


'''simple docstring'''



from __future__ import annotations

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

__A	=(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__A	=[ord(letter) for letter in string.ascii_lowercase]
__A	={ord(char) for char in VALID_CHARS}

__A	=["the", "be", "to", "of", "and", "in", "that", "have"]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     ""
			UpperCAmelCase__  :					int
			UpperCAmelCase__  :					int
			UpperCAmelCase__  :					int

			for keychar, cipherchar in zip(cycle(UpperCamelCase__					) ,      UpperCamelCase__					):
						UpperCAmelCase__  :					List[Any]        =     cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(UpperCamelCase__					)

			return decoded







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					list[str]        =     []
			for key in product(UpperCamelCase__ ,      repeat=3					):
						UpperCAmelCase__  :					Optional[int]        =     try_key(UpperCamelCase__ ,      UpperCamelCase__					)
						if encoded is not None:
									possibles.append(UpperCamelCase__					)
			return possibles







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







def     _UpperCamelCase      (		UpperCamelCase__ = "p059_cipher.txt"					):
			UpperCAmelCase__  :					list[int]
			UpperCAmelCase__  :					list[str]
			UpperCAmelCase__  :					str
			UpperCAmelCase__  :					str
			UpperCAmelCase__  :					str        =     Path(UpperCamelCase__					).parent.joinpath(UpperCamelCase__					).read_text(encoding="""utf-8"""					)

			UpperCAmelCase__  :					int        =     [int(UpperCamelCase__					) for number in data.strip().split(""","""					)]

			UpperCAmelCase__  :					Any        =     filter_valid_chars(UpperCamelCase__					)
			for common_word in COMMON_WORDS:
						UpperCAmelCase__  :					int        =     filter_common_word(UpperCamelCase__ ,      UpperCamelCase__					)
						if len(UpperCamelCase__					) == 1:
									break

			UpperCAmelCase__  :					str        =     possibles[0]
			return sum(ord(UpperCamelCase__					) for char in decoded_text					)


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



'''simple docstring'''



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)