


'''simple docstring'''


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__magic_name__			=      {
    '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__			=      {
    '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__			=      {
    '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__			=      {
    'num_train_timesteps': 40,
    'sigma_min': 0.0_0_2,
    'sigma_max': 80.0,
}

__magic_name__			=      {
    'num_train_timesteps': 201,
    'sigma_min': 0.0_0_2,
    'sigma_max': 80.0,
}

__magic_name__			=      {
    'num_train_timesteps': 151,
    'sigma_min': 0.0_0_2,
    'sigma_max': 80.0,
}




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      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      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple=False):
      A_						:  Dict          =				checkpoint[F'{old_prefix}.in_layers.0.weight']
      A_						:  Optional[int]          =				checkpoint[F'{old_prefix}.in_layers.0.bias']
      A_						:  Union[str, Any]          =				checkpoint[F'{old_prefix}.in_layers.2.weight']
      A_						:  Union[str, Any]          =				checkpoint[F'{old_prefix}.in_layers.2.bias']
      A_						:  List[str]          =				checkpoint[F'{old_prefix}.emb_layers.1.weight']
      A_						:  int          =				checkpoint[F'{old_prefix}.emb_layers.1.bias']
      A_						:  int          =				checkpoint[F'{old_prefix}.out_layers.0.weight']
      A_						:  Union[str, Any]          =				checkpoint[F'{old_prefix}.out_layers.0.bias']
      A_						:  Optional[Any]          =				checkpoint[F'{old_prefix}.out_layers.3.weight']
      A_						:  Optional[int]          =				checkpoint[F'{old_prefix}.out_layers.3.bias']

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

      return new_checkpoint




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Optional[Any]=None):
      A_						:  Optional[Any]          =				checkpoint[F'{old_prefix}.qkv.weight'].chunk(3							,						dim=0)
      A_						:  List[Any]          =				checkpoint[F'{old_prefix}.qkv.bias'].chunk(3							,						dim=0)

      A_						:  List[Any]          =				checkpoint[F'{old_prefix}.norm.weight']
      A_						:  Tuple          =				checkpoint[F'{old_prefix}.norm.bias']

      A_						:  Union[str, Any]          =				weight_q.squeeze(-1).squeeze(-1)
      A_						:  Dict          =				bias_q.squeeze(-1).squeeze(-1)
      A_						:  int          =				weight_k.squeeze(-1).squeeze(-1)
      A_						:  Tuple          =				bias_k.squeeze(-1).squeeze(-1)
      A_						:  Optional[Any]          =				weight_v.squeeze(-1).squeeze(-1)
      A_						:  Dict          =				bias_v.squeeze(-1).squeeze(-1)

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

      return new_checkpoint




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Tuple):
      A_						:  List[Any]          =				torch.load(lowerCamelCase							,						map_location="""cpu""")
      A_						:  Optional[Any]          =				{}

      A_						:  List[Any]          =				checkpoint["""time_embed.0.weight"""]
      A_						:  Dict          =				checkpoint["""time_embed.0.bias"""]
      A_						:  Optional[Any]          =				checkpoint["""time_embed.2.weight"""]
      A_						:  Any          =				checkpoint["""time_embed.2.bias"""]

      if unet_config["num_class_embeds"] is not None:
            A_						:  Union[str, Any]          =				checkpoint["""label_emb.weight"""]

      A_						:  Union[str, Any]          =				checkpoint["""input_blocks.0.0.weight"""]
      A_						:  List[str]          =				checkpoint["""input_blocks.0.0.bias"""]

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

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

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

            if i != len(lowerCamelCase) - 1:
                  A_						:  str          =				F'down_blocks.{i}.downsamplers.0'
                  A_						:  List[str]          =				F'input_blocks.{current_layer}.0'
                  A_						:  Tuple          =				convert_resnet(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
                  current_layer += 1

            A_						:  str          =				current_channels

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

      A_						:  Optional[Any]          =				0
      A_						:  int          =				unet_config["""up_block_types"""]

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

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

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

      A_						:  str          =				checkpoint["""out.0.weight"""]
      A_						:  Dict          =				checkpoint["""out.0.bias"""]
      A_						:  Optional[Any]          =				checkpoint["""out.2.weight"""]
      A_						:  int          =				checkpoint["""out.2.bias"""]

      return new_checkpoint


if __name__ == "__main__":
  __magic_name__			=      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__			=      parser.parse_args()
  __magic_name__			=      strabool(args.class_cond)

  __magic_name__			=      os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

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

  if not args.class_cond:
    __magic_name__			=      None

  __magic_name__			=      con_pt_to_diffuser(args.unet_path, unet_config)

  __magic_name__			=      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__			=      CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __magic_name__			=      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__			=      CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __magic_name__			=      CMStochasticIterativeScheduler(**scheduler_config)

  __magic_name__			=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)




'''simple docstring'''


from ..utils import DummyObject, requires_backends



class 	__lowerCAmelCase		(							metaclass=__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""torch""", """torchsde"""]






  def __init__(							self :       Any	,*_a :       Union[str, Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        requires_backends(self	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       Optional[int]	,*_a :       List[Any]	,**_a :       Any ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       List[Any]	,*_a :       Tuple	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )
