters()		,    lr=1E-3     )
														lowercase     ,	lowercase         =      dummy_dataloaders()
														# Train baseline
														lowercase         =      Accelerator()
														lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      accelerator.prepare(
														    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														# Save initial
														lowercase         =      os.path.join(lowerCAmelCase__		,    "initial"     )
														accelerator.save_state(lowerCAmelCase__     )
														((lowercase)     ,	(lowercase))         =      model.a.item(), model.b.item()
														lowercase         =      optimizer.state_dict()
														lowercase         =      train(3		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														((lowercase)     ,	(lowercase))         =      model.a.item(), model.b.item()
														lowercase         =      optimizer.state_dict()

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

														lowercase         =      train(2		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														# Save everything
														lowercase         =      os.path.join(lowerCAmelCase__		,    "checkpoint"     )
														accelerator.save_state(lowerCAmelCase__     )

														# Load everything back in and make sure all states work
														accelerator.load_state(lowerCAmelCase__     )
														test_rands += train(1		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														((lowercase)     ,	(lowercase))         =      model.a.item(), model.b.item()
														lowercase         =      optimizer.state_dict()
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[Any]:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42     )
														lowercase         =      DummyModel()
														lowercase         =      torch.optim.Adam(params=model.parameters()		,    lr=1E-3     )
														lowercase     ,	lowercase         =      dummy_dataloaders()
														lowercase         =      ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__     )

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

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

														lowercase         =      train(2		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														# Save everything
														accelerator.save_state()

														# Load everything back in and make sure all states work
														accelerator.load_state(os.path.join(lowerCAmelCase__		,    "checkpoints"		,    "checkpoint_1"     )     )
														test_rands += train(1		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														((lowercase)     ,	(lowercase))         =      model.a.item(), model.b.item()
														lowercase         =      optimizer.state_dict()
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->str:
										lowercase         =      torch.tensor([1, 2, 3]     )
										lowercase         =      torch.tensor([2, 3, 4]     )
										lowercase         =      DummyModel()
										lowercase         =      torch.optim.Adam(net.parameters()     )
										lowercase         =      Accelerator()
										with self.assertRaises(lowerCAmelCase__     ) as ve:
														accelerator.register_for_checkpointing(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										with tempfile.TemporaryDirectory() as tmpdir:
														set_seed(42     )
														lowercase         =      DummyModel()
														lowercase         =      torch.optim.Adam(params=model.parameters()		,    lr=1E-3     )
														lowercase         =      torch.optim.lr_scheduler.StepLR(lowerCAmelCase__		,    step_size=1		,    gamma=0.99     )
														lowercase     ,	lowercase         =      dummy_dataloaders()
														lowercase         =      ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__     )
														# Train baseline
														lowercase         =      Accelerator(project_dir=lowerCAmelCase__		,    project_config=lowerCAmelCase__     )
														lowercase     ,	lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      accelerator.prepare(
														    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														# Save initial
														accelerator.save_state()
														lowercase         =      scheduler.state_dict()
														train(3		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
														self.assertNotEqual(lowerCAmelCase__		,    scheduler.state_dict()     )

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





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





						@require_cuda
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Union[str, Any]:
										lowercase         =      ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
										execute_subprocess_async(lowerCAmelCase__		,    env=os.environ.copy()     )


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


# Function to print upper half of diamond (pyramid)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(0		,    __magic_name__						):
								for _ in range(0		,    n - i - 1						):  # printing spaces
												print(" "		,    end=""						)
								for _ in range(0		,    i + 1						):  # printing stars
												print("* "		,    end=""						)
								print()

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(__magic_name__		,    0		,    -1						):
								for _ in range(__magic_name__		,    0		,    -1						):  # printing stars
												print("* "		,    end=""						)
								print()
								for _ in range(n - i + 1		,    0		,    -1						):  # printing spaces
												print(" "		,    end=""						)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if n <= 0:
								print("       ...       ....        nothing printing :("						)
								return
				floyd(__magic_name__						)  # upper half
				reverse_floyd(__magic_name__						)  # lower half


if __name__ == "__main__":
						print(r"| /\ | |- |  |-  |--| |\  /| |-")
						print(r"|/  \| |- |_ |_  |__| | \/ | |_")
						_snake_case      :							Union[str, Any]     	=    1
						while K:
												_snake_case      :							Dict     	=    int(input("enter the number and , and see the magic : "))
												print()
												pretty_print(user_number)
												_snake_case      :							Tuple     	=    int(input("press 0 to exit... and 1 to continue..."))

						print("Good Bye...")
