


'''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


__magic_name__			=      logging.getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Tuple=2							,						lowerCamelCase  :      Tuple=3							,						lowerCamelCase  :      Tuple=16							,						lowerCamelCase  :      int = 10							,						lowerCamelCase  :      int = 2):

      def get_dataset(lowerCamelCase  :      Optional[int]):
            A_						:  Tuple          =				torch.randn(batch_size * n_batches							,						1)
            return TensorDataset(lowerCamelCase							,						a * x + b + 0.1 * torch.randn(batch_size * n_batches							,						1))

      A_						:  str          =				get_dataset(lowerCamelCase)
      A_						:  Optional[Any]          =				get_dataset(lowerCamelCase)
      A_						:  List[Any]          =				DataLoader(lowerCamelCase							,						shuffle=lowerCamelCase							,						batch_size=lowerCamelCase							,						num_workers=4)
      A_						:  Optional[Any]          =				DataLoader(lowerCamelCase							,						shuffle=lowerCamelCase							,						batch_size=lowerCamelCase							,						num_workers=4)
      return (train_dataloader, valid_dataloader)




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      str=None):
      A_						:  List[str]          =				[]
      for epoch in range(lowerCamelCase):
            # Train quickly
            model.train()
            for batch in dataloader:
                  A_						:  int          =				batch
                  A_						:  Optional[int]          =				model(lowerCamelCase)
                  A_						:  str          =				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 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Optional[int] ):

        '''simple docstring'''




        super().__init__()
        A_						:  Optional[Any]          =				nn.Parameter(torch.randn(1 ) )
        A_						:  List[str]          =				nn.Parameter(torch.randn(1 ) )






  def        _a							(							self :       List[Any]	,_a :       int ):

        '''simple docstring'''




        return x * self.a + self.b







class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







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

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  Optional[Any]          =				DummyModel()
              A_						:  Optional[int]          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  int          =				dummy_dataloaders()
              A_						:  int          =				ProjectConfiguration(total_limit=1	,project_dir=_a	,automatic_checkpoint_naming=_a )
              # Train baseline
              A_						:  Optional[Any]          =				Accelerator(project_config=_a )
              A_						:  Union[str, Any]          =				accelerator.prepare(
                  _a	,_a	,_a	,_a )
              # Save initial
              accelerator.save_state()

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






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

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  Optional[Any]          =				DummyModel()
              A_						:  Dict          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  Optional[Any]          =				dummy_dataloaders()
              # Train baseline
              A_						:  List[Any]          =				Accelerator()
              A_						:  Dict          =				accelerator.prepare(
                  _a	,_a	,_a	,_a )
              # Save initial
              A_						:  Dict          =				os.path.join(_a	,"""initial""" )
              accelerator.save_state(_a )
              (A_)						:  Optional[Any]          =				model.a.item(), model.b.item()
              A_						:  Tuple          =				optimizer.state_dict()
              A_						:  List[str]          =				train(3	,_a	,_a	,_a	,_a )
              (A_)						:  Union[str, Any]          =				model.a.item(), model.b.item()
              A_						:  Optional[int]          =				optimizer.state_dict()

              # Train partially
              set_seed(42 )
              A_						:  Optional[Any]          =				DummyModel()
              A_						:  List[Any]          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  Tuple          =				dummy_dataloaders()
              A_						:  Optional[Any]          =				Accelerator()
              A_						:  Optional[int]          =				accelerator.prepare(
                  _a	,_a	,_a	,_a )
              accelerator.load_state(_a )
              (A_)						:  Any          =				model.a.item(), model.b.item()
              A_						:  str          =				optimizer.state_dict()
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )

              A_						:  Tuple          =				train(2	,_a	,_a	,_a	,_a )
              # Save everything
              A_						:  Any          =				os.path.join(_a	,"""checkpoint""" )
              accelerator.save_state(_a )

              # Load everything back in and make sure all states work
              accelerator.load_state(_a )
              test_rands += train(1	,_a	,_a	,_a	,_a )
              (A_)						:  List[str]          =				model.a.item(), model.b.item()
              A_						:  Optional[Any]          =				optimizer.state_dict()
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  List[str]          =				DummyModel()
              A_						:  List[str]          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  Dict          =				dummy_dataloaders()
              A_						:  Dict          =				ProjectConfiguration(automatic_checkpoint_naming=_a )

              # Train baseline
              A_						:  int          =				Accelerator(project_dir=_a	,project_config=_a )
              A_						:  Tuple          =				accelerator.prepare(
                  _a	,_a	,_a	,_a )
              # Save initial
              accelerator.save_state()
              (A_)						:  Tuple          =				model.a.item(), model.b.item()
              A_						:  Union[str, Any]          =				optimizer.state_dict()
              A_						:  Tuple          =				train(3	,_a	,_a	,_a	,_a )
              (A_)						:  Any          =				model.a.item(), model.b.item()
              A_						:  List[Any]          =				optimizer.state_dict()

              # Train partially
              set_seed(42 )
              A_						:  Any          =				DummyModel()
              A_						:  Tuple          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  Any          =				dummy_dataloaders()
              A_						:  Union[str, Any]          =				ProjectConfiguration(iteration=1	,automatic_checkpoint_naming=_a )
              A_						:  List[Any]          =				Accelerator(project_dir=_a	,project_config=_a )
              A_						:  Union[str, Any]          =				accelerator.prepare(
                  _a	,_a	,_a	,_a )
              accelerator.load_state(os.path.join(_a	,"""checkpoints"""	,"""checkpoint_0""" ) )
              (A_)						:  Optional[int]          =				model.a.item(), model.b.item()
              A_						:  List[str]          =				optimizer.state_dict()
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )

