"""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__	,      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__	,      SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[Any]			=	accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   SCREAMING_SNAKE_CASE__	,      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 os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE__:List[str]			=	3



def 					_lowerCamelCase(					a							):
       print("Generating primitive root of p"							)
       while True:
              __a     				=  random.randrange(3 ,  a							)
              if pow(a ,  2 ,  a							) == 1:
                     continue
              if pow(a ,  a ,  a							) == 1:
                     continue
              return g



def 					_lowerCamelCase(					a							):
       print("Generating prime p..."							)
       __a     				=  rabin_miller.generate_large_prime(a							)  # select large prime number.
       __a     				=  primitive_root(a							)  # one primitive root on modulo p.
       __a     				=  random.randrange(3 ,  a							)  # private_key -> have to be greater than 2 for safety.
       __a     				=  cryptomath.find_mod_inverse(pow(a ,  a ,  a							) ,  a							)

       __a     				=  (key_size, e_a, e_a, p)
       __a     				=  (key_size, d)

       return public_key, private_key



def 					_lowerCamelCase(					a ,  a							):
       if os.path.exists(F"{name}_pubkey.txt"							) or os.path.exists(F"{name}_privkey.txt"							):
              print("\nWARNING:"							)
              print(
                  F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                  "Use a different name or delete these files and re-run this program."							)
              sys.exit()

       __a				, __a     				=  generate_key(a							)
       print(F"\nWriting public key to file {name}_pubkey.txt..."							)
       with open(F"{name}_pubkey.txt" ,  "w"							) as fo:
              fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"							)

       print(F"Writing private key to file {name}_privkey.txt..."							)
       with open(F"{name}_privkey.txt" ,  "w"							) as fo:
              fo.write(F"{private_key[0]},{private_key[1]}"							)



def 					_lowerCamelCase(					):
       print("Making key files..."							)
       make_key_files("elgamal" ,  2_0_4_8							)
       print("Key files generation successful"							)


if __name__ == "__main__":
   main()
