
"""simple docstring"""




import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_=0    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = np.random.RandomState(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    ).images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 1_28, 1_28, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									SCREAMING_SNAKE_CASE_						 = PNDMScheduler.from_config(pipe.scheduler.config       ,       skip_prk_steps=SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    ).images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 1_28, 1_28, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									SCREAMING_SNAKE_CASE_						 = LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    ).images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 1_28, 1_28, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									SCREAMING_SNAKE_CASE_						 = EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    ).images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 1_28, 1_28, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									SCREAMING_SNAKE_CASE_						 = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    ).images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 1_28, 1_28, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									SCREAMING_SNAKE_CASE_						 = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    ).images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 1_28, 1_28, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = 3 * [inputs['''prompt''']]

									# forward
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = output.images[0, -3:, -3:, -1]

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = 3 * [inputs.pop('''prompt'''    )]

									SCREAMING_SNAKE_CASE_						 = pipe.tokenizer(
									    SCREAMING_SNAKE_CASE_       ,       padding='''max_length'''       ,       max_length=pipe.tokenizer.model_max_length       ,       truncation=SCREAMING_SNAKE_CASE_       ,       return_tensors='''np'''       ,       )
									SCREAMING_SNAKE_CASE_						 = text_inputs['''input_ids''']

									SCREAMING_SNAKE_CASE_						 = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

									SCREAMING_SNAKE_CASE_						 = prompt_embeds

									# forward
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = output.images[0, -3:, -3:, -1]

									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1e-4







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,       provider='''CPUExecutionProvider'''    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = 3 * ['''this is a negative prompt''']
									SCREAMING_SNAKE_CASE_						 = negative_prompt
									SCREAMING_SNAKE_CASE_						 = 3 * [inputs['''prompt''']]

									# forward
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = output.images[0, -3:, -3:, -1]

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_						 = 3 * [inputs.pop('''prompt'''    )]

									SCREAMING_SNAKE_CASE_						 = []
									for p in [prompt, negative_prompt]:
											SCREAMING_SNAKE_CASE_						 = pipe.tokenizer(
											    SCREAMING_SNAKE_CASE_       ,       padding='''max_length'''       ,       max_length=pipe.tokenizer.model_max_length       ,       truncation=SCREAMING_SNAKE_CASE_       ,       return_tensors='''np'''       ,       )
											SCREAMING_SNAKE_CASE_						 = text_inputs['''input_ids''']

											embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = embeds

									# forward
									SCREAMING_SNAKE_CASE_						 = pipe(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = output.images[0, -3:, -3:, -1]

									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1e-4







@nightly
@require_onnxruntime
@require_torch_gpu
class  snake_case     (							unittest.TestCase   ):

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ort.SessionOptions()
									SCREAMING_SNAKE_CASE_						 = False
									return options

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(
									    '''CompVis/stable-diffusion-v1-4'''       ,       revision='''onnx'''       ,       safety_checker=SCREAMING_SNAKE_CASE_       ,       feature_extractor=SCREAMING_SNAKE_CASE_       ,       provider=self.gpu_provider       ,       sess_options=self.gpu_options       ,       )
									sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''A painting of a squirrel eating a burger'''
									np.random.seed(0    )
									SCREAMING_SNAKE_CASE_						 = sd_pipe([prompt]       ,       guidance_scale=6.0       ,       num_inference_steps=10       ,       output_type='''np'''    )
									SCREAMING_SNAKE_CASE_						 = output.images

									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = DDIMScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''       ,       subfolder='''scheduler'''       ,       revision='''onnx'''    )
									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''       ,       revision='''onnx'''       ,       scheduler=SCREAMING_SNAKE_CASE_       ,       safety_checker=SCREAMING_SNAKE_CASE_       ,       feature_extractor=SCREAMING_SNAKE_CASE_       ,       provider=self.gpu_provider       ,       sess_options=self.gpu_options       ,       )
									sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''open neural network exchange'''
									SCREAMING_SNAKE_CASE_						 = np.random.RandomState(0    )
									SCREAMING_SNAKE_CASE_						 = sd_pipe([prompt]       ,       guidance_scale=7.5       ,       num_inference_steps=10       ,       generator=SCREAMING_SNAKE_CASE_       ,       output_type='''np'''    )
									SCREAMING_SNAKE_CASE_						 = output.images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''       ,       subfolder='''scheduler'''       ,       revision='''onnx'''    )
									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''       ,       revision='''onnx'''       ,       scheduler=SCREAMING_SNAKE_CASE_       ,       safety_checker=SCREAMING_SNAKE_CASE_       ,       feature_extractor=SCREAMING_SNAKE_CASE_       ,       provider=self.gpu_provider       ,       sess_options=self.gpu_options       ,       )
									sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''open neural network exchange'''
									SCREAMING_SNAKE_CASE_						 = np.random.RandomState(0    )
									SCREAMING_SNAKE_CASE_						 = sd_pipe([prompt]       ,       guidance_scale=7.5       ,       num_inference_steps=10       ,       generator=SCREAMING_SNAKE_CASE_       ,       output_type='''np'''    )
									SCREAMING_SNAKE_CASE_						 = output.images
									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 0

