




'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_		       =						logging.get_logger(__name__)
def  SCREAMING_SNAKE_CASE_ (							) -> str:
   _SCREAMING_SNAKE_CASE       =      DetaConfig(
       num_queries=9_00       ,     encoder_ffn_dim=20_48       ,     decoder_ffn_dim=20_48       ,     num_feature_levels=5       ,     assign_first_stage=_UpperCAmelCase       ,     with_box_refine=_UpperCAmelCase       ,     two_stage=_UpperCAmelCase       ,     )

   # set labels
   _SCREAMING_SNAKE_CASE       =      91
   _SCREAMING_SNAKE_CASE       =      "huggingface/label-files"
   _SCREAMING_SNAKE_CASE       =      "coco-detection-id2label.json"
   _SCREAMING_SNAKE_CASE       =      json.load(open(cached_download(hf_hub_url(_UpperCAmelCase       ,     _UpperCAmelCase       ,     repo_type="dataset"  )  )       ,     "r"  )  )
   _SCREAMING_SNAKE_CASE       =      {int(_UpperCAmelCase  ): v for k, v in idalabel.items()}
   _SCREAMING_SNAKE_CASE       =      idalabel
   _SCREAMING_SNAKE_CASE       =      {v: k for k, v in idalabel.items()}

   return config
def  SCREAMING_SNAKE_CASE_ (							__A				: Tuple  ) -> Tuple:
   _SCREAMING_SNAKE_CASE       =      []

   # stem
   # fmt: off
   rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")  )
   rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")  )
   rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")  )
   rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")  )
   rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")  )
   # stages
   for stage_idx in range(len(config.backbone_config.depths  )  ):
      for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                )  )
         # 3 convs
         for i in range(3  ):
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                )  )
            rename_keys.append(
                (
                    f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                    f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                )  )
            # transformer encoder
   for i in range(config.encoder_layers  ):
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")  )
      rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")  )

   # transformer decoder
   for i in range(config.decoder_layers  ):
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")  )
      rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")  )

   # fmt: on

   return rename_keys
def  SCREAMING_SNAKE_CASE_ (							__A				: List[str]       ,     __A				: List[Any]       ,     __A				: List[str]  ) -> List[Any]:
   _SCREAMING_SNAKE_CASE       =      dct.pop(_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE       =      val
def  SCREAMING_SNAKE_CASE_ (							__A				: int       ,     __A				: Union[str, Any]  ) -> Tuple:
   # transformer decoder self-attention layers
   _SCREAMING_SNAKE_CASE       =      config.d_model
   for i in range(config.decoder_layers  ):
      # read in weights + bias of input projection layer of self-attention
      _SCREAMING_SNAKE_CASE       =      state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""  )
      _SCREAMING_SNAKE_CASE       =      state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""  )
      # next, add query, keys and values (in that order) to the state dict
      _SCREAMING_SNAKE_CASE       =      in_proj_weight[:hidden_size, :]
      _SCREAMING_SNAKE_CASE       =      in_proj_bias[:hidden_size]
      _SCREAMING_SNAKE_CASE       =      in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      _SCREAMING_SNAKE_CASE       =      in_proj_bias[hidden_size : hidden_size * 2]
      _SCREAMING_SNAKE_CASE       =      in_proj_weight[-hidden_size:, :]
      _SCREAMING_SNAKE_CASE       =      in_proj_bias[-hidden_size:]
def  SCREAMING_SNAKE_CASE_ (							) -> Any:
   _SCREAMING_SNAKE_CASE       =      "http://images.cocodataset.org/val2017/000000039769.jpg"
   _SCREAMING_SNAKE_CASE       =      Image.open(requests.get(_UpperCAmelCase       ,     stream=_UpperCAmelCase  ).raw  )

   return im
@torch.no_grad()
def  SCREAMING_SNAKE_CASE_ (							__A				: Optional[Any]       ,     __A				: Tuple       ,     __A				: Optional[Any]  ) -> List[str]:
   _SCREAMING_SNAKE_CASE       =      get_deta_config()

   # load original state dict
   if model_name == "deta-resnet-50":
      _SCREAMING_SNAKE_CASE       =      "adet_checkpoint0011.pth"
   elif model_name == "deta-resnet-50-24-epochs":
      _SCREAMING_SNAKE_CASE       =      "adet_2x_checkpoint0023.pth"
   else:
      raise ValueError(f"""Model name {model_name} not supported"""  )
   _SCREAMING_SNAKE_CASE       =      hf_hub_download(repo_id="nielsr/deta-checkpoints"       ,     filename=_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE       =      torch.load(_UpperCAmelCase       ,     map_location="cpu"  )["model"]

