

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import BridgeTowerImageProcessor





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Dict[str, int] = None     , __lowerCamelCase					:			int = 32     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Union[int, float] = 1 / 255     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]     , __lowerCamelCase					:			Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Optional[int]=7     , __lowerCamelCase					:			str=30     , __lowerCamelCase					:			Any=400     , __lowerCamelCase					:			Tuple=3     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   do_resize
       SCREAMING_SNAKE_CASE										=   size if size is not None else {"shortest_edge": 288}
       SCREAMING_SNAKE_CASE										=   size_divisor
       SCREAMING_SNAKE_CASE										=   do_rescale
       SCREAMING_SNAKE_CASE										=   rescale_factor
       SCREAMING_SNAKE_CASE										=   do_normalize
       SCREAMING_SNAKE_CASE										=   do_center_crop
       SCREAMING_SNAKE_CASE										=   image_mean
       SCREAMING_SNAKE_CASE										=   image_std
       SCREAMING_SNAKE_CASE										=   do_pad
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   min_resolution
       SCREAMING_SNAKE_CASE										=   max_resolution





   def      _snake_case		(       self					:			Tuple			):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[str]=False			):
       if not batched:
           SCREAMING_SNAKE_CASE										=   self.size["shortest_edge"]
           SCREAMING_SNAKE_CASE										=   image_inputs[0]
           if isinstance(__lowerCamelCase     , Image.Image			):
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   image.size
           else:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   image.shape[1], image.shape[2]
           SCREAMING_SNAKE_CASE										=   size / min(__lowerCamelCase     , __lowerCamelCase			)
           if h < w:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   size, scale * w
           else:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   scale * h, size

           SCREAMING_SNAKE_CASE										=   int((1333 / 800) * size			)
           if max(__lowerCamelCase     , __lowerCamelCase			) > max_size:
               SCREAMING_SNAKE_CASE										=   max_size / max(__lowerCamelCase     , __lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   newh * scale
               SCREAMING_SNAKE_CASE										=   neww * scale

           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   int(newh + 0.5			), int(neww + 0.5			)
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

       else:
           SCREAMING_SNAKE_CASE										=   []
           for image in image_inputs:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.get_expected_values([image]			)
               expected_values.append((expected_height, expected_width)			)
           SCREAMING_SNAKE_CASE										=   max(__lowerCamelCase     , key=lambda __lowerCamelCase			: item[0]			)[0]
           SCREAMING_SNAKE_CASE										=   max(__lowerCamelCase     , key=lambda __lowerCamelCase			: item[1]			)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      BridgeTowerImageProcessor if is_vision_available() else None





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   BridgeTowerImageProcessingTester(self			)





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       return self.image_processor_tester.prepare_image_processor_dict()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_mean"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_std"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_normalize"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_resize"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "size"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "size_divisor"			)			)





   def      _snake_case		(       self					:			Tuple			):
       pass





   def      _snake_case		(       self					:			int			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , Image.Image			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (1, self.image_processor_tester.num_channels, expected_height, expected_width)     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase     , batched=__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     , )





   def      _snake_case		(       self					:			Any			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , numpify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , np.ndarray			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (1, self.image_processor_tester.num_channels, expected_height, expected_width)     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase     , batched=__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     , )





   def      _snake_case		(       self					:			Optional[int]			):
       # Initialize image processor
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , torchify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , torch.Tensor			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (1, self.image_processor_tester.num_channels, expected_height, expected_width)     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.image_processor_tester.get_expected_values(__lowerCamelCase     , batched=__lowerCamelCase			)
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     , )







'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase									=   {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    '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									=   {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    '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									=   {
    '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									=   {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 		_UpperCAmelCase (				__A				: Tuple   ):
				if isinstance(__A    ,		__A   ):
								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 		_UpperCAmelCase (				__A				: str    ,		__A				: Tuple    ,		__A				: Optional[int]    ,		__A				: int    ,		__A				: Optional[Any]=False   ):
				a_		:	str									=   checkpoint[f'{old_prefix}.in_layers.0.weight']
				a_		:	Dict									=   checkpoint[f'{old_prefix}.in_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.in_layers.2.weight']
				a_		:	Any									=   checkpoint[f'{old_prefix}.in_layers.2.bias']
				a_		:	Any									=   checkpoint[f'{old_prefix}.emb_layers.1.weight']
				a_		:	int									=   checkpoint[f'{old_prefix}.emb_layers.1.bias']
				a_		:	Union[str, Any]									=   checkpoint[f'{old_prefix}.out_layers.0.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.weight']
				a_		:	List[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.bias']

				if has_skip:
								a_		:	int									=   checkpoint[f'{old_prefix}.skip_connection.weight']
								a_		:	List[Any]									=   checkpoint[f'{old_prefix}.skip_connection.bias']

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]    ,		__A				: List[str]=None   ):
				a_					,      a_					,      a_		:	Any									=   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_		:	List[str]									=   checkpoint[f'{old_prefix}.norm.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.norm.bias']

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

				a_		:	Union[str, Any]									=   (
				    checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1   ).squeeze(-1   )
				)
				a_		:	Any									=   checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1   ).squeeze(-1   )

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str   ):
				a_		:	int									=   torch.load(__A    ,		map_location='''cpu'''   )
				a_		:	Union[str, Any]									=   {}

				a_		:	int									=   checkpoint['''time_embed.0.weight''']
				a_		:	Tuple									=   checkpoint['''time_embed.0.bias''']
				a_		:	List[Any]									=   checkpoint['''time_embed.2.weight''']
				a_		:	Union[str, Any]									=   checkpoint['''time_embed.2.bias''']

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

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

				a_		:	Optional[int]									=   unet_config['''down_block_types''']
				a_		:	Union[str, Any]									=   unet_config['''layers_per_block''']
				a_		:	Optional[Any]									=   unet_config['''attention_head_dim''']
				a_		:	str									=   unet_config['''block_out_channels''']
				a_		:	Any									=   1
				a_		:	Union[str, Any]									=   channels_list[0]

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

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

								if i != len(__A   ) - 1:
												a_		:	Dict									=   f'down_blocks.{i}.downsamplers.0'
												a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
												a_		:	Union[str, Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
												current_layer += 1

								a_		:	Any									=   current_channels

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

				a_		:	str									=   0
				a_		:	Optional[int]									=   unet_config['''up_block_types''']

				for i, layer_type in enumerate(__A   ):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Tuple									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'output_blocks.{current_layer}.0'
																a_		:	Optional[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

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

												if i != len(__A   ) - 1:
																a_		:	Dict									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Any									=   f'output_blocks.{current_layer-1}.2'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	Dict									=   checkpoint['''out.0.weight''']
				a_		:	int									=   checkpoint['''out.0.bias''']
				a_		:	int									=   checkpoint['''out.2.weight''']
				a_		:	str									=   checkpoint['''out.2.bias''']

				return new_checkpoint


if __name__ == "__main__":
		__lowerCAmelCase									=   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									=   parser.parse_args()
		__lowerCAmelCase									=   strabool(args.class_cond)

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

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

		if not args.class_cond:
				__lowerCAmelCase									=   None

		__lowerCAmelCase									=   con_pt_to_diffuser(args.unet_path, unet_config)

		__lowerCAmelCase									=   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									=   CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   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									=   CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		__lowerCAmelCase									=   CMStochasticIterativeScheduler(**scheduler_config)

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




