"""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


SCREAMING_SNAKE_CASE__:str			=	logging.getLogger(__name__)



def 					_lowerCamelCase(					a=2 ,  a=3 ,  a=1_6 ,  a = 1_0 ,  a = 2							):

							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 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a=None							):
							__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 				snake_case__     (   nn.Module   ):


	def __init__(				self		):
								super().__init__()
								__a     				=  nn.Parameter(torch.randn(1		)		)
								__a     				=  nn.Parameter(torch.randn(1		)		)






	def 		a__ (				self  ,						lowerCamelCase		):
								return x * self.a + self.b






class 				snake_case__     (   unittest.TestCase   ):


	def 		a__ (				self		):
								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=lowerCamelCase  ,						automatic_checkpoint_naming=lowerCamelCase		)
															# Train baseline
															__a     				=  Accelerator(project_config=lowerCamelCase		)
															__a				, __a				, __a				, __a     				=  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 		a__ (				self		):
								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(
															    lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															# Save initial
															__a     				=  os.path.join(lowerCamelCase  ,						"initial"		)
															accelerator.save_state(lowerCamelCase		)
															((__a)				, (__a))     				=  model.a.item(), model.b.item()
															__a     				=  optimizer.state_dict()
															__a     				=  train(3  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															((__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(
															    lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															accelerator.load_state(lowerCamelCase		)
															((__a)				, (__a))     				=  model.a.item(), model.b.item()
															__a     				=  optimizer.state_dict()
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)

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


	def 		a__ (				self		):
								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=lowerCamelCase		)

															# Train baseline
															__a     				=  Accelerator(project_dir=lowerCamelCase  ,						project_config=lowerCamelCase		)
															__a				, __a				, __a				, __a     				=  accelerator.prepare(
															    lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															# Save initial
															accelerator.save_state()
															((__a)				, (__a))     				=  model.a.item(), model.b.item()
															__a     				=  optimizer.state_dict()
															__a     				=  train(3  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															((__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=lowerCamelCase		)
															__a     				=  Accelerator(project_dir=lowerCamelCase  ,						project_config=lowerCamelCase		)
															__a				, __a				, __a				, __a     				=  accelerator.prepare(
															    lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															accelerator.load_state(os.path.join(lowerCamelCase  ,						"checkpoints"  ,						"checkpoint_0"		)		)
															((__a)				, (__a))     				=  model.a.item(), model.b.item()
															__a     				=  optimizer.state_dict()
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)

															__a     				=  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		)
															((__a)				, (__a))     				=  model.a.item(), model.b.item()
															__a     				=  optimizer.state_dict()
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)
															self.assertEqual(lowerCamelCase  ,						lowerCamelCase		)


	def 		a__ (				self		):
								__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(lowerCamelCase		) as ve:
															accelerator.register_for_checkpointing(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
								__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 		a__ (				self		):
								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(lowerCamelCase  ,						step_size=1  ,						gamma=0.99		)
															__a				, __a     				=  dummy_dataloaders()
															__a     				=  ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase		)
															# Train baseline
															__a     				=  Accelerator(project_dir=lowerCamelCase  ,						project_config=lowerCamelCase		)
															__a				, __a				, __a				, __a				, __a     				=  accelerator.prepare(
															    lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)
															# Save initial
															accelerator.save_state()
															__a     				=  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 		a__ (				self		):
								with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42		)
															__a     				=  DummyModel()
															__a     				=  ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase  ,						total_limit=2		)
															# Train baseline
															__a     				=  Accelerator(project_dir=lowerCamelCase  ,						project_config=lowerCamelCase		)
															__a     				=  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 		a__ (				self		):
								__a     				=  ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__		)]
								execute_subprocess_async(lowerCamelCase  ,						env=os.environ.copy()		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__:Optional[Any]			=	"""/tmp/accelerate/state_checkpointing"""
			SCREAMING_SNAKE_CASE__:str			=	DummyModel()
			SCREAMING_SNAKE_CASE__:int			=	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__:Optional[Any]			=	dummy_dataloaders()
			SCREAMING_SNAKE_CASE__:str			=	ProjectConfiguration(automatic_checkpoint_naming=True)
			# Train baseline
			SCREAMING_SNAKE_CASE__:Dict			=	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__:Optional[Any]			=	accelerator.prepare(
			    model, optimizer, train_dataloader, valid_dataloader, scheduler
			)
			SCREAMING_SNAKE_CASE__:Union[str, 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:
						SCREAMING_SNAKE_CASE__:str			=	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__:List[str]			=	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()

"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE__:Any			=	{
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
SCREAMING_SNAKE_CASE__:Optional[int]			=	"""ETAOINSHRDLCUMWFGYPBVKJXQZ"""
SCREAMING_SNAKE_CASE__:Tuple			=	"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def 					_lowerCamelCase(					a							):
       __a     				=  {letter: 0 for letter in string.ascii_uppercase}
       for letter in message.upper():
              if letter in LETTERS:
                     letter_count[letter] += 1

       return letter_count



def 					_lowerCamelCase(					a							):
       return x[0]



def 					_lowerCamelCase(					a							):
       __a     				=  get_letter_count(a							)
       __a     				=  {
           freq: [] for letter, freq in letter_to_freq.items()
       }
       for letter in LETTERS:
              freq_to_letter[letter_to_freq[letter]].append(a							)

       __a     				=  {}

       for freq in freq_to_letter:
              freq_to_letter[freq].sort(key=ETAOIN.find ,  reverse=a							)
              __a     				=  "".join(freq_to_letter[freq]							)

       __a     				=  list(freq_to_letter_str.items()							)
       freq_pairs.sort(key=a ,  reverse=a							)

       __a     				=  [freq_pair[1] for freq_pair in freq_pairs]

       return "".join(a							)



def 					_lowerCamelCase(					a							):
       __a     				=  get_frequency_order(a							)
       __a     				=  0
       for common_letter in ETAOIN[:6]:
              if common_letter in freq_order[:6]:
                     match_score += 1

       for uncommon_letter in ETAOIN[-6:]:
              if uncommon_letter in freq_order[-6:]:
                     match_score += 1

       return match_score


if __name__ == "__main__":
   import doctest

   doctest.testmod()
