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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
A     :	List[Any]    = 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')







@dataclass
class 			__A:
				snake_case_						   =    field(
				    default='''cifar10'''  ,   metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''The column name of the images in the files.'''}			)
				snake_case_						   =    field(default=a  ,   metadata={'''help''': '''A folder containing the training data.'''}			)
				snake_case_						   =    field(default=a  ,   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=a  ,   metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=a  ,   metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }  ,   )




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    {}
								if self.train_dir is not None:
												__a										=    self.train_dir
								if self.validation_dir is not None:
												__a										=    self.validation_dir
								__a										=    data_files if data_files else None







@dataclass
class 			__A:
				snake_case_						   =    field(
				    default=a  ,   metadata={
				        '''help''': (
				            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}			)
				snake_case_						   =    field(
				    default=a  ,   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=a  ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}			)
				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=a  ,   metadata={'''help''': '''Name or path of preprocessor config.'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }  ,   )
				snake_case_						   =    field(
				    default=0.75  ,   metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}			)
				snake_case_						   =    field(
				    default=a  ,   metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}			)







@dataclass
class 			__A( a			):
				snake_case_						   =    field(
				    default=1E-3  ,   metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}			)



def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    torch.stack([example['''pixel_values'''] for example in examples]  )
				return {"pixel_values": pixel_values}


def     __lowerCAmelCase			(			)       ->  int:
				# 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										=    HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)  )
				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										=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								__a							,     __a							,     __a										=    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_mae'''						,    a__						,    a__  )

				# 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										=    training_args.get_process_log_level()
				logger.setLevel(a__  )
				transformers.utils.logging.set_verbosity(a__  )
				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										=    None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								__a										=    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										=    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										=    None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,    a__  ) and data_args.train_val_split > 0.0:
								__a										=    ds['''train'''].train_test_split(data_args.train_val_split  )
								__a										=    split['''train''']
								__a										=    split['''test''']

				# Load pretrained model and image processor
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				__a										=    {
				    '''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:
								__a										=    ViTMAEConfig.from_pretrained(model_args.config_name						,    **a__  )
				elif model_args.model_name_or_path:
								__a										=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,    **a__  )
				else:
								__a										=    ViTMAEConfig()
								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}"""  )

    # adapt config
				config.update(
				    {
				        '''mask_ratio''': model_args.mask_ratio,
				        '''norm_pix_loss''': model_args.norm_pix_loss,
				    }  )

				# create image processor
				if model_args.image_processor_name:
								__a										=    ViTImageProcessor.from_pretrained(model_args.image_processor_name						,    **a__  )
				elif model_args.model_name_or_path:
								__a										=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,    **a__  )
				else:
								__a										=    ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								__a										=    ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path						,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )						,    config=a__						,    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										=    ViTMAEForPreTraining(a__  )

				if training_args.do_train:
								__a										=    ds['''train'''].column_names
				else:
								__a										=    ds['''validation'''].column_names

				if data_args.image_column_name is not None:
								__a										=    data_args.image_column_name
				elif "image" in column_names:
								__a										=    '''image'''
				elif "img" in column_names:
								__a										=    '''img'''
				else:
								__a										=    column_names[0]

				# transformations as done in original MAE paper
				# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
				if "shortest_edge" in image_processor.size:
								__a										=    image_processor.size['''shortest_edge''']
				else:
								__a										=    (image_processor.size['''height'''], image_processor.size['''width'''])
				__a										=    Compose(
				    [
				        Lambda(lambda a__  : img.convert('''RGB'''  ) if img.mode != "RGB" else img  ),
				        RandomResizedCrop(a__						,    scale=(0.2, 1.0)						,    interpolation=InterpolationMode.BICUBIC  ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean						,    std=image_processor.image_std  ),
				    ]  )

				def preprocess_images(a__  ):
								__a										=    [transforms(a__  ) for image in 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										=    ds['''train'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
								# Set the training transforms
								ds["train"].set_transform(a__  )

				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										=    (
												    ds['''validation'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
												)
								# Set the validation transforms
								ds["validation"].set_transform(a__  )

