code: 
  
  
  
  
  """simple docstring"""
  
  import getopt
  import json
  import os
  
  # import numpy as np
  import sys
  from collections import OrderedDict
  
  import datasets
  import numpy as np
  import torch
  from modeling_frcnn import GeneralizedRCNN
  from processing_image import Preprocess
  
  from utils import Config
  __A 		=			False
  __A 		=			Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  __A 		=			datasets.Features(
      OrderedDict(
          {
              "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
              "img_id": datasets.Value("int32"),
              "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  class 			lowerCamelCase__    :
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE=sys.argv[1:]							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				None
  					snake_case		:						List[Any]								=				None
  					snake_case		:						Any								=				None
  					snake_case		:						int								=				1
  					snake_case		:						Optional[Any]								=				getopt.getopt(lowerCamelCase__				, "i:o:b:s"				, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
  					for opt, arg in opts:
  							if opt in ("-i", "--inputdir"):
  									snake_case		:						Union[str, Any]								=				arg
  							elif opt in ("-o", "--outfile"):
  									snake_case		:						Tuple								=				arg
  							elif opt in ("-b", "--batch_size"):
  									snake_case		:						Any								=				int(lowerCamelCase__							)
  							elif opt in ("-s", "--subset_list"):
  									snake_case		:						List[str]								=				arg
  
  					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
  					assert outputfile is not None and not os.path.isfile(lowerCamelCase__							), F'''{outputfile}'''
  					if subset_list is not None:
  							with open(os.path.realpath(lowerCamelCase__							)							) as f:
  									snake_case		:						Optional[int]								=				{self._vqa_file_split()[0] for x in tryload(lowerCamelCase__							)}
  					else:
  							snake_case		:						Optional[Any]								=				None
  
  					snake_case		:						Optional[int]								=				CONFIG
  					if torch.cuda.is_available():
  							snake_case		:						Tuple								=				"cuda"
  					snake_case		:						Union[str, Any]								=				os.path.realpath(lowerCamelCase__							)
  					snake_case		:						Any								=				os.path.realpath(lowerCamelCase__							)
  					snake_case		:						Optional[Any]								=				Preprocess(self.config							)
  					snake_case		:						Tuple								=				GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"				, config=self.config							)
  					snake_case		:						str								=				batch_size if batch_size != 0 else 1
  					snake_case		:						Optional[int]								=				DEFAULT_SCHEMA
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				int(file.split("."							)[0].split("_"							)[-1]							)
  					snake_case		:						Tuple								=				os.path.join(self.inputdir				, lowerCamelCase__							)
  					return (img_id, filepath)
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				[]
  					for i, file in enumerate(os.listdir(self.inputdir							)							):
  							if self.subset_list is not None and i not in self.subset_list:
  									continue
  							batch.append(self._vqa_file_split(lowerCamelCase__							)							)
  							if len(lowerCamelCase__							) == self.batch:
  									snake_case		:						str								=				batch
  									snake_case		:						Tuple								=				[]
  									yield list(map(lowerCamelCase__				, zip(*lowerCamelCase__							)							)							)
  
  					for i in range(1							):
  							yield list(map(lowerCamelCase__				, zip(*lowerCamelCase__							)							)							)
  
  
  
  			def __call__(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if not TEST:
  							snake_case		:						Any								=				datasets.ArrowWriter(features=self.schema				, path=self.outputfile							)
  					# do file generator
  					for i, (img_ids, filepaths) in enumerate(self.file_generator							):
  							snake_case		:						str								=				self.preprocess(lowerCamelCase__							)
  							snake_case		:						Tuple								=				self.model(
  							    lowerCamelCase__				, lowerCamelCase__				, scales_yx=lowerCamelCase__				, padding="max_detections"				, max_detections=self.config.MAX_DETECTIONS				, pad_value=0				, return_tensors="np"				, location="cpu"				, )
  							snake_case		:						List[Any]								=				output_dict.pop("normalized_boxes"							)
  							if not TEST:
  									snake_case		:						Union[str, Any]								=				np.array(lowerCamelCase__							)
  									snake_case		:						List[str]								=				self.schema.encode_batch(lowerCamelCase__							)
  									writer.write_batch(lowerCamelCase__							)
  							if TEST:
  									break
  							# finalizer the writer
  					if not TEST:
  							snake_case		:						Any								=				writer.finalize()
  							print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''							)
  
