



"""simple docstring"""




import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase:				Optional[int]    							=  logging.get_logger(__name__)


def   lowercase__     (			_UpperCAmelCase				)     ->			Optional[int]:






      '''simple docstring'''


      lowercase  :      List[str]					=   ConvNextVaConfig()

      if "atto" in checkpoint_url:
            lowercase  :      Any					=   [2, 2, 6, 2]
            lowercase  :      Any					=   [40, 80, 1_60, 3_20]
      if "femto" in checkpoint_url:
            lowercase  :      Optional[int]					=   [2, 2, 6, 2]
            lowercase  :      Tuple					=   [48, 96, 1_92, 3_84]
      if "pico" in checkpoint_url:
            lowercase  :      Optional[int]					=   [2, 2, 6, 2]
            lowercase  :      Any					=   [64, 1_28, 2_56, 5_12]
      if "nano" in checkpoint_url:
            lowercase  :      Optional[Any]					=   [2, 2, 8, 2]
            lowercase  :      Dict					=   [80, 1_60, 3_20, 6_40]
      if "tiny" in checkpoint_url:
            lowercase  :      Optional[Any]					=   [3, 3, 9, 3]
            lowercase  :      int					=   [96, 1_92, 3_84, 7_68]
      if "base" in checkpoint_url:
            lowercase  :      Union[str, Any]					=   [3, 3, 27, 3]
            lowercase  :      List[Any]					=   [1_28, 2_56, 5_12, 10_24]
      if "large" in checkpoint_url:
            lowercase  :      int					=   [3, 3, 27, 3]
            lowercase  :      Dict					=   [1_92, 3_84, 7_68, 15_36]
      if "huge" in checkpoint_url:
            lowercase  :      Any					=   [3, 3, 27, 3]
            lowercase  :      Dict					=   [3_52, 7_04, 14_08, 28_16]

      lowercase  :      Tuple					=   10_00
      lowercase  :      List[Any]					=   'imagenet-1k-id2label.json'
      lowercase  :      Tuple					=   (1, 10_00)

      lowercase  :      Any					=   'huggingface/label-files'
      lowercase  :      Optional[Any]					=   num_labels
      lowercase  :      str					=   json.load(open(hf_hub_download(_UpperCAmelCase	,      _UpperCAmelCase	,      repo_type='dataset'				)	,      'r'				)				)
      lowercase  :      Dict					=   {int(_UpperCAmelCase				): v for k, v in idalabel.items()}

      lowercase  :      List[str]					=   idalabel
      lowercase  :      Any					=   {v: k for k, v in idalabel.items()}
      lowercase  :      Optional[int]					=   hidden_sizes
      lowercase  :      Optional[Any]					=   depths

      return config, expected_shape


def   lowercase__     (			_UpperCAmelCase				)     ->			Union[str, Any]:






      '''simple docstring'''


      if "downsample_layers.0.0" in name:
            lowercase  :      Union[str, Any]					=   name.replace('downsample_layers.0.0'	,      'embeddings.patch_embeddings'				)
      if "downsample_layers.0.1" in name:
            lowercase  :      Dict					=   name.replace('downsample_layers.0.1'	,      'embeddings.norm'				)  # we rename to layernorm later on
      if "downsample_layers.1.0" in name:
            lowercase  :      List[str]					=   name.replace('downsample_layers.1.0'	,      'stages.1.downsampling_layer.0'				)
      if "downsample_layers.1.1" in name:
            lowercase  :      Tuple					=   name.replace('downsample_layers.1.1'	,      'stages.1.downsampling_layer.1'				)
      if "downsample_layers.2.0" in name:
            lowercase  :      Dict					=   name.replace('downsample_layers.2.0'	,      'stages.2.downsampling_layer.0'				)
      if "downsample_layers.2.1" in name:
            lowercase  :      Tuple					=   name.replace('downsample_layers.2.1'	,      'stages.2.downsampling_layer.1'				)
      if "downsample_layers.3.0" in name:
            lowercase  :      Optional[int]					=   name.replace('downsample_layers.3.0'	,      'stages.3.downsampling_layer.0'				)
      if "downsample_layers.3.1" in name:
            lowercase  :      List[Any]					=   name.replace('downsample_layers.3.1'	,      'stages.3.downsampling_layer.1'				)
      if "stages" in name and "downsampling_layer" not in name:
            # stages.0.0. for instance should be renamed to stages.0.layers.0.
            lowercase  :      Optional[int]					=   name[: len('stages.0'				)] + '.layers' + name[len('stages.0'				) :]
      if "gamma" in name:
            lowercase  :      Tuple					=   name.replace('gamma'	,      'weight'				)
      if "beta" in name:
            lowercase  :      Dict					=   name.replace('beta'	,      'bias'				)
      if "stages" in name:
            lowercase  :      Dict					=   name.replace('stages'	,      'encoder.stages'				)
      if "norm" in name:
            lowercase  :      Optional[int]					=   name.replace('norm'	,      'layernorm'				)
      if "head" in name:
            lowercase  :      Union[str, Any]					=   name.replace('head'	,      'classifier'				)

      return name


def   lowercase__     (			)     ->			List[Any]:






      '''simple docstring'''


      lowercase  :      Any					=   'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowercase  :      Union[str, Any]					=   Image.open(requests.get(_UpperCAmelCase	,      stream=_UpperCAmelCase				).raw				)
      return im


def   lowercase__     (			_UpperCAmelCase				)     ->			List[Any]:






