


'''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      ):
   '''simple docstring'''





   if hor == 1_2_8:
      _a					:  List[Any]						=  ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
      _a					:  Tuple						=  (3_2, 1_2_8, 2_5_6)
      _a					:  Dict						=  ('UpResnetBlock1D', 'UpResnetBlock1D')

   elif hor == 3_2:
      _a					:  Tuple						=  ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
      _a					:  Union[str, Any]						=  (3_2, 6_4, 1_2_8, 2_5_6)
      _a					:  List[Any]						=  ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
   _a					:  Optional[Any]						=  torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''      )
   _a					:  Optional[int]						=  model.state_dict()
   _a					:  Optional[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': 1_4,
       'out_channels': 1_4,
       'extra_in_channels': 0,
       'time_embedding_type': 'positional',
       'flip_sin_to_cos': False,
       'freq_shift': 1,
       'sample_size': 6_5_5_3_6,
       'mid_block_type': 'MidResTemporalBlock1D',
       'act_fn': 'mish',
   }
   _a					:  Any						=  UNetaDModel(**__snake_case      )
   print(f'''length of state dict: {len(state_dict.keys()      )}'''      )
   print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()      )}'''      )
   _a					:  Any						=  dict(zip(model.state_dict().keys()		,	hf_value_function.state_dict().keys()      )      )
   for k, v in mapping.items():
      _a					:  List[str]						=  state_dict.pop(__snake_case      )
   hf_value_function.load_state_dict(__snake_case      )

   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(__snake_case		,	__snake_case      )


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  str						=  {
       'in_channels': 1_4,
       'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
       'up_block_types': (),
       'out_block_type': 'ValueFunction',
       'mid_block_type': 'ValueFunctionMidBlock1D',
       'block_out_channels': (3_2, 6_4, 1_2_8, 2_5_6),
       'layers_per_block': 1,
       'downsample_each_block': True,
       'sample_size': 6_5_5_3_6,
       'out_channels': 1_4,
       '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',
   }

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

   _a					:  List[str]						=  dict(zip(state_dict.keys()		,	hf_value_function.state_dict().keys()      )      )
   for k, v in mapping.items():
      _a					:  Optional[Any]						=  state_dict.pop(__snake_case      )

   hf_value_function.load_state_dict(__snake_case      )

   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(__snake_case		,	__snake_case      )


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



'''simple docstring'''

def snake_case_      (					__snake_case  :							int = 200)			->					int:
   lowerCAmelCase_			=      [1, 2, 5, 10, 20, 50, 100, 200]
   lowerCAmelCase_			=      [0] * (pence + 1)
   lowerCAmelCase_			=      1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(__snake_case	,   pence + 1	,   1):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(2_00) == 7_36_82





