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

					elif hor == 3_2:
										SCREAMING_SNAKE_CASE    			=       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										SCREAMING_SNAKE_CASE    			=       (3_2, 6_4, 1_2_8, 2_5_6)
										SCREAMING_SNAKE_CASE    			=       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					SCREAMING_SNAKE_CASE    			=       torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"  )
					SCREAMING_SNAKE_CASE    			=       model.state_dict()
					SCREAMING_SNAKE_CASE    			=       {
					    "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",
					}
					SCREAMING_SNAKE_CASE    			=       UNetaDModel(**UpperCAmelCase__  )
					print(F"length of state dict: {len(state_dict.keys()  )}"  )
					print(F"length of value function dict: {len(hf_value_function.state_dict().keys()  )}"  )
					SCREAMING_SNAKE_CASE    			=       dict(zip(model.state_dict().keys()      , hf_value_function.state_dict().keys()  )  )
					for k, v in mapping.items():
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(UpperCAmelCase__  )
					hf_value_function.load_state_dict(UpperCAmelCase__  )

					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(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       {
					    "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",
					}

					SCREAMING_SNAKE_CASE    			=       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"  )
					SCREAMING_SNAKE_CASE    			=       model
					SCREAMING_SNAKE_CASE    			=       UNetaDModel(**UpperCAmelCase__  )
					print(F"length of state dict: {len(state_dict.keys()  )}"  )
					print(F"length of value function dict: {len(hf_value_function.state_dict().keys()  )}"  )

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

					hf_value_function.load_state_dict(UpperCAmelCase__  )

					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(UpperCAmelCase__      , UpperCAmelCase__  )


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





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




