




from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__lowerCAmelCase :str     =    (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__lowerCAmelCase :list[int]     =    [ord(letter) for letter in string.ascii_lowercase]
__lowerCAmelCase :set[int]     =    {ord(char) for char in VALID_CHARS}

__lowerCAmelCase :list[str]     =    ["the", "be", "to", "of", "and", "in", "that", "have"]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					str				       =   ""
					_snake_case       :					int
					_snake_case       :					int
					_snake_case       :					int

					for keychar, cipherchar in zip(cycle(UpperCAmelCase				)  ,  UpperCAmelCase				):
										_snake_case       :					Any				       =   cipherchar ^ keychar
										if decodedchar not in VALID_INTS:
															return None
										decoded += chr(UpperCAmelCase				)

					return decoded







def    A				(							UpperCAmelCase				):
					_snake_case       :					list[str]				       =   []
					for key in product(UpperCAmelCase  ,  repeat=3				):
										_snake_case       :					Union[str, Any]				       =   try_key(UpperCAmelCase  ,  UpperCAmelCase				)
										if encoded is not None:
															possibles.append(UpperCAmelCase				)
					return possibles







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return [possible for possible in possibles if common_word in possible.lower()]







def    A				(							UpperCAmelCase = "p059_cipher.txt"				):
					_snake_case       :					list[int]
					_snake_case       :					list[str]
					_snake_case       :					str
					_snake_case       :					str
					_snake_case       :					str				       =   Path(UpperCAmelCase				).parent.joinpath(UpperCAmelCase				).read_text(encoding="utf-8"				)

					_snake_case       :					List[str]				       =   [int(UpperCAmelCase				) for number in data.strip().split(","				)]

					_snake_case       :					Any				       =   filter_valid_chars(UpperCAmelCase				)
					for common_word in COMMON_WORDS:
										_snake_case       :					Optional[int]				       =   filter_common_word(UpperCAmelCase  ,  UpperCAmelCase				)
										if len(UpperCAmelCase				) == 1:
															break

					_snake_case       :					Optional[Any]				       =   possibles[0]
					return sum(ord(UpperCAmelCase				) for char in decoded_text				)


if __name__ == "__main__":
							print(F'''{solution() = }''')






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				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					Tuple				       =   {
					    "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": 65_536,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					_snake_case       :					Any				       =   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()				)}"""				)
					_snake_case       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   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    A				(							):
					_snake_case       :					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": 65_536,
					    "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",
					}

					_snake_case       :					Dict				       =   torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
					_snake_case       :					Optional[int]				       =   model
					_snake_case       :					List[str]				       =   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()				)}"""				)

					_snake_case       :					Optional[int]				       =   dict(zip(state_dict.keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Any				       =   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()