code: 
  
  
  
  
  
  
  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, 20_48), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int=sys.argv[1:]			)			->       Tuple:
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=1
             __UpperCAmelCase							,   __UpperCAmelCase    							=getopt.getopt(__SCREAMING_SNAKE_CASE   ,    """i:o:b:s"""   ,    ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]			)
             for opt, arg in opts:
                    if opt in ("-i", "--inputdir"):
                           __UpperCAmelCase    							=arg
                    elif opt in ("-o", "--outfile"):
                           __UpperCAmelCase    							=arg
                    elif opt in ("-b", "--batch_size"):
                           __UpperCAmelCase    							=int(__SCREAMING_SNAKE_CASE			)
                    elif opt in ("-s", "--subset_list"):
                           __UpperCAmelCase    							=arg
  
             assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
             assert outputfile is not None and not os.path.isfile(__SCREAMING_SNAKE_CASE			), f'''{outputfile}'''
             if subset_list is not None:
                    with open(os.path.realpath(__SCREAMING_SNAKE_CASE			)			) as f:
                           __UpperCAmelCase    							={self._vqa_file_split()[0] for x in tryload(__SCREAMING_SNAKE_CASE			)}
             else:
                    __UpperCAmelCase    							=None
  
             __UpperCAmelCase    							=CONFIG
             if torch.cuda.is_available():
                    __UpperCAmelCase    							="""cuda"""
             __UpperCAmelCase    							=os.path.realpath(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=os.path.realpath(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=Preprocess(self.config			)
             __UpperCAmelCase    							=GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""   ,    config=self.config			)
             __UpperCAmelCase    							=batch_size if batch_size != 0 else 1
             __UpperCAmelCase    							=DEFAULT_SCHEMA
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]			)			->       Any:
             __UpperCAmelCase    							=int(file.split("""."""			)[0].split("""_"""			)[-1]			)
             __UpperCAmelCase    							=os.path.join(self.inputdir   ,    __SCREAMING_SNAKE_CASE			)
             return (img_id, filepath)
  
  
  
      @property
      def _a       (							self			:    Tuple			)			->       Dict:
             __UpperCAmelCase    							=[]
             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(__SCREAMING_SNAKE_CASE			)			)
                    if len(__SCREAMING_SNAKE_CASE			) == self.batch:
                           __UpperCAmelCase    							=batch
                           __UpperCAmelCase    							=[]
                           yield list(map(__SCREAMING_SNAKE_CASE   ,    zip(*__SCREAMING_SNAKE_CASE			)			)			)
  
             for i in range(1			):
                    yield list(map(__SCREAMING_SNAKE_CASE   ,    zip(*__SCREAMING_SNAKE_CASE			)			)			)
  
  
  
      def __call__(							self			:    str			)			->       Optional[int]:
             # make writer
             if not TEST:
                    __UpperCAmelCase    							=datasets.ArrowWriter(features=self.schema   ,    path=self.outputfile			)
             # do file generator
             for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                    __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase    							=self.preprocess(__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=self.model(
                        __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    scales_yx=__SCREAMING_SNAKE_CASE   ,    padding="""max_detections"""   ,    max_detections=self.config.MAX_DETECTIONS   ,    pad_value=0   ,    return_tensors="""np"""   ,    location="""cpu"""   ,    )
                    __UpperCAmelCase    							=output_dict.pop("""normalized_boxes"""			)
                    if not TEST:
                           __UpperCAmelCase    							=np.array(__SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=self.schema.encode_batch(__SCREAMING_SNAKE_CASE			)
                           writer.write_batch(__SCREAMING_SNAKE_CASE			)
                    if TEST:
                           break
                    # finalizer the writer
             if not TEST:
                    __UpperCAmelCase							,   __UpperCAmelCase    							=writer.finalize()
                    print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''			)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Optional[int]		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         try:
                __UpperCAmelCase    							=json.load(A_		)
                try:
                       __UpperCAmelCase    							=list(data.keys()		)
                except Exception:
                       __UpperCAmelCase    							=[d["""img_id"""] for d in data]
         except Exception:
                try:
                       __UpperCAmelCase    							=eval(stream.read()		)
                except Exception:
                       __UpperCAmelCase    							=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: 
  
  
  
  
  
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       str   =   (DPMSolverMultistepScheduler,)
      lowerCamelCase  :       List[str]   =   (('num_inference_steps', 25),)
  
  
  
      def _a       (							self			:    Dict   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Dict:
             __UpperCAmelCase    							={
                 """num_train_timesteps""": 1000,
                 """beta_start""": 0.0_001,
                 """beta_end""": 0.02,
                 """beta_schedule""": """linear""",
                 """solver_order""": 2,
                 """prediction_type""": """epsilon""",
                 """thresholding""": False,
                 """sample_max_value""": 1.0,
                 """algorithm_type""": """dpmsolver++""",
                 """solver_type""": """midpoint""",
                 """lower_order_final""": False,
                 """lambda_min_clipped""": -float("""inf"""			),
                 """variance_type""": None,
             }
  
             config.update(**__SCREAMING_SNAKE_CASE			)
             return config
  
  
  
      def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0   ,    **__SCREAMING_SNAKE_CASE			:    int			)			->       Dict:
             __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
             __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=self.dummy_sample
             __UpperCAmelCase    							=0.1 * sample
             __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                    # copy over dummy past residuals
                    __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]
  
