


'''simple docstring'''
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
__snake_case				 =	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''')

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


def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					with open(__a					,   '''rb'''  ) as f:
										UpperCamelCase__  :Union[str, Any]				   =					Image.open(__a  )
										return im.convert('''RGB'''  )
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							field(
		    default=A__      ,						metadata={
		        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
		    }      ,						)
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'A folder containing the training data.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'A folder containing the validation data.'}   )
		_a       					=							field(
		    default=0.1_5      ,						metadata={'help': 'Percent to split off of train for validation.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }      ,						)
		_a       					=							field(
		    default=A__      ,						metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }      ,						)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							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   lowercase      :

		"""simple docstring"""

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



def      a (     __a  )    ->      Optional[int]:



					'''simple docstring'''






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


def      a (     )    ->      Optional[int]:



					'''simple docstring'''






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

					UpperCamelCase__  :Tuple				   =					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.
					UpperCamelCase__  :int				   =					None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										UpperCamelCase__  :str				   =					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__  :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__  :Tuple				   =					{}
										if data_args.train_dir is not None:
															UpperCamelCase__  :Tuple				   =					os.path.join(data_args.train_dir					,   '''**'''  )
										if data_args.validation_dir is not None:
															UpperCamelCase__  :Union[str, Any]				   =					os.path.join(data_args.validation_dir					,   '''**'''  )
										UpperCamelCase__  :Optional[Any]				   =					load_dataset(
										    '''imagefolder'''					,   data_files=__a					,   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__  :List[Any]				   =					None if '''validation''' in dataset.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split					,   __a  ) and data_args.train_val_split > 0.0:
										UpperCamelCase__  :Union[str, Any]				   =					dataset['''train'''].train_test_split(data_args.train_val_split  )
										UpperCamelCase__  :str				   =					split['''train''']
										UpperCamelCase__  :Tuple				   =					split['''test''']

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

					# Load the accuracy metric from the datasets package
					UpperCamelCase__  :Union[str, Any]				   =					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(__a  ):
										return metric.compute(predictions=np.argmax(p.predictions					,   axis=1  )					,   references=p.label_ids  )

					UpperCamelCase__  :str				   =					AutoConfig.from_pretrained(
					    model_args.config_name or model_args.model_name_or_path					,   num_labels=len(__a  )					,   labelaid=__a					,   idalabel=__a					,   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__  :str				   =					AutoModelForImageClassification.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					,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes					,   )
					UpperCamelCase__  :Optional[Any]				   =					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__  :Optional[int]				   =					image_processor.size['''shortest_edge''']
					else:
										UpperCamelCase__  :str				   =					(image_processor.size['''height'''], image_processor.size['''width'''])
					UpperCamelCase__  :int				   =					Normalize(mean=image_processor.image_mean					,   std=image_processor.image_std  )
					UpperCamelCase__  :Optional[Any]				   =					Compose(
					    [
					        RandomResizedCrop(__a  ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        normalize,
					    ]  )
					UpperCamelCase__  :Optional[int]				   =					Compose(
					    [
					        Resize(__a  ),
					        CenterCrop(__a  ),
					        ToTensor(),
					        normalize,
					    ]  )

					def train_transforms(__a  ):
										UpperCamelCase__  :Optional[int]				   =					[
										    _train_transforms(pil_img.convert('''RGB'''  )  ) for pil_img in example_batch['''image''']
										]
										return example_batch

					def val_transforms(__a  ):
										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__  :Union[str, Any]				   =					(
															    dataset['''train'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
															)
										# Set the training transforms
										dataset["train"].set_transform(__a  )

					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__  :List[str]				   =					(
															    dataset['''validation'''].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
															)
										# Set the validation transforms
										dataset["validation"].set_transform(__a  )

