



"""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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int    )  ->       Dict:





     '''simple docstring'''

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

     elif hor == 32:
          _UpperCAmelCase      :     Optional[Any]        =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          _UpperCAmelCase      :     Union[str, Any]        =   (32, 64, 128, 256)
          _UpperCAmelCase      :     Union[str, Any]        =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     _UpperCAmelCase      :     Any        =   torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'    )
     _UpperCAmelCase      :     Optional[int]        =   model.state_dict()
     _UpperCAmelCase      :     Optional[int]        =   {
         "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": 65_536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     _UpperCAmelCase      :     Optional[int]        =   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()    )}'    )
     _UpperCAmelCase      :     str        =   dict(zip(model.state_dict().keys()						,  hf_value_function.state_dict().keys()    )    )
     for k, v in mapping.items():
          _UpperCAmelCase      :     Optional[Any]        =   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 						__snake_case						(				)  ->       Tuple:





     '''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": 65_536,
         "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[int]        =   model
     _UpperCAmelCase      :     Any        =   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()    )}'    )

     _UpperCAmelCase      :     Union[str, Any]        =   dict(zip(state_dict.keys()						,  hf_value_function.state_dict().keys()    )    )
     for k, v in mapping.items():
          _UpperCAmelCase      :     List[Any]        =   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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   [], []
     while len(SCREAMING_SNAKE_CASE__    ) > 1:
          _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   min(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )
          start.append(SCREAMING_SNAKE_CASE__    )
          end.append(SCREAMING_SNAKE_CASE__    )
          collection.remove(SCREAMING_SNAKE_CASE__    )
          collection.remove(SCREAMING_SNAKE_CASE__    )
     end.reverse()
     return start + collection + end


if __name__ == "__main__":
 _lowerCAmelCase    : int							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase    : List[str]							=							[int(item) for item in user_input.split(",")]
 print(*merge_sort(unsorted), sep=",")
