



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def  snake_case_					(     _SCREAMING_SNAKE_CASE ):
	__lowercase									=      fname.split(os.path.sep )[-1]
	return re.search(R"^(.*)_\d+\.jpg$"	,						__a ).groups()[0]

class 	_A (			A__						):


						'''simple docstring'''





						def __init__(						self						:				Optional[Any]				,   lowerCamelCase						:				str				,   lowerCamelCase						:				int=None				,   lowerCamelCase						:				Tuple=None						):






							'''simple docstring'''



							__lowercase									=      file_names
							__lowercase									=      image_transform
							__lowercase									=      label_to_id



						def __len__(						self						:				List[Any]						):






							'''simple docstring'''



							return len(self.file_names						)



						def __getitem__(						self						:				Any				,   lowerCamelCase						:				Tuple						):






							'''simple docstring'''



							__lowercase									=      self.file_names[idx]
							__lowercase									=      PIL.Image.open(UpperCamelCase_						)
							__lowercase									=      raw_image.convert("RGB"						)
							if self.image_transform is not None:
								__lowercase									=      self.image_transform(UpperCamelCase_						)
							__lowercase									=      extract_label(UpperCamelCase_						)
							if self.label_to_id is not None:
								__lowercase									=      self.label_to_id[label]
							return {"image": image, "label": label}



def  snake_case_					(     _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE ):






	if args.with_tracking:
		__lowercase									=      Accelerator(
		    cpu=args.cpu	,						mixed_precision=args.mixed_precision	,						log_with="all"	,						project_dir=args.project_dir )
	else:
		__lowercase									=      Accelerator(cpu=args.cpu	,						mixed_precision=args.mixed_precision )

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowercase									=      config['''lr''']
	__lowercase									=      int(config["num_epochs"] )
	__lowercase									=      int(config["seed"] )
	__lowercase									=      int(config["batch_size"] )
	__lowercase									=      config['''image_size''']
	if not isinstance(__a	,						(list, tuple) ):
		__lowercase									=      (image_size, image_size)

	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps	,						"isdigit" ):
		if args.checkpointing_steps == "epoch":
			__lowercase									=      args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			__lowercase									=      int(args.checkpointing_steps )
		else:
			raise ValueError(
			    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
	else:
		__lowercase									=      None

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		__lowercase									=      os.path.split(__a )[-1].split("." )[0]
		accelerator.init_trackers(__a	,						__a )

	# Grab all the image filenames
	__lowercase									=      [os.path.join(args.data_dir	,						__a ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

	# Build the label correspondences
	__lowercase									=      [extract_label(__a ) for fname in file_names]
	__lowercase									=      list(set(__a ) )
	id_to_label.sort()
	__lowercase									=      {lbl: i for i, lbl in enumerate(__a )}

	# Set the seed before splitting the data.
	np.random.seed(__a )
	torch.manual_seed(__a )
	torch.cuda.manual_seed_all(__a )

	# Split our filenames between train and validation
	__lowercase									=      np.random.permutation(len(__a ) )
	__lowercase									=      int(0.8 * len(__a ) )
	__lowercase									=      random_perm[:cut]
	__lowercase									=      random_perm[cut:]

	# For training we use a simple RandomResizedCrop
	__lowercase									=      Compose([RandomResizedCrop(__a	,						scale=(0.5, 1.0) ), ToTensor()] )
	__lowercase									=      PetsDataset(
	    [file_names[i] for i in train_split]	,						image_transform=__a	,						label_to_id=__a )

	# For evaluation, we use a deterministic Resize
	__lowercase									=      Compose([Resize(__a ), ToTensor()] )
	__lowercase									=      PetsDataset([file_names[i] for i in eval_split]	,						image_transform=__a	,						label_to_id=__a )

	# Instantiate dataloaders.
	__lowercase									=      DataLoader(__a	,						shuffle=__a	,						batch_size=__a	,						num_workers=4 )
	__lowercase									=      DataLoader(__a	,						shuffle=__a	,						batch_size=__a	,						num_workers=4 )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowercase									=      create_model("resnet50d"	,						pretrained=__a	,						num_classes=len(__a ) )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	__lowercase									=      model.to(accelerator.device )

	# Freezing the base model
	for param in model.parameters():
		__lowercase									=      False
	for param in model.get_classifier().parameters():
		__lowercase									=      True

	# We normalize the batches of images to be a bit faster.
	__lowercase									=      torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
	__lowercase									=      torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

