





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A					:    Optional[Any]   =  {
    '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					:    Dict   =  {
    '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					:    List[Any]   =  {
    '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					:    Any   =  {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

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

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



def        _a						(    UpperCAmelCase     )				->				Union[str, Any]:







   """simple docstring"""


   if isinstance(__snake_case			,  __snake_case     ):
      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     )				->				str:







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

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

   return new_checkpoint



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=None     )				->				str:







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

   lowerCamelCase__   :						Dict					   =							checkpoint[f"{old_prefix}.norm.weight"]
   lowerCamelCase__   :						int					   =							checkpoint[f"{old_prefix}.norm.bias"]

   lowerCamelCase__   :						Tuple					   =							weight_q.squeeze(-1     ).squeeze(-1     )
   lowerCamelCase__   :						str					   =							bias_q.squeeze(-1     ).squeeze(-1     )
   lowerCamelCase__   :						Any					   =							weight_k.squeeze(-1     ).squeeze(-1     )
   lowerCamelCase__   :						List[Any]					   =							bias_k.squeeze(-1     ).squeeze(-1     )
   lowerCamelCase__   :						Dict					   =							weight_v.squeeze(-1     ).squeeze(-1     )
   lowerCamelCase__   :						Optional[Any]					   =							bias_v.squeeze(-1     ).squeeze(-1     )

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

   return new_checkpoint



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				str:







   """simple docstring"""
   lowerCamelCase__   :						Any					   =							torch.load(__snake_case			,  map_location='''cpu'''     )
   lowerCamelCase__   :						int					   =							{}

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

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

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

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

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

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

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

      lowerCamelCase__   :						Optional[int]					   =							current_channels

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

   lowerCamelCase__   :						int					   =							0
   lowerCamelCase__   :						Union[str, Any]					   =							unet_config['up_block_types']

   for i, layer_type in enumerate(__snake_case     ):
      if layer_type == "ResnetUpsampleBlock2D":
         for j in range(layers_per_block + 1     ):
            lowerCamelCase__   :						Dict					   =							f"up_blocks.{i}.resnets.{j}"
            lowerCamelCase__   :						Dict					   =							f"output_blocks.{current_layer}.0"
            lowerCamelCase__   :						Dict					   =							convert_resnet(__snake_case			,  __snake_case			,  __snake_case			,  __snake_case			,  has_skip=__snake_case     )
            current_layer += 1

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

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

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

   return new_checkpoint


if __name__ == "__main__":
       _A					:    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					:    Tuple   =  parser.parse_args()
       _A					:    str   =  strabool(args.class_cond)

       _A					:    Optional[int]   =  os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

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

       if not args.class_cond:
              _A					:    Optional[int]   =  None

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

       _A					:    List[Any]   =  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					:    str   =  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					:    Optional[int]   =  CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       _A					:    str   =  CMStochasticIterativeScheduler(**scheduler_config)

       _A					:    Union[str, Any]   =  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)











def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






