



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 	__a					(   lowerCAmelCase_       : str    )				->			Dict:






						'''simple docstring'''



						if hor == 1_28:
												UpperCAmelCase_=       ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												UpperCAmelCase_=       (32, 1_28, 2_56)
												UpperCAmelCase_=       ("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 32:
												UpperCAmelCase_=       ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												UpperCAmelCase_=       (32, 64, 1_28, 2_56)
												UpperCAmelCase_=       ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						UpperCAmelCase_=       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""    )
						UpperCAmelCase_=       model.state_dict()
						UpperCAmelCase_=       {
						    """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""",
						}
						UpperCAmelCase_=       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()    )}"""    )
						UpperCAmelCase_=       dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()    )    )
						for k, v in mapping.items():
												UpperCAmelCase_=       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 	__a					(   )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       {
						    """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""",
						}

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

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









def 	__a					(   lowerCAmelCase_       : Dict    )				->			Dict:






						'''simple docstring'''



						return [
						    {
						        0: [1, 2],
						        1: [0, 2],
						        2: [0, 1, 3, 5],
						        3: [2, 4],
						        4: [3],
						        5: [2, 6, 8],
						        6: [5, 7],
						        7: [6, 8],
						        8: [5, 7],
						    },
						    {
						        0: [6],
						        1: [9],
						        2: [4, 5],
						        3: [4],
						        4: [2, 3],
						        5: [2],
						        6: [0, 7],
						        7: [6],
						        8: [],
						        9: [1],
						    },
						    {
						        0: [4],
						        1: [6],
						        2: [],
						        3: [5, 6, 7],
						        4: [0, 6],
						        5: [3, 8, 9],
						        6: [1, 3, 4, 7],
						        7: [3, 6, 8, 9],
						        8: [5, 7],
						        9: [5, 7],
						    },
						    {
						        0: [1, 3],
						        1: [0, 2, 4],
						        2: [1, 3, 4],
						        3: [0, 2, 4],
						        4: [1, 2, 3],
						    },
						][index]



def 	__a					(   lowerCAmelCase_       : dict[int, list[int]]    )				->			list[tuple[int, int]]:






						'''simple docstring'''



						UpperCAmelCase_=       0
						UpperCAmelCase_=       len(lowerCAmelCase_    )  # No of vertices in graph
						UpperCAmelCase_=       [0] * n
						UpperCAmelCase_=       [False] * n

						def dfs(lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    ):
												UpperCAmelCase_=       True
												UpperCAmelCase_=       id_
												id_ += 1
												for to in graph[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,id_    )
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )
																								if id_ <= low[to]:
																														bridges.append((at, to) if at < to else (to, at)    )
																		else:
																								# This edge is a back edge and cannot be a bridge
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )

						UpperCAmelCase_=       []
						for i in range(lowerCAmelCase_    ):
												if not visited[i]:
																		dfs(lowerCAmelCase_      ,-1      ,lowerCAmelCase_      ,id_    )
						return bridges


if __name__ == "__main__":
	import doctest

	doctest.testmod()




