


'''simple docstring'''






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


a_       :     Dict 							=     logging.get_logger(__name__)

a_       :     Any 							=     {
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""layoutlm"""
 def __init__(			self   ,					__magic_name__=3_05_22   ,					__magic_name__=7_68   ,					__magic_name__=12   ,					__magic_name__=12   ,					__magic_name__=30_72   ,					__magic_name__="gelu"   ,					__magic_name__=0.1   ,					__magic_name__=0.1   ,					__magic_name__=5_12   ,					__magic_name__=2   ,					__magic_name__=0.0_2   ,					__magic_name__=1e-12   ,					__magic_name__=0   ,					__magic_name__="absolute"   ,					__magic_name__=True   ,					__magic_name__=10_24   ,					**__magic_name__   ,					)       ->				str:
      super().__init__(pad_token_id=__magic_name__   ,					**__magic_name__    )
      _a         =				vocab_size
      _a         =				hidden_size
      _a         =				num_hidden_layers
      _a         =				num_attention_heads
      _a         =				hidden_act
      _a         =				intermediate_size
      _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         =				position_embedding_type
      _a         =				use_cache
      _a         =				max_ad_position_embeddings





class 			a  (  _SCREAMING_SNAKE_CASE							):
 def __init__(			self   ,					__magic_name__   ,					__magic_name__ = "default"   ,					__magic_name__ = None   ,					)       ->				int:
      super().__init__(__magic_name__   ,					task=__magic_name__   ,					patching_specs=__magic_name__    )
      _a         =				config.max_ad_position_embeddings - 1
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'sequence'}),
              ('bbox', {0: 'batch', 1: 'sequence'}),
              ('attention_mask', {0: 'batch', 1: 'sequence'}),
              ('token_type_ids', {0: 'batch', 1: 'sequence'}),
          ]    )




 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__ = -1   ,					__magic_name__ = -1   ,					__magic_name__ = False   ,					__magic_name__ = None   ,					)       ->				Mapping[str, Any]:
      _a         =				super().generate_dummy_inputs(
          __magic_name__   ,					batch_size=__magic_name__   ,					seq_length=__magic_name__   ,					is_pair=__magic_name__   ,					framework=__magic_name__    )

      # Generate a dummy bbox
      _a         =				[48, 84, 73, 1_28]

      if not framework == TensorType.PYTORCH:
           raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'    )

      if not is_torch_available():
           raise ValueError('Cannot generate dummy inputs without PyTorch installed.'    )
      import torch

      _a   , _a         =				input_dict['input_ids'].shape
      _a         =				torch.tensor([*[box] * seq_length]    ).tile(__magic_name__   ,					1   ,					1    )
      return input_dict





'''simple docstring'''






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	int:




     '''simple docstring'''



     if a < b:
          return gcd(lowerCAmelCase__					,    lowerCAmelCase__		)
     if a % b == 0:
          return b
     return gcd(lowerCAmelCase__					,    a % b		)






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	int:




     '''simple docstring'''



     if y == 0:
          return 1
     _a         =				power(lowerCAmelCase__					,    y // 2					,    lowerCAmelCase__		) % mod
     _a         =				(temp * temp) % mod
     if y % 2 == 1:
          _a         =				(temp * x) % mod
     return temp






def 						_A					(lowerCAmelCase__	:int		) ->	bool:




     '''simple docstring'''



     _a         =				2
     while b < n:
          if gcd(lowerCAmelCase__					,    lowerCAmelCase__		) == 1 and power(lowerCAmelCase__					,    n - 1					,    lowerCAmelCase__		) != 1:
               return False
          b += 1
     return True


if __name__ == "__main__":
 a_       :     Any 							=     int(input("Enter number: ").strip())
 if is_carmichael_number(number):
  print(f'''{number} is a Carmichael Number.''')
 else:
  print(f'''{number} is not a Carmichael Number.''')

