





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     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Optional[Any]			      =	fname.split(os.path.sep     )[-1]
   return re.search(r"""^(.*)_\d+\.jpg$"""  ,_lowerCAmelCase     ).groups()[0]

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__      ,       a__=None      ,       a__=None       ):
         A_     :				str			      =	file_names
         A_     :				List[str]			      =	image_transform
         A_     :				List[Any]			      =	label_to_id
      def __len__(		self       ):
         return len(self.file_names       )






      def __getitem__(		self      ,       a__       ):
         A_     :				Tuple			      =	self.file_names[idx]
         A_     :				Tuple			      =	PIL.Image.open(a__       )
         A_     :				str			      =	raw_image.convert("""RGB"""       )
         if self.image_transform is not None:
            A_     :				List[Any]			      =	self.image_transform(a__       )
         A_     :				Tuple			      =	extract_label(a__       )
         if self.label_to_id is not None:
            A_     :				List[Any]			      =	self.label_to_id[label]
         return {"image": image, "label": label}






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   if args.with_tracking:
      A_     :				Union[str, Any]			      =	Accelerator(
          cpu=args.cpu  ,mixed_precision=args.mixed_precision  ,log_with="""all"""  ,project_dir=args.project_dir     )
   else:
      A_     :				Optional[int]			      =	Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision     )

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_     :				Optional[Any]			      =	config["""lr"""]
   A_     :				List[str]			      =	int(config["""num_epochs"""]     )
   A_     :				List[str]			      =	int(config["""seed"""]     )
   A_     :				int			      =	int(config["""batch_size"""]     )
   A_     :				List[Any]			      =	config["""image_size"""]
   if not isinstance(_lowerCAmelCase  ,(list, tuple)     ):
      A_     :				Optional[int]			      =	(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":
         A_     :				Any			      =	args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A_     :				str			      =	int(args.checkpointing_steps     )
      else:
         raise ValueError(
             f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
   else:
      A_     :				Any			      =	None

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

   # Grab all the image filenames
   A_     :				List[str]			      =	[os.path.join(args.data_dir  ,_lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(""".jpg"""     )]

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

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

   # Split our filenames between train and validation
   A_     :				Dict			      =	np.random.permutation(len(_lowerCAmelCase     )     )
   A_     :				List[str]			      =	int(0.8 * len(_lowerCAmelCase     )     )
   A_     :				Any			      =	random_perm[:cut]
   A_     :				int			      =	random_perm[cut:]

   # For training we use a simple RandomResizedCrop
   A_     :				Optional[int]			      =	Compose([RandomResizedCrop(_lowerCAmelCase  ,scale=(0.5, 1.0)     ), ToTensor()]     )
   A_     :				Any			      =	PetsDataset(
       [file_names[i] for i in train_split]  ,image_transform=_lowerCAmelCase  ,label_to_id=_lowerCAmelCase     )

   # For evaluation, we use a deterministic Resize
   A_     :				Tuple			      =	Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
   A_     :				Optional[int]			      =	PetsDataset([file_names[i] for i in eval_split]  ,image_transform=_lowerCAmelCase  ,label_to_id=_lowerCAmelCase     )

   # Instantiate dataloaders.
   A_     :				List[Any]			      =	DataLoader(_lowerCAmelCase  ,shuffle=_lowerCAmelCase  ,batch_size=_lowerCAmelCase  ,num_workers=4     )
   A_     :				Dict			      =	DataLoader(_lowerCAmelCase  ,shuffle=_lowerCAmelCase  ,batch_size=_lowerCAmelCase  ,num_workers=4     )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_     :				List[str]			      =	create_model("""resnet50d"""  ,pretrained=_lowerCAmelCase  ,num_classes=len(_lowerCAmelCase     )     )

   # 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).
   A_     :				Union[str, Any]			      =	model.to(accelerator.device     )

   # Freezing the base model
   for param in model.parameters():
      A_     :				Union[str, Any]			      =	False
   for param in model.get_classifier().parameters():
      A_     :				Any			      =	True

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

   # Instantiate optimizer
   A_     :				List[str]			      =	torch.optim.Adam(params=model.parameters()  ,lr=lr / 2_5     )

