

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase_       		=  {
    "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",
}

lowercase_       		=  {
    "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",
}

lowercase_       		=  {
    "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",
}

lowercase_       		=  {
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

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

lowercase_       		=  {
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      List[Any]:





    '''simple docstring'''






    if isinstance(SCREAMING_SNAKE_CASE__    ,			SCREAMING_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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=False      )							->      Any:





    '''simple docstring'''






    A__          =   checkpoint[f'{old_prefix}.in_layers.0.weight']
    A__          =   checkpoint[f'{old_prefix}.in_layers.0.bias']
    A__          =   checkpoint[f'{old_prefix}.in_layers.2.weight']
    A__          =   checkpoint[f'{old_prefix}.in_layers.2.bias']
    A__          =   checkpoint[f'{old_prefix}.emb_layers.1.weight']
    A__          =   checkpoint[f'{old_prefix}.emb_layers.1.bias']
    A__          =   checkpoint[f'{old_prefix}.out_layers.0.weight']
    A__          =   checkpoint[f'{old_prefix}.out_layers.0.bias']
    A__          =   checkpoint[f'{old_prefix}.out_layers.3.weight']
    A__          =   checkpoint[f'{old_prefix}.out_layers.3.bias']

    if has_skip:
        A__          =   checkpoint[f'{old_prefix}.skip_connection.weight']
        A__          =   checkpoint[f'{old_prefix}.skip_connection.bias']

    return new_checkpoint
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]=None      )							->      Tuple:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   checkpoint[f'{old_prefix}.qkv.weight'].chunk(3    ,			dim=0      )
    A__      ,						A__      ,						A__          =   checkpoint[f'{old_prefix}.qkv.bias'].chunk(3    ,			dim=0      )

    A__          =   checkpoint[f'{old_prefix}.norm.weight']
    A__          =   checkpoint[f'{old_prefix}.norm.bias']

    A__          =   weight_q.squeeze(-1      ).squeeze(-1      )
    A__          =   bias_q.squeeze(-1      ).squeeze(-1      )
    A__          =   weight_k.squeeze(-1      ).squeeze(-1      )
    A__          =   bias_k.squeeze(-1      ).squeeze(-1      )
    A__          =   weight_v.squeeze(-1      ).squeeze(-1      )
    A__          =   bias_v.squeeze(-1      ).squeeze(-1      )

    A__          =   (
        checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1      ).squeeze(-1      )
    )
    A__          =   checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1      ).squeeze(-1      )

    return new_checkpoint
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Dict      )							->      str:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'      )
    A__          =   {}

    A__          =   checkpoint['time_embed.0.weight']
    A__          =   checkpoint['time_embed.0.bias']
    A__          =   checkpoint['time_embed.2.weight']
    A__          =   checkpoint['time_embed.2.bias']

    if unet_config["num_class_embeds"] is not None:
        A__          =   checkpoint['label_emb.weight']

    A__          =   checkpoint['input_blocks.0.0.weight']
    A__          =   checkpoint['input_blocks.0.0.bias']

    A__          =   unet_config['down_block_types']
    A__          =   unet_config['layers_per_block']
    A__          =   unet_config['attention_head_dim']
    A__          =   unet_config['block_out_channels']
    A__          =   1
    A__          =   channels_list[0]

    for i, layer_type in enumerate(SCREAMING_SNAKE_CASE__      ):
        A__          =   channels_list[i]
        A__          =   current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(SCREAMING_SNAKE_CASE__      ):
                A__          =   f'down_blocks.{i}.resnets.{j}'
                A__          =   f'input_blocks.{current_layer}.0'
                A__          =   True if j == 0 and downsample_block_has_skip else False
                A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			has_skip=SCREAMING_SNAKE_CASE__      )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(SCREAMING_SNAKE_CASE__      ):
                A__          =   f'down_blocks.{i}.resnets.{j}'
                A__          =   f'input_blocks.{current_layer}.0'
                A__          =   True if j == 0 and downsample_block_has_skip else False
                A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			has_skip=SCREAMING_SNAKE_CASE__      )
                A__          =   f'down_blocks.{i}.attentions.{j}'
                A__          =   f'input_blocks.{current_layer}.1'
                A__          =   convert_attention(
                    SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                current_layer += 1

        if i != len(SCREAMING_SNAKE_CASE__      ) - 1:
            A__          =   f'down_blocks.{i}.downsamplers.0'
            A__          =   f'input_blocks.{current_layer}.0'
            A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
            current_layer += 1

