

"""simple docstring"""


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a					: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",
}

a					: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",
}

a					:Dict    = {
    "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					:Union[str, Any]    = {
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

a					:List[str]    = {
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

a					:int    = {
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}




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

      if has_skip:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	checkpoint[F'''{old_prefix}.skip_connection.weight''']
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None							)       ->				Tuple:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Any      		=	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3  ,					dim=0							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3  ,					dim=0							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	checkpoint[F'''{old_prefix}.norm.weight''']
      SCREAMING_SNAKE_CASE__   :      Any      		=	checkpoint[F'''{old_prefix}.norm.bias''']

      SCREAMING_SNAKE_CASE__   :      str      		=	weight_q.squeeze(-1							).squeeze(-1							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	bias_q.squeeze(-1							).squeeze(-1							)
      SCREAMING_SNAKE_CASE__   :      Dict      		=	weight_k.squeeze(-1							).squeeze(-1							)
      SCREAMING_SNAKE_CASE__   :      int      		=	bias_k.squeeze(-1							).squeeze(-1							)
      SCREAMING_SNAKE_CASE__   :      str      		=	weight_v.squeeze(-1							).squeeze(-1							)
      SCREAMING_SNAKE_CASE__   :      Dict      		=	bias_v.squeeze(-1							).squeeze(-1							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	(
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
      )
      SCREAMING_SNAKE_CASE__   :      Dict      		=	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

      return new_checkpoint




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)
      SCREAMING_SNAKE_CASE__   :      Dict      		=	{}

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

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

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	checkpoint["""input_blocks.0.0.weight"""]
      SCREAMING_SNAKE_CASE__   :      Dict      		=	checkpoint["""input_blocks.0.0.bias"""]

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

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

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

            if i != len(__lowerCAmelCase							) - 1:
                  SCREAMING_SNAKE_CASE__   :      str      		=	F'''down_blocks.{i}.downsamplers.0'''
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	F'''input_blocks.{current_layer}.0'''
                  SCREAMING_SNAKE_CASE__   :      List[Any]      		=	convert_resnet(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
                  current_layer += 1

            SCREAMING_SNAKE_CASE__   :      Tuple      		=	current_channels

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

      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	unet_config["""up_block_types"""]

      for i, layer_type in enumerate(__lowerCAmelCase							):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1							):
                        SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	F'''up_blocks.{i}.resnets.{j}'''
                        SCREAMING_SNAKE_CASE__   :      str      		=	F'''output_blocks.{current_layer}.0'''
                        SCREAMING_SNAKE_CASE__   :      Tuple      		=	convert_resnet(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					has_skip=__lowerCAmelCase							)
                        current_layer += 1

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

                  if i != len(__lowerCAmelCase							) - 1:
                        SCREAMING_SNAKE_CASE__   :      List[Any]      		=	F'''up_blocks.{i}.upsamplers.0'''
                        SCREAMING_SNAKE_CASE__   :      List[str]      		=	F'''output_blocks.{current_layer-1}.2'''
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	convert_resnet(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	checkpoint["""out.0.weight"""]
      SCREAMING_SNAKE_CASE__   :      Dict      		=	checkpoint["""out.0.bias"""]
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	checkpoint["""out.2.weight"""]
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	checkpoint["""out.2.bias"""]

      return new_checkpoint


if __name__ == "__main__":
  a					:List[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.")

  a					:Union[str, Any]    = parser.parse_args()
  a					:int    = strabool(args.class_cond)

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

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

  if not args.class_cond:
    a					:Any    = None

  a					:Optional[Any]    = con_pt_to_diffuser(args.unet_path, unet_config)

  a					:Dict    = 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					:Optional[int]    = CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    a					: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)):
    a					:List[str]    = CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

  a					:List[Any]    = CMStochasticIterativeScheduler(**scheduler_config)

  a					:str    = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)







"""simple docstring"""


a					:List[str]    = [
    (1_000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	0
      while place < len(__lowerCAmelCase							):
            if (place + 1 < len(__lowerCAmelCase							)) and (vals[roman[place]] < vals[roman[place + 1]]):
                  total += vals[roman[place + 1]] - vals[roman[place]]
                  place += 2
            else:
                  total += vals[roman[place]]
                  place += 1
      return total




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for arabic, roman in ROMAN:
            ((SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__))   :      List[str]      		=	divmod(__lowerCAmelCase  ,					__lowerCAmelCase							)
            result.append(roman * factor							)
            if number == 0:
                  break
      return "".join(__lowerCAmelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




