

'''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
__snake_case			:     Optional[int]										=      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')

__snake_case			:     List[str]										=      list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__snake_case			:     int										=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class lowerCamelCase			:



			'''simple docstring'''






			__snake_case        =	field(
			    default='cifar10'	,						metadata={'help': 'Name of a dataset from the datasets package'}						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}	,						)
			__snake_case        =	field(default=lowercase_	,						metadata={'help': 'A folder containing the training data.'}						)
			__snake_case        =	field(default=lowercase_	,						metadata={'help': 'A folder containing the validation data.'}						)
			__snake_case        =	field(
			    default=0.15	,						metadata={'help': 'Percent to split off of train for validation.'}						)
			__snake_case        =	field(default=32	,						metadata={'help': 'The size of the square patches to use for masking.'}						)
			__snake_case        =	field(
			    default=0.6	,						metadata={'help': 'Percentage of patches to mask.'}	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
			            'value if set.'
			        )
			    }	,						)


			def lowercase__						(				self	:						str			)      ->		str:





									'''simple docstring'''
									A__					:       List[Any]     ={}
									if self.train_dir is not None:
															A__					:       str     =self.train_dir
									if self.validation_dir is not None:
															A__					:       List[str]     =self.validation_dir
									A__					:       Union[str, Any]     =data_files if data_files else None



@dataclass
class lowerCamelCase			:



			'''simple docstring'''






			__snake_case        =	field(
			    default=lowercase_	,						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.'
			        )
			    }	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowercase_						)}	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
			__snake_case        =	field(
			    default=lowercase_	,						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'
			        )
			    }	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}	,						)
			__snake_case        =	field(
			    default='main'	,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,						)
			__snake_case        =	field(default=lowercase_	,						metadata={'help': 'Name or path of preprocessor config.'}						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={
			        'help': (
			            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
			        )
			    }	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={
			        'help': (
			            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
			        )
			    }	,						)
			__snake_case        =	field(
			    default=lowercase_	,						metadata={'help': 'Stride to use for the encoder.'}	,						)



class lowerCamelCase			:



			'''simple docstring'''






			def __init__(				self	:						Dict		,      lowerCAmelCase_	:						Optional[int]=1_92		,      lowerCAmelCase_	:						Dict=32		,      lowerCAmelCase_	:						Optional[int]=4		,      lowerCAmelCase_	:						int=0.6			)      ->		str:





									'''simple docstring'''
									A__					:       Tuple     =input_size
									A__					:       Union[str, Any]     =mask_patch_size
									A__					:       Union[str, Any]     =model_patch_size
									A__					:       int     =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__					:       str     =self.input_size // self.mask_patch_size
									A__					:       Optional[Any]     =self.mask_patch_size // self.model_patch_size

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



			def __call__(				self	:						List[str]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[str]     =np.random.permutation(self.token_count			)[: self.mask_count]
									A__					:       List[Any]     =np.zeros(self.token_count		,      dtype=lowerCAmelCase_			)
									A__					:       List[Any]     =1

									A__					:       Optional[int]     =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	(       __snake_case					:		Optional[Any]	)	->							List[str]:






						"""simple docstring"""





						A__					:       Tuple     =torch.stack([example["""pixel_values"""] for example in examples]	)
						A__					:       Tuple     =torch.stack([example["""mask"""] for example in examples]	)
						return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 						__lowerCamelCase	(       )	->							Dict:






						"""simple docstring"""





						A__					:       Optional[int]     =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__					:       Union[str, Any]     =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												A__   ,						A__   ,						A__					:       List[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""",		__snake_case,		__snake_case	)

						# 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__					:       List[str]     =training_args.get_process_log_level()
						logger.setLevel(__snake_case	)
						transformers.utils.logging.set_verbosity(__snake_case	)
						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__					:       str     =None
						if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
												A__					:       Dict     =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__					:       Union[str, Any]     =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__					:       List[Any]     =None if """validation""" in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split,		__snake_case	) and data_args.train_val_split > 0.0:
												A__					:       List[Any]     =ds["""train"""].train_test_split(data_args.train_val_split	)
												A__					:       Tuple     =split["""train"""]
												A__					:       Tuple     =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__					:       List[Any]     =AutoConfig.from_pretrained(model_args.config_name_or_path,		**__snake_case	)
						elif model_args.model_name_or_path:
												A__					:       str     =AutoConfig.from_pretrained(model_args.model_name_or_path,		**__snake_case	)
						else:
												A__					:       List[Any]     =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(__snake_case,		"""decoder_type"""	):
												A__					:       Any     ="""simmim"""

