




'''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


UpperCamelCase_						:			Any  	=						logging.get_logger(__name__)

UpperCamelCase_						:			Dict  	=						{
    '''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				(						snake_case__							):
     SCREAMING_SNAKE_CASE_							:    Dict    				=						"layoutlm"






     def __init__( self					,_SCREAMING_SNAKE_CASE=30_522					,_SCREAMING_SNAKE_CASE=768					,_SCREAMING_SNAKE_CASE=12					,_SCREAMING_SNAKE_CASE=12					,_SCREAMING_SNAKE_CASE=3_072					,_SCREAMING_SNAKE_CASE="gelu"					,_SCREAMING_SNAKE_CASE=0.1					,_SCREAMING_SNAKE_CASE=0.1					,_SCREAMING_SNAKE_CASE=512					,_SCREAMING_SNAKE_CASE=2					,_SCREAMING_SNAKE_CASE=0.0_2					,_SCREAMING_SNAKE_CASE=1e-12					,_SCREAMING_SNAKE_CASE=0					,_SCREAMING_SNAKE_CASE="absolute"					,_SCREAMING_SNAKE_CASE=True					,_SCREAMING_SNAKE_CASE=1_024					,**_SCREAMING_SNAKE_CASE					,)							->			Union[str, Any]:
          super().__init__(pad_token_id=__UpperCAmelCase					,**__UpperCAmelCase    )
          _snake_case							=      vocab_size
          _snake_case							=      hidden_size
          _snake_case							=      num_hidden_layers
          _snake_case							=      num_attention_heads
          _snake_case							=      hidden_act
          _snake_case							=      intermediate_size
          _snake_case							=      hidden_dropout_prob
          _snake_case							=      attention_probs_dropout_prob
          _snake_case							=      max_position_embeddings
          _snake_case							=      type_vocab_size
          _snake_case							=      initializer_range
          _snake_case							=      layer_norm_eps
          _snake_case							=      position_embedding_type
          _snake_case							=      use_cache
          _snake_case							=      max_ad_position_embeddings







class    _a				(						snake_case__							):






     def __init__( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE = "default"					,_SCREAMING_SNAKE_CASE = None					,)							->			str:
          super().__init__(__UpperCAmelCase					,task=__UpperCAmelCase					,patching_specs=__UpperCAmelCase    )
          _snake_case							=      config.max_ad_position_embeddings - 1






     @property
     def 				_lowercase	( 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 				_lowercase	( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE = -1					,_SCREAMING_SNAKE_CASE = -1					,_SCREAMING_SNAKE_CASE = False					,_SCREAMING_SNAKE_CASE = None					,)							->			Mapping[str, Any]:
          _snake_case							=      super().generate_dummy_inputs(
              __UpperCAmelCase					,batch_size=__UpperCAmelCase					,seq_length=__UpperCAmelCase					,is_pair=__UpperCAmelCase					,framework=__UpperCAmelCase    )

          # Generate a dummy bbox
          _snake_case							=      [48, 84, 73, 128]

          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

          _snake_case							,					_snake_case							=      input_dict["input_ids"].shape
          _snake_case							=      torch.tensor([*[box] * seq_length]    ).tile(__UpperCAmelCase					,1					,1    )
          return input_dict







from __future__ import annotations



def        __a     (    lowerCAmelCase_   :					int				)      -> int | None:






						'''simple docstring'''



						for r in range(1			,lowerCAmelCase_				):
												UpperCAmelCase_=   []
												for x in range(n - 1				):
																		UpperCAmelCase_=   pow(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)
																		if val in li:
																								break
																		li.append(lowerCAmelCase_				)
												else:
																		return r
						return None


if __name__ == "__main__":
	__A				    =						int(input('''Enter a prime number q: '''))
	__A				    =						find_primitive(q)
	if a is None:
		print(f'Cannot find the primitive for the value: {a!r}')
	else:
		__A				    =						int(input('''Enter private key of A: '''))
		__A				    =						pow(a, a_private, q)
		__A				    =						int(input('''Enter private key of B: '''))
		__A				    =						pow(a, b_private, q)

		__A				    =						pow(b_public, a_private, q)
		__A				    =						pow(a_public, b_private, q)

		print('''The key value generated by A is: ''', a_secret)
		print('''The key value generated by B is: ''', b_secret)