	# Instantiate optimizer
	__lowercase									=      torch.optim.Adam(params=model.parameters()	,						lr=lr / 2_5 )

	# Instantiate learning rate scheduler
	__lowercase									=      OneCycleLR(optimizer=__a	,						max_lr=__a	,						epochs=__a	,						steps_per_epoch=len(__a ) )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	__lowercase									=      accelerator.prepare(
	    __a	,						__a	,						__a	,						__a	,						__a )
	# We need to keep track of how many total steps we have iterated over
	__lowercase									=      0
	# We also need to keep track of the starting epoch so files are named properly
	__lowercase									=      0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""" )
			accelerator.load_state(args.resume_from_checkpoint )
			__lowercase									=      os.path.basename(args.resume_from_checkpoint )
		else:
			# Get the most recent checkpoint
			__lowercase									=      [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
			dirs.sort(key=os.path.getctime )
			__lowercase									=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		__lowercase									=      os.path.splitext(__a )[0]

		if "epoch" in training_difference:
			__lowercase									=      int(training_difference.replace("epoch_"	,						"" ) ) + 1
			__lowercase									=      None
		else:
			__lowercase									=      int(training_difference.replace("step_"	,						"" ) )
			__lowercase									=      resume_step // len(__a )
			resume_step -= starting_epoch * len(__a )

    # Now we train the model
	for epoch in range(__a	,						__a ):
		model.train()
		if args.with_tracking:
			__lowercase									=      0
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			__lowercase									=      accelerator.skip_first_batches(__a	,						__a )
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			__lowercase									=      train_dataloader
		for batch in active_dataloader:
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			__lowercase									=      {k: v.to(accelerator.device ) for k, v in batch.items()}
			__lowercase									=      (batch['''image'''] - mean) / std
			__lowercase									=      model(__a )
			__lowercase									=      torch.nn.functional.cross_entropy(__a	,						batch["label"] )
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(__a )
			optimizer.step()
			lr_scheduler.step()
			optimizer.zero_grad()
			overall_step += 1
			if isinstance(__a	,						__a ):
				__lowercase									=      F"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						__lowercase									=      os.path.join(args.output_dir	,						__a )
					accelerator.save_state(__a )
		model.eval()
		__lowercase									=      0
		__lowercase									=      0
		for step, batch in enumerate(__a ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			__lowercase									=      {k: v.to(accelerator.device ) for k, v in batch.items()}
			__lowercase									=      (batch['''image'''] - mean) / std
			with torch.no_grad():
				__lowercase									=      model(__a )
			__lowercase									=      outputs.argmax(dim=-1 )
			__lowercase									=      accelerator.gather_for_metrics((predictions, batch["label"]) )
			__lowercase									=      predictions == references
			num_elems += accurate_preds.shape[0]
			accurate += accurate_preds.long().sum()

		__lowercase									=      accurate.item() / num_elems
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}: {1_0_0 * eval_metric:.2f}""" )
		if args.with_tracking:
			accelerator.log(
			    {
			        "accuracy": 1_0_0 * eval_metric,
			        "train_loss": total_loss.item() / len(__a ),
			        "epoch": epoch,
			    }	,						step=__a	,						)
		if checkpointing_steps == "epoch":
			__lowercase									=      F"""epoch_{epoch}"""
			if args.output_dir is not None:
				__lowercase									=      os.path.join(args.output_dir	,						__a )
			accelerator.save_state(__a )

	if args.with_tracking:
		accelerator.end_training()



def  snake_case_					(     ):
	__lowercase									=      argparse.ArgumentParser(description="Simple example of training script." )
	parser.add_argument("--data_dir"	,						required=__a	,						help="The data folder on disk." )
	parser.add_argument("--fp16"	,						action="store_true"	,						help="If passed, will use FP16 training." )
	parser.add_argument(
	    "--mixed_precision"	,						type=__a	,						default=__a	,						choices=["no", "fp16", "bf16", "fp8"]	,						help="Whether to use mixed precision. Choose"
	    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
	    "and an Nvidia Ampere GPU."	,						)
	parser.add_argument("--cpu"	,						action="store_true"	,						help="If passed, will train on the CPU." )
	parser.add_argument(
	    "--checkpointing_steps"	,						type=__a	,						default=__a	,						help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."	,						)
	parser.add_argument(
	    "--output_dir"	,						type=__a	,						default="."	,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,						)
	parser.add_argument(
	    "--resume_from_checkpoint"	,						type=__a	,						default=__a	,						help="If the training should continue from a checkpoint folder."	,						)
	parser.add_argument(
	    "--with_tracking"	,						action="store_true"	,						help="Whether to load in all available experiment trackers from the environment and use them for logging."	,						)
	parser.add_argument(
	    "--project_dir"	,						type=__a	,						default="logs"	,						help="Location on where to store experiment tracking logs` and relevent project information"	,						)
	__lowercase									=      parser.parse_args()
	__lowercase									=      {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
	training_function(__a	,						__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