						# adapt config
						A__					:       str     =model_args.image_size if model_args.image_size is not None else config.image_size
						A__					:       Tuple     =model_args.patch_size if model_args.patch_size is not None else config.patch_size
						A__					:       Optional[Any]     =(
						    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__					:       Dict     =AutoImageProcessor.from_pretrained(model_args.image_processor_name,		**__snake_case	)
						elif model_args.model_name_or_path:
												A__					:       str     =AutoImageProcessor.from_pretrained(model_args.model_name_or_path,		**__snake_case	)
						else:
												A__					:       int     ={
												    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
												}
												A__					:       str     =IMAGE_PROCESSOR_TYPES[model_args.model_type]()

						# create model
						if model_args.model_name_or_path:
												A__					:       List[Any]     =AutoModelForMaskedImageModeling.from_pretrained(
												    model_args.model_name_or_path,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path	),		config=__snake_case,		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(__snake_case	)

						if training_args.do_train:
												A__					:       int     =ds["""train"""].column_names
						else:
												A__					:       List[Any]     =ds["""validation"""].column_names

						if data_args.image_column_name is not None:
												A__					:       str     =data_args.image_column_name
						elif "image" in column_names:
												A__					:       Dict     ="""image"""
						elif "img" in column_names:
												A__					:       Union[str, Any]     ="""img"""
						else:
												A__					:       List[str]     =column_names[0]

						# transformations as done in original SimMIM paper
						# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
						A__					:       Optional[Any]     =Compose(
						    [
						        Lambda(lambda __snake_case	: 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__					:       int     =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(__snake_case					:		Tuple	):
												A__					:       Union[str, Any]     =[transforms(__snake_case	) for image in examples[image_column_name]]
												A__					:       Tuple     =[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__					:       Any     =ds["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
												# Set the training transforms
												ds["train"].set_transform(__snake_case	)

						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__					:       Optional[Any]     =(
																		    ds["""validation"""].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
																		)
												# Set the validation transforms
												ds["validation"].set_transform(__snake_case	)

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

						# Training
						if training_args.do_train:
												A__					:       Any     =None
												if training_args.resume_from_checkpoint is not None:
																		A__					:       Optional[int]     =training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		A__					:       List[Any]     =last_checkpoint
												A__					:       Union[str, Any]     =trainer.train(resume_from_checkpoint=__snake_case	)
												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__					:       Tuple     =trainer.evaluate()
												trainer.log_metrics("""eval""",		__snake_case	)
												trainer.save_metrics("""eval""",		__snake_case	)

						# Write model card and (optionally) push to hub
						A__					:       Optional[int]     ={
						    """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(**__snake_case	)
						else:
												trainer.create_model_card(**__snake_case	)


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,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class lowerCamelCase			(  lowercase_	,						lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	StableDiffusionInstructPixaPixPipeline
			__snake_case        =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			__snake_case        =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case        =	IMAGE_TO_IMAGE_IMAGE_PARAMS
			__snake_case        =	IMAGE_TO_IMAGE_IMAGE_PARAMS


			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       int     =UNetaDConditionModel(
									    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=8		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      )
									A__					:       str     =PNDMScheduler(skip_prk_steps=lowerCAmelCase_			)
									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		,      )
									torch.manual_seed(0			)
									A__					:       List[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=10_00		,      )
									A__					:       Tuple     =CLIPTextModel(lowerCAmelCase_			)
									A__					:       int     =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

									A__					:       Union[str, Any]     ={
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						Dict=0			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =floats_tensor((1, 3, 32, 32)		,      rng=random.Random(lowerCAmelCase_			)			).to(lowerCAmelCase_			)
									A__					:       str     =image.cpu().permute(0		,      2		,      3		,      1			)[0]
									A__					:       List[str]     =Image.fromarray(np.uinta(lowerCAmelCase_			)			).convert("""RGB"""			)
									if str(lowerCAmelCase_			).startswith("""mps"""			):
															A__					:       Any     =torch.manual_seed(lowerCAmelCase_			)
									else:
															A__					:       int     =torch.Generator(device=lowerCAmelCase_			).manual_seed(lowerCAmelCase_			)
									A__					:       Optional[Any]     ={
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """image_guidance_scale""": 1,
									    """output_type""": """numpy""",
									}
									return inputs


			def lowercase__						(				self	:						Any			)      ->		str:





									'''simple docstring'''
									A__					:       Any     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       Any     =self.get_dummy_components()
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Dict     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       List[Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       List[Any]     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       Dict     =image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A__					:       Tuple     =np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]			)

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


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Optional[int]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       List[str]     =self.get_dummy_components()
									A__					:       Union[str, Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Optional[Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Optional[int]     ="""french fries"""
									A__					:       Tuple     =sd_pipe(**lowerCAmelCase_		,      negative_prompt=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =output.images
									A__					:       List[str]     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A__					:       Tuple     =np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]			)

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


			def lowercase__						(				self	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       str     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       str     =self.get_dummy_components()
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Tuple     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Dict     =[inputs["""prompt"""]] * 2

