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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
__lowercase											=      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-classification/requirements.txt''')

__lowercase											=      list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowercase											=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    with open(SCREAMING_SNAKE_CASE						,    '''rb'''			) as f:
        __UpperCamelCase  :Dict    						=	Image.open(SCREAMING_SNAKE_CASE			)
        return im.convert('''RGB'''			)


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
        }		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}				)
    a__			:		Optional[str]      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """A folder containing the training data."""}				)
    a__			:		Optional[str]      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """A folder containing the validation data."""}				)
    a__			:		Optional[float]      			=   field(
        default=0.1_5		,			metadata={"""help""": """Percent to split off of train for validation."""}				)
    a__			:		Optional[int]      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }		,			)
    a__			:		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     UpperCamelCase__     (			self) ->			Any:
        if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
            raise ValueError(
                '''You must specify either a dataset name from the hub or a train and/or validation directory.''')


@dataclass
class 			lowerCamelCase_  :






    '''simple docstring'''






    a__			:		str      			=   field(
        default="""google/vit-base-patch16-224-in21k"""		,			metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCAmelCase_				)}		,			)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
    a__			:		Optional[str]      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}				)
    a__			:		str      			=   field(
        default="""main"""		,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,			)
    a__			:		str      			=   field(default=UpperCAmelCase_		,			metadata={"""help""": """Name or path of preprocessor config."""}				)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }		,			)
    a__			:		bool      			=   field(
        default=UpperCAmelCase_		,			metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}		,			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	torch.stack([example['''pixel_values'''] for example in examples]			)
    __UpperCamelCase  :Dict    						=	torch.tensor([example['''labels'''] for example in examples]			)
    return {"pixel_values": pixel_values, "labels": labels}



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	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.
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
    else:
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Optional[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_image_classification'''						,    SCREAMING_SNAKE_CASE						,    SCREAMING_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()

    __UpperCamelCase  :Optional[int]    						=	training_args.get_process_log_level()
    logger.setLevel(SCREAMING_SNAKE_CASE			)
    transformers.utils.logging.set_verbosity(SCREAMING_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.
    __UpperCamelCase  :int    						=	None
    if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
        __UpperCamelCase  :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.'''			)

    # Set seed before initializing model.
    set_seed(training_args.seed			)

    # Initialize our dataset and prepare it for the 'image-classification' task.
    if data_args.dataset_name is not None:
        __UpperCamelCase  :Optional[Any]    						=	load_dataset(
            data_args.dataset_name						,    data_args.dataset_config_name						,    cache_dir=model_args.cache_dir						,    task='''image-classification'''						,    use_auth_token=True if model_args.use_auth_token else None						,    )
    else:
        __UpperCamelCase  :Any    						=	{}
        if data_args.train_dir is not None:
            __UpperCamelCase  :int    						=	os.path.join(data_args.train_dir						,    '''**'''			)
        if data_args.validation_dir is not None:
            __UpperCamelCase  :Any    						=	os.path.join(data_args.validation_dir						,    '''**'''			)
        __UpperCamelCase  :str    						=	load_dataset(
            '''imagefolder'''						,    data_files=SCREAMING_SNAKE_CASE						,    cache_dir=model_args.cache_dir						,    task='''image-classification'''						,    )

    # If we don't have a validation split, split off a percentage of train as validation.
    __UpperCamelCase  :Optional[int]    						=	None if '''validation''' in dataset.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split						,    SCREAMING_SNAKE_CASE			) and data_args.train_val_split > 0.0:
        __UpperCamelCase  :Optional[int]    						=	dataset['''train'''].train_test_split(data_args.train_val_split			)
        __UpperCamelCase  :int    						=	split['''train''']
        __UpperCamelCase  :List[Any]    						=	split['''test''']

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    __UpperCamelCase  :Tuple    						=	dataset['''train'''].features['''labels'''].names
    __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	{}, {}
    for i, label in enumerate(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[int]    						=	str(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Optional[Any]    						=	label

    # Load the accuracy metric from the datasets package
    __UpperCamelCase  :Dict    						=	evaluate.load('''accuracy'''			)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(SCREAMING_SNAKE_CASE			):
        return metric.compute(predictions=np.argmax(p.predictions						,    axis=1			)						,    references=p.label_ids			)

    __UpperCamelCase  :Tuple    						=	AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path						,    num_labels=len(SCREAMING_SNAKE_CASE			)						,    labelaid=SCREAMING_SNAKE_CASE						,    idalabel=SCREAMING_SNAKE_CASE						,    finetuning_task='''image-classification'''						,    cache_dir=model_args.cache_dir						,    revision=model_args.model_revision						,    use_auth_token=True if model_args.use_auth_token else None						,    )
    __UpperCamelCase  :List[Any]    						=	AutoModelForImageClassification.from_pretrained(
        model_args.model_name_or_path						,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)						,    config=SCREAMING_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						,    ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,    )
    __UpperCamelCase  :str    						=	AutoImageProcessor.from_pretrained(
        model_args.image_processor_name or model_args.model_name_or_path						,    cache_dir=model_args.cache_dir						,    revision=model_args.model_revision						,    use_auth_token=True if model_args.use_auth_token else None						,    )

