

'''simple docstring'''



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


UpperCAmelCase :       str			   =     logging.getLogger(__name__)

UpperCAmelCase :       Optional[int]			   =     'pytorch_model.bin'







@dataclasses.dataclass
class 						lowerCAmelCase__   :



 """simple docstring"""



 lowerCAmelCase__  					=      dataclasses.field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} ,					)







@dataclasses.dataclass
class 						lowerCAmelCase__   :



 """simple docstring"""



 lowerCAmelCase__  					=      dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}					)
 lowerCAmelCase__  					=      dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "A csv or a json file containing the validation data."}					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "The name of the task to train on."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "The list of labels for the task."}					)







@dataclasses.dataclass
class 						lowerCAmelCase__   :



 """simple docstring"""



 lowerCAmelCase__  					=      dataclasses.field(
     metadata={"help": "The output directory where the model predictions and checkpoints will be written."}					)
 lowerCAmelCase__  					=      dataclasses.field(
     default="accuracy" ,					metadata={"help": "The evaluation metric used for the task."}					)
 lowerCAmelCase__  					=      dataclasses.field(
     default="no" ,					metadata={
         "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
     } ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=10 ,					metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=0.0 ,					metadata={
         "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
     } ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=0.0 ,					metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=100 ,					metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} ,					)
 lowerCAmelCase__  					=      dataclasses.field(
     default=a ,					metadata={"help": "Random seed for initialization."} ,					)







def    a__       (  a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      datasets.concatenate_datasets([infer_input, infer_output]      ,    axis=1						)

     if args.do_filter_by_confidence:
          __SCREAMING_SNAKE_CASE     =      dataset.filter(lambda a__						: example["probability"] > args.confidence_threshold						)

     if args.do_filter_by_val_performance:
          assert eval_result >= 0.0 and eval_result <= 1.0
          __SCREAMING_SNAKE_CASE     =      int(eval_result * len(a__						)						)
          print(a__						)
          __SCREAMING_SNAKE_CASE     =      dataset.sort("""probability"""      ,    reverse=a__						)
          __SCREAMING_SNAKE_CASE     =      dataset.select(range(a__						)						)

     __SCREAMING_SNAKE_CASE     =      dataset.remove_columns(["""label""", """probability"""]						)
     __SCREAMING_SNAKE_CASE     =      dataset.rename_column("""prediction"""      ,    """label"""						)
     __SCREAMING_SNAKE_CASE     =      dataset.map(lambda a__						: {"label": idalabel[example["label"]]}						)
     __SCREAMING_SNAKE_CASE     =      dataset.shuffle(seed=args.seed						)

     __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    F'train_pseudo.{args.data_file_extension}'						)
     if args.data_file_extension == "csv":
          dataset.to_csv(a__      ,    index=a__						)
     else:
          dataset.to_json(a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__      ,    **a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      Accelerator()
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,    datefmt="""%m/%d/%Y %H:%M:%S"""      ,    level=logging.INFO      ,    )
     logger.info(accelerator.state						)

     # Setup logging, we only want one process per machine to log things on the
     # screen. accelerator.is_local_main_process is only True for one process per
     # machine.
     logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR						)

     if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
     else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()

     __SCREAMING_SNAKE_CASE     =      STModelArguments(model_name_or_path=a__						)
     __SCREAMING_SNAKE_CASE     =      STDataArguments(train_file=a__      ,    infer_file=a__						)
     __SCREAMING_SNAKE_CASE     =      STTrainingArguments(output_dir=a__						)
     __SCREAMING_SNAKE_CASE     =      argparse.Namespace()

     for arg_class in (model_args, data_args, training_args):
          for key, value in vars(a__						).items():
               setattr(a__      ,    a__      ,    a__						)

     for key, value in kwargs.items():
          if hasattr(a__      ,    a__						):
               setattr(a__      ,    a__      ,    a__						)

    # Sanity checks
     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      None

     # You need to provide the training data and the data to predict on
     assert args.train_file is not None
     assert args.infer_file is not None
     __SCREAMING_SNAKE_CASE     =      args.train_file
     __SCREAMING_SNAKE_CASE     =      args.infer_file

     if args.evaluation_strategy != IntervalStrategy.NO.value:
          assert args.eval_file is not None
          __SCREAMING_SNAKE_CASE     =      args.eval_file

     for key in data_files:
          __SCREAMING_SNAKE_CASE     =      data_files[key].split("""."""						)[-1]
          assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
          if args.data_file_extension is None:
               __SCREAMING_SNAKE_CASE     =      extension
          else:
               assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'

     assert (
         args.eval_metric in datasets.list_metrics()
     ), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

