

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


a		=logging.get_logger(__name__)

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






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[int]										=				'''table-transformer'''
							_UpperCAmelCase   :  Optional[Any]										=				['''past_key_values''']
							_UpperCAmelCase   :  Tuple										=				{
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}
							def __init__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str=True       ,SCREAMING_SNAKE_CASE__		:      Any=None       ,SCREAMING_SNAKE_CASE__		:      Dict=3       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_0_0       ,SCREAMING_SNAKE_CASE__		:      Tuple=6       ,SCREAMING_SNAKE_CASE__		:      str=2_0_4_8       ,SCREAMING_SNAKE_CASE__		:      List[Any]=8       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=6       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_0_4_8       ,SCREAMING_SNAKE_CASE__		:      List[str]=8       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.0       ,SCREAMING_SNAKE_CASE__		:      Tuple=0.0       ,SCREAMING_SNAKE_CASE__		:      List[Any]=True       ,SCREAMING_SNAKE_CASE__		:      str="relu"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_5_6       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=0.1       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.0       ,SCREAMING_SNAKE_CASE__		:      List[str]=0.0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0.02       ,SCREAMING_SNAKE_CASE__		:      List[str]=1.0       ,SCREAMING_SNAKE_CASE__		:      Any=False       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]="sine"       ,SCREAMING_SNAKE_CASE__		:      int="resnet50"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=True       ,SCREAMING_SNAKE_CASE__		:      List[str]=False       ,SCREAMING_SNAKE_CASE__		:      Any=1       ,SCREAMING_SNAKE_CASE__		:      int=5       ,SCREAMING_SNAKE_CASE__		:      Tuple=2       ,SCREAMING_SNAKE_CASE__		:      str=1       ,SCREAMING_SNAKE_CASE__		:      Dict=1       ,SCREAMING_SNAKE_CASE__		:      int=5       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2       ,SCREAMING_SNAKE_CASE__		:      int=0.1       ,**SCREAMING_SNAKE_CASE__		:      Dict       ,):
								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.')
										__lowerCamelCase    :    int 	=			CONFIG_MAPPING['resnet'](out_features=['stage4'])
									elif isinstance(__A       ,__A):
										__lowerCamelCase    :    str 	=			backbone_config.get('model_type')
										__lowerCamelCase    :    Optional[int] 	=			CONFIG_MAPPING[backbone_model_type]
										__lowerCamelCase    :    List[Any] 	=			config_class.from_dict(__A)
									# set timm attributes to None
									__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			None, None, None

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


							@property
							def    lowerCAmelCase					(		self		:      Any):
								return self.d_model






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[int]										=				version.parse('''1.11'''    )
							@property
							def    lowerCAmelCase					(		self		:      Dict):
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								        ('pixel_mask', {0: 'batch'}),
								    ])
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return 1E-5


							@property
							def    lowerCAmelCase					(		self		:      Tuple):
								return 1_2







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

a		=3






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	print('Generating primitive root of p'      )
	while True:
		__lowerCamelCase    :    Tuple 	=			random.randrange(3     ,			lowerCamelCase__      )
		if pow(lowerCamelCase__     ,			2     ,			lowerCamelCase__      ) == 1:
			continue
		if pow(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      ) == 1:
			continue
		return g






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					tuple[tuple[int, int, int, int], tuple[int, int]]:
	print('Generating prime p...'      )
	__lowerCamelCase    :    List[str] 	=			rabin_miller.generate_large_prime(lowerCamelCase__      )  # select large prime number.
	__lowerCamelCase    :    Dict 	=			primitive_root(lowerCamelCase__      )  # one primitive root on modulo p.
	__lowerCamelCase    :    Optional[int] 	=			random.randrange(3     ,			lowerCamelCase__      )  # private_key -> have to be greater than 2 for safety.
	__lowerCamelCase    :    List[Any] 	=			cryptomath.find_mod_inverse(pow(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )     ,			lowerCamelCase__      )

	__lowerCamelCase    :    int 	=			(key_size, e_a, e_a, p)
	__lowerCamelCase    :    str 	=			(key_size, d)

	return public_key, private_key






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	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()

	__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			generate_key(lowerCamelCase__      )
	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        SCREAMING_SNAKE_CASE__	(  )  ->					None:
	print('Making key files...'      )
	make_key_files('elgamal'     ,			2_0_4_8      )
	print('Key files generation successful'      )


if __name__ == "__main__":
					main()




