


'''simple docstring'''




import argparse

import torch

from diffusers import UNetaDConditionModel





def      _a		(lowercase__ :      Union[str, Any]       ,      lowercase__ :      int       ,      lowercase__ :      Dict       ,      lowercase__ :      Dict=None       ,      lowercase__ :      Any=None       ,      lowercase__ :      Tuple=None	) -> Union[str, Any]:






     """simple docstring"""
     assert isinstance(lowercase__       ,      lowercase__	), "Paths should be a list of dicts containing 'old' and 'new' keys."

     # Splits the attention layers into three variables.
     if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
               __snake_case    			=	old_checkpoint[path]
               __snake_case    			=	old_tensor.shape[0] // 3

               __snake_case    			=	(-1, channels) if len(old_tensor.shape	) == 3 else (-1)

               __snake_case    			=	old_tensor.shape[0] // config['num_head_channels'] // 3

               __snake_case    			=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
               __snake_case			,					__snake_case			,					__snake_case    			=	old_tensor.split(channels // num_heads       ,      dim=1	)

               __snake_case    			=	query.reshape(lowercase__	)
               __snake_case    			=	key.reshape(lowercase__	)
               __snake_case    			=	value.reshape(lowercase__	)

     for path in paths:
          __snake_case    			=	path['new']

          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    __snake_case    			=	new_path.replace(replacement['old']       ,      replacement['new']	)

        # proj_attn.weight has to be converted from conv 1D to linear
          __snake_case    			=	old_checkpoint[path['old']]
          __snake_case    			=	['proj_attn.weight']
          __snake_case    			=	['proj_out.weight', 'proj_in.weight']
          if any(k in new_path for k in names	):
               __snake_case    			=	weight[:, :, 0]
          elif any(k in new_path for k in names_a	) and len(weight.shape	) > 2 and ".attentions." not in new_path:
               __snake_case    			=	weight[:, :, 0]
          else:
               __snake_case    			=	weight





def      _a		(lowercase__ :      Tuple       ,      lowercase__ :      List[str]=0	) -> Optional[Any]:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          __snake_case    			=	old_item

          #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
          #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

          #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
          #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

          #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

          mapping.append({'old': old_item, 'new': new_item}	)

     return mapping





def      _a		(lowercase__ :      List[str]       ,      lowercase__ :      Tuple=1	) -> List[Any]:






     """simple docstring"""
     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'	)[n_shave_prefix_segments:]	)
     else:
          return ".".join(path.split('.'	)[:n_shave_prefix_segments]	)





def      _a		(lowercase__ :      str       ,      lowercase__ :      Tuple=0	) -> Optional[int]:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          mapping.append({'old': old_item, 'new': old_item}	)

     return mapping





def      _a		(lowercase__ :      Dict       ,      lowercase__ :      Tuple=0	) -> int:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          __snake_case    			=	old_item.replace('in_layers.0'       ,      'norm1'	)
          __snake_case    			=	new_item.replace('in_layers.2'       ,      'conv1'	)

          __snake_case    			=	new_item.replace('out_layers.0'       ,      'norm2'	)
          __snake_case    			=	new_item.replace('out_layers.3'       ,      'conv2'	)

          __snake_case    			=	new_item.replace('emb_layers.1'       ,      'time_emb_proj'	)
          __snake_case    			=	new_item.replace('skip_connection'       ,      'conv_shortcut'	)

          __snake_case    			=	shave_segments(lowercase__       ,      n_shave_prefix_segments=lowercase__	)

          if "temopral_conv" not in old_item:
               mapping.append({'old': old_item, 'new': new_item}	)

     return mapping





def      _a		(lowercase__ :      List[Any]       ,      lowercase__ :      Any       ,      lowercase__ :      Optional[Any]=None       ,      lowercase__ :      Dict=False	) -> int:






     """simple docstring"""
     __snake_case    			=	{}
     __snake_case    			=	list(checkpoint.keys()	)

