


'''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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Any:
     """simple docstring"""




     if hor == 128:
          __a       =   ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
          __a       =   (32, 128, 256)
          __a       =   ("""UpResnetBlock1D""", """UpResnetBlock1D""")

     elif hor == 32:
          __a       =   ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
          __a       =   (32, 64, 128, 256)
          __a       =   ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
     __a       =   torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''		)
     __a       =   model.state_dict()
     __a       =   {
         """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""",
     }
     __a       =   UNetaDModel(**__SCREAMING_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       =   dict(zip(model.state_dict().keys()				,  hf_value_function.state_dict().keys()		)		)
     for k, v in mapping.items():
          __a       =   state_dict.pop(__SCREAMING_SNAKE_CASE		)
     hf_value_function.load_state_dict(__SCREAMING_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(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      )				->					int:
     """simple docstring"""




     __a       =   {
         """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""",
     }

     __a       =   torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""		)
     __a       =   model
     __a       =   UNetaDModel(**__SCREAMING_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       =   dict(zip(state_dict.keys()				,  hf_value_function.state_dict().keys()		)		)
     for k, v in mapping.items():
          __a       =   state_dict.pop(__SCREAMING_SNAKE_CASE		)

     hf_value_function.load_state_dict(__SCREAMING_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(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)


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










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   []
     __a       =   set({"""(""", """[""", """{"""}		)
     __a       =   set({""")""", """]""", """}"""}		)
     __a       =   {"""{""": """}""", """[""": """]""", """(""": """)"""}

     for i in range(len(__SCREAMING_SNAKE_CASE		)		):
          if s[i] in open_brackets:
               stack.append(s[i]		)

          elif s[i] in closed_brackets and (
              len(__SCREAMING_SNAKE_CASE		) == 0 or (len(__SCREAMING_SNAKE_CASE		) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(__SCREAMING_SNAKE_CASE		) == 0




def        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   input("""Enter sequence of brackets: """		)
     if is_balanced(__SCREAMING_SNAKE_CASE		):
          print(__SCREAMING_SNAKE_CASE				,  """is balanced"""		)
     else:
          print(__SCREAMING_SNAKE_CASE				,  """is not balanced"""		)


if __name__ == "__main__":
 main()