   # Instantiate learning rate scheduler
   A_     :				Optional[Any]			      =	OneCycleLR(optimizer=_lowerCAmelCase  ,max_lr=_lowerCAmelCase  ,epochs=_lowerCAmelCase  ,steps_per_epoch=len(_lowerCAmelCase     )     )

   # 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.
   A_      ,				A_      ,				A_      ,				A_      ,				A_     :				Optional[Any]			      =	accelerator.prepare(
       _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
   # We need to keep track of how many total steps we have iterated over
   A_     :				str			      =	0
   # We also need to keep track of the starting epoch so files are named properly
   A_     :				Dict			      =	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     )
         A_     :				Any			      =	os.path.basename(args.resume_from_checkpoint     )
      else:
         # Get the most recent checkpoint
         A_     :				Tuple			      =	[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
         dirs.sort(key=os.path.getctime     )
         A_     :				Any			      =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A_     :				str			      =	os.path.splitext(_lowerCAmelCase     )[0]

      if "epoch" in training_difference:
         A_     :				Tuple			      =	int(training_difference.replace("""epoch_"""  ,""""""     )     ) + 1
         A_     :				Optional[Any]			      =	None
      else:
         A_     :				Any			      =	int(training_difference.replace("""step_"""  ,""""""     )     )
         A_     :				Union[str, Any]			      =	resume_step // len(_lowerCAmelCase     )
         resume_step -= starting_epoch * len(_lowerCAmelCase     )

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

      A_     :				Tuple			      =	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(_lowerCAmelCase     ),
                 """epoch""": epoch,
             }  ,step=_lowerCAmelCase  ,)
      if checkpointing_steps == "epoch":
         A_     :				Optional[int]			      =	f"""epoch_{epoch}"""
         if args.output_dir is not None:
            A_     :				List[Any]			      =	os.path.join(args.output_dir  ,_lowerCAmelCase     )
         accelerator.save_state(_lowerCAmelCase     )

   if args.with_tracking:
      accelerator.end_training()



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	argparse.ArgumentParser(description="""Simple example of training script."""     )
   parser.add_argument("""--data_dir"""  ,required=_lowerCAmelCase  ,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=_lowerCAmelCase  ,default=_lowerCAmelCase  ,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=_lowerCAmelCase  ,default=_lowerCAmelCase  ,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=_lowerCAmelCase  ,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=_lowerCAmelCase  ,default=_lowerCAmelCase  ,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=_lowerCAmelCase  ,default="""logs"""  ,help="""Location on where to store experiment tracking logs` and relevent project information"""  ,)
   A_     :				Any			      =	parser.parse_args()
   A_     :				Dict			      =	{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
   training_function(_lowerCAmelCase  ,_lowerCAmelCase     )


if __name__ == "__main__":
      main()













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    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      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       ):
         torch.manual_seed(0       )
         A_     :				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      ,       )
         A_     :				int			      =	PNDMScheduler(skip_prk_steps=a__       )
         torch.manual_seed(0       )
         A_     :				int			      =	AutoencoderKL(
             block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,       latent_channels=4      ,       )
         torch.manual_seed(0       )
         A_     :				List[Any]			      =	CLIPTextConfig(
             bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       )
         A_     :				List[Any]			      =	CLIPTextModel(a__       )
         A_     :				int			      =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

         A_     :				Tuple			      =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=0       ):
         A_     :				Tuple			      =	floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(a__       )       ).to(a__       )
         A_     :				List[Any]			      =	image.cpu().permute(0      ,       2      ,       3      ,       1       )[0]
         A_     :				Union[str, Any]			      =	Image.fromarray(np.uinta(a__       )       ).convert("""RGB"""       )
         if str(a__       ).startswith("""mps"""       ):
            A_     :				Dict			      =	torch.manual_seed(a__       )
         else:
            A_     :				List[Any]			      =	torch.Generator(device=a__       ).manual_seed(a__       )
         A_     :				Optional[int]			      =	{
             """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       ):
         A_     :				Union[str, Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				List[Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				List[Any]			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				List[str]			      =	self.get_dummy_inputs(a__       )
         A_     :				str			      =	sd_pipe(**a__       ).images
         A_     :				Dict			      =	image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         A_     :				List[str]			      =	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       ):
         A_     :				List[Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				Tuple			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				Tuple			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

