
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A			      =      logging.get_logger(__name__)

A			      =      {
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''donut-swin'''

					_lowerCAmelCase			     =	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}
					def __init__(     self,    A=224,    A=4,    A=3,    A=96,    A=[2, 2, 6, 2],    A=[3, 6, 12, 24],    A=7,    A=4.0,    A=True,    A=0.0,    A=0.0,    A=0.1,    A="gelu",    A=False,    A=0.02,    A=1e-5,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )

									lowerCamelCase					:			int									= image_size
									lowerCamelCase					:			Any									= patch_size
									lowerCamelCase					:			Dict									= num_channels
									lowerCamelCase					:			Union[str, Any]									= embed_dim
									lowerCamelCase					:			str									= depths
									lowerCamelCase					:			Optional[int]									= len(A      )
									lowerCamelCase					:			Union[str, Any]									= num_heads
									lowerCamelCase					:			Any									= window_size
									lowerCamelCase					:			Optional[int]									= mlp_ratio
									lowerCamelCase					:			Dict									= qkv_bias
									lowerCamelCase					:			Any									= hidden_dropout_prob
									lowerCamelCase					:			List[Any]									= attention_probs_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= drop_path_rate
									lowerCamelCase					:			List[str]									= hidden_act
									lowerCamelCase					:			Dict									= use_absolute_embeddings
									lowerCamelCase					:			str									= layer_norm_eps
									lowerCamelCase					:			Tuple									= initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCamelCase					:			Any									= int(embed_dim * 2 ** (len(A      ) - 1)      )







'''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__							:							list[int]       ,							UpperCAmelCase__							:							tuple[int, ...]):
				lowerCamelCase					:			str									= ""
				lowerCamelCase					:			int
				lowerCamelCase					:			int
				lowerCamelCase					:			int

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

				return decoded





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





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





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

				lowerCamelCase					:			Tuple									= [int(UpperCAmelCase__) for number in data.strip().split(',')]

				lowerCamelCase					:			str									= filter_valid_chars(UpperCAmelCase__)
				for common_word in COMMON_WORDS:
								lowerCamelCase					:			List[str]									= filter_common_word(UpperCAmelCase__       ,							UpperCAmelCase__)
								if len(UpperCAmelCase__) == 1:
												break

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


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