				# Compute absolute learning rate
				__a										=    (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								__a										=    training_args.base_learning_rate * total_train_batch_size / 256

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

				# Training
				if training_args.do_train:
								__a										=    None
								if training_args.resume_from_checkpoint is not None:
												__a										=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												__a										=    last_checkpoint
								__a										=    trainer.train(resume_from_checkpoint=a__  )
								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										=    trainer.evaluate()
								trainer.log_metrics('''eval'''						,    a__  )
								trainer.save_metrics('''eval'''						,    a__  )

				# Write model card and (optionally) push to hub
				__a										=    {
				    '''tasks''': '''masked-auto-encoding''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''masked-auto-encoding'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**a__  )
				else:
								trainer.create_model_card(**a__  )


def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class 			__A( a  ,   a  ,   unittest.TestCase			):
				snake_case_						   =    AutoencoderKL
				snake_case_						   =    '''sample'''
				snake_case_						   =    1E-2




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    4
								__a										=    3
								__a										=    (32, 32)

								__a										=    floats_tensor((batch_size, num_channels) + sizes				).to(_snake_case				)

								return {"sample": image}




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								return (3, 32, 32)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								return (3, 32, 32)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    {
								    '''block_out_channels''': [32, 64],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								__a										=    self.dummy_input
								return init_dict, inputs_dict




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								pass




				@unittest.skipIf(torch_device == '''mps'''		,     '''Gradient checkpointing skipped on MPS'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a							,     __a										=    self.prepare_init_args_and_inputs_for_common()
								__a										=    self.model_class(**_snake_case				)
								model.to(_snake_case				)

								assert not model.is_gradient_checkpointing and model.training

								__a										=    model(**_snake_case				).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model.zero_grad()

								__a										=    torch.randn_like(_snake_case				)
								__a										=    (out - labels).mean()
								loss.backward()

								# re-instantiate the model now enabling gradient checkpointing
								__a										=    self.model_class(**_snake_case				)
								# clone model
								model_a.load_state_dict(model.state_dict()				)
								model_a.to(_snake_case				)
								model_a.enable_gradient_checkpointing()

								assert model_a.is_gradient_checkpointing and model_a.training

								__a										=    model_a(**_snake_case				).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model_a.zero_grad()
								__a										=    (out_a - labels).mean()
								loss_a.backward()

								# compare the output and parameters gradients
								self.assertTrue((loss - loss_a).abs() < 1E-5				)
								__a										=    dict(model.named_parameters()				)
								__a										=    dict(model_a.named_parameters()				)
								for name, param in named_params.items():
												self.assertTrue(torch_all_close(param.grad.data		,     named_params_a[name].grad.data		,     atol=5E-5				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a							,     __a										=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''		,     output_loading_info=_snake_case				)
								self.assertIsNotNone(_snake_case				)
								self.assertEqual(len(loading_info['''missing_keys''']				)		,     0				)

								model.to(_snake_case				)
								__a										=    model(**self.dummy_input				)

								assert image is not None, "Make sure output is not None"




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


								'''simple docstring'''




								__a										=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
								__a										=    model.to(_snake_case				)
								model.eval()

								if torch_device == "mps":
												__a										=    torch.manual_seed(0				)
								else:
												__a										=    torch.Generator(device=_snake_case				).manual_seed(0				)

								__a										=    torch.randn(
								    1		,     model.config.in_channels		,     model.config.sample_size		,     model.config.sample_size		,     generator=torch.manual_seed(0				)		,     )
								__a										=    image.to(_snake_case				)
								with torch.no_grad():
												__a										=    model(_snake_case		,     sample_posterior=_snake_case		,     generator=_snake_case				).sample

