





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  )		->				Union[str, Any]:
 if hor == 128:
  UpperCamelCase			:      int 			=     ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  UpperCamelCase			:      Tuple 			=     (32, 128, 256)
  UpperCamelCase			:      List[str] 			=     ("UpResnetBlock1D", "UpResnetBlock1D")

 elif hor == 32:
  UpperCamelCase			:      Union[str, Any] 			=     ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  UpperCamelCase			:      str 			=     (32, 64, 128, 256)
  UpperCamelCase			:      Union[str, Any] 			=     ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
 UpperCamelCase			:      List[Any] 			=     torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""  )
 UpperCamelCase			:      Optional[int] 			=     model.state_dict()
 UpperCamelCase			:      Union[str, Any] 			=     {
     "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			:      List[Any] 			=     dict(zip(model.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()							,     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_       (  )		->				int:
 UpperCamelCase			:      Tuple 			=     {
     "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			:      Tuple 			=     torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"  )
 UpperCamelCase			:      int 			=     model
 UpperCamelCase			:      Tuple 			=     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			:      List[str] 			=     dict(zip(state_dict.keys()							,     hf_value_function.state_dict().keys()  )  )
 for k, v in mapping.items():
  UpperCamelCase			:      Union[str, Any] 			=     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()







from __future__ import annotations



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

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

 UpperCamelCase			:      Any 			=     [2]

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

 return primes



def 	A_       (  _lowerCAmelCase = 100_0000  )		->				int:
 UpperCamelCase			:      Union[str, Any] 			=     prime_sieve(_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     0

 for i in range(len(_lowerCAmelCase  )  ):
  for j in range(i + length							,     len(_lowerCAmelCase  )  ):
   UpperCamelCase			:      Dict 			=     sum(primes[i:j]  )
   if sol >= ceiling:
    break

   if sol in primes:
    UpperCamelCase			:      int 			=     j - i
    UpperCamelCase			:      Dict 			=     sol

 return largest


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