


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__      			=    logging.get_logger(__name__)

snake_case__      			=    {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class        lowerCAmelCase_			(							__a):
						lowerCamelCase_    =  '''sew'''




						def __init__(						self    : str ,  __A    : List[Any]=32 ,  __A    : str=768 ,  __A    : int=12 ,  __A    : int=12 ,  __A    : List[str]=3072 ,  __A    : str=2 ,  __A    : Tuple="gelu" ,  __A    : Tuple=0.1 ,  __A    : List[str]=0.1 ,  __A    : Any=0.1 ,  __A    : str=0.0 ,  __A    : Optional[Any]=0.1 ,  __A    : str=0.1 ,  __A    : str=0.02 ,  __A    : List[str]=1E-5 ,  __A    : str="group" ,  __A    : List[Any]="gelu" ,  __A    : Optional[int]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,  __A    : int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,  __A    : List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,  __A    : Optional[Any]=False ,  __A    : Dict=128 ,  __A    : Optional[Any]=16 ,  __A    : List[Any]=True ,  __A    : Union[str, Any]=0.05 ,  __A    : Dict=10 ,  __A    : Union[str, Any]=2 ,  __A    : List[str]=0.0 ,  __A    : Optional[int]=10 ,  __A    : List[str]=0 ,  __A    : Tuple="mean" ,  __A    : Union[str, Any]=False ,  __A    : Dict=False ,  __A    : Dict=256 ,  __A    : Union[str, Any]=0 ,  __A    : List[Any]=1 ,  __A    : Optional[int]=2 ,  **__A    : int ,  )   ->str:






									"""simple docstring"""






									super().__init__(**__UpperCamelCase ,  pad_token_id=__UpperCamelCase ,  bos_token_id=__UpperCamelCase ,  eos_token_id=__UpperCamelCase    )
									a__			:Any    	=     hidden_size
									a__			:List[Any]    	=     feat_extract_norm
									a__			:Union[str, Any]    	=     feat_extract_activation
									a__			:List[Any]    	=     list(__UpperCamelCase    )
									a__			:List[Any]    	=     list(__UpperCamelCase    )
									a__			:int    	=     list(__UpperCamelCase    )
									a__			:int    	=     conv_bias
									a__			:Union[str, Any]    	=     num_conv_pos_embeddings
									a__			:Union[str, Any]    	=     num_conv_pos_embedding_groups
									a__			:List[Any]    	=     len(self.conv_dim    )
									a__			:Dict    	=     num_hidden_layers
									a__			:Dict    	=     intermediate_size
									a__			:int    	=     squeeze_factor
									a__			:Optional[Any]    	=     hidden_act
									a__			:Tuple    	=     num_attention_heads
									a__			:Tuple    	=     hidden_dropout
									a__			:List[str]    	=     attention_dropout
									a__			:str    	=     activation_dropout
									a__			:Optional[Any]    	=     feat_proj_dropout
									a__			:Tuple    	=     final_dropout
									a__			:int    	=     layerdrop
									a__			:Tuple    	=     layer_norm_eps
									a__			:Dict    	=     initializer_range
									a__			:Dict    	=     vocab_size

									if (
									    (len(self.conv_stride    ) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
									    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
									):
												raise ValueError(
												    "Configuration for convolutional layers is incorrect."
												    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
												    F'''but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)'''
												    F'''= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									a__			:Any    	=     apply_spec_augment
									a__			:int    	=     mask_time_prob
									a__			:Dict    	=     mask_time_length
									a__			:Optional[int]    	=     mask_time_min_masks
									a__			:int    	=     mask_feature_prob
									a__			:Dict    	=     mask_feature_length
									a__			:Tuple    	=     mask_feature_min_masks

									# ctc loss
									a__			:Optional[Any]    	=     ctc_loss_reduction
									a__			:List[str]    	=     ctc_zero_infinity

									# sequence classification
									a__			:Any    	=     use_weighted_layer_sum
									a__			:Any    	=     classifier_proj_size






						@property
						def 			_snake_case       (						self    : Union[str, Any]    )   ->Tuple:






									"""simple docstring"""






									return functools.reduce(operator.mul ,  self.conv_stride ,  1    )



import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812






def     lowerCAmelCase_			(   ):
						print("""Making key files..."""			)
						make_key_files("""rsa"""		, 1024			)
						print("""Key files generation successful."""			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		random.randrange(2 ** (key_size - 1)		, 2 ** (key_size)			)
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			) == 1:
																		break

						print("""Calculating d that is mod inverse of e..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				int			):
						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()

						UpperCamelCase_,UpperCamelCase_		:							str  =		generate_key(_SCREAMING_SNAKE_CASE			)
						print(f'''\nWriting public key to file {name}_pubkey.txt...'''			)
						with open(f'''{name}_pubkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{public_key[0]},{public_key[1]}'''			)

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


if __name__ == "__main__":
	main()






