





import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)



def 		A_    (					_lowerCAmelCase					)						->			str:






 """simple docstring"""




 if hor == 128:
  UpperCamelCase     : List[str]	     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  UpperCamelCase     : Dict	     =       (32, 128, 256)
  UpperCamelCase     : Dict	     =       ("UpResnetBlock1D", "UpResnetBlock1D")

 elif hor == 32:
  UpperCamelCase     : Optional[int]	     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  UpperCamelCase     : str	     =       (32, 64, 128, 256)
  UpperCamelCase     : int	     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
 UpperCamelCase     : Optional[Any]	     =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""					)
 UpperCamelCase     : Union[str, Any]	     =       model.state_dict()
 UpperCamelCase     : List[str]	     =       {
     "down_block_types": down_block_types,
     "block_out_channels": block_out_channels,
     "up_block_types": up_block_types,
     "layers_per_block": 1,
     "use_timestep_embedding": True,
     "out_block_type": "OutConv1DBlock",
     "norm_num_groups": 8,
     "downsample_each_block": False,
     "in_channels": 14,
     "out_channels": 14,
     "extra_in_channels": 0,
     "time_embedding_type": "positional",
     "flip_sin_to_cos": False,
     "freq_shift": 1,
     "sample_size": 6_5536,
     "mid_block_type": "MidResTemporalBlock1D",
     "act_fn": "mish",
 }
 UpperCamelCase     : int	     =       UNetaDModel(**_lowerCAmelCase					)
 print(F"""length of state dict: {len(state_dict.keys()					)}"""					)
 print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()					)}"""					)
 UpperCamelCase     : Optional[int]	     =       dict(zip(model.state_dict().keys()							,       hf_value_function.state_dict().keys()					)					)
 for k, v in mapping.items():
  UpperCamelCase     : Dict	     =       state_dict.pop(_lowerCAmelCase					)
 hf_value_function.load_state_dict(_lowerCAmelCase					)

 torch.save(hf_value_function.state_dict()							,       F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""					)
 with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""							,       "w"					) as f:
  json.dump(_lowerCAmelCase							,       _lowerCAmelCase					)



def 		A_    (					)						->			Optional[Any]:






 """simple docstring"""




 UpperCamelCase     : Union[str, Any]	     =       {
     "in_channels": 14,
     "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
     "up_block_types": (),
     "out_block_type": "ValueFunction",
     "mid_block_type": "ValueFunctionMidBlock1D",
     "block_out_channels": (32, 64, 128, 256),
     "layers_per_block": 1,
     "downsample_each_block": True,
     "sample_size": 6_5536,
     "out_channels": 14,
     "extra_in_channels": 0,
     "time_embedding_type": "positional",
     "use_timestep_embedding": True,
     "flip_sin_to_cos": False,
     "freq_shift": 1,
     "norm_num_groups": 8,
     "act_fn": "mish",
 }

 UpperCamelCase     : Optional[Any]	     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"					)
 UpperCamelCase     : Optional[Any]	     =       model
 UpperCamelCase     : List[Any]	     =       UNetaDModel(**_lowerCAmelCase					)
 print(F"""length of state dict: {len(state_dict.keys()					)}"""					)
 print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()					)}"""					)

 UpperCamelCase     : Optional[int]	     =       dict(zip(state_dict.keys()							,       hf_value_function.state_dict().keys()					)					)
 for k, v in mapping.items():
  UpperCamelCase     : Tuple	     =       state_dict.pop(_lowerCAmelCase					)

 hf_value_function.load_state_dict(_lowerCAmelCase					)

 torch.save(hf_value_function.state_dict()							,       "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"					)
 with open("hub/hopper-medium-v2/value_function/config.json"							,       "w"					) as f:
  json.dump(_lowerCAmelCase							,       _lowerCAmelCase					)


if __name__ == "__main__":
    unet(32)
    # unet(128)
    value_function()







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())
