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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					Optional[int]:







  """simple docstring"""





  if hor == 1_28:
    SCREAMING_SNAKE_CASE__										=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
    SCREAMING_SNAKE_CASE__										=      (32, 1_28, 2_56)
    SCREAMING_SNAKE_CASE__										=      ('UpResnetBlock1D', 'UpResnetBlock1D')

  elif hor == 32:
    SCREAMING_SNAKE_CASE__										=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
    SCREAMING_SNAKE_CASE__										=      (32, 64, 1_28, 2_56)
    SCREAMING_SNAKE_CASE__										=      ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
  SCREAMING_SNAKE_CASE__										=      torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
  SCREAMING_SNAKE_CASE__										=      model.state_dict()
  SCREAMING_SNAKE_CASE__										=      {
      '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_55_36,
      'mid_block_type': 'MidResTemporalBlock1D',
      'act_fn': 'mish',
  }
  SCREAMING_SNAKE_CASE__										=      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()	)}"""	)
  SCREAMING_SNAKE_CASE__										=      dict(zip(model.state_dict().keys() ,     hf_value_function.state_dict().keys()	)	)
  for k, v in mapping.items():
    SCREAMING_SNAKE_CASE__										=      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 	__SCREAMING_SNAKE_CASE  (					)							->					Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {
      '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, 1_28, 2_56),
      'layers_per_block': 1,
      'downsample_each_block': True,
      'sample_size': 6_55_36,
      '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',
  }

  SCREAMING_SNAKE_CASE__										=      torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""	)
  SCREAMING_SNAKE_CASE__										=      model
  SCREAMING_SNAKE_CASE__										=      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()	)}"""	)

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





'''simple docstring'''

def      __lowerCamelCase     (      )			->      Tuple:
		for n in range(1						,			1000000  ):
				yield n * (n + 1) // 2



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		_a     :      Any        =							1
		_a     :      Tuple        =							2
		while i * i <= n:
				_a     :      Tuple        =							0
				while n % i == 0:
						n //= i
						multiplicity += 1
				divisors_count *= multiplicity + 1
				i += 1
		if n > 1:
				divisors_count *= 2
		return divisors_count



def      __lowerCamelCase     (      )			->      str:
		return next(i for i in triangle_number_generator() if count_divisors(lowerCAmelCase_  ) > 500  )


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