     # If passed along, set the training seed now.
     if args.seed is not None:
          set_seed(args.seed						)

     logger.info("""Creating the initial data directory for self-training..."""						)
     __SCREAMING_SNAKE_CASE     =      F'{args.output_dir}/self-train_iter-{{}}'.format
     __SCREAMING_SNAKE_CASE     =      data_dir_format(0						)

     if accelerator.is_main_process:
          if args.output_dir is not None:
               os.makedirs(args.output_dir      ,    exist_ok=a__						)
               os.makedirs(a__      ,    exist_ok=a__						)
     accelerator.wait_for_everyone()

     __SCREAMING_SNAKE_CASE     =      None
     __SCREAMING_SNAKE_CASE     =      None
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      False
     # Show the progress bar
     __SCREAMING_SNAKE_CASE     =      tqdm(range(args.max_selftrain_iterations						)      ,    disable=not accelerator.is_local_main_process						)

     # Self-train
     for iteration in range(0      ,    int(args.max_selftrain_iterations						)						):
          __SCREAMING_SNAKE_CASE     =      data_dir_format(a__						)
          assert os.path.exists(a__						)

          # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
          # iteration > 0
          __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """stage-1"""						)
          __SCREAMING_SNAKE_CASE     =      {
              """accelerator""": accelerator,
              """model_name_or_path""": args.model_name_or_path,
              """cache_dir""": args.cache_dir,
              """do_train""": True,
              """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
              """do_eval""": True if args.eval_file is not None else False,
              """eval_file""": data_files["""eval"""],
              """do_predict""": True,
              """infer_file""": data_files["""infer"""],
              """task_name""": args.task_name,
              """label_list""": args.label_list,
              """output_dir""": current_output_dir,
              """eval_metric""": args.eval_metric,
              """evaluation_strategy""": args.evaluation_strategy,
              """early_stopping_patience""": args.early_stopping_patience,
              """early_stopping_threshold""": args.early_stopping_threshold,
              """seed""": args.seed,
          }
          # Add additional training arguments
          for key, value in kwargs.items():
               if key not in arguments_dict and not hasattr(a__      ,    a__						):
                    arguments_dict.update({key: value}						)

          __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """best-checkpoint"""      ,    a__						)
          if os.path.exists(a__						):
               logger.info(
                   """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""      ,    a__      ,    a__      ,    )
          else:
               logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""      ,    a__						)
               finetune(**a__						)
               accelerator.wait_for_everyone()
               assert os.path.exists(a__						)
               logger.info("""Self-training job completed: iteration: %d, stage: 1."""      ,    a__						)

          if iteration > 0 and args.finetune_on_labeled_data:
               # Stage 2 (optional): fine-tuning on the original labeled data
               __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """best-checkpoint"""						)
               __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """stage-2"""						)
               # Update arguments_dict
               __SCREAMING_SNAKE_CASE     =      model_path
               __SCREAMING_SNAKE_CASE     =      data_files["""train"""]
               __SCREAMING_SNAKE_CASE     =      current_output_dir

               __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """best-checkpoint"""      ,    a__						)
               if os.path.exists(a__						):
                    logger.info(
                        """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""      ,    a__      ,    a__      ,    )
               else:
                    logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""      ,    a__						)
                    finetune(**a__						)
                    accelerator.wait_for_everyone()
                    assert os.path.exists(a__						)
                    logger.info("""Self-training job completed: iteration: %d, stage: 2."""      ,    a__						)

          __SCREAMING_SNAKE_CASE     =      iteration
          __SCREAMING_SNAKE_CASE     =      data_dir_format(iteration + 1						)