  
  
  
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
  		try:
  				snake_case		:						Dict								=				json.load(lowercase__   )
  				try:
  						snake_case		:						List[Any]								=				list(data.keys()   )
  				except Exception:
  						snake_case		:						List[Any]								=				[d["img_id"] for d in data]
  		except Exception:
  				try:
  						snake_case		:						Optional[Any]								=				eval(stream.read()   )
  				except Exception:
  						snake_case		:						Any								=				stream.read().split("\n"   )
  		return data
  
  
  if __name__ == "__main__":
  							__A 		=			Extract(sys.argv[1:])
  							extract()
  							if not TEST:
  														__A 		=			datasets.Dataset.from_file(extract.outputfile)
  														# wala!
  														# print(np.array(dataset[0:2]["roi_features"]).shape)
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      load_image,
      load_numpy,
      nightly,
      require_torch_gpu,
      skip_mps,
      torch_device,
  )
  
  from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
  			a__ :      Optional[Any]								=     RePaintPipeline
  			a__ :      List[Any]								=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  			a__ :      List[Any]								=     PipelineTesterMixin.required_optional_params - {
  			    """latents""",
  			    """num_images_per_prompt""",
  			    """callback""",
  			    """callback_steps""",
  			}
  			a__ :      List[Any]								=     IMAGE_INPAINTING_BATCH_PARAMS
  			a__ :      List[str]								=     False
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					torch.manual_seed(0							)
  					snake_case		:						Union[str, Any]								=				UNetaDModel(
  					    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=3				, out_channels=3				, down_block_types=("DownBlock2D", "AttnDownBlock2D")				, up_block_types=("AttnUpBlock2D", "UpBlock2D")				, )
  					snake_case		:						Union[str, Any]								=				RePaintScheduler()
  					snake_case		:						Optional[Any]								=				{"unet": unet, "scheduler": scheduler}
  					return components
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
  							snake_case		:						str								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						List[Any]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Any								=				np.random.RandomState(SCREAMING_SNAKE_CASE							).standard_normal((1, 3, 32, 32)							)
  					snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						List[str]								=				(image > 0).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						int								=				{
  					    "image": image,
  					    "mask_image": mask,
  					    "generator": generator,
  					    "num_inference_steps": 5,
  					    "eta": 0.0,
  					    "jump_length": 2,
  					    "jump_n_sample": 2,
  					    "output_type": "numpy",
  					}
  					return inputs
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
  					snake_case		:						Any								=				self.get_dummy_components()
  					snake_case		:						Optional[int]								=				RePaintPipeline(**SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				sd_pipe.to(SCREAMING_SNAKE_CASE							)
  					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				sd_pipe(**SCREAMING_SNAKE_CASE							).images
  					snake_case		:						Optional[int]								=				image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 32, 32, 3)
  					snake_case		:						List[str]								=				np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]							)
  
  					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_local()
  
  
  			@unittest.skip("non-deterministic pipeline"							)
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_inference_batch_single_identical()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_dict_tuple_outputs_equivalent()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_optional_components()
  
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_attention_slicing_forward_pass()
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256.png"							)
  					snake_case		:						Any								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
  					snake_case		:						List[Any]								=				load_numpy(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256_result.npy"							)
  
  					snake_case		:						str								=				"google/ddpm-ema-celebahq-256"
  					snake_case		:						Optional[Any]								=				UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				RePaintPipeline(unet=SCREAMING_SNAKE_CASE				, scheduler=SCREAMING_SNAKE_CASE							).to(SCREAMING_SNAKE_CASE							)
  					repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  					repaint.enable_attention_slicing()
  
  					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
  					snake_case		:						int								=				repaint(
  					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, num_inference_steps=250				, eta=0.0				, jump_length=10				, jump_n_sample=10				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, )
  					snake_case		:						List[Any]								=				output.images[0]
  
  					assert image.shape == (256, 256, 3)
  					assert np.abs(expected_image - image							).mean() < 1E-2
  
  