





"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a_ =			{
    """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""",
}

a_ =			{
    """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""",
}

a_ =			{
    """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""",
}

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

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

a_ =			{
    """num_train_timesteps""": 151,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}







def 		__lowercase     (   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 		__lowercase     (   snake_case_  :  Dict				,snake_case_  :  Optional[int]				,snake_case_  :  List[Any]				,snake_case_  :  Optional[int]				,snake_case_  :  List[Any]=False						)   ->Any:



    '''simple docstring'''







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

    if has_skip:
        __A    :						int						 =     checkpoint[F"""{old_prefix}.skip_connection.weight"""]
        __A    :						Optional[int]						 =     checkpoint[F"""{old_prefix}.skip_connection.bias"""]

    return new_checkpoint







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  List[str]				,snake_case_  :  List[Any]				,snake_case_  :  List[Any]				,snake_case_  :  List[str]=None						)   ->Tuple:



    '''simple docstring'''







    __A ,       __A ,       __A    :						Dict						 =     checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3				,dim=0						)
    __A ,       __A ,       __A    :						List[Any]						 =     checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3				,dim=0						)

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

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

    __A    :						Optional[Any]						 =     (
        checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1						).squeeze(-1						)
    )
    __A    :						Tuple						 =     checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1						).squeeze(-1						)

    return new_checkpoint







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Dict						)   ->str:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     torch.load(SCREAMING_SNAKE_CASE__				,map_location='''cpu'''						)
    __A    :						str						 =     {}

    __A    :						List[str]						 =     checkpoint['''time_embed.0.weight''']
    __A    :						Optional[int]						 =     checkpoint['''time_embed.0.bias''']
    __A    :						int						 =     checkpoint['''time_embed.2.weight''']
    __A    :						int						 =     checkpoint['''time_embed.2.bias''']

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

    __A    :						Dict						 =     checkpoint['''input_blocks.0.0.weight''']
    __A    :						str						 =     checkpoint['''input_blocks.0.0.bias''']

    __A    :						str						 =     unet_config['''down_block_types''']
    __A    :						Optional[int]						 =     unet_config['''layers_per_block''']
    __A    :						str						 =     unet_config['''attention_head_dim''']
    __A    :						str						 =     unet_config['''block_out_channels''']
    __A    :						List[Any]						 =     1
    __A    :						List[Any]						 =     channels_list[0]

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

        __A    :						Union[str, Any]						 =     current_channels

    # hardcoded the mid-block for now
    __A    :						Optional[Any]						 =     '''mid_block.resnets.0'''
    __A    :						int						 =     '''middle_block.0'''
    __A    :						int						 =     convert_resnet(SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__						)
    __A    :						List[Any]						 =     '''mid_block.attentions.0'''
    __A    :						List[str]						 =     '''middle_block.1'''
    __A    :						int						 =     convert_attention(SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__						)
    __A    :						Any						 =     '''mid_block.resnets.1'''
    __A    :						Optional[Any]						 =     '''middle_block.2'''
    __A    :						Any						 =     convert_resnet(SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__						)

    __A    :						int						 =     0
    __A    :						Optional[int]						 =     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    :						Optional[int]						 =     F"""up_blocks.{i}.resnets.{j}"""
                __A    :						Any						 =     F"""output_blocks.{current_layer}.0"""
                __A    :						Dict						 =     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    :						Optional[Any]						 =     F"""up_blocks.{i}.upsamplers.0"""
                __A    :						Optional[Any]						 =     F"""output_blocks.{current_layer-1}.1"""
                __A    :						Optional[Any]						 =     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    :						Tuple						 =     F"""up_blocks.{i}.resnets.{j}"""
                __A    :						Union[str, Any]						 =     F"""output_blocks.{current_layer}.0"""
                __A    :						Optional[int]						 =     convert_resnet(SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,has_skip=SCREAMING_SNAKE_CASE__						)
                __A    :						Union[str, Any]						 =     F"""up_blocks.{i}.attentions.{j}"""
                __A    :						str						 =     F"""output_blocks.{current_layer}.1"""
                __A    :						Optional[int]						 =     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    :						str						 =     F"""up_blocks.{i}.upsamplers.0"""
                __A    :						List[Any]						 =     F"""output_blocks.{current_layer-1}.2"""
                __A    :						List[Any]						 =     convert_resnet(SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__				,SCREAMING_SNAKE_CASE__						)

    __A    :						List[Any]						 =     checkpoint['''out.0.weight''']
    __A    :						Optional[Any]						 =     checkpoint['''out.0.bias''']
    __A    :						Union[str, Any]						 =     checkpoint['''out.2.weight''']
    __A    :						int						 =     checkpoint['''out.2.bias''']

    return new_checkpoint


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

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

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

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

       if not args.class_cond:
              a_ =			None

       a_ =			con_pt_to_diffuser(args.unet_path, unet_config)

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

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

       a_ =			CMStochasticIterativeScheduler(**scheduler_config)

       a_ =			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