          __SCREAMING_SNAKE_CASE     =      AutoConfig.from_pretrained(os.path.join(a__      ,    """best-checkpoint"""						)						)
          __SCREAMING_SNAKE_CASE     =      config.idalabel
          __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """eval_results_best-checkpoint.json"""						)
          __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """test_results_best-checkpoint.json"""						)
          assert os.path.exists(a__						)

          with open(a__      ,    """r"""						) as f:
               __SCREAMING_SNAKE_CASE     =      float(json.load(a__						)[args.eval_metric]						)
          __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    """infer_output_best-checkpoint.csv"""						)
          assert os.path.exists(a__						)
          # Loading the dataset from local csv or json files.
          __SCREAMING_SNAKE_CASE     =      load_dataset(args.data_file_extension      ,    data_files={"""data""": data_files["""infer"""]}						)["""data"""]
          __SCREAMING_SNAKE_CASE     =      load_dataset("""csv"""      ,    data_files={"""data""": infer_output_file}						)["""data"""]

          if accelerator.is_main_process:
               os.makedirs(a__      ,    exist_ok=a__						)
               shutil.copy(a__      ,    os.path.join(a__      ,    F'eval_results_iter-{iteration}.json'						)						)
               if os.path.exists(a__						):
                    shutil.copy(a__      ,    os.path.join(a__      ,    F'test_results_iter-{iteration}.json'						)						)
               create_pseudo_labeled_data(a__      ,    a__      ,    a__      ,    a__      ,    a__      ,    a__						)
          accelerator.wait_for_everyone()

          __SCREAMING_SNAKE_CASE     =      os.path.join(a__      ,    F'train_pseudo.{args.data_file_extension}'						)

          if args.evaluation_strategy != IntervalStrategy.NO.value:
               __SCREAMING_SNAKE_CASE     =      eval_result

               if best_iteration is None:
                    __SCREAMING_SNAKE_CASE     =      new_iteration
                    __SCREAMING_SNAKE_CASE     =      new_eval_result
               else:
                    if new_eval_result - best_eval_result > args.early_stopping_threshold:
                         __SCREAMING_SNAKE_CASE     =      new_iteration
                         __SCREAMING_SNAKE_CASE     =      new_eval_result
                         __SCREAMING_SNAKE_CASE     =      0
                    else:
                         if new_eval_result == best_eval_result:
                              __SCREAMING_SNAKE_CASE     =      new_iteration
                              __SCREAMING_SNAKE_CASE     =      new_eval_result
                         early_stopping_patience_counter += 1

                    if early_stopping_patience_counter >= args.early_stopping_patience:
                         __SCREAMING_SNAKE_CASE     =      True

          progress_bar.update(1						)

          if should_training_stop:
               break

     if best_iteration is not None:
          # Save the best iteration
          logger.info("""Best iteration: %d"""      ,    a__						)
          logger.info("""Best evaluation result: %s = %f"""      ,    args.eval_metric      ,    a__						)
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(a__      ,    F'eval_results_iter-{iteration}.json'						)      ,    os.path.join(a__      ,    """eval_results_best-iteration.json"""						)      ,    )
     else:
          # Assume that the last iteration is the best
          logger.info("""Best iteration: %d"""      ,    args.max_selftrain_iterations - 1						)
          logger.info("""Best evaluation result: %s = %f"""      ,    args.eval_metric      ,    a__						)
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(a__      ,    F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'						)      ,    os.path.join(a__      ,    """eval_results_best-iteration.json"""						)      ,    )









'''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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      StableDiffusionInpaintPipeline
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__  					=      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__  					=      frozenset([]					)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDConditionModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=9					,							out_channels=4					,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,							cross_attention_dim=32					,							attention_head_dim=(2, 4)					,							use_linear_projection=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE			)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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					,							sample_size=128					,							)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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=1_000					,							hidden_act="""gelu"""					,							projection_dim=512					,							)
      __SCREAMING_SNAKE_CASE     =      CLIPTextModel(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      __SCREAMING_SNAKE_CASE     =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]=0			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)					,							rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			)[0]
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((64, 64)			)
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
      if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
           __SCREAMING_SNAKE_CASE     =      torch.manual_seed(__SCREAMING_SNAKE_CASE			)
      else:
           __SCREAMING_SNAKE_CASE     =      torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": init_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
      }
      return inputs
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE     =      self.get_dummy_components()
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe.to(__SCREAMING_SNAKE_CASE			)
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe(**__SCREAMING_SNAKE_CASE			).images
      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




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







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



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> str:


      """simple docstring"""




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE			)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 9E-3
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							safety_checker=__SCREAMING_SNAKE_CASE					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 5E-1


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,							subfolder="""scheduler"""			)
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing(1			)
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=2					,							output_type="""np"""					,							)

      __SCREAMING_SNAKE_CASE     =      torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






