import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase			:							Any     ={
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

lowerCAmelCase			:							Any     ={
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

lowerCAmelCase			:							Any     ={
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

lowerCAmelCase			:							List[Any]     ={
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowerCAmelCase			:							str     ={
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowerCAmelCase			:							Optional[Any]     ={
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}







def 						A__  (					__A	):







    '''simple docstring'''
    if isinstance(snake_case__   , snake_case__	):
        return v
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("""boolean value expected"""	)







def 						A__  (					__A   , __A   , __A   , __A   , __A=False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
    _lowerCamelCase     :		int 						=						checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
    _lowerCamelCase     :		Dict 						=						checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
    _lowerCamelCase     :		str 						=						checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
    _lowerCamelCase     :		int 						=						checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
    _lowerCamelCase     :		List[str] 						=						checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
    _lowerCamelCase     :		Tuple 						=						checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
    _lowerCamelCase     :		Union[str, Any] 						=						checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
    _lowerCamelCase     :		Optional[Any] 						=						checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
    _lowerCamelCase     :		List[Any] 						=						checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

    if has_skip:
        _lowerCamelCase     :		Optional[Any] 						=						checkpoint[F"""{old_prefix}.skip_connection.weight"""]
        _lowerCamelCase     :		List[Any] 						=						checkpoint[F"""{old_prefix}.skip_connection.bias"""]

    return new_checkpoint







def 						A__  (					__A   , __A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Any 						=						checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3   , dim=0	)
    _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Any 						=						checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3   , dim=0	)

    _lowerCamelCase     :		Union[str, Any] 						=						checkpoint[F"""{old_prefix}.norm.weight"""]
    _lowerCamelCase     :		Optional[Any] 						=						checkpoint[F"""{old_prefix}.norm.bias"""]

    _lowerCamelCase     :		Optional[int] 						=						weight_q.squeeze(-1	).squeeze(-1	)
    _lowerCamelCase     :		Union[str, Any] 						=						bias_q.squeeze(-1	).squeeze(-1	)
    _lowerCamelCase     :		Tuple 						=						weight_k.squeeze(-1	).squeeze(-1	)
    _lowerCamelCase     :		List[Any] 						=						bias_k.squeeze(-1	).squeeze(-1	)
    _lowerCamelCase     :		Tuple 						=						weight_v.squeeze(-1	).squeeze(-1	)
    _lowerCamelCase     :		Union[str, Any] 						=						bias_v.squeeze(-1	).squeeze(-1	)

    _lowerCamelCase     :		int 						=						(
        checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1	).squeeze(-1	)
    )
    _lowerCamelCase     :		int 						=						checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1	).squeeze(-1	)

    return new_checkpoint







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						torch.load(snake_case__   , map_location="""cpu"""	)
    _lowerCamelCase     :		Union[str, Any] 						=						{}

    _lowerCamelCase     :		List[str] 						=						checkpoint["""time_embed.0.weight"""]
    _lowerCamelCase     :		List[str] 						=						checkpoint["""time_embed.0.bias"""]
    _lowerCamelCase     :		Any 						=						checkpoint["""time_embed.2.weight"""]
    _lowerCamelCase     :		Tuple 						=						checkpoint["""time_embed.2.bias"""]

    if unet_config["num_class_embeds"] is not None:
        _lowerCamelCase     :		Dict 						=						checkpoint["""label_emb.weight"""]

    _lowerCamelCase     :		Union[str, Any] 						=						checkpoint["""input_blocks.0.0.weight"""]
    _lowerCamelCase     :		Union[str, Any] 						=						checkpoint["""input_blocks.0.0.bias"""]

