



'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase			=    16
UpperCAmelCase			=    32






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						List[Any]							)			->						Dict:







	"""simple docstring"""
	# Initialize accelerator
	if args.with_tracking:
		lowerCAmelCase    =	Accelerator(
		    cpu=args.cpu							,		mixed_precision=args.mixed_precision							,		log_with="""all"""							,		project_dir=args.project_dir							)
	else:
		lowerCAmelCase    =	Accelerator(cpu=args.cpu							,		mixed_precision=args.mixed_precision							)

	if hasattr(args.checkpointing_steps							,		"""isdigit"""							):
		if args.checkpointing_steps == "epoch":
			lowerCAmelCase    =	args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			lowerCAmelCase    =	int(args.checkpointing_steps							)
		else:
			raise ValueError(
			    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'							)
	else:
		lowerCAmelCase    =	None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCAmelCase    =	config["""lr"""]
	lowerCAmelCase    =	int(config["""num_epochs"""]							)
	lowerCAmelCase    =	int(config["""seed"""]							)
	lowerCAmelCase    =	int(config["""batch_size"""]							)

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		lowerCAmelCase    =	os.path.split(_SCREAMING_SNAKE_CASE							)[-1].split("""."""							)[0]
		accelerator.init_trackers(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase    =	AutoTokenizer.from_pretrained("""bert-base-cased"""							)
	lowerCAmelCase    =	load_dataset("""glue"""							,		"""mrpc"""							)
	lowerCAmelCase    =	evaluate.load("""glue"""							,		"""mrpc"""							)

	def tokenize_function(_SCREAMING_SNAKE_CASE     :						Optional[int]							):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase    =	tokenizer(examples["""sentence1"""]							,		examples["""sentence2"""]							,		truncation=_SCREAMING_SNAKE_CASE							,		max_length=_SCREAMING_SNAKE_CASE							)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		lowerCAmelCase    =	datasets.map(
		    _SCREAMING_SNAKE_CASE							,		batched=_SCREAMING_SNAKE_CASE							,		remove_columns=["""idx""", """sentence1""", """sentence2"""]							,		)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCAmelCase    =	tokenized_datasets.rename_column("""label"""							,		"""labels"""							)

	# If the batch size is too big we use gradient accumulation
	lowerCAmelCase    =	1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		lowerCAmelCase    =	batch_size // MAX_GPU_BATCH_SIZE
		lowerCAmelCase    =	MAX_GPU_BATCH_SIZE

	def collate_fn(_SCREAMING_SNAKE_CASE     :						Optional[int]							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		lowerCAmelCase    =	128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			lowerCAmelCase    =	16
		elif accelerator.mixed_precision != "no":
			lowerCAmelCase    =	8
		else:
			lowerCAmelCase    =	None

		return tokenizer.pad(
		    _SCREAMING_SNAKE_CASE							,		padding="""longest"""							,		max_length=_SCREAMING_SNAKE_CASE							,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE							,		return_tensors="""pt"""							,		)

	# Instantiate dataloaders.
	lowerCAmelCase    =	DataLoader(
	    tokenized_datasets["""train"""]							,		shuffle=_SCREAMING_SNAKE_CASE							,		collate_fn=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	DataLoader(
	    tokenized_datasets["""validation"""]							,		shuffle=_SCREAMING_SNAKE_CASE							,		collate_fn=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)

	set_seed(_SCREAMING_SNAKE_CASE							)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase    =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,		return_dict=_SCREAMING_SNAKE_CASE							)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	lowerCAmelCase    =	model.to(accelerator.device							)

	# Instantiate optimizer
	lowerCAmelCase    =	AdamW(params=model.parameters()							,		lr=_SCREAMING_SNAKE_CASE							)

	# Instantiate scheduler
	lowerCAmelCase    =	get_linear_schedule_with_warmup(
	    optimizer=_SCREAMING_SNAKE_CASE							,		num_warmup_steps=100							,		num_training_steps=(len(_SCREAMING_SNAKE_CASE							) * num_epochs) // gradient_accumulation_steps							,		)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	accelerator.prepare(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# We need to keep track of how many total steps we have iterated over
	lowerCAmelCase    =	0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCAmelCase    =	0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'							)
			accelerator.load_state(args.resume_from_checkpoint							)
			lowerCAmelCase    =	os.path.basename(args.resume_from_checkpoint							)
		else:
			# Get the most recent checkpoint
			lowerCAmelCase    =	[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
			dirs.sort(key=os.path.getctime							)
			lowerCAmelCase    =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		lowerCAmelCase    =	os.path.splitext(_SCREAMING_SNAKE_CASE							)[0]

		if "epoch" in training_difference:
			lowerCAmelCase    =	int(training_difference.replace("""epoch_"""							,		""""""							)							) + 1
			lowerCAmelCase    =	None
		else:
			lowerCAmelCase    =	int(training_difference.replace("""step_"""							,		""""""							)							)
			lowerCAmelCase    =	resume_step // len(_SCREAMING_SNAKE_CASE							)
			resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE							)