     __snake_case    			=	'model.diffusion_model.'

     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith('model_ema'	) for k in keys	) > 1_0_0 and extract_ema:
          print(f'Checkpoint {path} has both EMA and non-EMA weights.'	)
          print(
              'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
              ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'	)
          for key in keys:
               if key.startswith('model.diffusion_model'	):
                    __snake_case    			=	'model_ema.' + ''.join(key.split('.'	)[1:]	)
                    __snake_case    			=	checkpoint.pop(lowercase__	)
     else:
          if sum(k.startswith('model_ema'	) for k in keys	) > 1_0_0:
               print(
                   'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
                   ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'	)

          for key in keys:
               __snake_case    			=	checkpoint.pop(lowercase__	)

     __snake_case    			=	{}

     __snake_case    			=	unet_state_dict['time_embed.0.weight']
     __snake_case    			=	unet_state_dict['time_embed.0.bias']
     __snake_case    			=	unet_state_dict['time_embed.2.weight']
     __snake_case    			=	unet_state_dict['time_embed.2.bias']

     if config["class_embed_type"] is None:
          # No parameters to port
          ...
     elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
          __snake_case    			=	unet_state_dict['label_emb.0.0.weight']
          __snake_case    			=	unet_state_dict['label_emb.0.0.bias']
          __snake_case    			=	unet_state_dict['label_emb.0.2.weight']
          __snake_case    			=	unet_state_dict['label_emb.0.2.bias']
     else:
          raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'	)

     __snake_case    			=	unet_state_dict['input_blocks.0.0.weight']
     __snake_case    			=	unet_state_dict['input_blocks.0.0.bias']

     __snake_case    			=	[v for v in unet_state_dict if v.startswith('input_blocks.0.1'	)]
     __snake_case    			=	renew_attention_paths(lowercase__	)
     __snake_case    			=	{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
     assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     __snake_case    			=	unet_state_dict['out.0.weight']
     __snake_case    			=	unet_state_dict['out.0.bias']
     __snake_case    			=	unet_state_dict['out.2.weight']
     __snake_case    			=	unet_state_dict['out.2.bias']

     # Retrieves the keys for the input blocks only
     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in unet_state_dict if 'input_blocks' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
         for layer_id in range(lowercase__	)
     }

     # Retrieves the keys for the middle blocks only
     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in unet_state_dict if 'middle_block' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]
         for layer_id in range(lowercase__	)
     }

     # Retrieves the keys for the output blocks only
     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in unet_state_dict if 'output_blocks' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]
         for layer_id in range(lowercase__	)
     }

     for i in range(1       ,      lowercase__	):
          __snake_case    			=	(i - 1) // (config['layers_per_block'] + 1)
          __snake_case    			=	(i - 1) % (config['layers_per_block'] + 1)

          __snake_case    			=	[
              key for key in input_blocks[i] if f'input_blocks.{i}.0' in key and f'input_blocks.{i}.0.op' not in key
          ]
          __snake_case    			=	[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
          __snake_case    			=	[key for key in input_blocks[i] if f'input_blocks.{i}.2' in key]

          if f'input_blocks.{i}.op.weight' in unet_state_dict:
               __snake_case    			=	unet_state_dict.pop(
                   f'input_blocks.{i}.op.weight'	)
               __snake_case    			=	unet_state_dict.pop(
                   f'input_blocks.{i}.op.bias'	)

          __snake_case    			=	renew_resnet_paths(lowercase__	)
          __snake_case    			=	{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
          assign_to_checkpoint(
              lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

          __snake_case    			=	[key for key in resnets if 'temopral_conv' in key]
          __snake_case    			=	renew_temp_conv_paths(lowercase__	)
          __snake_case    			=	{
              'old': f'input_blocks.{i}.0.temopral_conv',
              'new': f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
          }
          assign_to_checkpoint(
              lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

          if len(lowercase__	):
               __snake_case    			=	renew_attention_paths(lowercase__	)
               __snake_case    			=	{'old': f'input_blocks.{i}.1', 'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

          if len(lowercase__	):
               __snake_case    			=	renew_attention_paths(lowercase__	)
               __snake_case    			=	{
                   'old': f'input_blocks.{i}.2',
                   'new': f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
               }
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     __snake_case    			=	middle_blocks[0]
     __snake_case    			=	[key for key in resnet_a if 'temopral_conv' in key]
     __snake_case    			=	middle_blocks[1]
     __snake_case    			=	middle_blocks[2]
     __snake_case    			=	middle_blocks[3]
     __snake_case    			=	[key for key in resnet_a if 'temopral_conv' in key]

