

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 a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= ""
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42

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

	return decoded





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	for key in product(lowercase__			, repeat=3	):
		snake_case_      		= try_key(lowercase__			, lowercase__	)
		if encoded is not None:
			possibles.append(lowercase__	)
	return possibles





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	return [possible for possible in possibles if common_word in possible.lower()]





def a(lowercase__ = "p059_cipher.txt"	):





	'''simple docstring'''
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= Path(lowercase__	).parent.joinpath(lowercase__	).read_text(encoding='utf-8'	)

	snake_case_      		= [int(lowercase__	) for number in data.strip().split(','	)]

	snake_case_      		= filter_valid_chars(lowercase__	)
	for common_word in COMMON_WORDS:
		snake_case_      		= filter_common_word(lowercase__			, lowercase__	)
		if len(lowercase__	) == 1:
			break

	snake_case_      		= possibles[0]
	return sum(ord(lowercase__	) for char in decoded_text	)


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



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A											=     logging.get_logger(__name__)

A											=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """bit"""
			__A						  =  ["""preactivation""", """bottleneck"""]
			__A						  =  ["""SAME""", """VALID"""]


			def __init__(    self ,				__UpperCamelCase=3 ,				__UpperCamelCase=64 ,				__UpperCamelCase=[2_56, 5_12, 10_24, 20_48] ,				__UpperCamelCase=[3, 4, 6, 3] ,				__UpperCamelCase="preactivation" ,				__UpperCamelCase="relu" ,				__UpperCamelCase=None ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=False ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types							)}"""							)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_      		= global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
				snake_case_      		= num_channels
				snake_case_      		= embedding_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= layer_type
				snake_case_      		= hidden_act
				snake_case_      		= global_padding
				snake_case_      		= num_groups
				snake_case_      		= drop_path_rate
				snake_case_      		= embedding_dynamic_padding
				snake_case_      		= output_stride
				snake_case_      		= width_factor

				snake_case_      		= ['stem'] + [f"""stage{idx}""" for idx in range(1 ,				len(__UpperCamelCase							) + 1							)]
				snake_case_		,       snake_case_      		= get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase ,				out_indices=__UpperCamelCase ,				stage_names=self.stage_names							)
