
'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowercase					:							List[str]   =      {
    "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",
}

lowercase					:							Optional[int]   =      {
    "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",
}

lowercase					:							Optional[Any]   =      {
    "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",
}

lowercase					:							Any   =      {
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowercase					:							int   =      {
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

lowercase					:							Optional[int]   =      {
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[str]:
	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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A=False			) ->			Optional[Any]:
	_snake_case     		=	checkpoint[F'{old_prefix}.in_layers.0.weight']
	_snake_case     		=	checkpoint[F'{old_prefix}.in_layers.0.bias']
	_snake_case     		=	checkpoint[F'{old_prefix}.in_layers.2.weight']
	_snake_case     		=	checkpoint[F'{old_prefix}.in_layers.2.bias']
	_snake_case     		=	checkpoint[F'{old_prefix}.emb_layers.1.weight']
	_snake_case     		=	checkpoint[F'{old_prefix}.emb_layers.1.bias']
	_snake_case     		=	checkpoint[F'{old_prefix}.out_layers.0.weight']
	_snake_case     		=	checkpoint[F'{old_prefix}.out_layers.0.bias']
	_snake_case     		=	checkpoint[F'{old_prefix}.out_layers.3.weight']
	_snake_case     		=	checkpoint[F'{old_prefix}.out_layers.3.bias']

	if has_skip:
		_snake_case     		=	checkpoint[F'{old_prefix}.skip_connection.weight']
		_snake_case     		=	checkpoint[F'{old_prefix}.skip_connection.bias']

	return new_checkpoint







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A=None			) ->			List[Any]:
	_snake_case		,				_snake_case		,				_snake_case     		=	checkpoint[F'{old_prefix}.qkv.weight'].chunk(3    ,       dim=0			)
	_snake_case		,				_snake_case		,				_snake_case     		=	checkpoint[F'{old_prefix}.qkv.bias'].chunk(3    ,       dim=0			)

	_snake_case     		=	checkpoint[F'{old_prefix}.norm.weight']
	_snake_case     		=	checkpoint[F'{old_prefix}.norm.bias']

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

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

	return new_checkpoint







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Dict:
	_snake_case     		=	torch.load(__A    ,       map_location='cpu'			)
	_snake_case     		=	{}

	_snake_case     		=	checkpoint['time_embed.0.weight']
	_snake_case     		=	checkpoint['time_embed.0.bias']
	_snake_case     		=	checkpoint['time_embed.2.weight']
	_snake_case     		=	checkpoint['time_embed.2.bias']

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

	_snake_case     		=	checkpoint['input_blocks.0.0.weight']
	_snake_case     		=	checkpoint['input_blocks.0.0.bias']

	_snake_case     		=	unet_config['down_block_types']
	_snake_case     		=	unet_config['layers_per_block']
	_snake_case     		=	unet_config['attention_head_dim']
	_snake_case     		=	unet_config['block_out_channels']
	_snake_case     		=	1
	_snake_case     		=	channels_list[0]

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

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

		if i != len(__A			) - 1:
			_snake_case     		=	F'down_blocks.{i}.downsamplers.0'
			_snake_case     		=	F'input_blocks.{current_layer}.0'
			_snake_case     		=	convert_resnet(__A    ,       __A    ,       __A    ,       __A			)
			current_layer += 1

		_snake_case     		=	current_channels

	# hardcoded the mid-block for now
	_snake_case     		=	'mid_block.resnets.0'
	_snake_case     		=	'middle_block.0'
	_snake_case     		=	convert_resnet(__A    ,       __A    ,       __A    ,       __A			)
	_snake_case     		=	'mid_block.attentions.0'
	_snake_case     		=	'middle_block.1'
	_snake_case     		=	convert_attention(__A    ,       __A    ,       __A    ,       __A    ,       __A			)
	_snake_case     		=	'mid_block.resnets.1'
	_snake_case     		=	'middle_block.2'
	_snake_case     		=	convert_resnet(__A    ,       __A    ,       __A    ,       __A			)

	_snake_case     		=	0
	_snake_case     		=	unet_config['up_block_types']

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

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

			if i != len(__A			) - 1:
				_snake_case     		=	F'up_blocks.{i}.upsamplers.0'
				_snake_case     		=	F'output_blocks.{current_layer-1}.2'
				_snake_case     		=	convert_resnet(__A    ,       __A    ,       __A    ,       __A			)

	_snake_case     		=	checkpoint['out.0.weight']
	_snake_case     		=	checkpoint['out.0.bias']
	_snake_case     		=	checkpoint['out.2.weight']
	_snake_case     		=	checkpoint['out.2.bias']

	return new_checkpoint


if __name__ == "__main__":
					lowercase					:							Union[str, Any]   =      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.")

					lowercase					:							List[Any]   =      parser.parse_args()
					lowercase					:							Any   =      strabool(args.class_cond)

					lowercase					:							Optional[int]   =      os.path.basename(args.unet_path)
					print(F'''Checkpoint: {ckpt_name}''')

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

					if not args.class_cond:
										lowercase					:							List[str]   =      None

					lowercase					:							str   =      con_pt_to_diffuser(args.unet_path, unet_config)

					lowercase					:							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:
										lowercase					:							Any   =      CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										lowercase					:							List[Any]   =      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)):
										lowercase					:							int   =      CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

					lowercase					:							Optional[int]   =      CMStochasticIterativeScheduler(**scheduler_config)

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



'''simple docstring'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

