






"""simple docstring"""




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__magic_name__:					List[Any]							=     {
    "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",
}

__magic_name__:					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",
}

__magic_name__:					Tuple							=     {
    "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",
}

__magic_name__:					Tuple							=     {
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__magic_name__:					Optional[int]							=     {
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__magic_name__:					int							=     {
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						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,   _A,   _A,   _A,   _A=False			):






						"""simple docstring"""







						__magic_name__      :       List[str]       						= checkpoint[f'{old_prefix}.in_layers.0.weight']
						__magic_name__      :       List[str]       						= checkpoint[f'{old_prefix}.in_layers.0.bias']
						__magic_name__      :       Any       						= checkpoint[f'{old_prefix}.in_layers.2.weight']
						__magic_name__      :       str       						= checkpoint[f'{old_prefix}.in_layers.2.bias']
						__magic_name__      :       List[Any]       						= checkpoint[f'{old_prefix}.emb_layers.1.weight']
						__magic_name__      :       Union[str, Any]       						= checkpoint[f'{old_prefix}.emb_layers.1.bias']
						__magic_name__      :       Dict       						= checkpoint[f'{old_prefix}.out_layers.0.weight']
						__magic_name__      :       Optional[Any]       						= checkpoint[f'{old_prefix}.out_layers.0.bias']
						__magic_name__      :       Optional[Any]       						= checkpoint[f'{old_prefix}.out_layers.3.weight']
						__magic_name__      :       Dict       						= checkpoint[f'{old_prefix}.out_layers.3.bias']

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

						return new_checkpoint

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A=None			):






						"""simple docstring"""







						__magic_name__      :       str       						= checkpoint[f'{old_prefix}.qkv.weight'].chunk(3,   dim=0			)
						__magic_name__      :       int       						= checkpoint[f'{old_prefix}.qkv.bias'].chunk(3,   dim=0			)

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

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

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

						return new_checkpoint

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						__magic_name__      :       str       						= torch.load(_A,   map_location="""cpu"""			)
						__magic_name__      :       Tuple       						= {}

						__magic_name__      :       List[str]       						= checkpoint["""time_embed.0.weight"""]
						__magic_name__      :       Union[str, Any]       						= checkpoint["""time_embed.0.bias"""]
						__magic_name__      :       int       						= checkpoint["""time_embed.2.weight"""]
						__magic_name__      :       List[Any]       						= checkpoint["""time_embed.2.bias"""]

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

						__magic_name__      :       int       						= checkpoint["""input_blocks.0.0.weight"""]
						__magic_name__      :       List[str]       						= checkpoint["""input_blocks.0.0.bias"""]

						__magic_name__      :       Union[str, Any]       						= unet_config["""down_block_types"""]
						__magic_name__      :       int       						= unet_config["""layers_per_block"""]
						__magic_name__      :       str       						= unet_config["""attention_head_dim"""]
						__magic_name__      :       Tuple       						= unet_config["""block_out_channels"""]
						__magic_name__      :       List[Any]       						= 1
						__magic_name__      :       str       						= channels_list[0]

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

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

												if i != len(_A			) - 1:
																		__magic_name__      :       Dict       						= f'down_blocks.{i}.downsamplers.0'
																		__magic_name__      :       Dict       						= f'input_blocks.{current_layer}.0'
																		__magic_name__      :       List[str]       						= convert_resnet(_A,   _A,   _A,   _A			)
																		current_layer += 1

												__magic_name__      :       str       						= current_channels

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

						__magic_name__      :       Any       						= 0
						__magic_name__      :       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			):
																								__magic_name__      :       Optional[int]       						= f'up_blocks.{i}.resnets.{j}'
																								__magic_name__      :       str       						= f'output_blocks.{current_layer}.0'
																								__magic_name__      :       Any       						= convert_resnet(_A,   _A,   _A,   _A,   has_skip=_A			)
																								current_layer += 1

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

																		if i != len(_A			) - 1:
																								__magic_name__      :       Optional[int]       						= f'up_blocks.{i}.upsamplers.0'
																								__magic_name__      :       str       						= f'output_blocks.{current_layer-1}.2'
																								__magic_name__      :       Tuple       						= convert_resnet(_A,   _A,   _A,   _A			)

						__magic_name__      :       int       						= checkpoint["""out.0.weight"""]
						__magic_name__      :       List[Any]       						= checkpoint["""out.0.bias"""]
						__magic_name__      :       Union[str, Any]       						= checkpoint["""out.2.weight"""]
						__magic_name__      :       List[Any]       						= checkpoint["""out.2.bias"""]

						return new_checkpoint


if __name__ == "__main__":
		__magic_name__:					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.")

		__magic_name__:					Optional[Any]							=     parser.parse_args()
		__magic_name__:					int							=     strabool(args.class_cond)

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

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

		if not args.class_cond:
				__magic_name__:					Any							=     None

		__magic_name__:					List[Any]							=     con_pt_to_diffuser(args.unet_path, unet_config)

		__magic_name__:					Any							=     UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__magic_name__:					Any							=     CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__magic_name__:					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)):
				__magic_name__:					int							=     CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

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

		__magic_name__:					Union[str, Any]							=     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)








import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__magic_name__:					int							=     "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def    UpperCamelCase						(	_A,   _A=None			):






						"""simple docstring"""







						require_version(deps[pkg],   _A			)
