






'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase     (    _lowercase		):
							a    				=	(IPNDMScheduler,)
							a    				=	(("""num_inference_steps""", 50),)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							**UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	{"""num_train_timesteps""": 1_000}
														config.update(**lowerCamelCase_					)
														return config




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[Any]=0      ,							**UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Union[str, Any]	  =	dict(self.forward_default_kwargs					)
														lowerCamelCase__ :     Optional[int]	  =	kwargs.pop("""num_inference_steps"""      ,							lowerCamelCase_					)
														lowerCamelCase__ :     List[Any]	  =	self.dummy_sample
														lowerCamelCase__ :     int	  =	0.1 * sample
														lowerCamelCase__ :     List[Any]	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Any	  =	self.get_scheduler_config(**lowerCamelCase_					)
																					lowerCamelCase__ :     Any	  =	scheduler_class(**lowerCamelCase_					)
																					scheduler.set_timesteps(lowerCamelCase_					)
																					# copy over dummy past residuals
																					lowerCamelCase__ :     Optional[Any]	  =	dummy_past_residuals[:]

																					if time_step is None:
																												lowerCamelCase__ :     Any	  =	scheduler.timesteps[len(scheduler.timesteps					) // 2]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase_					)
																												lowerCamelCase__ :     int	  =	scheduler_class.from_pretrained(lowerCamelCase_					)
																												new_scheduler.set_timesteps(lowerCamelCase_					)
																												# copy over dummy past residuals
																												lowerCamelCase__ :     int	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     List[Any]	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample
																					lowerCamelCase__ :     Optional[Any]	  =	new_scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample

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

																					lowerCamelCase__ :     int	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample
																					lowerCamelCase__ :     Tuple	  =	new_scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample

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




							def 		lowerCamelCase_    (							self:    str					):
														pass




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Dict=0      ,							**UpperCamelCase__:    int					):
														lowerCamelCase__ :     Any	  =	dict(self.forward_default_kwargs					)
														lowerCamelCase__ :     Optional[Any]	  =	kwargs.pop("""num_inference_steps"""      ,							lowerCamelCase_					)
														lowerCamelCase__ :     Optional[int]	  =	self.dummy_sample
														lowerCamelCase__ :     Dict	  =	0.1 * sample
														lowerCamelCase__ :     Optional[Any]	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     Union[str, Any]	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     Dict	  =	scheduler_class(**lowerCamelCase_					)
																					scheduler.set_timesteps(lowerCamelCase_					)

																					# copy over dummy past residuals (must be after setting timesteps)
																					lowerCamelCase__ :     Union[str, Any]	  =	dummy_past_residuals[:]

																					if time_step is None:
																												lowerCamelCase__ :     int	  =	scheduler.timesteps[len(scheduler.timesteps					) // 2]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(lowerCamelCase_					)
																												lowerCamelCase__ :     List[Any]	  =	scheduler_class.from_pretrained(lowerCamelCase_					)
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(lowerCamelCase_					)

																												# copy over dummy past residual (must be after setting timesteps)
																												lowerCamelCase__ :     List[Any]	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     List[str]	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample
																					lowerCamelCase__ :     Dict	  =	new_scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample

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

																					lowerCamelCase__ :     Optional[int]	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample
																					lowerCamelCase__ :     Dict	  =	new_scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample

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




							def 		lowerCamelCase_    (							self:    Tuple      ,							**UpperCamelCase__:    Optional[int]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.scheduler_classes[0]
														lowerCamelCase__ :     Union[str, Any]	  =	self.get_scheduler_config(**lowerCamelCase_					)
														lowerCamelCase__ :     List[Any]	  =	scheduler_class(**lowerCamelCase_					)

														lowerCamelCase__ :     int	  =	10
														lowerCamelCase__ :     Dict	  =	self.dummy_model()
														lowerCamelCase__ :     List[Any]	  =	self.dummy_sample_deter
														scheduler.set_timesteps(lowerCamelCase_					)

														for i, t in enumerate(scheduler.timesteps					):
																					lowerCamelCase__ :     Any	  =	model(lowerCamelCase_      ,							lowerCamelCase_					)
																					lowerCamelCase__ :     Dict	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_					).prev_sample

														for i, t in enumerate(scheduler.timesteps					):
																					lowerCamelCase__ :     Optional[Any]	  =	model(lowerCamelCase_      ,							lowerCamelCase_					)
																					lowerCamelCase__ :     Optional[int]	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_					).prev_sample

														return sample




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Union[str, Any]	  =	dict(self.forward_default_kwargs					)

														lowerCamelCase__ :     Optional[int]	  =	kwargs.pop("""num_inference_steps"""      ,							lowerCamelCase_					)

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__ :     int	  =	self.get_scheduler_config()
																					lowerCamelCase__ :     Dict	  =	scheduler_class(**lowerCamelCase_					)

																					lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_sample
																					lowerCamelCase__ :     Any	  =	0.1 * sample

																					if num_inference_steps is not None and hasattr(lowerCamelCase_      ,							"""set_timesteps"""					):
																												scheduler.set_timesteps(lowerCamelCase_					)
																					elif num_inference_steps is not None and not hasattr(lowerCamelCase_      ,							"""set_timesteps"""					):
																												lowerCamelCase__ :     Dict	  =	num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					lowerCamelCase__ :     Tuple	  =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																					lowerCamelCase__ :     Any	  =	dummy_past_residuals[:]

																					lowerCamelCase__ :     Tuple	  =	scheduler.timesteps[5]
																					lowerCamelCase__ :     List[Any]	  =	scheduler.timesteps[6]

																					lowerCamelCase__ :     Any	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample
																					lowerCamelCase__ :     Optional[Any]	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample

																					self.assertEqual(output_a.shape      ,							sample.shape					)
																					self.assertEqual(output_a.shape      ,							output_a.shape					)

																					lowerCamelCase__ :     int	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample
																					lowerCamelCase__ :     List[Any]	  =	scheduler.step(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_					).prev_sample

																					self.assertEqual(output_a.shape      ,							sample.shape					)
																					self.assertEqual(output_a.shape      ,							output_a.shape					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														for timesteps in [100, 1_000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase_      ,							time_step=lowerCamelCase_					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														for t, num_inference_steps in zip([1, 5, 10]      ,							[10, 50, 100]					):
																					self.check_over_forward(num_inference_steps=lowerCamelCase_      ,							time_step=lowerCamelCase_					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.full_loop()
														lowerCamelCase__ :     Optional[Any]	  =	torch.mean(torch.abs(lowerCamelCase_					)					)

														assert abs(result_mean.item() - 2_540_529					) < 10









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''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''',
}

_A     :      Union[str, Any]   					={
    '''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''',
}

_A     :      Dict   					={
    '''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     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

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

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

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

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

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

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

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

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

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

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

							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     Any	  =	unet_config["""up_block_types"""]

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

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

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


if __name__ == "__main__":
			_A     :      Tuple   					=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     :      Tuple   					=parser.parse_args()
			_A     :      Optional[int]   					=strabool(args.class_cond)

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

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

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      Optional[int]   					=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     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=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     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

			_A     :      Optional[Any]   					=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

