





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


UpperCAmelCase						:       Optional[int]        =					logging.get_logger(__name__)

UpperCAmelCase						:       Any        =					{
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     '''nystromformer'''



				def __init__(      self					, _A=30000					, _A=768					, _A=12					, _A=12					, _A=3072					, _A="gelu_new"					, _A=0.1					, _A=0.1					, _A=510					, _A=2					, _A=64					, _A=64					, _A=65					, _A=False					, _A=0.0_2					, _A=1e-5					, _A=1					, _A=0					, _A=2					, **_A					, ):
									__A     :						Tuple          =    vocab_size
									__A     :						Union[str, Any]          =    max_position_embeddings
									__A     :						Any          =    hidden_size
									__A     :						List[str]          =    num_hidden_layers
									__A     :						Optional[int]          =    num_attention_heads
									__A     :						Any          =    intermediate_size
									__A     :						Any          =    hidden_act
									__A     :						Optional[Any]          =    hidden_dropout_prob
									__A     :						List[str]          =    attention_probs_dropout_prob
									__A     :						Union[str, Any]          =    initializer_range
									__A     :						Optional[int]          =    type_vocab_size
									__A     :						Optional[int]          =    segment_means_seq_len
									__A     :						Optional[int]          =    num_landmarks
									__A     :						List[Any]          =    conv_kernel_size
									__A     :						Optional[int]          =    inv_coeff_init_option
									__A     :						Optional[int]          =    layer_norm_eps
									super().__init__(pad_token_id=_A					, bos_token_id=_A					, eos_token_id=_A					, **_A					)











from __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