   # rename keys
   _SCREAMING_SNAKE_CASE       =      create_rename_keys(_UpperCAmelCase  )
   for src, dest in rename_keys:
      rename_key(_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase  )
   read_in_decoder_q_k_v(_UpperCAmelCase       ,     _UpperCAmelCase  )

   # fix some prefixes
   for key in state_dict.copy().keys():
      if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
         _SCREAMING_SNAKE_CASE       =      state_dict.pop(_UpperCAmelCase  )
         _SCREAMING_SNAKE_CASE       =      val
      if "input_proj" in key:
         _SCREAMING_SNAKE_CASE       =      state_dict.pop(_UpperCAmelCase  )
         _SCREAMING_SNAKE_CASE       =      val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         _SCREAMING_SNAKE_CASE       =      state_dict.pop(_UpperCAmelCase  )
         _SCREAMING_SNAKE_CASE       =      val

          # finally, create HuggingFace model and load state dict
   _SCREAMING_SNAKE_CASE       =      DetaForObjectDetection(_UpperCAmelCase  )
   model.load_state_dict(_UpperCAmelCase  )
   model.eval()

   _SCREAMING_SNAKE_CASE       =      "cuda" if torch.cuda.is_available() else "cpu"
   model.to(_UpperCAmelCase  )

   # load image processor
   _SCREAMING_SNAKE_CASE       =      DetaImageProcessor(format="coco_detection"  )

   # verify our conversion on image
   _SCREAMING_SNAKE_CASE       =      prepare_img()
   _SCREAMING_SNAKE_CASE       =      processor(images=_UpperCAmelCase       ,     return_tensors="pt"  )
   _SCREAMING_SNAKE_CASE       =      encoding["pixel_values"]
   _SCREAMING_SNAKE_CASE       =      model(pixel_values.to(_UpperCAmelCase  )  )

   # verify logits
   if model_name == "deta-resnet-50":
      _SCREAMING_SNAKE_CASE       =      torch.tensor(
          [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]  )
      _SCREAMING_SNAKE_CASE       =      torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]  )
   elif model_name == "deta-resnet-50-24-epochs":
      _SCREAMING_SNAKE_CASE       =      torch.tensor(
          [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]  )
      _SCREAMING_SNAKE_CASE       =      torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]  )

   assert torch.allclose(outputs.logits[0, :3, :3]       ,     expected_logits.to(_UpperCAmelCase  )       ,     atol=1e-4  )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,     expected_boxes.to(_UpperCAmelCase  )       ,     atol=1e-4  )
   print("Everything ok!"  )

   if pytorch_dump_folder_path:
      # Save model and processor
      logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""  )
      Path(_UpperCAmelCase  ).mkdir(exist_ok=_UpperCAmelCase  )
      model.save_pretrained(_UpperCAmelCase  )
      processor.save_pretrained(_UpperCAmelCase  )

   # Push to hub
   if push_to_hub:
      print("Pushing model and processor to hub..."  )
      model.push_to_hub(f"""jozhang97/{model_name}"""  )
      processor.push_to_hub(f"""jozhang97/{model_name}"""  )


if __name__ == "__main__":
 lowerCamelCase_		       =						argparse.ArgumentParser()

 parser.add_argument(
     '--model_name',
     type=str,
     default='deta-resnet-50',
     choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
     help='Name of the model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     help='Path to the folder to output PyTorch model.',
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )
 lowerCamelCase_		       =						parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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				(					a__   ):
 snake_case__    	=    (HeunDiscreteScheduler,)
 snake_case__    	=    1_0



 def 					UpperCamelCase__   (			self							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      {
      'num_train_timesteps': 11_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**_snake_case			)
  return config



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,    [0.0_002, 0.002, 0.02]			):
   self.check_over_configs(beta_start=_snake_case							,    beta_end=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for schedule in ["linear", "scaled_linear", "exp"]:
   self.check_over_configs(beta_schedule=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for clip_sample_range in [1.0, 2.0, 3.0]:
   self.check_over_configs(clip_sample_range=_snake_case							,    clip_sample=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for prediction_type in ["epsilon", "v_prediction", "sample"]:
   self.check_over_configs(prediction_type=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

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

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config(prediction_type='v_prediction'			)
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

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

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 4.69_34E-07			) < 1E-2
   assert abs(result_mean.item() - 6.11_12E-10			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

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

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if str(_snake_case			).startswith('cpu'			):
   # The following sum varies between 148 and 156 on mps. Why?
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  elif str(_snake_case			).startswith('mps'			):
   # Larger tolerance on mps
   assert abs(result_mean.item() - 0.0_002			) < 1E-2
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case							,    use_karras_sigmas=_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

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

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  assert abs(result_sum.item() - 0.00_015			) < 1E-2
  assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07			) < 1E-2





