





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


snake_case__					:							List[str]													=     {
    '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',
}

snake_case__					:							Any													=     {
    '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',
}

snake_case__					:							Optional[Any]													=     {
    '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',
}

snake_case__					:							str													=     {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

snake_case__					:							Tuple													=     {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

snake_case__					:							int													=     {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}



def _a				(	lowerCamelCase:   List[str] )	->						int:







      '''simple docstring'''






      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 _a				(	lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   int					,				lowerCamelCase:   Any					,				lowerCamelCase:   str					,				lowerCamelCase:   List[str]=False )	->						Dict:







      '''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 _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Dict					,				lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   str=None )	->						Optional[Any]:







      '''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 _a				(	lowerCamelCase:   str					,				lowerCamelCase:   Tuple )	->						Any:







      '''simple docstring'''






      __A          =				torch.load(lowerCamelCase					,				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(lowerCamelCase ):
            __A          =				channels_list[i]
            __A          =				current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(lowerCamelCase ):
                        __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(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				has_skip=lowerCamelCase )
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(lowerCamelCase ):
                        __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(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				has_skip=lowerCamelCase )
                        __A          =				F"""down_blocks.{i}.attentions.{j}"""
                        __A          =				F"""input_blocks.{current_layer}.1"""
                        __A          =				convert_attention(
                            lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        current_layer += 1

            if i != len(lowerCamelCase ) - 1:
                  __A          =				F"""down_blocks.{i}.downsamplers.0"""
                  __A          =				F"""input_blocks.{current_layer}.0"""
                  __A          =				convert_resnet(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                  current_layer += 1

            __A          =				current_channels

      # hardcoded the mid-block for now
      __A          =				'''mid_block.resnets.0'''
      __A          =				'''middle_block.0'''
      __A          =				convert_resnet(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      __A          =				'''mid_block.attentions.0'''
      __A          =				'''middle_block.1'''
      __A          =				convert_attention(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      __A          =				'''mid_block.resnets.1'''
      __A          =				'''middle_block.2'''
      __A          =				convert_resnet(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )

      __A          =				0
      __A          =				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          =				F"""up_blocks.{i}.resnets.{j}"""
                        __A          =				F"""output_blocks.{current_layer}.0"""
                        __A          =				convert_resnet(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				has_skip=lowerCamelCase )
                        current_layer += 1

                  if i != len(lowerCamelCase ) - 1:
                        __A          =				F"""up_blocks.{i}.upsamplers.0"""
                        __A          =				F"""output_blocks.{current_layer-1}.1"""
                        __A          =				convert_resnet(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
            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(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				has_skip=lowerCamelCase )
                        __A          =				F"""up_blocks.{i}.attentions.{j}"""
                        __A          =				F"""output_blocks.{current_layer}.1"""
                        __A          =				convert_attention(
                            lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        current_layer += 1

                  if i != len(lowerCamelCase ) - 1:
                        __A          =				F"""up_blocks.{i}.upsamplers.0"""
                        __A          =				F"""output_blocks.{current_layer-1}.2"""
                        __A          =				convert_resnet(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )

      __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__":
       snake_case__					:							Tuple													=     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.')

       snake_case__					:							Optional[int]													=     parser.parse_args()
       snake_case__					:							int													=     strabool(args.class_cond)

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

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

       if not args.class_cond:
              snake_case__					:							Dict													=     None

       snake_case__					:							List[Any]													=     con_pt_to_diffuser(args.unet_path, unet_config)

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

       snake_case__					:							Dict													=     CMStochasticIterativeScheduler(**scheduler_config)

       snake_case__					:							Any													=     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)












def _a				(	lowerCamelCase:   dict )	->						bool:







      '''simple docstring'''






      __A          =				set()
      # To detect a back edge, keep track of vertices currently in the recursion stack
      __A          =				set()
      return any(
          node not in visited and depth_first_search(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
          for node in graph )



def _a				(	lowerCamelCase:   dict					,				lowerCamelCase:   int					,				lowerCamelCase:   set					,				lowerCamelCase:   set )	->						bool:







      '''simple docstring'''






      visited.add(lowerCamelCase )
      rec_stk.add(lowerCamelCase )

      for node in graph[vertex]:
            if node not in visited:
                  if depth_first_search(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ):
                        return True
            elif node in rec_stk:
                  return True

    # The node needs to be removed from recursion stack before function ends
      rec_stk.remove(lowerCamelCase )
      return False


if __name__ == "__main__":
       from doctest import testmod

       testmod()