              A_						:  Optional[int]          =				train(2	,_a	,_a	,_a	,_a )
              # Save everything
              accelerator.save_state()

              # Load everything back in and make sure all states work
              accelerator.load_state(os.path.join(_a	,"""checkpoints"""	,"""checkpoint_1""" ) )
              test_rands += train(1	,_a	,_a	,_a	,_a )
              (A_)						:  List[Any]          =				model.a.item(), model.b.item()
              A_						:  Optional[int]          =				optimizer.state_dict()
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )
              self.assertEqual(_a	,_a )






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.tensor([1, 2, 3] )
        A_						:  str          =				torch.tensor([2, 3, 4] )
        A_						:  str          =				DummyModel()
        A_						:  Tuple          =				torch.optim.Adam(net.parameters() )
        A_						:  List[str]          =				Accelerator()
        with self.assertRaises(_a ) as ve:
              accelerator.register_for_checkpointing(_a	,_a	,_a	,_a )
        A_						:  Union[str, Any]          =				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 :       Tuple ):

        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmpdir:
              set_seed(42 )
              A_						:  List[str]          =				DummyModel()
              A_						:  Any          =				torch.optim.Adam(params=model.parameters()	,lr=1e-3 )
              A_						:  str          =				torch.optim.lr_scheduler.StepLR(_a	,step_size=1	,gamma=0.99 )
              A_						:  List[str]          =				dummy_dataloaders()
              A_						:  Union[str, Any]          =				ProjectConfiguration(automatic_checkpoint_naming=_a )
              # Train baseline
              A_						:  List[Any]          =				Accelerator(project_dir=_a	,project_config=_a )
              A_						:  str          =				accelerator.prepare(
                  _a	,_a	,_a	,_a	,_a )
              # Save initial
              accelerator.save_state()
              A_						:  List[Any]          =				scheduler.state_dict()
              train(3	,_a	,_a	,_a	,_a	,_a )
              self.assertNotEqual(_a	,scheduler.state_dict() )

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






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

        '''simple docstring'''




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






  @require_cuda
  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  List[str]          =				["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
        execute_subprocess_async(_a	,env=os.environ.copy() )


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

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							KandinskyVaaControlnetPipeline
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  a_  					=							[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return self.time_input_dim






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 100






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  List[Any]          =				{
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A_						:  Tuple          =				UNetaDConditionModel(**_a )
        return model






  @property
  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				VQModel(**self.dummy_movq_kwargs )
        return model






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.dummy_unet
        A_						:  int          =				self.dummy_movq

        A_						:  Tuple          =				DDIMScheduler(
            num_train_timesteps=1000	,beta_schedule="""linear"""	,beta_start=0.00085	,beta_end=0.012	,clip_sample=_a	,set_alpha_to_one=_a	,steps_offset=1	,prediction_type="""epsilon"""	,thresholding=_a	,)

        A_						:  int          =				{
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






  def        _a							(							self :       Dict	,_a :       str	,_a :       Union[str, Any]=0 ):

        '''simple docstring'''




        A_						:  Dict          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(_a ) ).to(_a )
        A_						:  int          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1 ) ).to(
            _a )

        # create hint
        A_						:  List[Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("""mps""" ):
              A_						:  Optional[Any]          =				torch.manual_seed(_a )
        else:
              A_						:  str          =				torch.Generator(device=_a ).manual_seed(_a )
        A_						:  List[Any]          =				{
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Dict ):

        '''simple docstring'''




        A_						:  List[Any]          =				"""cpu"""

        A_						:  List[str]          =				self.get_dummy_components()

        A_						:  Tuple          =				self.pipeline_class(**_a )
        A_						:  Dict          =				pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        A_						:  Tuple          =				pipe(**self.get_dummy_inputs(_a ) )
        A_						:  Tuple          =				output.images

        A_						:  Optional[Any]          =				pipe(
            **self.get_dummy_inputs(_a )	,return_dict=_a	,)[0]

        A_						:  Tuple          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A_						:  List[Any]          =				np.array(
            [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )

        assert (
            np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )

        A_						:  Optional[int]          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png""" )
        A_						:  Optional[int]          =				torch.from_numpy(np.array(_a ) ).float() / 255.0
        A_						:  List[Any]          =				hint.permute(2	,0	,1 ).unsqueeze(0 )

        A_						:  List[Any]          =				KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""	,torch_dtype=torch.floataa )
        pipe_prior.to(_a )

        A_						:  Union[str, Any]          =				KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,torch_dtype=torch.floataa )
        A_						:  Union[str, Any]          =				pipeline.to(_a )
        pipeline.set_progress_bar_config(disable=_a )

        A_						:  Optional[Any]          =				"""A robot, 4k photo"""

        A_						:  Any          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_   ,					A_						:  List[str]          =				pipe_prior(
            _a	,generator=_a	,num_inference_steps=5	,negative_prompt=""""""	,).to_tuple()

        A_						:  int          =				torch.Generator(device="""cuda""" ).manual_seed(0 )
        A_						:  List[Any]          =				pipeline(
            image_embeds=_a	,negative_image_embeds=_a	,hint=_a	,generator=_a	,num_inference_steps=100	,output_type="""np"""	,)

        A_						:  Dict          =				output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_a	,_a )
