





"""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 					lowerCAmelCase_			(	__a		)					->		List[str]:







  """simple docstring"""





  if hor == 128:
    lowerCamelCase__:				Union[str, Any]        =("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
    lowerCamelCase__:				Optional[int]        =(32, 128, 256)
    lowerCamelCase__:				Tuple        =("UpResnetBlock1D", "UpResnetBlock1D")

  elif hor == 32:
    lowerCamelCase__:				Tuple        =("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
    lowerCamelCase__:				List[Any]        =(32, 64, 128, 256)
    lowerCamelCase__:				List[str]        =("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
  lowerCamelCase__:				Dict        =torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""		)
  lowerCamelCase__:				Optional[int]        =model.state_dict()
  lowerCamelCase__:				str        ={
      "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": 65536,
      "mid_block_type": "MidResTemporalBlock1D",
      "act_fn": "mish",
  }
  lowerCamelCase__:				List[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()		)}"""		)
  lowerCamelCase__:				Tuple        =dict(zip(model.state_dict().keys()     ,   hf_value_function.state_dict().keys()		)		)
  for k, v in mapping.items():
    lowerCamelCase__:				Dict        =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 					lowerCAmelCase_			(	)					->		int:







  """simple docstring"""





  lowerCamelCase__:				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": 65536,
      "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__:				Union[str, Any]        =torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"		)
  lowerCamelCase__:				Any        =model
  lowerCamelCase__:				str        =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__:				List[str]        =dict(zip(state_dict.keys()     ,   hf_value_function.state_dict().keys()		)		)
  for k, v in mapping.items():
    lowerCamelCase__:				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()








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =num_of_nodes
   lowerCamelCase__:				list[list[int]]        =[]
   lowerCamelCase__:				dict[int, int]        ={}

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   self.m_edges.append([u_node, v_node, weight])

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int)	->int:



   '''simple docstring'''



   if self.m_component[u_node] == u_node:
     return u_node
   return self.find_component(self.m_component[u_node])

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if self.m_component[u_node] != u_node:
     for k in self.m_component:
       lowerCamelCase__:				Dict        =self.find_component(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       list[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if component_size[u_node] <= component_size[v_node]:
     lowerCamelCase__:				Optional[int]        =v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(UpperCAmelCase_)

   elif component_size[u_node] >= component_size[v_node]:
     lowerCamelCase__:				Tuple        =self.find_component(UpperCAmelCase_)
     component_size[u_node] += component_size[v_node]
     self.set_component(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]
   lowerCamelCase__:				List[str]        =0

   lowerCamelCase__:				list[Any]        =[-1] * self.m_num_of_nodes

   # A list of components (initialized to all of the nodes)
   for node in range(self.m_num_of_nodes):
     self.m_component.update({node: node})
     component_size.append(1)

   lowerCamelCase__:				List[str]        =self.m_num_of_nodes

   while num_of_components > 1:
     for edge in self.m_edges:
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

       lowerCamelCase__:				List[Any]        =self.m_component[u]
       lowerCamelCase__:				str        =self.m_component[v]

       if u_component != v_component:

         for component in (u_component, v_component):
           if (
               minimum_weight_edge[component] == -1
               or minimum_weight_edge[component][2] > w
           ):
             lowerCamelCase__:				Union[str, Any]        =[u, v, w]

     for edge in minimum_weight_edge:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
         lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

         lowerCamelCase__:				str        =self.m_component[u]
         lowerCamelCase__:				Any        =self.m_component[v]

         if u_component != v_component:
           mst_weight += w
           self.union(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
           print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
           num_of_components -= 1

     lowerCamelCase__:				Tuple        =[-1] * self.m_num_of_nodes
   print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")





def 					lowerCAmelCase_			(	)					->		None:







  """simple docstring"""







if __name__ == "__main__":
     import doctest

     doctest.testmod()

