
from __future__ import annotations

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

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

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[int]		,						_lowerCamelCase				:    tuple[int, ...])      ->					str | None:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      ""
							__UpperCamelCase			:     int
							__UpperCamelCase			:     int
							__UpperCamelCase			:     int

							for keychar, cipherchar in zip(cycle(_lowerCamelCase)		,						_lowerCamelCase):
														__UpperCamelCase			:     Optional[int]         =      cipherchar ^ keychar
														if decodedchar not in VALID_INTS:
																					return None
														decoded += chr(_lowerCamelCase)

							return decoded




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[int])      ->					list[str]:






							'''simple docstring'''





							__UpperCamelCase			:     list[str]         =      []
							for key in product(_lowerCamelCase		,						repeat=3):
														__UpperCamelCase			:     int         =      try_key(_lowerCamelCase		,						_lowerCamelCase)
														if encoded is not None:
																					possibles.append(_lowerCamelCase)
							return possibles




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list[str]		,						_lowerCamelCase				:    str)      ->					list[str]:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str = "p059_cipher.txt")      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     list[int]
							__UpperCamelCase			:     list[str]
							__UpperCamelCase			:     str
							__UpperCamelCase			:     str
							__UpperCamelCase			:     str         =      Path(_lowerCamelCase).parent.joinpath(_lowerCamelCase).read_text(encoding="utf-8")

							__UpperCamelCase			:     Optional[int]         =      [int(_lowerCamelCase) for number in data.strip().split(",")]

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

							__UpperCamelCase			:     List[Any]         =      possibles[0]
							return sum(ord(_lowerCamelCase) 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


lowercase						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      List[str]									= {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 				lowerCamelCase__							(       __lowercase  ,		__lowercase):




	'''simple docstring'''




	_A			=  'nat'

	_A			=  {
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(      self	:Optional[Any]  ,     a	:Any=4  ,     a	:Any=3  ,     a	:int=6_4  ,     a	:Dict=[3, 4, 6, 5]  ,     a	:Dict=[2, 4, 8, 1_6]  ,     a	:Optional[Any]=7  ,     a	:Any=3.0  ,     a	:Optional[int]=True  ,     a	:int=0.0  ,     a	:Union[str, Any]=0.0  ,     a	:List[Any]=0.1  ,     a	:str="gelu"  ,     a	:Union[str, Any]=0.02  ,     a	:Tuple=1E-5  ,     a	:str=0.0  ,     a	:Optional[int]=None  ,     a	:Dict=None  ,     **a	:Optional[Any]  ,     )	->				int:
								super().__init__(**a		)

								__UpperCamelCase			:     Any         =      patch_size
								__UpperCamelCase			:     str         =      num_channels
								__UpperCamelCase			:     List[Any]         =      embed_dim
								__UpperCamelCase			:     str         =      depths
								__UpperCamelCase			:     str         =      len(a		)
								__UpperCamelCase			:     Optional[Any]         =      num_heads
								__UpperCamelCase			:     Dict         =      kernel_size
								__UpperCamelCase			:     Union[str, Any]         =      mlp_ratio
								__UpperCamelCase			:     Union[str, Any]         =      qkv_bias
								__UpperCamelCase			:     List[str]         =      hidden_dropout_prob
								__UpperCamelCase			:     Any         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Any         =      drop_path_rate
								__UpperCamelCase			:     Any         =      hidden_act
								__UpperCamelCase			:     Tuple         =      layer_norm_eps
								__UpperCamelCase			:     Dict         =      initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__UpperCamelCase			:     int         =      int(embed_dim * 2 ** (len(a		) - 1)		)
								__UpperCamelCase			:     List[Any]         =      layer_scale_init_value
								__UpperCamelCase			:     Optional[Any]         =      ["stem"] + [f'stage{idx}' for idx in range(1  ,     len(a		) + 1		)]
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      get_aligned_output_features_output_indices(
								    out_features=a  ,     out_indices=a  ,     stage_names=self.stage_names		)