      '''simple docstring'''


      if "224" in checkpoint_url:
            lowercase  :      Union[str, Any]					=   2_24
            lowercase  :      List[Any]					=   2_24 / 2_56
      elif "384" in checkpoint_url:
            lowercase  :      Optional[int]					=   3_84
            lowercase  :      int					=   None
      else:
            lowercase  :      Any					=   5_12
            lowercase  :      Any					=   None

      return ConvNextImageProcessor(
          size=_UpperCAmelCase	,      crop_pct=_UpperCAmelCase	,      image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	,      image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]	,      resample=PILImageResampling.BICUBIC	,      )


@torch.no_grad()
def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			Optional[Any]:






      '''simple docstring'''


      print('Downloading original model from checkpoint...'				)
      # define ConvNeXTV2 configuration based on URL
      lowercase	,    lowercase  :      Optional[int]					=   get_convnextva_config(_UpperCAmelCase				)
      # load original state_dict from URL
      lowercase  :      List[str]					=   torch.hub.load_state_dict_from_url(_UpperCAmelCase				)['model']

      print('Converting model parameters...'				)
      # rename keys
      for key in state_dict.copy().keys():
            lowercase  :      Dict					=   state_dict.pop(_UpperCAmelCase				)
            lowercase  :      Tuple					=   val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
            lowercase  :      List[str]					=   state_dict.pop(_UpperCAmelCase				)
            if not key.startswith('classifier'				):
                  lowercase  :      List[str]					=   'convnextv2.' + key
            lowercase  :      Union[str, Any]					=   val

      # load HuggingFace model
      lowercase  :      List[str]					=   ConvNextVaForImageClassification(_UpperCAmelCase				)
      model.load_state_dict(_UpperCAmelCase				)
      model.eval()

      # Check outputs on an image, prepared by ConvNextImageProcessor
      lowercase  :      List[Any]					=   convert_preprocessor(_UpperCAmelCase				)
      lowercase  :      Optional[Any]					=   preprocessor(images=prepare_img()	,      return_tensors='pt'				)
      lowercase  :      List[Any]					=   model(**_UpperCAmelCase				).logits

      # note: the logits below were obtained without center cropping
      if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
            lowercase  :      List[str]					=   torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
            lowercase  :      Optional[int]					=   torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
            lowercase  :      int					=   torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
            lowercase  :      Tuple					=   torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
            lowercase  :      Optional[Any]					=   torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
            lowercase  :      Any					=   torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
            lowercase  :      Any					=   torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
            lowercase  :      Optional[int]					=   torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
            lowercase  :      Dict					=   torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
            lowercase  :      Union[str, Any]					=   torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
            lowercase  :      Union[str, Any]					=   torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
            lowercase  :      Union[str, Any]					=   torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
            lowercase  :      Union[str, Any]					=   torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
            lowercase  :      int					=   torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
            lowercase  :      int					=   torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
            lowercase  :      Optional[Any]					=   torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
            lowercase  :      Union[str, Any]					=   torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]				)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
            lowercase  :      List[str]					=   torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]				)
      else:
            raise ValueError(f'''Unknown URL: {checkpoint_url}'''				)

      assert torch.allclose(logits[0, :5]	,      _UpperCAmelCase	,      atol=1e-3				)
      assert logits.shape == expected_shape
      print('Model outputs match the original results!'				)

      if save_model:
            print('Saving model to local...'				)
            # Create folder to save model
            if not os.path.isdir(_UpperCAmelCase				):
                  os.mkdir(_UpperCAmelCase				)

            model.save_pretrained(_UpperCAmelCase				)
            preprocessor.save_pretrained(_UpperCAmelCase				)

      lowercase  :      Tuple					=   'convnextv2'
      if "atto" in checkpoint_url:
            model_name += "-atto"
      if "femto" in checkpoint_url:
            model_name += "-femto"
      if "pico" in checkpoint_url:
            model_name += "-pico"
      if "nano" in checkpoint_url:
            model_name += "-nano"
      elif "tiny" in checkpoint_url:
            model_name += "-tiny"
      elif "base" in checkpoint_url:
            model_name += "-base"
      elif "large" in checkpoint_url:
            model_name += "-large"
      elif "huge" in checkpoint_url:
            model_name += "-huge"
      if "22k" in checkpoint_url and "1k" not in checkpoint_url:
            model_name += "-22k"
      elif "22k" in checkpoint_url and "1k" in checkpoint_url:
            model_name += "-22k-1k"
      elif "1k" in checkpoint_url:
            model_name += "-1k"
      if "224" in checkpoint_url:
            model_name += "-224"
      elif "384" in checkpoint_url:
            model_name += "-384"
      elif "512" in checkpoint_url:
            model_name += "-512"

      if push_to_hub:
            print(f'''Pushing {model_name} to the hub...'''				)
            model.push_to_hub(_UpperCAmelCase				)
            preprocessor.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
     _UpperCamelCase:				List[str]    							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
         type=str,
         help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='model',
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--save_model', action='store_true', help='Save model to local')
     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

     _UpperCamelCase:				int    							=  parser.parse_args()
     convert_convnextva_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
     )





"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2
