





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCamelCase	:					Dict			  =					{
    """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	:					Optional[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""",
}

__lowerCamelCase	:					Union[str, 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""",
}

__lowerCamelCase	:					int			  =					{
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__lowerCamelCase	:					List[str]			  =					{
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__lowerCamelCase	:					List[Any]			  =					{
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}



def 		A_    (					_lowerCAmelCase					)						->			List[Any]:
 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							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=False					)						->			Optional[int]:
 UpperCamelCase     : str	     =       checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
 UpperCamelCase     : int	     =       checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
 UpperCamelCase     : Dict	     =       checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
 UpperCamelCase     : str	     =       checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
 UpperCamelCase     : Tuple	     =       checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
 UpperCamelCase     : str	     =       checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
 UpperCamelCase     : Any	     =       checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
 UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
 UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
 UpperCamelCase     : Any	     =       checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

 if has_skip:
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""{old_prefix}.skip_connection.weight"""]
  UpperCamelCase     : Dict	     =       checkpoint[F"""{old_prefix}.skip_connection.bias"""]

 return new_checkpoint



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None					)						->			str:
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3							,       dim=0					)
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3							,       dim=0					)

 UpperCamelCase     : Tuple	     =       checkpoint[F"""{old_prefix}.norm.weight"""]
 UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""{old_prefix}.norm.bias"""]

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

 UpperCamelCase     : Optional[Any]	     =       (
     checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1					).squeeze(-1					)
 )
 UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1					).squeeze(-1					)

 return new_checkpoint



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 UpperCamelCase     : Dict	     =       torch.load(_lowerCAmelCase							,       map_location="cpu"					)
 UpperCamelCase     : int	     =       {}

 UpperCamelCase     : Dict	     =       checkpoint["time_embed.0.weight"]
 UpperCamelCase     : List[Any]	     =       checkpoint["time_embed.0.bias"]
 UpperCamelCase     : List[str]	     =       checkpoint["time_embed.2.weight"]
 UpperCamelCase     : Optional[int]	     =       checkpoint["time_embed.2.bias"]

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

 UpperCamelCase     : List[str]	     =       checkpoint["input_blocks.0.0.weight"]
 UpperCamelCase     : Tuple	     =       checkpoint["input_blocks.0.0.bias"]

 UpperCamelCase     : List[Any]	     =       unet_config["down_block_types"]
 UpperCamelCase     : Any	     =       unet_config["layers_per_block"]
 UpperCamelCase     : Tuple	     =       unet_config["attention_head_dim"]
 UpperCamelCase     : Union[str, Any]	     =       unet_config["block_out_channels"]
 UpperCamelCase     : List[Any]	     =       1
 UpperCamelCase     : Optional[int]	     =       channels_list[0]

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

  elif layer_type == "AttnDownBlock2D":
   for j in range(_lowerCAmelCase					):
    UpperCamelCase     : int	     =       F"""down_blocks.{i}.resnets.{j}"""
    UpperCamelCase     : Dict	     =       F"""input_blocks.{current_layer}.0"""
    UpperCamelCase     : str	     =       True if j == 0 and downsample_block_has_skip else False
    UpperCamelCase     : Dict	     =       convert_resnet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       has_skip=_lowerCAmelCase					)
    UpperCamelCase     : Any	     =       F"""down_blocks.{i}.attentions.{j}"""
    UpperCamelCase     : str	     =       F"""input_blocks.{current_layer}.1"""
    UpperCamelCase     : Tuple	     =       convert_attention(
        _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
    current_layer += 1

  if i != len(_lowerCAmelCase					) - 1:
   UpperCamelCase     : Optional[int]	     =       F"""down_blocks.{i}.downsamplers.0"""
   UpperCamelCase     : int	     =       F"""input_blocks.{current_layer}.0"""
   UpperCamelCase     : Optional[Any]	     =       convert_resnet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
   current_layer += 1

