


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowercase							:   Optional[int]						=						{
    "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							:   int						=						{
    "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							:   Optional[Any]						=						{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

_lowercase							:   List[str]						=						{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

_lowercase							:   str						=						{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}





def 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> List[str]:





	"""simple docstring"""







	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 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Any				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Dict=False    )			-> Union[str, Any]:





	"""simple docstring"""







	A             =					checkpoint[f'{old_prefix}.in_layers.0.weight']
	A             =					checkpoint[f'{old_prefix}.in_layers.0.bias']
	A             =					checkpoint[f'{old_prefix}.in_layers.2.weight']
	A             =					checkpoint[f'{old_prefix}.in_layers.2.bias']
	A             =					checkpoint[f'{old_prefix}.emb_layers.1.weight']
	A             =					checkpoint[f'{old_prefix}.emb_layers.1.bias']
	A             =					checkpoint[f'{old_prefix}.out_layers.0.weight']
	A             =					checkpoint[f'{old_prefix}.out_layers.0.bias']
	A             =					checkpoint[f'{old_prefix}.out_layers.3.weight']
	A             =					checkpoint[f'{old_prefix}.out_layers.3.bias']

	if has_skip:
		A             =					checkpoint[f'{old_prefix}.skip_connection.weight']
		A             =					checkpoint[f'{old_prefix}.skip_connection.bias']

	return new_checkpoint





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  str				,       UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Dict=None    )			-> str:





	"""simple docstring"""







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

	A             =					checkpoint[f'{old_prefix}.norm.weight']
	A             =					checkpoint[f'{old_prefix}.norm.bias']

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

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

	return new_checkpoint





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  List[Any]    )			-> str:





	"""simple docstring"""







	A             =					torch.load(UpperCamelCase__				,       map_location="""cpu"""    )
	A             =					{}

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

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

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

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

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

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

		if i != len(UpperCamelCase__    ) - 1:
			A             =					f'down_blocks.{i}.downsamplers.0'
			A             =					f'input_blocks.{current_layer}.0'
			A             =					convert_resnet(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )
			current_layer += 1

		A             =					current_channels

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

	A             =					0
	A             =					unet_config["""up_block_types"""]

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

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

			if i != len(UpperCamelCase__    ) - 1:
				A             =					f'up_blocks.{i}.upsamplers.0'
				A             =					f'output_blocks.{current_layer-1}.2'
				A             =					convert_resnet(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )

	A             =					checkpoint["""out.0.weight"""]
	A             =					checkpoint["""out.0.bias"""]
	A             =					checkpoint["""out.2.weight"""]
	A             =					checkpoint["""out.2.bias"""]

	return new_checkpoint


if __name__ == "__main__":
				_lowercase							:   str						=						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							:   Union[str, Any]						=						parser.parse_args()
				_lowercase							:   List[Any]						=						strabool(args.class_cond)

				_lowercase							:   str						=						os.path.basename(args.unet_path)
				print(f'''Checkpoint: {ckpt_name}''')

				# Get U-Net config
				if "imagenet64" in ckpt_name:
								_lowercase							:   Optional[int]						=						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							:   Optional[int]						=						TEST_UNET_CONFIG
				else:
								raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

				if not args.class_cond:
								_lowercase							:   int						=						None

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

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

				_lowercase							:   Dict						=						CMStochasticIterativeScheduler(**scheduler_config)

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







_lowercase							:   Dict						=						"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
				from .utils import rich



