import functools
import operator

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


A_      =      logging.get_logger(__name__)

A_      =      {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      __lowercase					(    UpperCAmelCase__    ):
						lowercase					 =     "unispeech"

						def __init__(	self				:      List[str]							,							__lowerCamelCase				:      Dict=32							,							__lowerCamelCase				:      List[str]=7_68							,							__lowerCamelCase				:      str=12							,							__lowerCamelCase				:      int=12							,							__lowerCamelCase				:      int=30_72							,							__lowerCamelCase				:      Any="gelu"							,							__lowerCamelCase				:      List[Any]=0.1							,							__lowerCamelCase				:      List[Any]=0.1							,							__lowerCamelCase				:      Optional[int]=0.1							,							__lowerCamelCase				:      str=0.0							,							__lowerCamelCase				:      int=0.0							,							__lowerCamelCase				:      Dict=0.1							,							__lowerCamelCase				:      int=0.1							,							__lowerCamelCase				:      List[str]=0.02							,							__lowerCamelCase				:      Tuple=1E-5							,							__lowerCamelCase				:      Optional[int]="group"							,							__lowerCamelCase				:      List[str]="gelu"							,							__lowerCamelCase				:      Optional[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)							,							__lowerCamelCase				:      Optional[int]=(5, 2, 2, 2, 2, 2, 2)							,							__lowerCamelCase				:      List[Any]=(10, 3, 3, 3, 3, 2, 2)							,							__lowerCamelCase				:      int=False							,							__lowerCamelCase				:      Union[str, Any]=1_28							,							__lowerCamelCase				:      List[Any]=16							,							__lowerCamelCase				:      List[str]=False							,							__lowerCamelCase				:      List[Any]=True							,							__lowerCamelCase				:      List[Any]=0.05							,							__lowerCamelCase				:      Dict=10							,							__lowerCamelCase				:      Union[str, Any]=2							,							__lowerCamelCase				:      List[Any]=0.0							,							__lowerCamelCase				:      Tuple=10							,							__lowerCamelCase				:      Optional[Any]=0							,							__lowerCamelCase				:      str=3_20							,							__lowerCamelCase				:      Dict=2							,							__lowerCamelCase				:      int=0.1							,							__lowerCamelCase				:      Optional[int]=1_00							,							__lowerCamelCase				:      Optional[Any]=2_56							,							__lowerCamelCase				:      Any=2_56							,							__lowerCamelCase				:      Any=0.1							,							__lowerCamelCase				:      str="mean"							,							__lowerCamelCase				:      Optional[Any]=False							,							__lowerCamelCase				:      Tuple=False							,							__lowerCamelCase				:      str=2_56							,							__lowerCamelCase				:      List[str]=80							,							__lowerCamelCase				:      Tuple=0							,							__lowerCamelCase				:      Optional[Any]=1							,							__lowerCamelCase				:      Optional[Any]=2							,							__lowerCamelCase				:      str=0.5							,							**__lowerCamelCase				:      Union[str, Any]							,							)							->		Any:


									'''simple docstring'''

									super().__init__(**lowerCamelCase__							,							pad_token_id=lowerCamelCase__							,							bos_token_id=lowerCamelCase__							,							eos_token_id=lowerCamelCase__   )
									lowercase							=		hidden_size
									lowercase							=		feat_extract_norm
									lowercase							=		feat_extract_activation
									lowercase							=		list(lowerCamelCase__   )
									lowercase							=		list(lowerCamelCase__   )
									lowercase							=		list(lowerCamelCase__   )
									lowercase							=		conv_bias
									lowercase							=		num_conv_pos_embeddings
									lowercase							=		num_conv_pos_embedding_groups
									lowercase							=		len(self.conv_dim   )
									lowercase							=		num_hidden_layers
									lowercase							=		intermediate_size
									lowercase							=		hidden_act
									lowercase							=		num_attention_heads
									lowercase							=		hidden_dropout
									lowercase							=		attention_dropout
									lowercase							=		activation_dropout
									lowercase							=		feat_proj_dropout
									lowercase							=		final_dropout
									lowercase							=		layerdrop
									lowercase							=		layer_norm_eps
									lowercase							=		initializer_range
									lowercase							=		num_ctc_classes
									lowercase							=		vocab_size
									lowercase							=		do_stable_layer_norm
									lowercase							=		use_weighted_layer_sum
									lowercase							=		classifier_proj_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)`, but is `len(config.conv_dim) ='''
												    f' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'
												    f' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'   )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									lowercase							=		apply_spec_augment
									lowercase							=		mask_time_prob
									lowercase							=		mask_time_length
									lowercase							=		mask_time_min_masks
									lowercase							=		mask_feature_prob
									lowercase							=		mask_feature_length
									lowercase							=		mask_feature_min_masks

									# parameters for pretraining with codevector quantized representations
									lowercase							=		num_codevectors_per_group
									lowercase							=		num_codevector_groups
									lowercase							=		contrastive_logits_temperature
									lowercase							=		feat_quantizer_dropout
									lowercase							=		num_negatives
									lowercase							=		codevector_dim
									lowercase							=		proj_codevector_dim
									lowercase							=		diversity_loss_weight

									# ctc loss
									lowercase							=		ctc_loss_reduction
									lowercase							=		ctc_zero_infinity

									# pretraining loss
									lowercase							=		replace_prob



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


									'''simple docstring'''

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






import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

A_      =      3






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			print('''Generating primitive root of p'''       )
			while True:
						lowercase							=		random.randrange(3,		UpperCAmelCase       )
						if pow(UpperCAmelCase,		2,		UpperCAmelCase       ) == 1:
									continue
						if pow(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       ) == 1:
									continue
						return g






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple[tuple[int, int, int, int], tuple[int, int]]:



			"""simple docstring"""







			print('''Generating prime p...'''       )
			lowercase							=		rabin_miller.generate_large_prime(UpperCAmelCase       )  # select large prime number.
			lowercase							=		primitive_root(UpperCAmelCase       )  # one primitive root on modulo p.
			lowercase							=		random.randrange(3,		UpperCAmelCase       )  # private_key -> have to be greater than 2 for safety.
			lowercase							=		cryptomath.find_mod_inverse(pow(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       ),		UpperCAmelCase       )

			lowercase							=		(key_size, e_a, e_a, p)
			lowercase							=		(key_size, d)

			return public_key, private_key






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       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							=		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()