    _lowerCamelCase     :		Tuple 						=						unet_config["""down_block_types"""]
    _lowerCamelCase     :		Tuple 						=						unet_config["""layers_per_block"""]
    _lowerCamelCase     :		Dict 						=						unet_config["""attention_head_dim"""]
    _lowerCamelCase     :		int 						=						unet_config["""block_out_channels"""]
    _lowerCamelCase     :		Tuple 						=						1
    _lowerCamelCase     :		List[Any] 						=						channels_list[0]

    for i, layer_type in enumerate(snake_case__	):
        _lowerCamelCase     :		Tuple 						=						channels_list[i]
        _lowerCamelCase     :		str 						=						current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(snake_case__	):
                _lowerCamelCase     :		Optional[int] 						=						F"""down_blocks.{i}.resnets.{j}"""
                _lowerCamelCase     :		Union[str, Any] 						=						F"""input_blocks.{current_layer}.0"""
                _lowerCamelCase     :		List[Any] 						=						True if j == 0 and downsample_block_has_skip else False
                _lowerCamelCase     :		Tuple 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__   , has_skip=snake_case__	)
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(snake_case__	):
                _lowerCamelCase     :		List[Any] 						=						F"""down_blocks.{i}.resnets.{j}"""
                _lowerCamelCase     :		Optional[int] 						=						F"""input_blocks.{current_layer}.0"""
                _lowerCamelCase     :		Optional[int] 						=						True if j == 0 and downsample_block_has_skip else False
                _lowerCamelCase     :		Optional[Any] 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__   , has_skip=snake_case__	)
                _lowerCamelCase     :		Any 						=						F"""down_blocks.{i}.attentions.{j}"""
                _lowerCamelCase     :		Tuple 						=						F"""input_blocks.{current_layer}.1"""
                _lowerCamelCase     :		int 						=						convert_attention(
                    snake_case__   , snake_case__   , snake_case__   , snake_case__   , snake_case__	)
                current_layer += 1

        if i != len(snake_case__	) - 1:
            _lowerCamelCase     :		Any 						=						F"""down_blocks.{i}.downsamplers.0"""
            _lowerCamelCase     :		int 						=						F"""input_blocks.{current_layer}.0"""
            _lowerCamelCase     :		int 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__	)
            current_layer += 1

        _lowerCamelCase     :		Dict 						=						current_channels

    # hardcoded the mid-block for now
    _lowerCamelCase     :		Any 						=						"""mid_block.resnets.0"""
    _lowerCamelCase     :		Any 						=						"""middle_block.0"""
    _lowerCamelCase     :		Dict 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__	)
    _lowerCamelCase     :		Tuple 						=						"""mid_block.attentions.0"""
    _lowerCamelCase     :		Dict 						=						"""middle_block.1"""
    _lowerCamelCase     :		Any 						=						convert_attention(snake_case__   , snake_case__   , snake_case__   , snake_case__   , snake_case__	)
    _lowerCamelCase     :		int 						=						"""mid_block.resnets.1"""
    _lowerCamelCase     :		str 						=						"""middle_block.2"""
    _lowerCamelCase     :		Tuple 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__	)

    _lowerCamelCase     :		Any 						=						0
    _lowerCamelCase     :		str 						=						unet_config["""up_block_types"""]

    for i, layer_type in enumerate(snake_case__	):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1	):
                _lowerCamelCase     :		List[str] 						=						F"""up_blocks.{i}.resnets.{j}"""
                _lowerCamelCase     :		Tuple 						=						F"""output_blocks.{current_layer}.0"""
                _lowerCamelCase     :		List[Any] 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__   , has_skip=snake_case__	)
                current_layer += 1

            if i != len(snake_case__	) - 1:
                _lowerCamelCase     :		List[str] 						=						F"""up_blocks.{i}.upsamplers.0"""
                _lowerCamelCase     :		Union[str, Any] 						=						F"""output_blocks.{current_layer-1}.1"""
                _lowerCamelCase     :		Any 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__	)
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1	):
                _lowerCamelCase     :		Optional[int] 						=						F"""up_blocks.{i}.resnets.{j}"""
                _lowerCamelCase     :		Any 						=						F"""output_blocks.{current_layer}.0"""
                _lowerCamelCase     :		List[str] 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__   , has_skip=snake_case__	)
                _lowerCamelCase     :		int 						=						F"""up_blocks.{i}.attentions.{j}"""
                _lowerCamelCase     :		Optional[int] 						=						F"""output_blocks.{current_layer}.1"""
                _lowerCamelCase     :		Union[str, Any] 						=						convert_attention(
                    snake_case__   , snake_case__   , snake_case__   , snake_case__   , snake_case__	)
                current_layer += 1

