



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''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

__A       =						{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

__A       =						{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Any:






						'''simple docstring'''



						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 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any=False    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

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

						return new_checkpoint



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Any=None    )				->			Any:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3      ,dim=0    )
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3      ,dim=0    )

						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.norm.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.norm.bias"""]

						UpperCAmelCase_=       weight_q.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       bias_q.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       weight_k.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       bias_k.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       weight_v.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       bias_v.squeeze(-1    ).squeeze(-1    )

						UpperCAmelCase_=       (
						    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1    ).squeeze(-1    )
						)
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1    ).squeeze(-1    )

						return new_checkpoint



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : int    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       {}

						UpperCAmelCase_=       checkpoint["""time_embed.0.weight"""]
						UpperCAmelCase_=       checkpoint["""time_embed.0.bias"""]
						UpperCAmelCase_=       checkpoint["""time_embed.2.weight"""]
						UpperCAmelCase_=       checkpoint["""time_embed.2.bias"""]

						if unet_config["num_class_embeds"] is not None:
												UpperCAmelCase_=       checkpoint["""label_emb.weight"""]

						UpperCAmelCase_=       checkpoint["""input_blocks.0.0.weight"""]
						UpperCAmelCase_=       checkpoint["""input_blocks.0.0.bias"""]

						UpperCAmelCase_=       unet_config["""down_block_types"""]
						UpperCAmelCase_=       unet_config["""layers_per_block"""]
						UpperCAmelCase_=       unet_config["""attention_head_dim"""]
						UpperCAmelCase_=       unet_config["""block_out_channels"""]
						UpperCAmelCase_=       1
						UpperCAmelCase_=       channels_list[0]

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

												elif layer_type == "AttnDownBlock2D":
																		for j in range(lowerCAmelCase_    ):
																								UpperCAmelCase_=       F"""down_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""input_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       True if j == 0 and downsample_block_has_skip else False
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								UpperCAmelCase_=       F"""down_blocks.{i}.attentions.{j}"""
																								UpperCAmelCase_=       F"""input_blocks.{current_layer}.1"""
																								UpperCAmelCase_=       convert_attention(
																								    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																								current_layer += 1

												if i != len(lowerCAmelCase_    ) - 1:
																		UpperCAmelCase_=       F"""down_blocks.{i}.downsamplers.0"""
																		UpperCAmelCase_=       F"""input_blocks.{current_layer}.0"""
																		UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																		current_layer += 1

												UpperCAmelCase_=       current_channels

						# hardcoded the mid-block for now
						UpperCAmelCase_=       """mid_block.resnets.0"""
						UpperCAmelCase_=       """middle_block.0"""
						UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						UpperCAmelCase_=       """mid_block.attentions.0"""
						UpperCAmelCase_=       """middle_block.1"""
						UpperCAmelCase_=       convert_attention(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						UpperCAmelCase_=       """mid_block.resnets.1"""
						UpperCAmelCase_=       """middle_block.2"""
						UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       0
						UpperCAmelCase_=       unet_config["""up_block_types"""]

						for i, layer_type in enumerate(lowerCAmelCase_    ):
												if layer_type == "ResnetUpsampleBlock2D":
																		for j in range(layers_per_block + 1    ):
																								UpperCAmelCase_=       F"""up_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								current_layer += 1

																		if i != len(lowerCAmelCase_    ) - 1:
																								UpperCAmelCase_=       F"""up_blocks.{i}.upsamplers.0"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer-1}.1"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												elif layer_type == "AttnUpBlock2D":
																		for j in range(layers_per_block + 1    ):
																								UpperCAmelCase_=       F"""up_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								UpperCAmelCase_=       F"""up_blocks.{i}.attentions.{j}"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer}.1"""
																								UpperCAmelCase_=       convert_attention(
																								    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																								current_layer += 1

																		if i != len(lowerCAmelCase_    ) - 1:
																								UpperCAmelCase_=       F"""up_blocks.{i}.upsamplers.0"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer-1}.2"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       checkpoint["""out.0.weight"""]
						UpperCAmelCase_=       checkpoint["""out.0.bias"""]
						UpperCAmelCase_=       checkpoint["""out.2.weight"""]
						UpperCAmelCase_=       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)









def 	__a					(   lowerCAmelCase_       : Dict    )				->			Dict:






						'''simple docstring'''



						return [
						    {
						        0: [1, 2],
						        1: [0, 2],
						        2: [0, 1, 3, 5],
						        3: [2, 4],
						        4: [3],
						        5: [2, 6, 8],
						        6: [5, 7],
						        7: [6, 8],
						        8: [5, 7],
						    },
						    {
						        0: [6],
						        1: [9],
						        2: [4, 5],
						        3: [4],
						        4: [2, 3],
						        5: [2],
						        6: [0, 7],
						        7: [6],
						        8: [],
						        9: [1],
						    },
						    {
						        0: [4],
						        1: [6],
						        2: [],
						        3: [5, 6, 7],
						        4: [0, 6],
						        5: [3, 8, 9],
						        6: [1, 3, 4, 7],
						        7: [3, 6, 8, 9],
						        8: [5, 7],
						        9: [5, 7],
						    },
						    {
						        0: [1, 3],
						        1: [0, 2, 4],
						        2: [1, 3, 4],
						        3: [0, 2, 4],
						        4: [1, 2, 3],
						    },
						][index]



def 	__a					(   lowerCAmelCase_       : dict[int, list[int]]    )				->			list[tuple[int, int]]:






						'''simple docstring'''



						UpperCAmelCase_=       0
						UpperCAmelCase_=       len(lowerCAmelCase_    )  # No of vertices in graph
						UpperCAmelCase_=       [0] * n
						UpperCAmelCase_=       [False] * n

						def dfs(lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    ):
												UpperCAmelCase_=       True
												UpperCAmelCase_=       id_
												id_ += 1
												for to in graph[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,id_    )
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )
																								if id_ <= low[to]:
																														bridges.append((at, to) if at < to else (to, at)    )
																		else:
																								# This edge is a back edge and cannot be a bridge
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )

						UpperCAmelCase_=       []
						for i in range(lowerCAmelCase_    ):
												if not visited[i]:
																		dfs(lowerCAmelCase_      ,-1      ,lowerCAmelCase_      ,id_    )
						return bridges


if __name__ == "__main__":
	import doctest

	doctest.testmod()




