





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A :   List[str]  			=  {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 10_00,
    '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 :   List[Any]  			=  {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 10_00,
    'block_out_channels': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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 :   Tuple  			=  {
    'sample_size': 2_56,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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 :   Dict  			=  {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A :   Dict  			=  {
    'num_train_timesteps': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A :   Optional[Any]  			=  {
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}



def 	_a						(   UpperCAmelCase					)    ->    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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       :						int        =							checkpoint[f"{old_prefix}.in_layers.0.weight"]
   lowerCamelCase__       :						List[Any]        =							checkpoint[f"{old_prefix}.in_layers.0.bias"]
   lowerCamelCase__       :						int        =							checkpoint[f"{old_prefix}.in_layers.2.weight"]
   lowerCamelCase__       :						List[str]        =							checkpoint[f"{old_prefix}.in_layers.2.bias"]
   lowerCamelCase__       :						Optional[Any]        =							checkpoint[f"{old_prefix}.emb_layers.1.weight"]
   lowerCamelCase__       :						Dict        =							checkpoint[f"{old_prefix}.emb_layers.1.bias"]
   lowerCamelCase__       :						Tuple        =							checkpoint[f"{old_prefix}.out_layers.0.weight"]
   lowerCamelCase__       :						int        =							checkpoint[f"{old_prefix}.out_layers.0.bias"]
   lowerCamelCase__       :						List[Any]        =							checkpoint[f"{old_prefix}.out_layers.3.weight"]
   lowerCamelCase__       :						List[str]        =							checkpoint[f"{old_prefix}.out_layers.3.bias"]

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

   return new_checkpoint



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							checkpoint[f"{old_prefix}.qkv.weight"].chunk(3      ,  dim=0					)
   lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						int        =							checkpoint[f"{old_prefix}.qkv.bias"].chunk(3      ,  dim=0					)

   lowerCamelCase__       :						List[Any]        =							checkpoint[f"{old_prefix}.norm.weight"]
   lowerCamelCase__       :						str        =							checkpoint[f"{old_prefix}.norm.bias"]

   lowerCamelCase__       :						Optional[int]        =							weight_q.squeeze(-1					).squeeze(-1					)
   lowerCamelCase__       :						Optional[Any]        =							bias_q.squeeze(-1					).squeeze(-1					)
   lowerCamelCase__       :						Optional[int]        =							weight_k.squeeze(-1					).squeeze(-1					)
   lowerCamelCase__       :						Any        =							bias_k.squeeze(-1					).squeeze(-1					)
   lowerCamelCase__       :						Optional[int]        =							weight_v.squeeze(-1					).squeeze(-1					)
   lowerCamelCase__       :						int        =							bias_v.squeeze(-1					).squeeze(-1					)

   lowerCamelCase__       :						Optional[Any]        =							(
       checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1					).squeeze(-1					)
   )
   lowerCamelCase__       :						Dict        =							checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1					).squeeze(-1					)

   return new_checkpoint



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						Optional[Any]        =							torch.load(UpperCAmelCase      ,  map_location='''cpu'''					)
   lowerCamelCase__       :						Dict        =							{}

   lowerCamelCase__       :						Optional[Any]        =							checkpoint['''time_embed.0.weight''']
   lowerCamelCase__       :						Optional[Any]        =							checkpoint['''time_embed.0.bias''']
   lowerCamelCase__       :						List[str]        =							checkpoint['''time_embed.2.weight''']
   lowerCamelCase__       :						Optional[Any]        =							checkpoint['''time_embed.2.bias''']

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

   lowerCamelCase__       :						Dict        =							checkpoint['''input_blocks.0.0.weight''']
   lowerCamelCase__       :						str        =							checkpoint['''input_blocks.0.0.bias''']

   lowerCamelCase__       :						Dict        =							unet_config['''down_block_types''']
   lowerCamelCase__       :						Any        =							unet_config['''layers_per_block''']
   lowerCamelCase__       :						List[str]        =							unet_config['''attention_head_dim''']
   lowerCamelCase__       :						List[Any]        =							unet_config['''block_out_channels''']
   lowerCamelCase__       :						List[str]        =							1
   lowerCamelCase__       :						str        =							channels_list[0]

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

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

      if i != len(UpperCAmelCase					) - 1:
         lowerCamelCase__       :						Dict        =							f"down_blocks.{i}.downsamplers.0"
         lowerCamelCase__       :						Optional[int]        =							f"input_blocks.{current_layer}.0"
         lowerCamelCase__       :						Optional[Any]        =							convert_resnet(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)
         current_layer += 1

      lowerCamelCase__       :						Optional[int]        =							current_channels

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

   lowerCamelCase__       :						Tuple        =							0
   lowerCamelCase__       :						Tuple        =							unet_config['''up_block_types''']

   for i, layer_type in enumerate(UpperCAmelCase					):
      if layer_type == "ResnetUpsampleBlock2D":
         for j in range(layers_per_block + 1					):
            lowerCamelCase__       :						Union[str, Any]        =							f"up_blocks.{i}.resnets.{j}"
            lowerCamelCase__       :						Tuple        =							f"output_blocks.{current_layer}.0"
            lowerCamelCase__       :						str        =							convert_resnet(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  has_skip=UpperCAmelCase					)
            current_layer += 1

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

         if i != len(UpperCAmelCase					) - 1:
            lowerCamelCase__       :						List[Any]        =							f"up_blocks.{i}.upsamplers.0"
            lowerCamelCase__       :						Tuple        =							f"output_blocks.{current_layer-1}.2"
            lowerCamelCase__       :						Any        =							convert_resnet(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)

   lowerCamelCase__       :						Dict        =							checkpoint['''out.0.weight''']
   lowerCamelCase__       :						List[str]        =							checkpoint['''out.0.bias''']
   lowerCamelCase__       :						Any        =							checkpoint['''out.2.weight''']
   lowerCamelCase__       :						Tuple        =							checkpoint['''out.2.bias''']

   return new_checkpoint


if __name__ == "__main__":
       _A :   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.')

       _A :   Optional[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 :   Union[str, Any]  			=  IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              _A :   Tuple  			=  LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              _A :   str  			=  TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              _A :   int  			=  None

       _A :   Any  			=  con_pt_to_diffuser(args.unet_path, unet_config)

       _A :   Optional[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:
              _A :   Any  			=  CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              _A :   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)):
              _A :   Union[str, Any]  			=  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)










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''					)

   lowerCamelCase__       :						List[str]        =							0
   while number:
      # This way we arrive at next set bit (next 1) instead of looping
      # through each bit and checking for 1s hence the
      # loop won't run 32 times it will only run the number of `1` times
      number &= number - 1
      count += 1
   return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()