    # Now we train the model
	for epoch in range(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
		model.train()
		if args.with_tracking:
			lowerCAmelCase    =	0
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			lowerCAmelCase    =	accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			lowerCAmelCase    =	train_dataloader
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE							):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device							)
			lowerCAmelCase    =	model(**_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	outputs.loss
			lowerCAmelCase    =	loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(_SCREAMING_SNAKE_CASE							)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase    =	f'step_{overall_step}'
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						lowerCAmelCase    =	os.path.join(args.output_dir							,		_SCREAMING_SNAKE_CASE							)
					accelerator.save_state(_SCREAMING_SNAKE_CASE							)

		model.eval()
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE							):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device							)
			with torch.no_grad():
				lowerCAmelCase    =	model(**_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	outputs.logits.argmax(dim=-1							)
			lowerCAmelCase,   lowerCAmelCase    =	accelerator.gather_for_metrics((predictions, batch["""labels"""])							)
			metric.add_batch(
			    predictions=_SCREAMING_SNAKE_CASE							,		references=_SCREAMING_SNAKE_CASE							,		)

		lowerCAmelCase    =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,		_SCREAMING_SNAKE_CASE							)
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(_SCREAMING_SNAKE_CASE							),
			        """epoch""": epoch,
			    }							,		step=_SCREAMING_SNAKE_CASE							,		)

		if checkpointing_steps == "epoch":
			lowerCAmelCase    =	f'epoch_{epoch}'
			if args.output_dir is not None:
				lowerCAmelCase    =	os.path.join(args.output_dir							,		_SCREAMING_SNAKE_CASE							)
			accelerator.save_state(_SCREAMING_SNAKE_CASE							)

	if args.with_tracking:
		accelerator.end_training()






def 		_snake_case		(					)			->						Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase    =	argparse.ArgumentParser(description="""Simple example of training script."""							)
	parser.add_argument(
	    """--mixed_precision"""							,		type=_SCREAMING_SNAKE_CASE							,		default=_SCREAMING_SNAKE_CASE							,		choices=["""no""", """fp16""", """bf16""", """fp8"""]							,		help="""Whether to use mixed precision. Choose"""
	    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
	    """and an Nvidia Ampere GPU."""							,		)
	parser.add_argument("""--cpu"""							,		action="""store_true"""							,		help="""If passed, will train on the CPU."""							)
	parser.add_argument(
	    """--checkpointing_steps"""							,		type=_SCREAMING_SNAKE_CASE							,		default=_SCREAMING_SNAKE_CASE							,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""							,		)
	parser.add_argument(
	    """--resume_from_checkpoint"""							,		type=_SCREAMING_SNAKE_CASE							,		default=_SCREAMING_SNAKE_CASE							,		help="""If the training should continue from a checkpoint folder."""							,		)
	parser.add_argument(
	    """--with_tracking"""							,		action="""store_true"""							,		help="""Whether to load in all available experiment trackers from the environment and use them for logging."""							,		)
	parser.add_argument(
	    """--output_dir"""							,		type=_SCREAMING_SNAKE_CASE							,		default="""."""							,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""							,		)
	parser.add_argument(
	    """--project_dir"""							,		type=_SCREAMING_SNAKE_CASE							,		default="""logs"""							,		help="""Location on where to store experiment tracking logs` and relevent project information"""							,		)
	lowerCAmelCase    =	parser.parse_args()
	lowerCAmelCase    =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				main()




'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Optional[Any]           =		StableDiffusionModelEditingPipeline
						UpperCAmelCase							:    List[str]           =		TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS


						def 				__snake_case    (					self      )	->	Optional[Any]:
							torch.manual_seed(0      )
							lowerCAmelCase    =	UNetaDConditionModel(
							    block_out_channels=(32, 64)	,       layers_per_block=2	,       sample_size=32	,       in_channels=4	,       out_channels=4	,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,       cross_attention_dim=32	,       )
							lowerCAmelCase    =	DDIMScheduler()
							torch.manual_seed(0      )
							lowerCAmelCase    =	AutoencoderKL(
							    block_out_channels=[32, 64]	,       in_channels=3	,       out_channels=3	,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,       latent_channels=4	,       )
							torch.manual_seed(0      )
							lowerCAmelCase    =	CLIPTextConfig(
							    bos_token_id=0	,       eos_token_id=2	,       hidden_size=32	,       intermediate_size=37	,       layer_norm_eps=1e-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1000	,       )
							lowerCAmelCase    =	CLIPTextModel(A_      )
							lowerCAmelCase    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							lowerCAmelCase    =	{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components


						def 				__snake_case    (					self	,       A_	,       A_=0      )	->	Optional[int]:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    # Setting height and width to None to prevent OOMs on CPU.
							    """height""": None,
							    """width""": None,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	List[str]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]      )

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


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	"""french fries"""
							lowerCAmelCase    =	sd_pipe(**A_	,       negative_prompt=A_      )
							lowerCAmelCase    =	output.images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

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

							lowerCAmelCase    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]      )

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


						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0_0_8_5	,       beta_end=0.0_1_2	,       beta_schedule="""scaled_linear"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

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

							lowerCAmelCase    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]      )

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


						def 				__snake_case    (					self      )	->	Optional[Any]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	PNDMScheduler()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							# the pipeline does not expect pndm so test if it raises error.
							with self.assertRaises(A_      ):
								lowerCAmelCase    =	sd_pipe(**A_      ).images


						def 				__snake_case    (					self      )	->	str:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3      )




						def 				__snake_case    (					self      )	->	Any:
							super().test_attention_slicing_forward_pass(expected_max_diff=5e-3      )







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


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	int:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


						def 				__snake_case    (					self	,       A_=0      )	->	Dict:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(A_	,       safety_checker=A_      )
							pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)

							lowerCAmelCase    =	np.array(
							    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]      )

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

							# make sure image changes after editing
							pipe.edit_model("""A pack of roses"""	,       """A pack of blue roses"""      )

							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(expected_slice - image_slice      ).max() > 1e-1




						def 				__snake_case    (					self      )	->	str:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	DDIMScheduler.from_pretrained(A_	,       subfolder="""scheduler"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(
							    A_	,       scheduler=A_	,       safety_checker=A_      )
							lowerCAmelCase    =	pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing(1      )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      )

							lowerCAmelCase    =	torch.cuda.max_memory_allocated()
							# make sure that less than 4.4 GB is allocated
							assert mem_bytes < 4.4 * 10**9