    # Define torchvision transforms to be applied to each image.
    if "shortest_edge" in image_processor.size:
        __UpperCamelCase  :str    						=	image_processor.size['''shortest_edge''']
    else:
        __UpperCamelCase  :Any    						=	(image_processor.size['''height'''], image_processor.size['''width'''])
    __UpperCamelCase  :Any    						=	Normalize(mean=image_processor.image_mean						,    std=image_processor.image_std			)
    __UpperCamelCase  :Optional[Any]    						=	Compose(
        [
            RandomResizedCrop(SCREAMING_SNAKE_CASE			),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]			)
    __UpperCamelCase  :Optional[int]    						=	Compose(
        [
            Resize(SCREAMING_SNAKE_CASE			),
            CenterCrop(SCREAMING_SNAKE_CASE			),
            ToTensor(),
            normalize,
        ]			)

    def train_transforms(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	[
            _train_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch['''image''']
        ]
        return example_batch

    def val_transforms(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Tuple    						=	[_val_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch['''image''']]
        return example_batch

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

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

    # Initalize our trainer
    __UpperCamelCase  :Optional[Any]    						=	Trainer(
        model=SCREAMING_SNAKE_CASE						,    args=SCREAMING_SNAKE_CASE						,    train_dataset=dataset['''train'''] if training_args.do_train else None						,    eval_dataset=dataset['''validation'''] if training_args.do_eval else None						,    compute_metrics=SCREAMING_SNAKE_CASE						,    tokenizer=SCREAMING_SNAKE_CASE						,    data_collator=SCREAMING_SNAKE_CASE						,    )

    # Training
    if training_args.do_train:
        __UpperCamelCase  :List[str]    						=	None
        if training_args.resume_from_checkpoint is not None:
            __UpperCamelCase  :Dict    						=	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            __UpperCamelCase  :str    						=	last_checkpoint
        __UpperCamelCase  :Dict    						=	trainer.train(resume_from_checkpoint=SCREAMING_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:
        __UpperCamelCase  :Union[str, Any]    						=	trainer.evaluate()
        trainer.log_metrics('''eval'''						,    SCREAMING_SNAKE_CASE			)
        trainer.save_metrics('''eval'''						,    SCREAMING_SNAKE_CASE			)

    # Write model card and (optionally) push to hub
    __UpperCamelCase  :str    						=	{
        '''finetuned_from''': model_args.model_name_or_path,
        '''tasks''': '''image-classification''',
        '''dataset''': data_args.dataset_name,
        '''tags''': ['''image-classification''', '''vision'''],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**SCREAMING_SNAKE_CASE			)
    else:
        trainer.create_model_card(**SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    main()






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   StableUnCLIPImgaImgPipeline
    a__			:		Optional[Any]      			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__			:		str      			=   frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__			:		List[Any]      			=   frozenset([]				)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	32
        __UpperCamelCase  :int    						=	embedder_hidden_size

        # image encoding components

        __UpperCamelCase  :Dict    						=	CLIPImageProcessor(crop_size=32				,				size=32)

        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase				,				projection_dim=__lowercase				,				num_hidden_layers=5				,				num_attention_heads=4				,				image_size=32				,				intermediate_size=37				,				patch_size=1				,				))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase  :Any    						=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0				,				eos_token_id=2				,				hidden_size=__lowercase				,				projection_dim=32				,				intermediate_size=37				,				layer_norm_eps=1E-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				))

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	UNetaDConditionModel(
            sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,				up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,				block_out_channels=(32, 64)				,				attention_head_dim=(2, 4)				,				class_embed_type='''projection'''				,				projection_class_embeddings_input_dim=embedder_projection_dim * 2				,				cross_attention_dim=__lowercase				,				layers_per_block=1				,				upcast_attention=__lowercase				,				use_linear_projection=__lowercase				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Dict    						=	DDIMScheduler(
            beta_schedule='''scaled_linear'''				,				beta_start=0.0_00_85				,				beta_end=0.0_12				,				prediction_type='''v_prediction'''				,				set_alpha_to_one=__lowercase				,				steps_offset=1				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	AutoencoderKL()

        __UpperCamelCase  :Dict    						=	{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0				,				__lowercase=True) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :int    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :int    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Tuple    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase  :Tuple    						=	input_image * 0.5 + 0.5
            __UpperCamelCase  :Any    						=	input_image.clamp(0				,				1)
            __UpperCamelCase  :Any    						=	input_image.cpu().permute(0				,				2				,				3				,				1).float().numpy()
            __UpperCamelCase  :int    						=	DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Dict    						=	self.get_dummy_components()
        __UpperCamelCase  :Dict    						=	StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase  :Any    						=	sd_pipe(**__lowercase).images
        __UpperCamelCase  :int    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

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







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


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






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Optional[int]    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :str    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Tuple    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :int    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :int    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase  :Any    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        __UpperCamelCase  :Any    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Tuple    						=	pipe(
            __lowercase				,				'''anime turtle'''				,				num_inference_steps=2				,				output_type='''np'''				,				)

        __UpperCamelCase  :str    						=	torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9