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

					# Training
					if training_args.do_train:
										UpperCamelCase__  :Dict				   =					None
										if training_args.resume_from_checkpoint is not None:
															UpperCamelCase__  :List[str]				   =					training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															UpperCamelCase__  :str				   =					last_checkpoint
										UpperCamelCase__  :List[str]				   =					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:
										UpperCamelCase__  :List[str]				   =					trainer.evaluate()
										trainer.log_metrics('''eval'''					,   __a  )
										trainer.save_metrics('''eval'''					,   __a  )

					# Write model card and (optionally) push to hub
					UpperCamelCase__  :Union[str, Any]				   =					{
					    '''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(**__a  )
					else:
										trainer.create_model_card(**__a  )


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   lowercase      (				A__      ,						A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Optional[Any]				   =					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       ,  )
							UpperCamelCase__  :List[str]				   =					PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
							torch.manual_seed(0				)
							UpperCamelCase__  :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				)
							UpperCamelCase__  :int				   =					CLIPTextConfig(
							    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
							UpperCamelCase__  :Tuple				   =					CLIPTextModel(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

							UpperCamelCase__  :Dict				   =					{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
							UpperCamelCase__  :List[Any]				   =					Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				)
							if str(UpperCamelCase_				).startswith('''mps'''				):
												UpperCamelCase__  :Tuple				   =					torch.manual_seed(UpperCamelCase_				)
							else:
												UpperCamelCase__  :List[Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :List[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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :str				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :List[str]				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_components()
							UpperCamelCase__  :Any				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Tuple				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					'''french fries'''
							UpperCamelCase__  :Any				   =					sd_pipe(**UpperCamelCase_       ,  negative_prompt=UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					output.images
							UpperCamelCase__  :Any				   =					image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :List[str]				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Dict				   =					self.get_dummy_components()
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Dict				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					[inputs['''prompt''']] * 2

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

							UpperCamelCase__  :Optional[Any]				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Any				   =					image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCamelCase__  :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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							UpperCamelCase__  :str				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Tuple				   =					image[0, -3:, -3:, -1]

							UpperCamelCase__  :Dict				   =					[round(UpperCamelCase_       ,  4				) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_				) for x in slice]				)				)

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, 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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					VaeImageProcessor(do_resize=UpperCamelCase_       ,  do_normalize=UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)				)[0]

							UpperCamelCase__  :List[Any]				   =					components['''vae''']
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)

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

							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				)[0]

							UpperCamelCase__  :Optional[int]				   =					np.abs(out - out_latents_inputs				).max()
							self.assertLess(UpperCamelCase_       ,  1e-4       ,  '''passing latents as image input generate different result from passing image'''				)



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

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					torch.manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
							UpperCamelCase__  :Dict				   =					{
							    '''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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[Any]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :str				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :int				   =					self.get_inputs()
							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Optional[int]				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :str				   =					DDIMScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Union[str, Any]				   =					self.get_inputs()
							UpperCamelCase__  :Dict				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[int]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Tuple				   =					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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					0

							def callback_fn(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				) -> None:
												UpperCamelCase__  :Optional[Any]				   =					True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Optional[Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :List[str]				   =					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:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Union[str, Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :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

							UpperCamelCase__  :Union[str, Any]				   =					False

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :Dict				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Dict				   =					self.get_inputs()
							pipe(**UpperCamelCase_       ,  callback=UpperCamelCase_       ,  callback_steps=1				)
							assert callback_fn.has_been_called
							assert number_of_steps == 3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :List[str]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing(1				)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__  :Any				   =					self.get_inputs()
							UpperCamelCase__  :Tuple				   =					pipe(**UpperCamelCase_				)

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__  :int				   =					inputs['''image'''].resize((504, 504)				)

							UpperCamelCase__  :Union[str, Any]				   =					'''timbrooks/instruct-pix2pix'''
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  )
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				)
							UpperCamelCase__  :str				   =					output.images[0]

							UpperCamelCase__  :str				   =					image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							UpperCamelCase__  :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