"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int      ):
     lowercase_	:				str													=	'huggingface/label-files'
     lowercase_	:				Tuple													=	'imagenet-1k-id2label.json'
     lowercase_	:				str													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			repo_type='dataset'      )     ,			'r'      )      )
     lowercase_	:				Union[str, Any]													=	{int(__SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
     lowercase_	:				Optional[Any]													=	{v: k for k, v in idalabel.items()}

     lowercase_	:				Optional[int]													=	'std_conv' if 'bit' in model_name else False

     # note that when using BiT as backbone for ViT-hybrid checkpoints,
     # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
     # config.conv_layer = "std_conv_same"
     lowercase_	:				Any													=	BitConfig(
         conv_layer=__SCREAMING_SNAKE_CASE     ,			num_labels=10_00     ,			idalabel=__SCREAMING_SNAKE_CASE     ,			labelaid=__SCREAMING_SNAKE_CASE     ,			)

     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[str]      ):
     if "stem.conv" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('stem.conv'     ,			'bit.embedder.convolution'      )
     if "blocks" in name:
          lowercase_	:				Any													=	name.replace('blocks'     ,			'layers'      )
     if "head.fc" in name:
          lowercase_	:				List[Any]													=	name.replace('head.fc'     ,			'classifier.1'      )
     if name.startswith('norm'      ):
          lowercase_	:				Union[str, Any]													=	'bit.' + name
     if "bit" not in name and "classifier" not in name:
          lowercase_	:				Tuple													=	'bit.encoder.' + name

     return name



def 			lowercase__(       ):
     lowercase_	:				str													=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase_	:				int													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,			stream=__SCREAMING_SNAKE_CASE      ).raw      )
     return im



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Tuple=False      ):
     lowercase_	:				Any													=	get_config(__SCREAMING_SNAKE_CASE      )

     # load original model from timm
     lowercase_	:				int													=	create_model(__SCREAMING_SNAKE_CASE     ,			pretrained=__SCREAMING_SNAKE_CASE      )
     timm_model.eval()

     # load state_dict of original model
     lowercase_	:				Any													=	timm_model.state_dict()
     for key in state_dict.copy().keys():
          lowercase_	:				Any													=	state_dict.pop(__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[str]													=	val.squeeze() if 'head' in key else val

     # load HuggingFace model
     lowercase_	:				List[str]													=	BitForImageClassification(__SCREAMING_SNAKE_CASE      )
     model.eval()
     model.load_state_dict(__SCREAMING_SNAKE_CASE      )

     # create image processor
     lowercase_	:				List[Any]													=	create_transform(**resolve_data_config({}     ,			model=__SCREAMING_SNAKE_CASE      )      )
     lowercase_	:				List[str]													=	transform.transforms

     lowercase_	:				List[Any]													=	{
         'bilinear': PILImageResampling.BILINEAR,
         'bicubic': PILImageResampling.BICUBIC,
         'nearest': PILImageResampling.NEAREST,
     }

     lowercase_	:				str													=	BitImageProcessor(
         do_resize=__SCREAMING_SNAKE_CASE     ,			size={'shortest_edge': timm_transforms[0].size}     ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]     ,			do_center_crop=__SCREAMING_SNAKE_CASE     ,			crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}     ,			do_normalize=__SCREAMING_SNAKE_CASE     ,			image_mean=timm_transforms[-1].mean.tolist()     ,			image_std=timm_transforms[-1].std.tolist()     ,			)

     lowercase_	:				Tuple													=	prepare_img()
     lowercase_	:				Tuple													=	transform(__SCREAMING_SNAKE_CASE      ).unsqueeze(0      )
     lowercase_	:				List[Any]													=	processor(__SCREAMING_SNAKE_CASE     ,			return_tensors='pt'      ).pixel_values

     # verify pixel values
     assert torch.allclose(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # verify logits
     with torch.no_grad():
          lowercase_	:				Optional[int]													=	model(__SCREAMING_SNAKE_CASE      )
          lowercase_	:				Any													=	outputs.logits

     print('Logits:'     ,			logits[0, :3]      )
     print('Predicted class:'     ,			model.config.idalabel[logits.argmax(-1      ).item()]      )
     lowercase_	:				Optional[int]													=	timm_model(__SCREAMING_SNAKE_CASE      )
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(__SCREAMING_SNAKE_CASE     ,			outputs.logits     ,			atol=1E-3      )
     print('Looks ok!'      )

     if pytorch_dump_folder_path is not None:
          Path(__SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE      )
          print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''      )
          model.save_pretrained(__SCREAMING_SNAKE_CASE      )
          processor.save_pretrained(__SCREAMING_SNAKE_CASE      )

     if push_to_hub:
          print(F'''Pushing model {model_name} and processor to the hub'''      )
          model.push_to_hub(F'''ybelkada/{model_name}'''      )
          processor.push_to_hub(F'''ybelkada/{model_name}'''      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="resnetv2_50x1_bitm",
           type=str,
           help="Name of the BiT timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub",
           action="store_true",
           help="Whether to push the model to the hub.",
       )

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     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__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str=False      ):
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.skip_connection.weight''']
          lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str=None      ):
     lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,			dim=0      )
     lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,			dim=0      )

     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.norm.weight''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.norm.bias''']

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

     lowercase_	:				List[str]													=	(
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1      ).squeeze(-1      )
     )
     lowercase_	:				int													=	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1      ).squeeze(-1      )

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Any													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )
     lowercase_	:				Any													=	{}

     lowercase_	:				Dict													=	checkpoint['time_embed.0.weight']
     lowercase_	:				Optional[Any]													=	checkpoint['time_embed.0.bias']
     lowercase_	:				List[str]													=	checkpoint['time_embed.2.weight']
     lowercase_	:				Any													=	checkpoint['time_embed.2.bias']

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

     lowercase_	:				Optional[Any]													=	checkpoint['input_blocks.0.0.weight']
     lowercase_	:				Optional[int]													=	checkpoint['input_blocks.0.0.bias']

     lowercase_	:				Dict													=	unet_config['down_block_types']
     lowercase_	:				int													=	unet_config['layers_per_block']
     lowercase_	:				int													=	unet_config['attention_head_dim']
     lowercase_	:				Any													=	unet_config['block_out_channels']
     lowercase_	:				Optional[Any]													=	1
     lowercase_	:				Tuple													=	channels_list[0]

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

          lowercase_	:				int													=	current_channels

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

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

     lowercase_	:				List[str]													=	checkpoint['out.0.weight']
     lowercase_	:				Dict													=	checkpoint['out.0.bias']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.weight']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.bias']

     return new_checkpoint


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

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

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

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

       if not args.class_cond:
              __SCREAMING_SNAKE_CASE    	=None

       __SCREAMING_SNAKE_CASE    	=con_pt_to_diffuser(args.unet_path, unet_config)

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

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

       __SCREAMING_SNAKE_CASE    	=CMStochasticIterativeScheduler(**scheduler_config)

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

