'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A			:      Union[str, Any]					      =				{
    '''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''',
}

_A			:      int					      =				{
    '''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''',
}

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

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

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

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







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Union[str, Any]:






 '''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      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       str						,		snake_case_       :       Dict						,		snake_case_       :       str						,		snake_case_       :       Dict=False      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

 if has_skip:
  __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.skip_connection.weight"""]
  __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.skip_connection.bias"""]

 return new_checkpoint







def      UpperCamelCase_						(					snake_case_       :       Any						,		snake_case_       :       List[str]						,		snake_case_       :       int						,		snake_case_       :       str						,		snake_case_       :       int=None      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3						,		dim=0      )
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3						,		dim=0      )

 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.norm.weight"""]
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.norm.bias"""]

 __lowerCAmelCase											=						weight_q.squeeze(-1      ).squeeze(-1      )
 __lowerCAmelCase											=						bias_q.squeeze(-1      ).squeeze(-1      )
 __lowerCAmelCase											=						weight_k.squeeze(-1      ).squeeze(-1      )
 __lowerCAmelCase											=						bias_k.squeeze(-1      ).squeeze(-1      )
 __lowerCAmelCase											=						weight_v.squeeze(-1      ).squeeze(-1      )
 __lowerCAmelCase											=						bias_v.squeeze(-1      ).squeeze(-1      )

 __lowerCAmelCase											=						(
     checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1      ).squeeze(-1      )
 )
 __lowerCAmelCase											=						checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1      ).squeeze(-1      )

 return new_checkpoint







def      UpperCamelCase_						(					snake_case_       :       str						,		snake_case_       :       int      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(lowerCAmelCase__						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						{}

 __lowerCAmelCase											=						checkpoint['''time_embed.0.weight''']
 __lowerCAmelCase											=						checkpoint['''time_embed.0.bias''']
 __lowerCAmelCase											=						checkpoint['''time_embed.2.weight''']
 __lowerCAmelCase											=						checkpoint['''time_embed.2.bias''']

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

 __lowerCAmelCase											=						checkpoint['''input_blocks.0.0.weight''']
 __lowerCAmelCase											=						checkpoint['''input_blocks.0.0.bias''']

 __lowerCAmelCase											=						unet_config['''down_block_types''']
 __lowerCAmelCase											=						unet_config['''layers_per_block''']
 __lowerCAmelCase											=						unet_config['''attention_head_dim''']
 __lowerCAmelCase											=						unet_config['''block_out_channels''']
 __lowerCAmelCase											=						1
 __lowerCAmelCase											=						channels_list[0]

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

  elif layer_type == "AttnDownBlock2D":
   for j in range(lowerCAmelCase__      ):
    __lowerCAmelCase											=						f"""down_blocks.{i}.resnets.{j}"""
    __lowerCAmelCase											=						f"""input_blocks.{current_layer}.0"""
    __lowerCAmelCase											=						True if j == 0 and downsample_block_has_skip else False
    __lowerCAmelCase											=						convert_resnet(lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		has_skip=lowerCAmelCase__      )
    __lowerCAmelCase											=						f"""down_blocks.{i}.attentions.{j}"""
    __lowerCAmelCase											=						f"""input_blocks.{current_layer}.1"""
    __lowerCAmelCase											=						convert_attention(
        lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__      )
    current_layer += 1

  if i != len(lowerCAmelCase__      ) - 1:
   __lowerCAmelCase											=						f"""down_blocks.{i}.downsamplers.0"""
   __lowerCAmelCase											=						f"""input_blocks.{current_layer}.0"""
   __lowerCAmelCase											=						convert_resnet(lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__      )
   current_layer += 1

  __lowerCAmelCase											=						current_channels

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

 __lowerCAmelCase											=						0
 __lowerCAmelCase											=						unet_config['''up_block_types''']

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

   if i != len(lowerCAmelCase__      ) - 1:
    __lowerCAmelCase											=						f"""up_blocks.{i}.upsamplers.0"""
    __lowerCAmelCase											=						f"""output_blocks.{current_layer-1}.1"""
    __lowerCAmelCase											=						convert_resnet(lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__      )
  elif layer_type == "AttnUpBlock2D":
   for j in range(layers_per_block + 1      ):
    __lowerCAmelCase											=						f"""up_blocks.{i}.resnets.{j}"""
    __lowerCAmelCase											=						f"""output_blocks.{current_layer}.0"""
    __lowerCAmelCase											=						convert_resnet(lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		has_skip=lowerCAmelCase__      )
    __lowerCAmelCase											=						f"""up_blocks.{i}.attentions.{j}"""
    __lowerCAmelCase											=						f"""output_blocks.{current_layer}.1"""
    __lowerCAmelCase											=						convert_attention(
        lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__      )
    current_layer += 1

   if i != len(lowerCAmelCase__      ) - 1:
    __lowerCAmelCase											=						f"""up_blocks.{i}.upsamplers.0"""
    __lowerCAmelCase											=						f"""output_blocks.{current_layer-1}.2"""
    __lowerCAmelCase											=						convert_resnet(lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__      )

 __lowerCAmelCase											=						checkpoint['''out.0.weight''']
 __lowerCAmelCase											=						checkpoint['''out.0.bias''']
 __lowerCAmelCase											=						checkpoint['''out.2.weight''']
 __lowerCAmelCase											=						checkpoint['''out.2.bias''']

 return new_checkpoint


if __name__ == "__main__":
 _A			:      int					      =				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			:      List[str]					      =				parser.parse_args()
 _A			:      Optional[Any]					      =				strabool(args.class_cond)

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

 # Get U-Net config
 if "imagenet64" in ckpt_name:
  _A			:      List[str]					      =				IMAGENET_64_UNET_CONFIG
 elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
  _A			:      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			:      List[Any]					      =				None

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

 _A			:      Optional[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			:      Dict					      =				CD_SCHEDULER_CONFIG
 elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
  _A			:      Optional[int]					      =				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[Any]					      =				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    __UpperCamelCase     (				lowerCAmelCase__							:				int = 5_0_0_0_0_0_0_0  ):
 __a :  int						=  set()
 __a :  str						=  int((limit - 2_4) ** (1 / 2)  )

 __a :  int						=  set(range(3  ,   prime_square_limit + 1  ,   2  )  )
 primes.add(2  )
 for p in range(3  ,   prime_square_limit + 1  ,   2  ):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p  ,   prime_square_limit + 1  ,   lowerCAmelCase__  )  )  )

 for primea in primes:
  __a :  Union[str, Any]						=  primea * primea
  for primea in primes:
   __a :  Union[str, Any]						=  primea * primea * primea
   if square + cube >= limit - 1_6:
    break
   for primea in primes:
    __a :  int						=  primea * primea * primea * primea
    __a :  Union[str, Any]						=  square + cube + tetr
    if total >= limit:
     break
    ret.add(lowerCAmelCase__  )

 return len(lowerCAmelCase__  )


if __name__ == "__main__":
      print(F"""{solution() = }""")
