"""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				:		Optional[int]	   =       logging.get_logger(__name__)

_a				:		Dict	   =       {
    '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 						__A							(			SCREAMING_SNAKE_CASE_		):
					_UpperCamelCase							:      Optional[Any]		 =		"detr"
					_UpperCamelCase							:      Union[str, Any]		 =		["past_key_values"]
					_UpperCamelCase							:      Any		 =		{
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}






					def __init__(		self    ,					a__=True    ,					a__=None    ,					a__=3    ,					a__=100    ,					a__=6    ,					a__=2048    ,					a__=8    ,					a__=6    ,					a__=2048    ,					a__=8    ,					a__=0.0    ,					a__=0.0    ,					a__=True    ,					a__="relu"    ,					a__=256    ,					a__=0.1    ,					a__=0.0    ,					a__=0.0    ,					a__=0.0_2    ,					a__=1.0    ,					a__=False    ,					a__="sine"    ,					a__="resnet50"    ,					a__=True    ,					a__=False    ,					a__=1    ,					a__=5    ,					a__=2    ,					a__=1    ,					a__=1    ,					a__=5    ,					a__=2    ,					a__=0.1    ,					**a__    ,					):
							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."""  )
											_lowerCAmelCase						:	Dict        =					CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]  )
									elif isinstance(a__    ,					a__  ):
											_lowerCAmelCase						:	str        =					backbone_config.get("""model_type"""  )
											_lowerCAmelCase						:	Any        =					CONFIG_MAPPING[backbone_model_type]
											_lowerCAmelCase						:	Union[str, Any]        =					config_class.from_dict(a__  )
									# set timm attributes to None
									_lowerCAmelCase						:	Dict        =					None, None, None

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






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






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






					@classmethod
					def    __A  (		cls    ,					a__    ,					**a__  ):
							return cls(backbone_config=a__    ,					**a__  )






					def    __A  (		self  ):
							_lowerCAmelCase						:	Union[str, Any]        =					copy.deepcopy(self.__dict__  )
							if output["backbone_config"] is not None:
									_lowerCAmelCase						:	Dict        =					self.backbone_config.to_dict()
							_lowerCAmelCase						:	Any        =					self.__class__.model_type
							return output







class 						__A							(			SCREAMING_SNAKE_CASE_		):
					_UpperCamelCase							:      Optional[int]		 =		version.parse("1.11"		)






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






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






					@property
					def    __A  (		self  ):
							return 12





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

  # Append pipe symbol (vertical bar) to identify spaces at the end.
  print(f"Output:\n{text + '|'}"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					0

  for symbol in message:
    plain_text[col] += symbol
    col += 1

    if (
        (col == num_cols)
        or (col == num_cols - 1)
        and (row >= num_rows - num_shaded_boxes)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