  UpperCamelCase     : Optional[int]	     =       current_channels

 # hardcoded the mid-block for now
 UpperCamelCase     : Any	     =       "mid_block.resnets.0"
 UpperCamelCase     : Tuple	     =       "middle_block.0"
 UpperCamelCase     : List[str]	     =       convert_resnet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       "mid_block.attentions.0"
 UpperCamelCase     : Optional[Any]	     =       "middle_block.1"
 UpperCamelCase     : Dict	     =       convert_attention(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       "mid_block.resnets.1"
 UpperCamelCase     : Tuple	     =       "middle_block.2"
 UpperCamelCase     : Dict	     =       convert_resnet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : Tuple	     =       0
 UpperCamelCase     : str	     =       unet_config["up_block_types"]

 for i, layer_type in enumerate(_lowerCAmelCase					):
  if layer_type == "ResnetUpsampleBlock2D":
   for j in range(layers_per_block + 1					):
    UpperCamelCase     : List[Any]	     =       F"""up_blocks.{i}.resnets.{j}"""
    UpperCamelCase     : List[Any]	     =       F"""output_blocks.{current_layer}.0"""
    UpperCamelCase     : int	     =       convert_resnet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       has_skip=_lowerCAmelCase					)
    current_layer += 1

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

   if i != len(_lowerCAmelCase					) - 1:
    UpperCamelCase     : int	     =       F"""up_blocks.{i}.upsamplers.0"""
    UpperCamelCase     : Union[str, Any]	     =       F"""output_blocks.{current_layer-1}.2"""
    UpperCamelCase     : Optional[int]	     =       convert_resnet(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : int	     =       checkpoint["out.0.weight"]
 UpperCamelCase     : List[str]	     =       checkpoint["out.0.bias"]
 UpperCamelCase     : Tuple	     =       checkpoint["out.2.weight"]
 UpperCamelCase     : List[Any]	     =       checkpoint["out.2.bias"]

 return new_checkpoint


if __name__ == "__main__":
    __lowerCamelCase	:					List[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.""")

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    __lowerCamelCase	:					str			  =					strabool(args.class_cond)

    __lowerCamelCase	:					Union[str, Any]			  =					os.path.basename(args.unet_path)
    print(f"""Checkpoint: {ckpt_name}""")

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

    if not args.class_cond:
        __lowerCamelCase	:					Dict			  =					None

    __lowerCamelCase	:					Dict			  =					con_pt_to_diffuser(args.unet_path, unet_config)

    __lowerCamelCase	:					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:
        __lowerCamelCase	:					List[str]			  =					CD_SCHEDULER_CONFIG
    elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
        __lowerCamelCase	:					Union[str, 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)):
        __lowerCamelCase	:					int			  =					CT_LSUN_256_SCHEDULER_CONFIG
    else:
        raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

    __lowerCamelCase	:					Tuple			  =					CMStochasticIterativeScheduler(**scheduler_config)

    __lowerCamelCase	:					Dict			  =					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)







import math



def 		A_    (					_lowerCAmelCase					)						->			list:
 UpperCamelCase     : str	     =       [True] * n
 UpperCamelCase     : Optional[int]	     =       False
 UpperCamelCase     : str	     =       False
 UpperCamelCase     : List[Any]	     =       True

 for i in range(3							,       int(n**0.5 + 1					)							,       2					):
  UpperCamelCase     : int	     =       i * 2
  while index < n:
   UpperCamelCase     : Optional[Any]	     =       False
   UpperCamelCase     : Optional[Any]	     =       index + i

 UpperCamelCase     : Optional[Any]	     =       [2]

 for i in range(3							,       _lowerCAmelCase							,       2					):
  if is_prime[i]:
   primes.append(_lowerCAmelCase					)

 return primes



def 		A_    (					_lowerCAmelCase = 9999_6666_3333					)						->			int:
 UpperCamelCase     : Tuple	     =       math.floor(math.sqrt(_lowerCAmelCase					)					) + 100
 UpperCamelCase     : Dict	     =       prime_sieve(_lowerCAmelCase					)

 UpperCamelCase     : Optional[Any]	     =       0
 UpperCamelCase     : Tuple	     =       0
 UpperCamelCase     : List[str]	     =       primes[prime_index]

 while (last_prime**2) <= limit:
  UpperCamelCase     : Dict	     =       primes[prime_index + 1]

  UpperCamelCase     : Any	     =       last_prime**2
  UpperCamelCase     : Union[str, Any]	     =       next_prime**2

  # Get numbers divisible by lps(current)
  UpperCamelCase     : Dict	     =       lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  UpperCamelCase     : List[str]	     =       upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  UpperCamelCase     : Dict	     =       0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  UpperCamelCase     : str	     =       next_prime
  prime_index += 1

 return matches_sum


if __name__ == "__main__":
    print(solution())
