

"""simple docstring"""




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 	UpperCAmelCase__   (				A__   )      ->      Union[str, Any]:




   """simple docstring"""
   if hor == 128:
      lowerCamelCase__						=			("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
      lowerCamelCase__						=			(32, 128, 256)
      lowerCamelCase__						=			("UpResnetBlock1D", "UpResnetBlock1D")

   elif hor == 32:
      lowerCamelCase__						=			("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
      lowerCamelCase__						=			(32, 64, 128, 256)
      lowerCamelCase__						=			("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
   lowerCamelCase__						=			torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'   )
   lowerCamelCase__						=			model.state_dict()
   lowerCamelCase__						=			{
       "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",
   }
   lowerCamelCase__						=			UNetaDModel(**A__   )
   print(f'length of state dict: {len(state_dict.keys()   )}'   )
   print(f'length of value function dict: {len(hf_value_function.state_dict().keys()   )}'   )
   lowerCamelCase__						=			dict(zip(model.state_dict().keys()	,    hf_value_function.state_dict().keys()   )   )
   for k, v in mapping.items():
      lowerCamelCase__						=			state_dict.pop(A__   )
   hf_value_function.load_state_dict(A__   )

   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(A__	,    A__   )



def 	UpperCAmelCase__   (				)      ->      str:




   """simple docstring"""
   lowerCamelCase__						=			{
       "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",
   }

   lowerCamelCase__						=			torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"   )
   lowerCamelCase__						=			model
   lowerCamelCase__						=			UNetaDModel(**A__   )
   print(f'length of state dict: {len(state_dict.keys()   )}'   )
   print(f'length of value function dict: {len(hf_value_function.state_dict().keys()   )}'   )

   lowerCamelCase__						=			dict(zip(state_dict.keys()	,    hf_value_function.state_dict().keys()   )   )
   for k, v in mapping.items():
      lowerCamelCase__						=			state_dict.pop(A__   )

   hf_value_function.load_state_dict(A__   )

   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(A__	,    A__   )


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







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if num <= 0:
      raise ValueError("Input must be a positive integer"   )

   lowerCamelCase__						=			[True] * (num + 1)

   lowerCamelCase__						=			2
   while p * p <= num:
      if primes[p]:
         for i in range(p * p	,    num + 1	,    A__   ):
            lowerCamelCase__						=			False
      p += 1

   return [prime for prime in range(2	,    num + 1   ) if primes[prime]]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a positive integer: ''').strip())
     print(prime_sieve_eratosthenes(user_num))




