



'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__lowerCAmelCase		     =  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

__lowerCAmelCase		     =  list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCAmelCase		     =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='cifar10'     ,		metadata={'help': 'Name of a dataset from the datasets package'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'A folder containing the training data.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'A folder containing the validation data.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=0.15     ,		metadata={'help': 'Percent to split off of train for validation.'}  )
							lowerCAmelCase			:       int  							=   field(default=3_2     ,		metadata={'help': 'The size of the square patches to use for masking.'}  )
							lowerCAmelCase			:       float  							=   field(
							    default=0.6     ,		metadata={'help': 'Percentage of patches to mask.'}     ,		)
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }     ,		)







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				{}
									if self.train_dir is not None:
											_a	: Optional[int]								=				self.train_dir
									if self.validation_dir is not None:
											_a	: str								=				self.validation_dir
									_a	: Any								=				data_files if data_files else None

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       str  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
							            'checkpoint identifier on the hub. '
							            'Don\'t set if you want to train a model from scratch.'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCamelCase  )}     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'Override some existing default config settings when a model is trained from scratch. Example: '
							            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}     ,		)
							lowerCAmelCase			:       str  							=   field(
							    default='main'     ,		metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,		)
							lowerCAmelCase			:       str  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'Name or path of preprocessor config.'}  )
							lowerCAmelCase			:       bool  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': (
							            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Stride to use for the encoder.'}     ,		)

class       __magic_name__		:







							def __init__(				self   :			int      ,_UpperCAmelCase   :			str=192      ,_UpperCAmelCase   :			Tuple=32      ,_UpperCAmelCase   :			Any=4      ,_UpperCAmelCase   :			Optional[int]=0.6						):
									_a	: int								=				input_size
									_a	: List[Any]								=				mask_patch_size
									_a	: Tuple								=				model_patch_size
									_a	: Tuple								=				mask_ratio

									if self.input_size % self.mask_patch_size != 0:
											raise ValueError('Input size must be divisible by mask patch size'						)
									if self.mask_patch_size % self.model_patch_size != 0:
											raise ValueError('Mask patch size must be divisible by model patch size'						)

									_a	: Optional[int]								=				self.input_size // self.mask_patch_size
									_a	: List[str]								=				self.mask_patch_size // self.model_patch_size

									_a	: Optional[int]								=				self.rand_size**2
									_a	: Any								=				int(np.ceil(self.token_count * self.mask_ratio						)						)







							def __call__(				self   :			Dict						):
									_a	: List[Any]								=				np.random.permutation(self.token_count						)[: self.mask_count]
									_a	: List[str]								=				np.zeros(self.token_count      ,dtype=_UpperCAmelCase						)
									_a	: Dict								=				1

									_a	: str								=				mask.reshape((self.rand_size, self.rand_size)						)
									_a	: List[str]								=				mask.repeat(self.scale      ,axis=0						).repeat(self.scale      ,axis=1						)

									return torch.tensor(mask.flatten()						)



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		_a	: int								=				torch.stack([example['pixel_values'] for example in examples]    )
		_a	: int								=				torch.stack([example['mask'] for example in examples]    )
		return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def 	__lowerCamelCase	( )     -> Tuple:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_a	: str								=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		if len(sys.argv    ) == 2 and sys.argv[1].endswith('.json'    ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_a			,   _a			,   _a	: List[str]								=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
		else:
				_a			,   _a			,   _a	: Any								=				parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mim'		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,     datefmt='%m/%d/%Y %H:%M:%S'		,     handlers=[logging.StreamHandler(sys.stdout    )]		,     )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_a	: Tuple								=				training_args.get_process_log_level()
		logger.setLevel(lowerCAmelCase_    )
		transformers.utils.logging.set_verbosity(lowerCAmelCase_    )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + f"""distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}"""    )
		logger.info(f"""Training/evaluation parameters {training_args}"""    )

		# Detecting last checkpoint.
		_a	: Optional[Any]								=				None
		if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
				_a	: int								=				get_last_checkpoint(training_args.output_dir    )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
						raise ValueError(
						    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    'Use --overwrite_output_dir to overcome.'    )
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'    )

    # Initialize our dataset.
		_a	: Optional[int]								=				load_dataset(
		    data_args.dataset_name		,     data_args.dataset_config_name		,     data_files=data_args.data_files		,     cache_dir=model_args.cache_dir		,     use_auth_token=True if model_args.use_auth_token else None		,     )

		# If we don't have a validation split, split off a percentage of train as validation.
		_a	: Tuple								=				None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split		,     lowerCAmelCase_    ) and data_args.train_val_split > 0.0:
				_a	: Tuple								=				ds['train'].train_test_split(data_args.train_val_split    )
				_a	: Union[str, Any]								=				split['train']
				_a	: List[Any]								=				split['test']

		# Create config
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_a	: str								=				{
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name_or_path:
				_a	: Union[str, Any]								=				AutoConfig.from_pretrained(model_args.config_name_or_path		,     **lowerCAmelCase_    )
		elif model_args.model_name_or_path:
				_a	: Union[str, Any]								=				AutoConfig.from_pretrained(model_args.model_name_or_path		,     **lowerCAmelCase_    )
		else:
				_a	: Dict								=				CONFIG_MAPPING[model_args.model_type]()
				logger.warning('You are instantiating a new config instance from scratch.'    )
				if model_args.config_overrides is not None:
						logger.info(f"""Overriding config: {model_args.config_overrides}"""    )
						config.update_from_string(model_args.config_overrides    )
						logger.info(f"""New config: {config}"""    )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
		if hasattr(lowerCAmelCase_		,     'decoder_type'    ):
				_a	: List[str]								=				'simmim'

