
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__       (       __lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


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

			elif hor == 32:
						lowerCAmelCase_      			=			("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						lowerCAmelCase_      			=			(32, 64, 128, 256)
						lowerCAmelCase_      			=			("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			lowerCAmelCase_      			=			torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""       )
			lowerCAmelCase_      			=			model.state_dict()
			lowerCAmelCase_      			=			{
			    "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_      			=			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()       )}"""       )
			lowerCAmelCase_      			=			dict(zip(model.state_dict().keys()					,							hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						lowerCAmelCase_      			=			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      lowerCamelCase__       (       ):




			"""simple docstring"""


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

			lowerCAmelCase_      			=			dict(zip(state_dict.keys()					,							hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						lowerCAmelCase_      			=			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      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			if upper_limit < 0:
						raise ValueError("Limit for the Catalan sequence must be ≥ 0"       )

			lowerCAmelCase_      			=			[0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			lowerCAmelCase_      			=			1
			if upper_limit > 0:
						lowerCAmelCase_      			=			1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2					,							upper_limit + 1       ):
						for j in range(__lowerCAmelCase       ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
			print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
			print("\n*** Enter -1 at any time to quit ***")
			print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
			try:
						while True:
									_A     =  int(input().strip())
									if N < 0:
												print("\n********* Goodbye!! ************")
												break
									else:
												print(f"""The Catalan numbers from 0 through {N} are:""")
												print(catalan_numbers(N))
												print("Try another upper limit for the sequence: ", end="")
			except (NameError, ValueError):
						print("\n********* Invalid input, goodbye! ************\n")

			import doctest

			doctest.testmod()