								__a										=    output[0, -1, -3:, -3:].flatten().cpu()

								# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
								# the expected output slices are not the same for CPU and GPU.
								if torch_device == "mps":
												__a										=    torch.tensor(
												    [
												        -4.0_078E-01,
												        -3.8_323E-04,
												        -1.2_681E-01,
												        -1.1_462E-01,
												        2.0_095E-01,
												        1.0_893E-01,
												        -8.8_247E-02,
												        -3.0_361E-01,
												        -9.8_644E-03,
												    ]				)
								elif torch_device == "cpu":
												__a										=    torch.tensor(
												    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
								else:
												__a										=    torch.tensor(
												    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]				)

								self.assertTrue(torch_all_close(_snake_case		,     _snake_case		,     rtol=1E-2				)				)







@slow
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								return F"""gaussian_noise_s={seed}_shape={'_'.join([str(_snake_case				) for s in shape]				)}.npy"""




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0		,     _snake_case=(4, 3, 512, 512)		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




								__a										=    torch.floataa if fpaa else torch.floataa
								__a										=    torch.from_numpy(load_hf_numpy(self.get_file_format(_snake_case		,     _snake_case				)				)				).to(_snake_case				).to(_snake_case				)
								return image




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case="CompVis/stable-diffusion-v1-4"		,     _snake_case=False				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    '''fp16''' if fpaa else None
								__a										=    torch.floataa if fpaa else torch.floataa

								__a										=    AutoencoderKL.from_pretrained(
								    _snake_case		,     subfolder='''vae'''		,     torch_dtype=_snake_case		,     revision=_snake_case		,     )
								model.to(_snake_case				).eval()

								return model




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=0				)    ->      Tuple:


								'''simple docstring'''




								if torch_device == "mps":
												return torch.manual_seed(_snake_case				)
								return torch.Generator(device=_snake_case				).manual_seed(_snake_case				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case		,     generator=_snake_case		,     sample_posterior=_snake_case				).sample

								assert sample.shape == image.shape

								__a										=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
								__a										=    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=3E-3				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
				        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
				        # fmt: on
				    ]				)
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     fpaa=_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case		,     generator=_snake_case		,     sample_posterior=_snake_case				).sample

								assert sample.shape == image.shape

								__a										=    sample[-1, -2:, :2, -2:].flatten().float().cpu()
								__a										=    torch.tensor(_snake_case				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-2				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)

								with torch.no_grad():
												__a										=    model(_snake_case				).sample

								assert sample.shape == image.shape

								__a										=    sample[-1, -2:, -2:, :2].flatten().float().cpu()
								__a										=    torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=3E-3				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
				        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
				        # fmt: on
				    ]				)
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								__a										=    sample[-1, -2:, :2, -2:].flatten().cpu()
								__a										=    torch.tensor(_snake_case				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-3				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
				        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
				        # fmt: on
				    ]				)
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=_snake_case				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								__a										=    sample[-1, -2:, :2, -2:].flatten().float().cpu()
								__a										=    torch.tensor(_snake_case				)

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=5E-3				)




				@parameterized.expand([(13,), (16,), (27,)]				)
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()		,     reason='''xformers is not required when using PyTorch 2.0.'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model(fpaa=_snake_case				)
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)		,     fpaa=_snake_case				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-1				)




				@parameterized.expand([(13,), (16,), (37,)]				)
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()		,     reason='''xformers is not required when using PyTorch 2.0.'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case		,     shape=(3, 4, 64, 64)				)

								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												__a										=    model.decode(_snake_case				).sample

								assert list(sample.shape				) == [3, 3, 512, 512]

								assert torch_all_close(_snake_case		,     _snake_case		,     atol=1E-2				)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
				        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
				        # fmt: on
				    ]				)
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.get_sd_vae_model()
								__a										=    self.get_sd_image(_snake_case				)
								__a										=    self.get_generator(_snake_case				)

								with torch.no_grad():
												__a										=    model.encode(_snake_case				).latent_dist
												__a										=    dist.sample(generator=_snake_case				)

								assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

								__a										=    sample[0, -1, -3:, -3:].flatten().cpu()
								__a										=    torch.tensor(_snake_case				)

								__a										=    3E-3 if torch_device != '''mps''' else 1E-2
								assert torch_all_close(_snake_case		,     _snake_case		,     atol=_snake_case				)