									def test_callback_fn(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) -> None:
											SCREAMING_SNAKE_CASE_						 = True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													SCREAMING_SNAKE_CASE_						 = latents[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE_						 = np.array(
													    [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]    )

													assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1e-3
											elif step == 5:
													assert latents.shape == (1, 4, 64, 64)
													SCREAMING_SNAKE_CASE_						 = latents[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE_						 = np.array(
													    [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]    )

													assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1e-3

									SCREAMING_SNAKE_CASE_						 = False

									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''       ,       revision='''onnx'''       ,       safety_checker=SCREAMING_SNAKE_CASE_       ,       feature_extractor=SCREAMING_SNAKE_CASE_       ,       provider=self.gpu_provider       ,       sess_options=self.gpu_options       ,       )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''Andromeda galaxy in a bottle'''

									SCREAMING_SNAKE_CASE_						 = np.random.RandomState(0    )
									pipe(
									    prompt=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=5       ,       guidance_scale=7.5       ,       generator=SCREAMING_SNAKE_CASE_       ,       callback=SCREAMING_SNAKE_CASE_       ,       callback_steps=1       ,       )
									assert test_callback_fn.has_been_called
									assert number_of_steps == 6







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''       ,       revision='''onnx'''       ,       safety_checker=SCREAMING_SNAKE_CASE_       ,       feature_extractor=SCREAMING_SNAKE_CASE_       ,       provider=self.gpu_provider       ,       sess_options=self.gpu_options       ,       )
									assert isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									assert pipe.safety_checker is None

									SCREAMING_SNAKE_CASE_						 = pipe('''example prompt'''       ,       num_inference_steps=2    ).images[0]
									assert image is not None

									# check that there's no error when saving a pipeline with one of the models being None
									with tempfile.TemporaryDirectory() as tmpdirname:
											pipe.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    )

									# sanity check that the pipeline still works
									assert pipe.safety_checker is None
									SCREAMING_SNAKE_CASE_						 = pipe('''example prompt'''       ,       num_inference_steps=2    ).images[0]
									assert image is not None

"""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


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a=2,							__a=3,							__a=16,							__a = 10,							__a = 2    ):

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

		SCREAMING_SNAKE_CASE_						 = get_dataset(__a    )
		SCREAMING_SNAKE_CASE_						 = get_dataset(__a    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							shuffle=__a,							batch_size=__a,							num_workers=4    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							shuffle=__a,							batch_size=__a,							num_workers=4    )
		return (train_dataloader, valid_dataloader)



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a=None    ):
		SCREAMING_SNAKE_CASE_						 = []
		for epoch in range(__a    ):
				# Train quickly
				model.train()
				for batch in dataloader:
						SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = batch
						SCREAMING_SNAKE_CASE_						 = model(__a    )
						SCREAMING_SNAKE_CASE_						 = 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    ):

									"""simple docstring"""







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







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return x * self.a + self.b







class  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator()
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       '''initial'''    )
											accelerator.save_state(SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											SCREAMING_SNAKE_CASE_						 = train(3       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()

											# Train partially
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = Accelerator()
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											accelerator.load_state(SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = train(2       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save everything
											SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoint'''    )
											accelerator.save_state(SCREAMING_SNAKE_CASE_    )

											# Load everything back in and make sure all states work
											accelerator.load_state(SCREAMING_SNAKE_CASE_    )
											test_rands += train(1       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )

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

											# Train partially
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(iteration=1       ,       automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_0'''    )    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = train(2       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save everything
											accelerator.save_state()

											# Load everything back in and make sure all states work
											accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_1'''    )    )
											test_rands += train(1       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""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(SCREAMING_SNAKE_CASE_    ) as ve:
											accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									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 _lowercase					(self    ):

									"""simple docstring"""







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

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

							def _lowercase					(self    ):

									"""simple docstring"""







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







							@require_cuda
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__    )]
									execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,       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=0.99)
						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()