'''simple docstring'''





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE_:     Tuple  ={
    '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',
}

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

SCREAMING_SNAKE_CASE_:     Tuple  ={
    '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',
}

SCREAMING_SNAKE_CASE_:     Any  ={
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'num_train_timesteps': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE_:     List[Any]  ={
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      Optional[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 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : Any					,    snake_case_       : Optional[int]					,    snake_case_       : Dict=False			)    ->      Optional[int]:


     '''simple docstring'''




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

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

     return new_checkpoint





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


     '''simple docstring'''




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

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

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

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

     return new_checkpoint





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : int			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)
     UpperCAmelCase_           =   {}

     UpperCAmelCase_           =   checkpoint["time_embed.0.weight"]
     UpperCAmelCase_           =   checkpoint["time_embed.0.bias"]
     UpperCAmelCase_           =   checkpoint["time_embed.2.weight"]
     UpperCAmelCase_           =   checkpoint["time_embed.2.bias"]

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

     UpperCAmelCase_           =   checkpoint["input_blocks.0.0.weight"]
     UpperCAmelCase_           =   checkpoint["input_blocks.0.0.bias"]

     UpperCAmelCase_           =   unet_config["down_block_types"]
     UpperCAmelCase_           =   unet_config["layers_per_block"]
     UpperCAmelCase_           =   unet_config["attention_head_dim"]
     UpperCAmelCase_           =   unet_config["block_out_channels"]
     UpperCAmelCase_           =   1
     UpperCAmelCase_           =   channels_list[0]

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

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

          UpperCAmelCase_           =   current_channels

     # hardcoded the mid-block for now
     UpperCAmelCase_           =   "mid_block.resnets.0"
     UpperCAmelCase_           =   "middle_block.0"
     UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   "mid_block.attentions.0"
     UpperCAmelCase_           =   "middle_block.1"
     UpperCAmelCase_           =   convert_attention(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
     UpperCAmelCase_           =   "mid_block.resnets.1"
     UpperCAmelCase_           =   "middle_block.2"
     UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   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			):
                    UpperCAmelCase_           =   f"""up_blocks.{i}.resnets.{j}"""
                    UpperCAmelCase_           =   f"""output_blocks.{current_layer}.0"""
                    UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    has_skip=snake_case_			)
                    current_layer += 1

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

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

     UpperCAmelCase_           =   checkpoint["out.0.weight"]
     UpperCAmelCase_           =   checkpoint["out.0.bias"]
     UpperCAmelCase_           =   checkpoint["out.2.weight"]
     UpperCAmelCase_           =   checkpoint["out.2.bias"]

     return new_checkpoint


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

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =parser.parse_args()
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =strabool(args.class_cond)

    SCREAMING_SNAKE_CASE_:     List[Any]  =os.path.basename(args.unet_path)
    print(f"Checkpoint: {ckpt_name}")

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

    if not args.class_cond:
        SCREAMING_SNAKE_CASE_:     Union[str, Any]  =None

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

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

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =CMStochasticIterativeScheduler(**scheduler_config)

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








import math






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     [True] * n
							a__             =     False
							a__             =     False
							a__             =     True

							for i in range(3					,    int(n**0.5 + 1   )					,    2   ):
														a__             =     i * 2
														while index < n:
																					a__             =     False
																					a__             =     index + i

							a__             =     [2]

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

							return primes






def 							__lowercase   ( __lowerCAmelCase							:				int = 9_9_9_9_6_6_6_6_3_3_3_3   ):
							a__             =     math.floor(math.sqrt(__lowerCAmelCase   )   ) + 1_0_0
							a__             =     prime_sieve(__lowerCAmelCase   )

							a__             =     0
							a__             =     0
							a__             =     primes[prime_index]

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

														a__             =     last_prime**2
														a__             =     next_prime**2

														# Get numbers divisible by lps(current)
														a__             =     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)
														a__             =     upper_bound - next_prime
														while current > lower_bound:
																					matches_sum += current
																					current -= next_prime

														# Remove the numbers divisible by both ups and lps
														a__             =     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
														a__             =     next_prime
														prime_index += 1

							return matches_sum


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




