

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	(      SCREAMING_SNAKE_CASE      ):
	if hor == 1_2_8:
		UpperCAmelCase__:	Any									= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		UpperCAmelCase__:	Tuple									= (3_2, 1_2_8, 2_5_6)
		UpperCAmelCase__:	Dict									= ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 3_2:
		UpperCAmelCase__:	List[str]									= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		UpperCAmelCase__:	Optional[Any]									= (3_2, 6_4, 1_2_8, 2_5_6)
		UpperCAmelCase__:	Any									= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	UpperCAmelCase__:	Tuple									= torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
	UpperCAmelCase__:	Any									= model.state_dict()
	UpperCAmelCase__:	Dict									= {
	    "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",
	}
	UpperCAmelCase__:	int									= 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()      )}"      )
	UpperCAmelCase__:	Any									= dict(zip(model.state_dict().keys()	,hf_value_function.state_dict().keys()      )      )
	for k, v in mapping.items():
		UpperCAmelCase__:	List[str]									= 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 			_A	(      ):
	UpperCAmelCase__:	Any									= {
	    "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",
	}

	UpperCAmelCase__:	Union[str, Any]									= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"      )
	UpperCAmelCase__:	Any									= model
	UpperCAmelCase__:	List[str]									= 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()      )}"      )

	UpperCAmelCase__:	Union[str, Any]									= dict(zip(state_dict.keys()	,hf_value_function.state_dict().keys()      )      )
	for k, v in mapping.items():
		UpperCAmelCase__:	Tuple									= 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()


def 			_A	(      SCREAMING_SNAKE_CASE      ):  # noqa: E741
	UpperCAmelCase__:	int									= len(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= 0
	UpperCAmelCase__:	Optional[int]									= [0] * n
	UpperCAmelCase__:	List[str]									= [False] * n
	UpperCAmelCase__:	List[str]									= [False] * n

	def dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		if parent == root:
			out_edge_count += 1
		UpperCAmelCase__:	List[str]									= True
		UpperCAmelCase__:	List[Any]									= at

		for to in l[at]:
			if to == parent:
				pass
			elif not visited[to]:
				UpperCAmelCase__:	str									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				UpperCAmelCase__:	Optional[Any]									= min(low[at]	,low[to]      )

				# AP found via bridge
				if at < low[to]:
					UpperCAmelCase__:	List[Any]									= True
				# AP found via cycle
				if at == low[to]:
					UpperCAmelCase__:	str									= True
			else:
				UpperCAmelCase__:	Union[str, Any]									= min(low[at]	,SCREAMING_SNAKE_CASE      )
		return out_edge_count

	for i in range(SCREAMING_SNAKE_CASE      ):
		if not visited[i]:
			UpperCAmelCase__:	Optional[Any]									= 0
			UpperCAmelCase__:	Union[str, Any]									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,-1	,SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Tuple									= out_edge_count > 1

	for x in range(len(SCREAMING_SNAKE_CASE      )      ):
		if is_art[x] is True:
			print(SCREAMING_SNAKE_CASE      )


# Adjacency list of graph
_lowerCAmelCase  :      Any			 ={
    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],
}
compute_ap(data)