






'''simple docstring'''







from __future__ import annotations

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

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

UpperCamelCase    :      list[str]  			=		["the", "be", "to", "of", "and", "in", "that", "have"]
def 						A__			(				__lowerCAmelCase		:			list[int]   ,    __lowerCAmelCase		:			tuple[int, ...]						):
						lowerCamelCase__         =  ""
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42

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

						return decoded
def 						A__			(				__lowerCAmelCase		:			list[int]						):
						lowerCamelCase__         =  []
						for key in product(__lowerCAmelCase   ,    repeat=3						):
												lowerCamelCase__         =  try_key(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if encoded is not None:
																		possibles.append(__lowerCAmelCase						)
						return possibles
def 						A__			(				__lowerCAmelCase		:			list[str]   ,    __lowerCAmelCase		:			str						):
						return [possible for possible in possibles if common_word in possible.lower()]
def 						A__			(				__lowerCAmelCase		:			str = "p059_cipher.txt"						):
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  42
						lowerCamelCase__         =  Path(__lowerCAmelCase						).parent.joinpath(__lowerCAmelCase						).read_text(encoding="""utf-8"""						)

						lowerCamelCase__         =  [int(__lowerCAmelCase						) for number in data.strip().split(""","""						)]

						lowerCamelCase__         =  filter_valid_chars(__lowerCAmelCase						)
						for common_word in COMMON_WORDS:
												lowerCamelCase__         =  filter_common_word(__lowerCAmelCase   ,    __lowerCAmelCase						)
												if len(__lowerCAmelCase						) == 1:
																		break

						lowerCamelCase__         =  possibles[0]
						return sum(ord(__lowerCAmelCase						) for char in decoded_text						)


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









'''simple docstring'''







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		:			Union[str, Any]						):
						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""": 6_5536,
						    """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 						A__			(				):
						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""": 6_5536,
						    """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()