                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(__SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE			)
                           new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                           # copy over dummy past residuals
                           __UpperCAmelCase    							=dummy_past_residuals[: new_scheduler.config.solver_order]
  
                    __UpperCAmelCase							,   __UpperCAmelCase    							=sample, sample
                    for t in range(__SCREAMING_SNAKE_CASE   ,    time_step + scheduler.config.solver_order + 1			):
                           __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                           __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
  
  
  
      def _a       (							self			:    Any			)			->       Union[str, Any]:
             pass
  
  
  
      def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0   ,    **__SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[Any]:
             __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
             __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=self.dummy_sample
             __UpperCAmelCase    							=0.1 * sample
             __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config()
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
                    # copy over dummy past residuals (must be after setting timesteps)
                    __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]
  
                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(__SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE			)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
                           # copy over dummy past residual (must be after setting timesteps)
                           __UpperCAmelCase    							=dummy_past_residuals[: new_scheduler.config.solver_order]
  
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                    __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                    assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
  
  
  
      def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    **__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Optional[Any]:
             if scheduler is None:
                    __UpperCAmelCase    							=self.scheduler_classes[0]
                    __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=10
             __UpperCAmelCase    							=self.dummy_model()
             __UpperCAmelCase    							=self.dummy_sample_deter
             scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
             for i, t in enumerate(scheduler.timesteps			):
                    __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).prev_sample
  
             return sample
  
  
  
      def _a       (							self			:    Dict			)			->       Optional[int]:
             __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
  
             __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
  
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config()
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
                    __UpperCAmelCase    							=self.dummy_sample
                    __UpperCAmelCase    							=0.1 * sample
  
                    if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                    elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                           __UpperCAmelCase    							=num_inference_steps
  
                    # copy over dummy past residuals (must be done after set_timesteps)
                    __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
                    __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]
  
                    __UpperCAmelCase    							=scheduler.timesteps[5]
                    __UpperCAmelCase    							=scheduler.timesteps[6]
  
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                    self.assertEqual(output_a.shape   ,    sample.shape			)
                    self.assertEqual(output_a.shape   ,    output_a.shape			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       Tuple:
             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       int:
             self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE			)
             for order in [1, 2, 3]:
                    for solver_type in ["midpoint", "heun"]:
                           for threshold in [0.5, 1.0, 2.0]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             thresholding=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    sample_max_value=__SCREAMING_SNAKE_CASE   ,    algorithm_type="""dpmsolver++"""   ,    solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    )
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       List[str]:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Tuple:
             for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                                if order == 3:
                                                       continue
                                         else:
                                                self.check_over_configs(
                                                    solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                         __UpperCAmelCase    							=self.full_loop(
                                             solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                         assert not torch.isnan(__SCREAMING_SNAKE_CASE			).any(), "Samples have nan numbers"
  
  
  
      def _a       (							self			:    Dict			)			->       str:
             self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
             self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
             self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
             self.check_over_configs(lambda_min_clipped=-5.1			)
  
  
  
      def _a       (							self			:    Any			)			->       List[str]:
             self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE			)
             self.check_over_configs(variance_type="""learned_range"""			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       int:
             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE   ,    time_step=0			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       str:
             __UpperCAmelCase    							=self.full_loop()
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.3_301			) < 1e-3
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       str:
             __UpperCAmelCase    							=self.full_loop(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0.87   ,    sample_max_value=0.5			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 1.1_364			) < 1e-3
  
  
  
      def _a       (							self			:    List[Any]			)			->       List[Any]:
             __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.2_251			) < 1e-3
  
  
  
      def _a       (							self			:    int			)			->       Union[str, Any]:
             __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""   ,    use_karras_sigmas=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.2_096			) < 1e-3
  
  
  
      def _a       (							self			:    int			)			->       Optional[Any]:
             # make sure that iterating over schedulers with same config names gives same results
             # for defaults
             __UpperCAmelCase    							=DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
             __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.3_301			) < 1e-3
  
             __UpperCAmelCase    							=DPMSolverSinglestepScheduler.from_config(scheduler.config			)
             __UpperCAmelCase    							=UniPCMultistepScheduler.from_config(scheduler.config			)
             __UpperCAmelCase    							=DEISMultistepScheduler.from_config(scheduler.config			)
             __UpperCAmelCase    							=DPMSolverMultistepScheduler.from_config(scheduler.config			)
  
             __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.3_301			) < 1e-3
  
  
  
      def _a       (							self			:    List[Any]			)			->       str:
             __UpperCAmelCase    							=self.scheduler_classes[0]
             __UpperCAmelCase    							=self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0			)
             __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=10
             __UpperCAmelCase    							=self.dummy_model()
             __UpperCAmelCase    							=self.dummy_sample_deter.half()
             scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
             for i, t in enumerate(scheduler.timesteps			):
                    __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).prev_sample
  
             assert sample.dtype == torch.floataa
  
  
  
      def _a       (							self			:    Optional[int]   ,    **__SCREAMING_SNAKE_CASE			:    Tuple			)			->       Optional[int]:
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
                    scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
                    assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps
  
  
  
  
  