
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase_						:     Any	       =							logging.get_logger(__name__)

UpperCamelCase_						:     Any	       =							{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}




class   __lowercase						(						__snake_case       ):
	_A       =   "table-transformer"
	_A       =   ["past_key_values"]
	_A       =   {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}






	def __init__(self				: int      ,	snake_case				: List[str]=True      ,	snake_case				: Optional[Any]=None      ,	snake_case				: List[Any]=3      ,	snake_case				: Dict=100      ,	snake_case				: Dict=6      ,	snake_case				: int=2048      ,	snake_case				: Union[str, Any]=8      ,	snake_case				: Dict=6      ,	snake_case				: Any=2048      ,	snake_case				: Optional[Any]=8      ,	snake_case				: int=0.0      ,	snake_case				: str=0.0      ,	snake_case				: Any=True      ,	snake_case				: Optional[int]="relu"      ,	snake_case				: List[str]=256      ,	snake_case				: int=0.1      ,	snake_case				: str=0.0      ,	snake_case				: int=0.0      ,	snake_case				: int=0.02      ,	snake_case				: Union[str, Any]=1.0      ,	snake_case				: Optional[int]=False      ,	snake_case				: str="sine"      ,	snake_case				: List[Any]="resnet50"      ,	snake_case				: Tuple=True      ,	snake_case				: Optional[int]=False      ,	snake_case				: Any=1      ,	snake_case				: Union[str, Any]=5      ,	snake_case				: List[Any]=2      ,	snake_case				: List[Any]=1      ,	snake_case				: Union[str, Any]=1      ,	snake_case				: List[str]=5      ,	snake_case				: str=2      ,	snake_case				: str=0.1      ,	**snake_case				: int      ,	) ->			Any:
							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."    )
																			_lowercase       :	Optional[int]           =   CONFIG_MAPPING["resnet"](out_features=["stage4"]    )
													elif isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	Any           =   backbone_config.get("model_type"    )
																			_lowercase       :	Tuple           =   CONFIG_MAPPING[backbone_model_type]
																			_lowercase       :	str           =   config_class.from_dict(snake_case    )
													# set timm attributes to None
													_lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   None, None, None

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






	@property
	def       _a(self				: Union[str, Any]    ) ->			int:
							return self.encoder_attention_heads






	@property
	def       _a(self				: List[Any]    ) ->			int:
							return self.d_model




class   __lowercase						(						__snake_case       ):
	_A       =   version.parse("1.11"       )






	@property
	def       _a(self				: int    ) ->			Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							        ("pixel_mask", {0: "batch"}),
							    ]    )






	@property
	def       _a(self				: Any    ) ->			float:
							return 1e-5






	@property
	def       _a(self				: int    ) ->			int:
							return 12






import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCamelCase_						:     List[Any]	       =							3



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			int:






						'''simple docstring'''

						print("Generating primitive root of p"  )
						while True:
												_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )
												if pow(_UpperCAmelCase ,			2 ,			_UpperCAmelCase  ) == 1:
																		continue
												if pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) == 1:
																		continue
												return g



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			tuple[tuple[int, int, int, int], tuple[int, int]]:






						'''simple docstring'''

						print("Generating prime p..."  )
						_lowercase       :	Optional[int]           =   rabin_miller.generate_large_prime(_UpperCAmelCase  )  # select large prime number.
						_lowercase       :	Dict           =   primitive_root(_UpperCAmelCase  )  # one primitive root on modulo p.
						_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )  # private_key -> have to be greater than 2 for safety.
						_lowercase       :	Tuple           =   cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) ,			_UpperCAmelCase  )

						_lowercase       :	List[Any]           =   (key_size, e_a, e_a, p)
						_lowercase       :	Union[str, Any]           =   (key_size, d)

						return public_key, private_key



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			None:






						'''simple docstring'''

						if os.path.exists(f"""{name}_pubkey.txt"""  ) or os.path.exists(f"""{name}_privkey.txt"""  ):
												print("\nWARNING:"  )
												print(
												    f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_lowercase      ,    _lowercase       :	Optional[Any]           =   generate_key(_UpperCAmelCase  )
						print(f"""\nWriting public key to file {name}_pubkey.txt..."""  )
						with open(f"""{name}_pubkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""  )

						print(f"""Writing private key to file {name}_privkey.txt..."""  )
						with open(f"""{name}_privkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{private_key[0]},{private_key[1]}"""  )



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						print("Making key files..."  )
						make_key_files("elgamal" ,			2048  )
						print("Key files generation successful"  )


if __name__ == "__main__":
							main()




