

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__       = logging.getLogger(__name__)
def      _lowerCAmelCase(	__A=2   ,       __A=3   ,       __A=16   ,       __A = 10   ,       __A = 2			):

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

		UpperCAmelCase					      =  get_dataset(__A			)
		UpperCAmelCase					      =  get_dataset(__A			)
		UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)
		UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)
		return (train_dataloader, valid_dataloader)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A   ,       __A=None			):
		UpperCAmelCase					      =  []
		for epoch in range(__A			):
				# Train quickly
				model.train()
				for batch in dataloader:
						UpperCAmelCase      ,	UpperCAmelCase					      =  batch
						UpperCAmelCase					      =  model(__A			)
						UpperCAmelCase					      =  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 						__magic_name__    (       nn.Module    ):


	def __init__(						self		: Union[str, Any]    )    ->      List[Any]:
			super().__init__()
			UpperCAmelCase					      =  nn.Parameter(torch.randn(1    )    )
			UpperCAmelCase					      =  nn.Parameter(torch.randn(1    )    )


	def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: Optional[int]    )    ->      Union[str, Any]:
			return x * self.a + self.b






class 						__magic_name__    (       unittest.TestCase    ):


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

					# Train partially
					set_seed(4_2    )
					UpperCAmelCase					      =  DummyModel()
					UpperCAmelCase					      =  torch.optim.Adam(params=model.parameters()			,						lr=1e-3    )
					UpperCAmelCase      ,	UpperCAmelCase					      =  dummy_dataloaders()
					UpperCAmelCase					      =  Accelerator()
					UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.prepare(
					    lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
					accelerator.load_state(lowerCAmelCase__    )
					((UpperCAmelCase)      ,	(UpperCAmelCase))					      =  model.a.item(), model.b.item()
					UpperCAmelCase					      =  optimizer.state_dict()
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )

					UpperCAmelCase					      =  train(2			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
					# Save everything
					UpperCAmelCase					      =  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__    )
					((UpperCAmelCase)      ,	(UpperCAmelCase))					      =  model.a.item(), model.b.item()
					UpperCAmelCase					      =  optimizer.state_dict()
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )


	def _UpperCamelCase	(						self		: List[Any]    )    ->      List[Any]:
			with tempfile.TemporaryDirectory() as tmpdir:
					set_seed(4_2    )
					UpperCAmelCase					      =  DummyModel()
					UpperCAmelCase					      =  torch.optim.Adam(params=model.parameters()			,						lr=1e-3    )
					UpperCAmelCase      ,	UpperCAmelCase					      =  dummy_dataloaders()
					UpperCAmelCase					      =  ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase__    )

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

					# Train partially
					set_seed(4_2    )
					UpperCAmelCase					      =  DummyModel()
					UpperCAmelCase					      =  torch.optim.Adam(params=model.parameters()			,						lr=1e-3    )
					UpperCAmelCase      ,	UpperCAmelCase					      =  dummy_dataloaders()
					UpperCAmelCase					      =  ProjectConfiguration(iteration=1			,						automatic_checkpoint_naming=lowerCAmelCase__    )
					UpperCAmelCase					      =  Accelerator(project_dir=lowerCAmelCase__			,						project_config=lowerCAmelCase__    )
					UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.prepare(
					    lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
					accelerator.load_state(os.path.join(lowerCAmelCase__			,						"checkpoints"			,						"checkpoint_0"    )    )
					((UpperCAmelCase)      ,	(UpperCAmelCase))					      =  model.a.item(), model.b.item()
					UpperCAmelCase					      =  optimizer.state_dict()
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )

					UpperCAmelCase					      =  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__    )
					((UpperCAmelCase)      ,	(UpperCAmelCase))					      =  model.a.item(), model.b.item()
					UpperCAmelCase					      =  optimizer.state_dict()
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )
					self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )


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


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






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCAmelCase__       = ""
lowerCAmelCase__       = ""
lowerCAmelCase__       = ""
lowerCAmelCase__       = 1  # (0 is vertical, 1 is horizontal)
def      _lowerCAmelCase(	):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_dataset(__A   ,       __A			)
  print("Processing..."			)
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  update_image_and_anno(__A   ,       __A   ,       __A			)

  for index, image in enumerate(__A			):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    UpperCAmelCase					      =  random_chars(32			)
    UpperCAmelCase					      =  paths[index].split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    UpperCAmelCase					      =  F"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
    cva.imwrite(F"/{file_root}.jpg"   ,       __A   ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
    print(F"Success {index+1}/{len(__A			)} with {file_name}"			)
    UpperCAmelCase					      =  []
    for anno in new_annos[index]:
      UpperCAmelCase					      =  F"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
      annos_list.append(__A			)
    with open(F"/{file_root}.txt"   ,       "w"			) as outfile:
      outfile.write("\n".join(line for line in annos_list			)			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for label_file in glob.glob(os.path.join(__A   ,       "*.txt"			)			):
    UpperCAmelCase					      =  label_file.split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    with open(__A			) as in_file:
      UpperCAmelCase					      =  in_file.readlines()
    UpperCAmelCase					      =  os.path.join(__A   ,       F"{label_name}.jpg"			)

    UpperCAmelCase					      =  []
    for obj_list in obj_lists:
      UpperCAmelCase					      =  obj_list.rstrip("\n"			).split(" "			)
      boxes.append(
          [
              int(obj[0]			),
              float(obj[1]			),
              float(obj[2]			),
              float(obj[3]			),
              float(obj[4]			),
          ]			)
    if not boxes:
      continue
    img_paths.append(__A			)
    labels.append(__A			)
  return img_paths, labels
def      _lowerCAmelCase(	__A   ,       __A   ,       __A = 1			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for idx in range(len(__A			)			):
    UpperCAmelCase					      =  []
    UpperCAmelCase					      =  img_list[idx]
    path_list.append(__A			)
    UpperCAmelCase					      =  anno_list[idx]
    UpperCAmelCase					      =  cva.imread(__A			)
    if flip_type == 1:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      for bbox in img_annos:
        UpperCAmelCase					      =  1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
    elif flip_type == 0:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      for bbox in img_annos:
        UpperCAmelCase					      =  1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
    new_annos_lists.append(__A			)
    new_imgs_list.append(__A			)
  return new_imgs_list, new_annos_lists, path_list
def      _lowerCAmelCase(	__A = 32			):
  assert number_char > 1, "The number of character should greater than 1"
  UpperCAmelCase					      =  ascii_lowercase + digits
  return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
      main()
      print("DONE ✅")