     __snake_case    			=	renew_resnet_paths(lowercase__	)
     __snake_case    			=	{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_temp_conv_paths(lowercase__	)
     __snake_case    			=	{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_resnet_paths(lowercase__	)
     __snake_case    			=	{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_temp_conv_paths(lowercase__	)
     __snake_case    			=	{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_attention_paths(lowercase__	)
     __snake_case    			=	{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     __snake_case    			=	renew_attention_paths(lowercase__	)
     __snake_case    			=	{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     for i in range(lowercase__	):
          __snake_case    			=	i // (config['layers_per_block'] + 1)
          __snake_case    			=	i % (config['layers_per_block'] + 1)
          __snake_case    			=	[shave_segments(lowercase__       ,      2	) for name in output_blocks[i]]
          __snake_case    			=	{}

          for layer in output_block_layers:
               __snake_case			,					__snake_case    			=	layer.split('.'	)[0], shave_segments(lowercase__       ,      1	)
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(lowercase__	)
               else:
                    __snake_case    			=	[layer_name]

          if len(lowercase__	) > 1:
               __snake_case    			=	[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
               __snake_case    			=	[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
               __snake_case    			=	[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

               __snake_case    			=	renew_resnet_paths(lowercase__	)
               __snake_case    			=	renew_resnet_paths(lowercase__	)

               __snake_case    			=	{'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

               __snake_case    			=	[key for key in resnets if 'temopral_conv' in key]
               __snake_case    			=	renew_temp_conv_paths(lowercase__	)
               __snake_case    			=	{
                   'old': f'output_blocks.{i}.0.temopral_conv',
                   'new': f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
               }
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

               __snake_case    			=	{k: sorted(lowercase__	) for k, v in output_block_list.items()}
               if ["conv.bias", "conv.weight"] in output_block_list.values():
                    __snake_case    			=	list(output_block_list.values()	).index(['conv.bias', 'conv.weight']	)
                    __snake_case    			=	unet_state_dict[
                        f'output_blocks.{i}.{index}.conv.weight'
                    ]
                    __snake_case    			=	unet_state_dict[
                        f'output_blocks.{i}.{index}.conv.bias'
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(lowercase__	) == 2:
                         __snake_case    			=	[]

               if len(lowercase__	):
                    __snake_case    			=	renew_attention_paths(lowercase__	)
                    __snake_case    			=	{
                        'old': f'output_blocks.{i}.1',
                        'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                    }
                    assign_to_checkpoint(
                        lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

               if len(lowercase__	):
                    __snake_case    			=	renew_attention_paths(lowercase__	)
                    __snake_case    			=	{
                        'old': f'output_blocks.{i}.2',
                        'new': f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                    }
                    assign_to_checkpoint(
                        lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)
          else:
               __snake_case    			=	renew_resnet_paths(lowercase__       ,      n_shave_prefix_segments=1	)
               for path in resnet_0_paths:
                    __snake_case    			=	'.'.join(['output_blocks', str(lowercase__	), path['old']]	)
                    __snake_case    			=	'.'.join(['up_blocks', str(lowercase__	), 'resnets', str(lowercase__	), path['new']]	)
                    __snake_case    			=	unet_state_dict[old_path]

               __snake_case    			=	[l for l in output_block_layers if 'temopral_conv' in l]
               for path in temopral_conv_paths:
                    __snake_case    			=	path.split('temopral_conv.'	)[-1]
                    __snake_case    			=	'.'.join(['output_blocks', str(lowercase__	), str(lowercase__	), 'temopral_conv', pruned_path]	)
                    __snake_case    			=	'.'.join(['up_blocks', str(lowercase__	), 'temp_convs', str(lowercase__	), pruned_path]	)
                    __snake_case    			=	unet_state_dict[old_path]

     return new_checkpoint


if __name__ == "__main__":
       _a      :      List[Any]       				=      argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       _a      :      Union[str, Any]       				=      parser.parse_args()

       _a      :      List[Any]       				=      torch.load(args.checkpoint_path, map_location="cpu")
       _a      :      int       				=      UNetaDConditionModel()

       _a      :      Dict       				=      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       _a      :      Any       				=      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       _a      :      List[Any]       				=      set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,      [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




