



"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__        =				{
    '''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''',
}

lowerCAmelCase__        =				{
    '''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''',
}

lowerCAmelCase__        =				{
    '''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''',
}

lowerCAmelCase__        =				{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

lowerCAmelCase__        =				{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

lowerCAmelCase__        =				{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def        snake_case_  (      A_					:		Optional[Any]						):


       '''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        snake_case_  (      A_					:		int,				A_					:		Tuple,				A_					:		Any,				A_					:		Tuple,				A_					:		Dict=False						):


       '''simple docstring'''





       _lowerCamelCase						:				Dict    			= checkpoint[F'''{old_prefix}.in_layers.0.weight''']
       _lowerCamelCase						:				List[str]    			= checkpoint[F'''{old_prefix}.in_layers.0.bias''']
       _lowerCamelCase						:				Tuple    			= checkpoint[F'''{old_prefix}.in_layers.2.weight''']
       _lowerCamelCase						:				int    			= checkpoint[F'''{old_prefix}.in_layers.2.bias''']
       _lowerCamelCase						:				str    			= checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
       _lowerCamelCase						:				Tuple    			= checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
       _lowerCamelCase						:				Optional[Any]    			= 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						:				Any    			= checkpoint[F'''{old_prefix}.out_layers.3.bias''']

       if has_skip:
              _lowerCamelCase						:				Dict    			= checkpoint[F'''{old_prefix}.skip_connection.weight''']
              _lowerCamelCase						:				int    			= checkpoint[F'''{old_prefix}.skip_connection.bias''']

       return new_checkpoint






def        snake_case_  (      A_					:		str,				A_					:		int,				A_					:		str,				A_					:		List[str],				A_					:		Union[str, Any]=None						):


       '''simple docstring'''





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

       _lowerCamelCase						:				Union[str, Any]    			= checkpoint[F'''{old_prefix}.norm.weight''']
       _lowerCamelCase						:				Optional[int]    			= checkpoint[F'''{old_prefix}.norm.bias''']

       _lowerCamelCase						:				Union[str, Any]    			= weight_q.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				List[Any]    			= bias_q.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				List[Any]    			= weight_k.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				Any    			= bias_k.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				Union[str, Any]    			= weight_v.squeeze(-1						).squeeze(-1						)
       _lowerCamelCase						:				List[Any]    			= bias_v.squeeze(-1						).squeeze(-1						)

       _lowerCamelCase						:				List[Any]    			= (
           checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1						).squeeze(-1						)
       )
       _lowerCamelCase						:				Tuple    			= checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1						).squeeze(-1						)

       return new_checkpoint






def        snake_case_  (      A_					:		str,				A_					:		Tuple						):


       '''simple docstring'''





       _lowerCamelCase						:				Union[str, Any]    			= torch.load(A_,				map_location='''cpu'''						)
       _lowerCamelCase						:				Dict    			= {}

       _lowerCamelCase						:				List[str]    			= checkpoint['''time_embed.0.weight''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''time_embed.0.bias''']
       _lowerCamelCase						:				List[str]    			= checkpoint['''time_embed.2.weight''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''time_embed.2.bias''']

       if unet_config["num_class_embeds"] is not None:
              _lowerCamelCase						:				List[str]    			= checkpoint['''label_emb.weight''']

       _lowerCamelCase						:				Any    			= checkpoint['''input_blocks.0.0.weight''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''input_blocks.0.0.bias''']

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

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

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

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

              _lowerCamelCase						:				Tuple    			= current_channels

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

       _lowerCamelCase						:				Optional[int]    			= 0
       _lowerCamelCase						:				str    			= unet_config['''up_block_types''']

       for i, layer_type in enumerate(A_						):
              if layer_type == "ResnetUpsampleBlock2D":
                     for j in range(layers_per_block + 1						):
                            _lowerCamelCase						:				List[str]    			= F'''up_blocks.{i}.resnets.{j}'''
                            _lowerCamelCase						:				Dict    			= F'''output_blocks.{current_layer}.0'''
                            _lowerCamelCase						:				Union[str, Any]    			= convert_resnet(A_,				A_,				A_,				A_,				has_skip=A_						)
                            current_layer += 1

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

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

       _lowerCamelCase						:				Optional[int]    			= checkpoint['''out.0.weight''']
       _lowerCamelCase						:				int    			= checkpoint['''out.0.bias''']
       _lowerCamelCase						:				Union[str, Any]    			= checkpoint['''out.2.weight''']
       _lowerCamelCase						:				Optional[int]    			= checkpoint['''out.2.bias''']

       return new_checkpoint


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

   lowerCAmelCase__        =				parser.parse_args()
   lowerCAmelCase__        =				strabool(args.class_cond)

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

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

   if not args.class_cond:
      lowerCAmelCase__        =				None

   lowerCAmelCase__        =				con_pt_to_diffuser(args.unet_path, unet_config)

   lowerCAmelCase__        =				UNetaDModel(**unet_config)
   image_unet.load_state_dict(converted_unet_ckpt)

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

   lowerCAmelCase__        =				CMStochasticIterativeScheduler(**scheduler_config)

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









def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if not isinstance(snake_case_       ,      snake_case_       ) or number < 0:
      raise ValueError('''Input must be a non-negative integer'''       )

   __snake_case							       =   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()

