


"""simple docstring"""







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  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


  '''simple docstring'''







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


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__=None		,					snake_case__=None						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							file_names
         lowerCAmelCase					:					Union[str, Any]								=							image_transform
         lowerCAmelCase					:					int								=							label_to_id

       def __len__(						self						):






         """simple docstring"""





         return len(self.file_names						)







       def __getitem__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							self.file_names[idx]
         lowerCAmelCase					:					Any								=							PIL.Image.open(snake_case__						)
         lowerCAmelCase					:					List[str]								=							raw_image.convert("RGB"						)
         if self.image_transform is not None:
           lowerCAmelCase					:					int								=							self.image_transform(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							extract_label(snake_case__						)
         if self.label_to_id is not None:
           lowerCAmelCase					:					Optional[Any]								=							self.label_to_id[label]
         return {"image": image, "label": label}

def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







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

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

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

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

  # Build the label correspondences
  lowerCAmelCase					:					Tuple								=							[extract_label(SCREAMING_SNAKE_CASE    ) for fname in file_names]
  lowerCAmelCase					:					Optional[int]								=							list(set(SCREAMING_SNAKE_CASE    )    )
  id_to_label.sort()
  lowerCAmelCase					:					int								=							{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE    )}

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

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

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

  # For evaluation, we use a deterministic Resize
  lowerCAmelCase					:					Union[str, Any]								=							Compose([Resize(SCREAMING_SNAKE_CASE    ), ToTensor()]    )
  lowerCAmelCase					:					Optional[int]								=							PetsDataset([file_names[i] for i in eval_split]    ,				image_transform=SCREAMING_SNAKE_CASE    ,				label_to_id=SCREAMING_SNAKE_CASE    )

  # Instantiate dataloaders.
  lowerCAmelCase					:					List[Any]								=							DataLoader(SCREAMING_SNAKE_CASE    ,				shuffle=SCREAMING_SNAKE_CASE    ,				batch_size=SCREAMING_SNAKE_CASE    ,				num_workers=4    )
  lowerCAmelCase					:					List[Any]								=							DataLoader(SCREAMING_SNAKE_CASE    ,				shuffle=SCREAMING_SNAKE_CASE    ,				batch_size=SCREAMING_SNAKE_CASE    ,				num_workers=4    )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase					:					Optional[Any]								=							create_model("resnet50d"    ,				pretrained=SCREAMING_SNAKE_CASE    ,				num_classes=len(SCREAMING_SNAKE_CASE    )    )

  # 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).
  lowerCAmelCase					:					int								=							model.to(accelerator.device    )

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

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

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

  # Instantiate learning rate scheduler
  lowerCAmelCase					:					Tuple								=							OneCycleLR(optimizer=SCREAMING_SNAKE_CASE    ,				max_lr=SCREAMING_SNAKE_CASE    ,				epochs=SCREAMING_SNAKE_CASE    ,				steps_per_epoch=len(SCREAMING_SNAKE_CASE    )    )

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

    if "epoch" in training_difference:
      lowerCAmelCase					:					Dict								=							int(training_difference.replace("epoch_"    ,				""    )    ) + 1
      lowerCAmelCase					:					str								=							None
    else:
      lowerCAmelCase					:					Tuple								=							int(training_difference.replace("step_"    ,				""    )    )
      lowerCAmelCase					:					int								=							resume_step // len(SCREAMING_SNAKE_CASE    )
      resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE    )

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

    lowerCAmelCase					:					str								=							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(SCREAMING_SNAKE_CASE    ),
              "epoch": epoch,
          }    ,				step=SCREAMING_SNAKE_CASE    ,				)
    if checkpointing_steps == "epoch":
      lowerCAmelCase					:					Any								=							f"""epoch_{epoch}"""
      if args.output_dir is not None:
        lowerCAmelCase					:					Optional[Any]								=							os.path.join(args.output_dir    ,				SCREAMING_SNAKE_CASE    )
      accelerator.save_state(SCREAMING_SNAKE_CASE    )

  if args.with_tracking:
    accelerator.end_training()
def  a__      (    ):


  '''simple docstring'''







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


if __name__ == "__main__":
     main()