        A__          =   current_channels

    # hardcoded the mid-block for now
    A__          =   'mid_block.resnets.0'
    A__          =   'middle_block.0'
    A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   'mid_block.attentions.0'
    A__          =   'middle_block.1'
    A__          =   convert_attention(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   'mid_block.resnets.1'
    A__          =   'middle_block.2'
    A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    A__          =   0
    A__          =   unet_config['up_block_types']

    for i, layer_type in enumerate(SCREAMING_SNAKE_CASE__      ):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1      ):
                A__          =   f'up_blocks.{i}.resnets.{j}'
                A__          =   f'output_blocks.{current_layer}.0'
                A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			has_skip=SCREAMING_SNAKE_CASE__      )
                current_layer += 1

            if i != len(SCREAMING_SNAKE_CASE__      ) - 1:
                A__          =   f'up_blocks.{i}.upsamplers.0'
                A__          =   f'output_blocks.{current_layer-1}.1'
                A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1      ):
                A__          =   f'up_blocks.{i}.resnets.{j}'
                A__          =   f'output_blocks.{current_layer}.0'
                A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			has_skip=SCREAMING_SNAKE_CASE__      )
                A__          =   f'up_blocks.{i}.attentions.{j}'
                A__          =   f'output_blocks.{current_layer}.1'
                A__          =   convert_attention(
                    SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                current_layer += 1

            if i != len(SCREAMING_SNAKE_CASE__      ) - 1:
                A__          =   f'up_blocks.{i}.upsamplers.0'
                A__          =   f'output_blocks.{current_layer-1}.2'
                A__          =   convert_resnet(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    A__          =   checkpoint['out.0.weight']
    A__          =   checkpoint['out.0.bias']
    A__          =   checkpoint['out.2.weight']
    A__          =   checkpoint['out.2.bias']

    return new_checkpoint


if __name__ == "__main__":
     lowercase_       		=  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.")

     lowercase_       		=  parser.parse_args()
     lowercase_       		=  strabool(args.class_cond)

     lowercase_       		=  os.path.basename(args.unet_path)
     print(f"""Checkpoint: {ckpt_name}""")

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

     if not args.class_cond:
          lowercase_       		=  None

     lowercase_       		=  con_pt_to_diffuser(args.unet_path, unet_config)

     lowercase_       		=  UNetaDModel(**unet_config)
     image_unet.load_state_dict(converted_unet_ckpt)

     # Get scheduler config
     if "cd" in ckpt_name or "test" in ckpt_name:
          lowercase_       		=  CD_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
          lowercase_       		=  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)):
          lowercase_       		=  CT_LSUN_256_SCHEDULER_CONFIG
     else:
          raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

     lowercase_       		=  CMStochasticIterativeScheduler(**scheduler_config)

     lowercase_       		=  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(DPMSolverSinglestepScheduler,)
    lowerCamelCase			=							(('num_inference_steps', 25),)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'prediction_type': 'epsilon',
            'thresholding': False,
            'sample_max_value': 1.0,
            'algorithm_type': 'dpmsolver++',
            'solver_type': 'midpoint',
            'lambda_min_clipped': -float('inf'		),
            'variance_type': None,
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=0,**lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)
            # copy over dummy past residuals
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   scheduler_class.from_pretrained(lowercase_		)
                new_scheduler.set_timesteps(lowercase_		)
                # copy over dummy past residuals
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__      ,						A__          =   sample, sample
            for t in range(lowercase_,time_step + scheduler.config.solver_order + 1		):
                A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
                A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

                assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       List[str]		)->    List[Any]:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any]=0,**lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)

            # copy over dummy past residuals (must be after setting timesteps)
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   scheduler_class.from_pretrained(lowercase_		)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(lowercase_		)

                # copy over dummy past residual (must be after setting timesteps)
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Optional[int]=None,**lowercase_ :       int		)->    int:





        '''simple docstring'''







        if scheduler is None:
            A__          =   self.scheduler_classes[0]
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)

        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(**lowercase_		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        return sample


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   5_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_574		) < 1E-3


    def 						snake_case__     (      self :       Optional[Any]		)->    List[Any]:





        '''simple docstring'''







        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3

        A__          =   DEISMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
        A__          =   UniPCMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        self.check_over_configs(thresholding=lowercase_		)
        for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
                for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            thresholding=lowercase_,prediction_type=lowercase_,sample_max_value=lowercase_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
                for order in [1, 2, 3]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        A__          =   self.full_loop(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"


    def 						snake_case__     (      self :       Optional[int]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowercase_		)
        self.check_over_configs(lower_order_final=lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(variance_type=lowercase_		)
        self.check_over_configs(variance_type='learned_range'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=lowercase_,time_step=0		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_248		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction'		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.1_453		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.0_649		) < 1E-3


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        assert sample.dtype == torch.floataa

