





"""simple docstring"""






def     __A      (			a_						:int   ,       a_						:str)		->		List[str]:
				__a :						List[str]			       =  ''''''
				for i in table:
								res += inp[i - 1]
				return res




def     __A      (			a_						:Dict)		->		Union[str, Any]:
				return data[1:] + data[0]




def     __A      (			a_						:Optional[int]   ,       a_						:Dict)		->		Dict:
				__a :						Dict			       =  ''''''
				for i in range(len(a_)):
								if a[i] == b[i]:
												res += "0"
								else:
												res += "1"
				return res




def     __A      (			a_						:Optional[int]   ,       a_						:Any)		->		Dict:
				__a :						Optional[int]			       =  int('''0b''' + data[0] + data[-1]   ,       2)
				__a :						List[Any]			       =  int('''0b''' + data[1:3]   ,       2)
				return bin(s[row][col])[2:]




def     __A      (			a_						:Union[str, Any]   ,       a_						:str   ,       a_						:Optional[Any]   ,       a_						:int   ,       a_						:List[str])		->		Any:
				__a :						Optional[int]			       =  message[:4]
				__a :						Optional[Any]			       =  message[4:]
				__a :						Optional[Any]			       =  apply_table(a_   ,       a_)
				__a :						Optional[Any]			       =  xor(a_   ,       a_)
				__a :						str			       =  apply_sbox(a_   ,       temp[:4])  # noqa: E741
				__a :						int			       =  apply_sbox(a_   ,       temp[4:])
				__a :						List[Any]			       =  '''0''' * (2 - len(a_)) + l  # noqa: E741
				__a :						int			       =  '''0''' * (2 - len(a_)) + r
				__a :						str			       =  apply_table(l + r   ,       a_)
				__a :						str			       =  xor(a_   ,       a_)
				return temp + right


if __name__ == "__main__":
		A 			=  input('''Enter 10 bit key: ''')
		A 			=  input('''Enter 8 bit message: ''')

		A 			=  [6, 3, 7, 4, 8, 5, 10, 9]
		A 			=  [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
		A 			=  [2, 4, 3, 1]
		A 			=  [2, 6, 3, 1, 4, 8, 5, 7]
		A 			=  [4, 1, 3, 5, 7, 2, 8, 6]
		A 			=  [4, 1, 2, 3, 2, 3, 4, 1]
		A 			=  [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
		A 			=  [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

		# key generation
		A 			=  apply_table(key, paa_table)
		A 			=  temp[:5]
		A 			=  temp[5:]
		A 			=  left_shift(left)
		A 			=  left_shift(right)
		A 			=  apply_table(left + right, pa_table)
		A 			=  left_shift(left)
		A 			=  left_shift(right)
		A 			=  left_shift(left)
		A 			=  left_shift(right)
		A 			=  apply_table(left + right, pa_table)

		# encryption
		A 			=  apply_table(message, IP)
		A 			=  function(expansion, sa, sa, keya, temp)
		A 			=  temp[4:] + temp[:4]
		A 			=  function(expansion, sa, sa, keya, temp)
		A 			=  apply_table(temp, IP_inv)
		print('''Cipher text is:''', CT)

		# decryption
		A 			=  apply_table(CT, IP)
		A 			=  function(expansion, sa, sa, keya, temp)
		A 			=  temp[4:] + temp[:4]
		A 			=  function(expansion, sa, sa, keya, temp)
		A 			=  apply_table(temp, IP_inv)
		print('''Plain text after decypting is:''', PT)






"""simple docstring"""






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A 			=  logging.get_logger(__name__)

A 			=  {
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''detr'''
					__lowerCAmelCase  =  ['''past_key_values''']
					__lowerCAmelCase  =  {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}







					def __init__(						self	,			_UpperCAmelCase=True	,			_UpperCAmelCase=None	,			_UpperCAmelCase=3	,			_UpperCAmelCase=100	,			_UpperCAmelCase=6	,			_UpperCAmelCase=2048	,			_UpperCAmelCase=8	,			_UpperCAmelCase=6	,			_UpperCAmelCase=2048	,			_UpperCAmelCase=8	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=True	,			_UpperCAmelCase="relu"	,			_UpperCAmelCase=256	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1.0	,			_UpperCAmelCase=False	,			_UpperCAmelCase="sine"	,			_UpperCAmelCase="resnet50"	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=1	,			_UpperCAmelCase=5	,			_UpperCAmelCase=2	,			_UpperCAmelCase=1	,			_UpperCAmelCase=1	,			_UpperCAmelCase=5	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.1	,			**_UpperCAmelCase	,			):
									if backbone_config is not None and use_timm_backbone:
													raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''     )

									if not use_timm_backbone:
													if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''     )
																	__a :						Any			       =  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']     )
													elif isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						Optional[int]			       =  backbone_config.get('''model_type'''     )
																	__a :						Tuple			       =  CONFIG_MAPPING[backbone_model_type]
																	__a :						Union[str, Any]			       =  config_class.from_dict(_UpperCAmelCase     )
													# set timm attributes to None
													__a       ,						__a       ,						__a :						Union[str, Any]			       =  None, None, None

									__a :						Union[str, Any]			       =  use_timm_backbone
									__a :						Any			       =  backbone_config
									__a :						Tuple			       =  num_channels
									__a :						int			       =  num_queries
									__a :						str			       =  d_model
									__a :						Any			       =  encoder_ffn_dim
									__a :						int			       =  encoder_layers
									__a :						Optional[int]			       =  encoder_attention_heads
									__a :						Any			       =  decoder_ffn_dim
									__a :						str			       =  decoder_layers
									__a :						Union[str, Any]			       =  decoder_attention_heads
									__a :						List[Any]			       =  dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						int			       =  activation_dropout
									__a :						Dict			       =  activation_function
									__a :						str			       =  init_std
									__a :						int			       =  init_xavier_std
									__a :						Optional[Any]			       =  encoder_layerdrop
									__a :						Optional[int]			       =  decoder_layerdrop
									__a :						str			       =  encoder_layers
									__a :						List[str]			       =  auxiliary_loss
									__a :						Optional[Any]			       =  position_embedding_type
									__a :						Any			       =  backbone
									__a :						Tuple			       =  use_pretrained_backbone
									__a :						int			       =  dilation
									# Hungarian matcher
									__a :						str			       =  class_cost
									__a :						Optional[Any]			       =  bbox_cost
									__a :						Any			       =  giou_cost
									# Loss coefficients
									__a :						List[str]			       =  mask_loss_coefficient
									__a :						Dict			       =  dice_loss_coefficient
									__a :						str			       =  bbox_loss_coefficient
									__a :						str			       =  giou_loss_coefficient
									__a :						Any			       =  eos_coefficient
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,			**_UpperCAmelCase     )







					@property
					def  _lowerCamelCase		(						self     ):
									return self.encoder_attention_heads







					@property
					def  _lowerCamelCase		(						self     ):
									return self.d_model







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return cls(backbone_config=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  copy.deepcopy(self.__dict__     )
									if output["backbone_config"] is not None:
													__a :						Tuple			       =  self.backbone_config.to_dict()
									__a :						Dict			       =  self.__class__.model_type
									return output






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  version.parse('''1.11'''							)







					@property
					def  _lowerCamelCase		(						self     ):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									        ('''pixel_mask''', {0: '''batch'''}),
									    ]     )







					@property
					def  _lowerCamelCase		(						self     ):
									return 1e-5







					@property
					def  _lowerCamelCase		(						self     ):
									return 12