



'''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      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
				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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      int:
		_a     :      Tuple        =							checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
		_a     :      int        =							checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
		_a     :      Tuple        =							checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
		_a     :      List[str]        =							checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
		_a     :      Optional[int]        =							checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
		_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
		_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
		_a     :      List[str]        =							checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
		_a     :      int        =							checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
		_a     :      int        =							checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

		if has_skip:
				_a     :      List[Any]        =							checkpoint[f"""{old_prefix}.skip_connection.weight"""]
				_a     :      Any        =							checkpoint[f"""{old_prefix}.skip_connection.bias"""]

		return new_checkpoint



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None  )			->      Optional[int]:
		_a       ,					_a       ,					_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3						,			dim=0  )
		_a       ,					_a       ,					_a     :      Optional[Any]        =							checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3						,			dim=0  )

		_a     :      int        =							checkpoint[f"""{old_prefix}.norm.weight"""]
		_a     :      Dict        =							checkpoint[f"""{old_prefix}.norm.bias"""]

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

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

		return new_checkpoint



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      List[Any]        =							torch.load(lowerCAmelCase_						,			map_location='cpu'  )
		_a     :      List[Any]        =							{}

		_a     :      List[str]        =							checkpoint['time_embed.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.0.bias']
		_a     :      Optional[Any]        =							checkpoint['time_embed.2.weight']
		_a     :      str        =							checkpoint['time_embed.2.bias']

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

		_a     :      Optional[Any]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      List[Any]        =							checkpoint['input_blocks.0.0.bias']

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

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

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

				if i != len(lowerCAmelCase_  ) - 1:
						_a     :      Optional[Any]        =							f"""down_blocks.{i}.downsamplers.0"""
						_a     :      Tuple        =							f"""input_blocks.{current_layer}.0"""
						_a     :      str        =							convert_resnet(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
						current_layer += 1

				_a     :      List[str]        =							current_channels

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

		_a     :      Union[str, Any]        =							0
		_a     :      str        =							unet_config['up_block_types']

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

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

						if i != len(lowerCAmelCase_  ) - 1:
								_a     :      Any        =							f"""up_blocks.{i}.upsamplers.0"""
								_a     :      int        =							f"""output_blocks.{current_layer-1}.2"""
								_a     :      List[Any]        =							convert_resnet(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      List[str]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      Tuple        =							checkpoint['out.2.weight']
		_a     :      Tuple        =							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)









def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
