'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCAmelCase  							=logging.get_logger(__name__)

__UpperCAmelCase  							={
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
  import detectrona




class 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  Optional[Any]							="layoutlmv2"






     def __init__(				self	:	Union[str, Any]						,       a	:	int=3_05_22						,       a	:	Any=7_68						,       a	:	Optional[Any]=12						,       a	:	Union[str, Any]=12						,       a	:	Optional[int]=30_72						,       a	:	Union[str, Any]="gelu"						,       a	:	Dict=0.1						,       a	:	List[str]=0.1						,       a	:	Any=5_12						,       a	:	Optional[Any]=2						,       a	:	List[str]=0.02						,       a	:	Tuple=1e-1_2						,       a	:	str=0						,       a	:	Any=10_24						,       a	:	List[str]=1_28						,       a	:	Optional[int]=32						,       a	:	Any=True						,       a	:	List[Any]=2_56						,       a	:	Dict=64						,       a	:	str=True						,       a	:	Optional[Any]=[7, 7, 2_56]						,       a	:	Optional[int]=1_28						,       a	:	Optional[Any]=1_28						,       a	:	Dict=True						,       a	:	int=True						,       a	:	str=False						,       a	:	Optional[int]=None						,       **a	:	Tuple						,       ):





         """simple docstring"""



         super().__init__(
             vocab_size=a						,       hidden_size=a						,       num_hidden_layers=a						,       num_attention_heads=a						,       intermediate_size=a						,       hidden_act=a						,       hidden_dropout_prob=a						,       attention_probs_dropout_prob=a						,       max_position_embeddings=a						,       type_vocab_size=a						,       initializer_range=a						,       layer_norm_eps=a						,       pad_token_id=a						,       **a						,       )
         __lowerCamelCase						=						max_ad_position_embeddings
         __lowerCamelCase						=						max_rel_pos
         __lowerCamelCase						=						rel_pos_bins
         __lowerCamelCase						=						fast_qkv
         __lowerCamelCase						=						max_rel_ad_pos
         __lowerCamelCase						=						rel_ad_pos_bins
         __lowerCamelCase						=						convert_sync_batchnorm
         __lowerCamelCase						=						image_feature_pool_shape
         __lowerCamelCase						=						coordinate_size
         __lowerCamelCase						=						shape_size
         __lowerCamelCase						=						has_relative_attention_bias
         __lowerCamelCase						=						has_spatial_attention_bias
         __lowerCamelCase						=						has_visual_segment_embedding
         __lowerCamelCase						=						(
             detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
         )






     @classmethod
     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]					):





         """simple docstring"""



         return {
             "MODEL.MASK_ON": True,
             "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
             "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
             "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
             "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
             "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
             "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
             "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
             "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
             "MODEL.POST_NMS_TOPK_TEST": 10_00,
             "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
             "MODEL.ROI_HEADS.NUM_CLASSES": 5,
             "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
             "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
             "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
             "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
             "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
             "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
             "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
             "MODEL.RESNETS.DEPTH": 1_01,
             "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
             "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
             "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
             "MODEL.RESNETS.NUM_GROUPS": 32,
             "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
             "MODEL.RESNETS.STRIDE_IN_1X1": False,
         }






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict					):





         """simple docstring"""



         __lowerCamelCase						=						detectrona.config.get_cfg()
         for k, v in self.detectrona_config_args.items():
             __lowerCamelCase						=						k.split('''.'''					)
             __lowerCamelCase						=						detectrona_config
             for attribute in attributes[:-1]:
                 __lowerCamelCase						=						getattr(a						,       a					)
             setattr(a						,       attributes[-1]						,       a					)

         return detectrona_config

'''simple docstring'''




__UpperCAmelCase  							=[chr(i) for i in range(3_2, 1_2_6)]
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(reversed(range(len(alphabets))))
__UpperCAmelCase  							=[]
__UpperCAmelCase  							=__UpperCAmelCase  							=__UpperCAmelCase  							=0






def __lowerCAmelCase				(							)			->					List[Any]:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    __lowerCamelCase						=						gear_one[0]
    gear_one.append(UpperCamelCase__       )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(UpperCamelCase__       )       ) == 0:
        __lowerCamelCase						=						gear_two[0]
        gear_two.append(UpperCamelCase__       )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(UpperCamelCase__       )       ) == 0:
            __lowerCamelCase						=						gear_three[0]
            gear_three.append(UpperCamelCase__       )
            del gear_three[0]
            gear_three_pos += 1






def __lowerCAmelCase				(							UpperCamelCase__       )			->					Optional[Any]:
    __lowerCamelCase						=						alphabets.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_one[target]
    __lowerCamelCase						=						gear_two[target]
    __lowerCamelCase						=						gear_three[target]
    __lowerCamelCase						=						reflector[target]
    __lowerCamelCase						=						gear_three.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_two.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_one.index(UpperCamelCase__       )
    code.append(alphabets[target]       )
    rotator()


if __name__ == "__main__":
  __UpperCAmelCase  							=list(input("Type your message:\n"))
  while True:
    try:
      __UpperCAmelCase  							=int(input("Please set token:(must be only digits)\n"))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("\n" + "".join(code))
  print(
      f'\nYour Token is {token} please write it down.\nIf you want to decode '
      "this message again you should input same digits as token!"
  )
