

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


a_    		=      logging.getLogger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  str=2		,							_UpperCamelCase		:  Any=3		,							_UpperCamelCase		:  Optional[int]=16		,							_UpperCamelCase		:  Optional[Any] = 10		,							_UpperCamelCase		:  Tuple = 2	)		->							Any:
 '''simple docstring'''


 def get_dataset(_UpperCamelCase		:  List[Any]	):
  SCREAMING_SNAKE_CASE								=			torch.randn(batch_size * n_batches		,							1	)
  return TensorDataset(__lowerCAmelCase		,							a * x + b + 0.1 * torch.randn(batch_size * n_batches		,							1	)	)

 SCREAMING_SNAKE_CASE								=			get_dataset(__lowerCAmelCase	)
 SCREAMING_SNAKE_CASE								=			get_dataset(__lowerCAmelCase	)
 SCREAMING_SNAKE_CASE								=			DataLoader(__lowerCAmelCase		,							shuffle=__lowerCAmelCase		,							batch_size=__lowerCAmelCase		,							num_workers=4	)
 SCREAMING_SNAKE_CASE								=			DataLoader(__lowerCAmelCase		,							shuffle=__lowerCAmelCase		,							batch_size=__lowerCAmelCase		,							num_workers=4	)
 return (train_dataloader, valid_dataloader)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Union[str, Any]=None	)		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 for epoch in range(__lowerCAmelCase	):
  # Train quickly
  model.train()
  for batch in dataloader:
   SCREAMING_SNAKE_CASE								=			batch
   SCREAMING_SNAKE_CASE								=			model(__lowerCAmelCase	)
   SCREAMING_SNAKE_CASE								=			torch.nn.functional.mse_loss(__lowerCAmelCase		,							__lowerCAmelCase	)
   accelerator.backward(__lowerCAmelCase	)
   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   ):

    def __init__(			self :      Union[str, Any]    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			nn.Parameter(torch.randn(1    )    )
     SCREAMING_SNAKE_CASE								=			nn.Parameter(torch.randn(1    )    )




    def UpperCamelCase			(			self :      str							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     return x * self.a + self.b




class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(4_2    )
      SCREAMING_SNAKE_CASE								=			DummyModel()
      SCREAMING_SNAKE_CASE								=			torch.optim.Adam(params=model.parameters()							,    lr=1E-3    )
      SCREAMING_SNAKE_CASE								=			dummy_dataloaders()
      SCREAMING_SNAKE_CASE								=			ProjectConfiguration(total_limit=1							,    project_dir=lowerCamelCase__							,    automatic_checkpoint_naming=lowerCamelCase__    )
      # Train baseline
      SCREAMING_SNAKE_CASE								=			Accelerator(project_config=lowerCamelCase__    )
      SCREAMING_SNAKE_CASE								=			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 :      Any    ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(4_2    )
      SCREAMING_SNAKE_CASE								=			DummyModel()
      SCREAMING_SNAKE_CASE								=			torch.optim.Adam(params=model.parameters()							,    lr=1E-3    )
      SCREAMING_SNAKE_CASE								=			dummy_dataloaders()
      # Train baseline
      SCREAMING_SNAKE_CASE								=			Accelerator()
      SCREAMING_SNAKE_CASE								=			accelerator.prepare(
          lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__    )
      # Save initial
      SCREAMING_SNAKE_CASE								=			os.path.join(lowerCamelCase__							,    'initial'    )
      accelerator.save_state(lowerCamelCase__    )
      (SCREAMING_SNAKE_CASE)								=			model.a.item(), model.b.item()
      SCREAMING_SNAKE_CASE								=			optimizer.state_dict()
      SCREAMING_SNAKE_CASE								=			train(3							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__    )
      (SCREAMING_SNAKE_CASE)								=			model.a.item(), model.b.item()
      SCREAMING_SNAKE_CASE								=			optimizer.state_dict()

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

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

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(4_2    )
      SCREAMING_SNAKE_CASE								=			DummyModel()
      SCREAMING_SNAKE_CASE								=			torch.optim.Adam(params=model.parameters()							,    lr=1E-3    )
      SCREAMING_SNAKE_CASE								=			dummy_dataloaders()
      SCREAMING_SNAKE_CASE								=			ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__    )

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

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

      SCREAMING_SNAKE_CASE								=			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__    )
      (SCREAMING_SNAKE_CASE)								=			model.a.item(), model.b.item()
      SCREAMING_SNAKE_CASE								=			optimizer.state_dict()
      self.assertEqual(lowerCamelCase__							,    lowerCamelCase__    )
      self.assertEqual(lowerCamelCase__							,    lowerCamelCase__    )
      self.assertEqual(lowerCamelCase__							,    lowerCamelCase__    )
      self.assertEqual(lowerCamelCase__							,    lowerCamelCase__    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.tensor([1, 2, 3]    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([2, 3, 4]    )
     SCREAMING_SNAKE_CASE								=			DummyModel()
     SCREAMING_SNAKE_CASE								=			torch.optim.Adam(net.parameters()    )
     SCREAMING_SNAKE_CASE								=			Accelerator()
     with self.assertRaises(lowerCamelCase__    ) as ve:
      accelerator.register_for_checkpointing(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__    )
     SCREAMING_SNAKE_CASE								=			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 :      List[str]    ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(4_2    )
      SCREAMING_SNAKE_CASE								=			DummyModel()
      SCREAMING_SNAKE_CASE								=			torch.optim.Adam(params=model.parameters()							,    lr=1E-3    )
      SCREAMING_SNAKE_CASE								=			torch.optim.lr_scheduler.StepLR(lowerCamelCase__							,    step_size=1							,    gamma=0.99    )
      SCREAMING_SNAKE_CASE								=			dummy_dataloaders()
      SCREAMING_SNAKE_CASE								=			ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__    )
      # Train baseline
      SCREAMING_SNAKE_CASE								=			Accelerator(project_dir=lowerCamelCase__							,    project_config=lowerCamelCase__    )
      SCREAMING_SNAKE_CASE								=			accelerator.prepare(
          lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__    )
      # Save initial
      accelerator.save_state()
      SCREAMING_SNAKE_CASE								=			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[Any]    ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(4_2    )
      SCREAMING_SNAKE_CASE								=			DummyModel()
      SCREAMING_SNAKE_CASE								=			ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__							,    total_limit=2    )
      # Train baseline
      SCREAMING_SNAKE_CASE								=			Accelerator(project_dir=lowerCamelCase__							,    project_config=lowerCamelCase__    )
      SCREAMING_SNAKE_CASE								=			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]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
     execute_subprocess_async(lowerCamelCase__							,    env=os.environ.copy()    )


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


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