            if i != len(snake_case__	) - 1:
                _lowerCamelCase     :		Union[str, Any] 						=						F"""up_blocks.{i}.upsamplers.0"""
                _lowerCamelCase     :		Any 						=						F"""output_blocks.{current_layer-1}.2"""
                _lowerCamelCase     :		Union[str, Any] 						=						convert_resnet(snake_case__   , snake_case__   , snake_case__   , snake_case__	)

    _lowerCamelCase     :		Dict 						=						checkpoint["""out.0.weight"""]
    _lowerCamelCase     :		Optional[Any] 						=						checkpoint["""out.0.bias"""]
    _lowerCamelCase     :		int 						=						checkpoint["""out.2.weight"""]
    _lowerCamelCase     :		int 						=						checkpoint["""out.2.bias"""]

    return new_checkpoint


if __name__ == "__main__":
    lowerCAmelCase			:							Any     =argparse.ArgumentParser()

    parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
    parser.add_argument(
        "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
    )
    parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

    lowerCAmelCase			:							List[str]     =parser.parse_args()
    lowerCAmelCase			:							Tuple     =strabool(args.class_cond)

    lowerCAmelCase			:							Optional[Any]     =os.path.basename(args.unet_path)
    print(F"""Checkpoint: {ckpt_name}""")

    # Get U-Net config
    if "imagenet64" in ckpt_name:
        lowerCAmelCase			:							List[Any]     =IMAGENET_64_UNET_CONFIG
    elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
        lowerCAmelCase			:							Dict     =LSUN_256_UNET_CONFIG
    elif "test" in ckpt_name:
        lowerCAmelCase			:							List[Any]     =TEST_UNET_CONFIG
    else:
        raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

    if not args.class_cond:
        lowerCAmelCase			:							List[str]     =None

    lowerCAmelCase			:							Tuple     =con_pt_to_diffuser(args.unet_path, unet_config)

    lowerCAmelCase			:							Optional[Any]     =UNetaDModel(**unet_config)
    image_unet.load_state_dict(converted_unet_ckpt)

    # Get scheduler config
    if "cd" in ckpt_name or "test" in ckpt_name:
        lowerCAmelCase			:							List[Any]     =CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        lowerCAmelCase			:							List[Any]     =CT_IMAGENET_64_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
        lowerCAmelCase			:							List[Any]     =CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

    lowerCAmelCase			:							List[str]     =CMStochasticIterativeScheduler(**scheduler_config)

    lowerCAmelCase			:							int     =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       (EulerDiscreteScheduler,)
      _snake_case    		=       10



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{
              """num_train_timesteps""": 1100,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**_UpperCamelCase)
          return config



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """simple docstring"""


          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=_UpperCamelCase    ,  beta_end=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Union[str, Any]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		str 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Any 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		str 						=						torch.manual_seed(0)

          _lowerCamelCase     :		str 						=						self.dummy_model()
          _lowerCamelCase     :		List[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		int 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		Any 						=						torch.manual_seed(0)

          _lowerCamelCase     :		int 						=						self.dummy_model()
          _lowerCamelCase     :		int 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		Dict 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[Any] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						output.prev_sample

          _lowerCamelCase     :		Union[str, Any] 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Optional[int] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
          assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		int 						=						self.get_scheduler_config()
          _lowerCamelCase     :		List[Any] 						=						scheduler_class(**_UpperCamelCase)

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

          _lowerCamelCase     :		Optional[Any] 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		Optional[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Tuple 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		List[Any] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[Any] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[int] 						=						self.get_scheduler_config()
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase    ,  use_karras_sigmas=_UpperCamelCase)

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

          _lowerCamelCase     :		int 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		str 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Optional[int] 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		Tuple 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		int 						=						output.prev_sample

          _lowerCamelCase     :		Tuple 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[str] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
          assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3