		# adapt config
		_a	: Tuple								=				model_args.image_size if model_args.image_size is not None else config.image_size
		_a	: List[str]								=				model_args.patch_size if model_args.patch_size is not None else config.patch_size
		_a	: List[str]								=				(
		    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
		)

		config.update(
		    {
		        'image_size': model_args.image_size,
		        'patch_size': model_args.patch_size,
		        'encoder_stride': model_args.encoder_stride,
		    }    )

		# create image processor
		if model_args.image_processor_name:
				_a	: Union[str, Any]								=				AutoImageProcessor.from_pretrained(model_args.image_processor_name		,     **lowerCAmelCase_    )
		elif model_args.model_name_or_path:
				_a	: Tuple								=				AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,     **lowerCAmelCase_    )
		else:
				_a	: List[Any]								=				{
				    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
				}
				_a	: Dict								=				IMAGE_PROCESSOR_TYPES[model_args.model_type]()

		# create model
		if model_args.model_name_or_path:
				_a	: Union[str, Any]								=				AutoModelForMaskedImageModeling.from_pretrained(
				    model_args.model_name_or_path		,     from_tf=bool('.ckpt' in model_args.model_name_or_path    )		,     config=lowerCAmelCase_		,     cache_dir=model_args.cache_dir		,     revision=model_args.model_revision		,     use_auth_token=True if model_args.use_auth_token else None		,     )
		else:
				logger.info('Training new model from scratch'    )
				_a	: int								=				AutoModelForMaskedImageModeling.from_config(lowerCAmelCase_    )

		if training_args.do_train:
				_a	: Dict								=				ds['train'].column_names
		else:
				_a	: Union[str, Any]								=				ds['validation'].column_names

		if data_args.image_column_name is not None:
				_a	: Optional[Any]								=				data_args.image_column_name
		elif "image" in column_names:
				_a	: Union[str, Any]								=				'image'
		elif "img" in column_names:
				_a	: Union[str, Any]								=				'img'
		else:
				_a	: int								=				column_names[0]

		# transformations as done in original SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		_a	: List[Any]								=				Compose(
		    [
		        Lambda(lambda lowerCAmelCase_    : img.convert('RGB'    ) if img.mode != "RGB" else img    ),
		        RandomResizedCrop(model_args.image_size		,     scale=(0.67, 1.0)		,     ratio=(3.0 / 4.0, 4.0 / 3.0)    ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean		,     std=image_processor.image_std    ),
		    ]    )

		# create mask generator
		_a	: Dict								=				MaskGenerator(
		    input_size=model_args.image_size		,     mask_patch_size=data_args.mask_patch_size		,     model_patch_size=model_args.patch_size		,     mask_ratio=data_args.mask_ratio		,     )

		def preprocess_images(lowerCAmelCase_    ):
				_a	: List[Any]								=				[transforms(lowerCAmelCase_    ) for image in examples[image_column_name]]
				_a	: List[str]								=				[mask_generator() for i in range(len(examples[image_column_name]    )    )]

				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'    )
				if data_args.max_train_samples is not None:
						_a	: Tuple								=				ds['train'].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
				# Set the training transforms
				ds["train"].set_transform(lowerCAmelCase_    )

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'    )
				if data_args.max_eval_samples is not None:
						_a	: str								=				(
						    ds['validation'].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
						)
				# Set the validation transforms
				ds["validation"].set_transform(lowerCAmelCase_    )

		# Initialize our trainer
		_a	: List[Any]								=				Trainer(
		    model=lowerCAmelCase_		,     args=lowerCAmelCase_		,     train_dataset=ds['train'] if training_args.do_train else None		,     eval_dataset=ds['validation'] if training_args.do_eval else None		,     tokenizer=lowerCAmelCase_		,     data_collator=lowerCAmelCase_		,     )

		# Training
		if training_args.do_train:
				_a	: int								=				None
				if training_args.resume_from_checkpoint is not None:
						_a	: Tuple								=				training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_a	: Optional[Any]								=				last_checkpoint
				_a	: Union[str, Any]								=				trainer.train(resume_from_checkpoint=lowerCAmelCase_    )
				trainer.save_model()
				trainer.log_metrics('train'		,     train_result.metrics    )
				trainer.save_metrics('train'		,     train_result.metrics    )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_a	: Dict								=				trainer.evaluate()
				trainer.log_metrics('eval'		,     lowerCAmelCase_    )
				trainer.save_metrics('eval'		,     lowerCAmelCase_    )

		# Write model card and (optionally) push to hub
		_a	: List[Any]								=				{
		    'finetuned_from': model_args.model_name_or_path,
		    'tasks': 'masked-image-modeling',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-image-modeling'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**lowerCAmelCase_    )
		else:
				trainer.create_model_card(**lowerCAmelCase_    )


if __name__ == "__main__":
							main()









'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				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      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, Any]								=				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      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

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







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