									A__					:       Optional[int]     =np.array(inputs["""image"""]			).astype(np.floataa			) / 255.0
									A__					:       List[str]     =torch.from_numpy(lowerCAmelCase_			).unsqueeze(0			).to(lowerCAmelCase_			)
									A__					:       Union[str, Any]     =image / 2 + 0.5
									A__					:       Optional[int]     =image.permute(0		,      3		,      1		,      2			)
									A__					:       Dict     =image.repeat(2		,      1		,      1		,      1			)

									A__					:       int     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A__					:       List[Any]     =np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]			)

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


			def lowercase__						(				self	:						str			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[Any]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       List[str]     =self.get_dummy_components()
									A__					:       List[str]     =EulerAncestralDiscreteScheduler(
									    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""			)
									A__					:       str     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       int     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Optional[Any]     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       Tuple     =image[0, -3:, -3:, -1]

									A__					:       List[str]     =[round(lowerCAmelCase_		,      4			) for x in image_slice.flatten().tolist()]
									print(""",""".join([str(lowerCAmelCase_			) for x in slice]			)			)

									assert image.shape == (1, 32, 32, 3)
									A__					:       Any     =np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]			)

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


			def lowercase__						(				self	:						List[Any]			)      ->		int:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



			def lowercase__						(				self	:						List[str]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.get_dummy_components()
									A__					:       Optional[Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Any     =VaeImageProcessor(do_resize=lowerCAmelCase_		,      do_normalize=lowerCAmelCase_			)
									A__					:       Dict     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       str     =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_		,      input_image_type="""pt"""			)			)[0]

									A__					:       List[Any]     =components["""vae"""]
									A__					:       Dict     =self.get_dummy_inputs_by_type(lowerCAmelCase_		,      input_image_type="""pt"""			)

									for image_param in self.image_latents_params:
															if image_param in inputs.keys():
																					A__					:       List[Any]     =vae.encode(inputs[image_param]			).latent_dist.mode()

									A__					:       Optional[Any]     =pipe(**lowerCAmelCase_			)[0]

									A__					:       Dict     =np.abs(out - out_latents_inputs			).max()
									self.assertLess(lowerCAmelCase_		,      1e-4		,      """passing latents as image input generate different result from passing image"""			)



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



			'''simple docstring'''






			def lowercase__						(				self	:						List[str]			)      ->		Union[str, Any]:





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int=0			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[Any]     =torch.manual_seed(lowerCAmelCase_			)
									A__					:       Optional[Any]     =load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""			)
									A__					:       List[Any]     ={
									    """prompt""": """turn him into a cyborg""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """image_guidance_scale""": 1.0,
									    """output_type""": """numpy""",
									}
									return inputs


			def lowercase__						(				self	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									A__					:       Optional[Any]     =pipe(**lowerCAmelCase_			).images
									A__					:       Tuple     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Dict     =np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]			)

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


			def lowercase__						(				self	:						str			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       str     =LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Union[str, Any]     =self.get_inputs()
									A__					:       Tuple     =pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       List[Any]     =np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]			)

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


			def lowercase__						(				self	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       str     =DDIMScheduler.from_config(pipe.scheduler.config			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									A__					:       List[str]     =pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Any     =np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]			)

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


			def lowercase__						(				self	:						Union[str, Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     =0

									def callback_fn(lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						torch.FloatTensor			) -> None:
															A__					:       Any     =True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					A__					:       List[str]     =latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					A__					:       Optional[Any]     =latents[0, -3:, -3:, -1]
																					A__					:       Tuple     =np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]			)

																					assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
															elif step == 2:
																					A__					:       List[Any]     =latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					A__					:       Dict     =latents[0, -3:, -3:, -1]
																					A__					:       List[Any]     =np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]			)

																					assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2

									A__					:       List[str]     =False

									A__					:       Optional[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_		,      torch_dtype=torch.floataa			)
									A__					:       int     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									pipe(**lowerCAmelCase_		,      callback=lowerCAmelCase_		,      callback_steps=1			)
									assert callback_fn.has_been_called
									assert number_of_steps == 3


			def lowercase__						(				self	:						int			)      ->		Optional[Any]:





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A__					:       Dict     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_		,      torch_dtype=torch.floataa			)
									A__					:       Union[str, Any]     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing(1			)
									pipe.enable_sequential_cpu_offload()

									A__					:       List[str]     =self.get_inputs()
									A__					:       Dict     =pipe(**lowerCAmelCase_			)

									A__					:       List[str]     =torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9



			def lowercase__						(				self	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Tuple     =self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A__					:       int     =inputs["""image"""].resize((5_04, 5_04)			)

									A__					:       Optional[int]     ="""timbrooks/instruct-pix2pix"""
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    lowerCAmelCase_		,      safety_checker=lowerCAmelCase_		,      )
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Dict     =pipe(**lowerCAmelCase_			)
									A__					:       Dict     =output.images[0]

									A__					:       int     =image[2_55:2_58, 3_83:3_86, -1]

									assert image.shape == (5_04, 5_04, 3)
									A__					:       Dict     =np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 5e-3