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

         assert image.shape == (1, 32, 32, 3)
         A_     :				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       ):
         A_     :				int			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Dict			      =	self.get_dummy_components()
         A_     :				Dict			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				str			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Dict			      =	self.get_dummy_inputs(a__       )
         A_     :				int			      =	[inputs["""prompt"""]] * 2

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

         A_     :				Optional[int]			      =	sd_pipe(**a__       ).images
         A_     :				Union[str, Any]			      =	image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         A_     :				Union[str, 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       ):
         A_     :				str			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_     :				Tuple			      =	self.get_dummy_components()
         A_     :				Dict			      =	EulerAncestralDiscreteScheduler(
             beta_start=0.0_0085      ,       beta_end=0.012      ,       beta_schedule="""scaled_linear"""       )
         A_     :				Union[str, Any]			      =	StableDiffusionInstructPixaPixPipeline(**a__       )
         A_     :				Union[str, Any]			      =	sd_pipe.to(a__       )
         sd_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	sd_pipe(**a__       ).images
         A_     :				Dict			      =	image[0, -3:, -3:, -1]

         A_     :				Any			      =	[round(a__      ,       4       ) for x in image_slice.flatten().tolist()]
         print(""",""".join([str(a__       ) for x in slice]       )       )

         assert image.shape == (1, 32, 32, 3)
         A_     :				Tuple			      =	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       ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






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

         A_     :				Optional[Any]			      =	pipe(**self.get_dummy_inputs_by_type(a__      ,       input_image_type="""pt"""       )       )[0]

         A_     :				Any			      =	components["""vae"""]
         A_     :				Any			      =	self.get_dummy_inputs_by_type(a__      ,       input_image_type="""pt"""       )

         for image_param in self.image_latents_params:
            if image_param in inputs.keys():
               A_     :				int			      =	vae.encode(inputs[image_param]       ).latent_dist.mode()

         A_     :				List[Any]			      =	pipe(**a__       )[0]

         A_     :				str			      =	np.abs(out - out_latents_inputs       ).max()
         self.assertLess(a__      ,       1E-4      ,       """passing latents as image input generate different result from passing image"""       )






@slow
@require_torch_gpu
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      def 		_lowerCamelCase      (		self      ,       a__=0       ):
         A_     :				str			      =	torch.manual_seed(a__       )
         A_     :				Dict			      =	load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""       )
         A_     :				List[Any]			      =	{
             """prompt""": """turn him into a cyborg""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """image_guidance_scale""": 1.0,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 512, 512, 3)
         A_     :				Optional[Any]			      =	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       ):
         A_     :				int			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         A_     :				int			      =	LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

         A_     :				int			      =	self.get_inputs()
         A_     :				List[Any]			      =	pipe(**a__       ).images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         A_     :				Any			      =	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__       )
         A_     :				Optional[int]			      =	DDIMScheduler.from_config(pipe.scheduler.config       )
         pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 512, 512, 3)
         A_     :				Optional[Any]			      =	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	0

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

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

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

         A_     :				Tuple			      =	False

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

         A_     :				Dict			      =	self.get_inputs()
         pipe(**a__      ,       callback=a__      ,       callback_steps=1       )
         assert callback_fn.has_been_called
         assert number_of_steps == 3
      def 		_lowerCamelCase      (		self       ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         A_     :				Optional[int]			      =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             """timbrooks/instruct-pix2pix"""      ,       safety_checker=a__      ,       torch_dtype=torch.floataa       )
         A_     :				Optional[Any]			      =	pipe.to(a__       )
         pipe.set_progress_bar_config(disable=a__       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         A_     :				List[Any]			      =	self.get_inputs()
         A_     :				int			      =	pipe(**a__       )

         A_     :				Dict			      =	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       ):
         A_     :				List[str]			      =	self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         A_     :				Dict			      =	inputs["""image"""].resize((504, 504)       )

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

         A_     :				Optional[int]			      =	pipe(**a__       )
         A_     :				Optional[Any]			      =	output.images[0]

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

         assert image.shape == (504, 504, 3)
         A_     :				Optional[Any]			      =	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






