"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_a				:		Union[str, Any]	   =       {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_a				:		Tuple	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"maskformer"
     _UpperCamelCase							:      Union[str, Any]		 =		{"hidden_size": "mask_feature_size"}
     _UpperCamelCase							:      str		 =		["resnet", "swin"]
     _UpperCamelCase							:      int		 =		["detr"]






     def __init__(		self    ,					a__ = 256    ,					a__ = 256    ,					a__ = 0.1    ,					a__ = False    ,					a__ = None    ,					a__ = None    ,					a__ = 0.0_2    ,					a__ = 1.0    ,					a__ = 1.0    ,					a__ = 1.0    ,					a__ = 2_0.0    ,					a__ = None    ,					**a__    ,					):
       if backbone_config is None:
         # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
         _lowerCAmelCase						:	Dict        =					SwinConfig(
             image_size=384    ,					in_channels=3    ,					patch_size=4    ,					embed_dim=128    ,					depths=[2, 2, 18, 2]    ,					num_heads=[4, 8, 16, 32]    ,					window_size=12    ,					drop_path_rate=0.3    ,					out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,					)

       if isinstance(a__    ,					a__  ):
         _lowerCAmelCase						:	Optional[Any]        =					backbone_config.pop("""model_type"""  )
         _lowerCAmelCase						:	List[Any]        =					CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase						:	str        =					config_class.from_dict(a__  )

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
         logger.warning_once(
             F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
             F"Supported model types: {','.join(self.backbones_supported  )}"  )

       if decoder_config is None:
         # fall back to https://huggingface.co/facebook/detr-resnet-50
         _lowerCAmelCase						:	str        =					DetrConfig()
       else:
         # verify that the decoder is supported
         _lowerCAmelCase						:	Optional[Any]        =					(
             decoder_config.pop("""model_type"""  ) if isinstance(a__    ,					a__  ) else decoder_config.model_type
         )
         if decoder_type not in self.decoders_supported:
           raise ValueError(
               F"Transformer Decoder {decoder_type} not supported, please use one of"
               F" {','.join(self.decoders_supported  )}"  )
         if isinstance(a__    ,					a__  ):
           _lowerCAmelCase						:	List[Any]        =					CONFIG_MAPPING[decoder_type]
           _lowerCAmelCase						:	Optional[Any]        =					config_class.from_dict(a__  )

       _lowerCAmelCase						:	Tuple        =					backbone_config
       _lowerCAmelCase						:	str        =					decoder_config
       # main feature dimension for the model
       _lowerCAmelCase						:	List[Any]        =					fpn_feature_size
       _lowerCAmelCase						:	Tuple        =					mask_feature_size
       # initializer
       _lowerCAmelCase						:	int        =					init_std
       _lowerCAmelCase						:	Union[str, Any]        =					init_xavier_std
       # Hungarian matcher && loss
       _lowerCAmelCase						:	int        =					cross_entropy_weight
       _lowerCAmelCase						:	Dict        =					dice_weight
       _lowerCAmelCase						:	Optional[Any]        =					mask_weight
       _lowerCAmelCase						:	Dict        =					use_auxiliary_loss
       _lowerCAmelCase						:	Union[str, Any]        =					no_object_weight
       _lowerCAmelCase						:	Optional[int]        =					output_auxiliary_logits

       _lowerCAmelCase						:	int        =					self.decoder_config.encoder_attention_heads
       _lowerCAmelCase						:	Tuple        =					self.decoder_config.num_hidden_layers
       super().__init__(**a__  )






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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					copy.deepcopy(self.__dict__  )
       _lowerCAmelCase						:	Optional[Any]        =					self.backbone_config.to_dict()
       _lowerCAmelCase						:	List[Any]        =					self.decoder_config.to_dict()
       _lowerCAmelCase						:	int        =					self.__class__.model_type
       return output





"""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()