"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    Optional[Any]       =KandinskyVaaImgaImgPipeline
       a  :    Optional[int]       =["image_embeds", "negative_image_embeds", "image"]
       a  :    Optional[int]       =[
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       a  :    str       =[
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       a  :    Dict       =False

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 32

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return self.time_input_dim * 4

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return 100

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)

         lowerCAmelCase					:					List[str]								=							{
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         lowerCAmelCase					:					int								=							UNetaDConditionModel(**snake_case__						)
         return model

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }

       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase					:					Optional[int]								=							VQModel(**self.dummy_movq_kwargs						)
         return model

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.dummy_unet
         lowerCAmelCase					:					Optional[int]								=							self.dummy_movq

         lowerCAmelCase					:					List[str]								=							{
             "num_train_timesteps": 1_000,
             "beta_schedule": "linear",
             "beta_start": 0.00085,
             "beta_end": 0.012,
             "clip_sample": False,
             "set_alpha_to_one": False,
             "steps_offset": 0,
             "prediction_type": "epsilon",
             "thresholding": False,
         }

         lowerCAmelCase					:					Tuple								=							DDIMScheduler(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							{
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }

         return components

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							floats_tensor((1, self.text_embedder_hidden_size)		,					rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase					:					Any								=							floats_tensor((1, self.text_embedder_hidden_size)		,					rng=random.Random(seed + 1						)						).to(
             snake_case__						)
         # create init_image
         lowerCAmelCase					:					List[Any]								=							floats_tensor((1, 3, 64, 64)		,					rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase					:					List[str]								=							image.cpu().permute(0		,					2		,					3		,					1						)[0]
         lowerCAmelCase					:					List[str]								=							Image.fromarray(np.uinta(snake_case__						)						).convert("RGB"						).resize((256, 256)						)

         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase					:					Optional[int]								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase					:					Optional[int]								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase					:					List[str]								=							{
             "image": init_image,
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "num_inference_steps": 10,
             "guidance_scale": 7.0,
             "strength": 0.2,
             "output_type": "np",
         }
         return inputs







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							"cpu"

         lowerCAmelCase					:					Dict								=							self.get_dummy_components()

         lowerCAmelCase					:					Union[str, Any]								=							self.pipeline_class(**snake_case__						)
         lowerCAmelCase					:					Optional[int]								=							pipe.to(snake_case__						)

         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase					:					int								=							pipe(**self.get_dummy_inputs(snake_case__						)						)
         lowerCAmelCase					:					Union[str, Any]								=							output.images

         lowerCAmelCase					:					Union[str, Any]								=							pipe(
             **self.get_dummy_inputs(snake_case__						)		,					return_dict=snake_case__		,					)[0]

         lowerCAmelCase					:					Optional[Any]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase					:					Optional[int]								=							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         lowerCAmelCase					:					int								=							np.array(
             [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]						)
         assert (
             np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""


@slow
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__    (		unittest.TestCase    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """simple docstring"""





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







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_img2img_frog.npy"						)

         lowerCAmelCase					:					Tuple								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
         lowerCAmelCase					:					Optional[Any]								=							"A red cartoon frog, 4k"

         lowerCAmelCase					:					int								=							KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"		,					torch_dtype=torch.floataa						)
         pipe_prior.to(snake_case__						)

         lowerCAmelCase					:					List[Any]								=							KandinskyVaaImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-decoder"		,					torch_dtype=torch.floataa						)
         lowerCAmelCase					:					Tuple								=							pipeline.to(snake_case__						)

         pipeline.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase					:					str								=							torch.Generator(device="cpu"						).manual_seed(0						)
         lowerCAmelCase						,  lowerCAmelCase					:					Optional[Any]								=							pipe_prior(
             snake_case__		,					generator=snake_case__		,					num_inference_steps=5		,					negative_prompt=""		,					).to_tuple()

         lowerCAmelCase					:					Tuple								=							pipeline(
             image=snake_case__		,					image_embeds=snake_case__		,					negative_image_embeds=snake_case__		,					generator=snake_case__		,					num_inference_steps=100		,					height=768		,					width=768		,					strength=0.2		,					output_type="np"		,					)

         lowerCAmelCase					:					Optional[Any]								=							output.images[0]

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

         assert_mean_pixel_difference(snake_case__		,					snake_case__						)





