





from __future__ import annotations



def _a       (			lowerCamelCase:     Tuple				)			->		Tuple:







      '''simple docstring'''






      if len(lowerCamelCase				) == 0:
            raise ValueError('''find_min() arg is an empty sequence'''				)
      __A      =       nums[0]
      for num in nums:
            __A      =       min(lowerCamelCase      ,       lowerCamelCase				)
      return min_num


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)












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


snake_case__      :							Optional[Any]									=  logging.get_logger(__name__)

snake_case__      :							List[str]									=  {
    '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_      (    _lowerCamelCase     ):
   lowerCAmelCase__       =				"""nystromformer"""




   def __init__(self			:Union[str, Any]					,     _UpperCamelCase			:Optional[int]=3_0000					,     _UpperCamelCase			:Optional[int]=768					,     _UpperCamelCase			:List[str]=12					,     _UpperCamelCase			:Dict=12					,     _UpperCamelCase			:Any=3072					,     _UpperCamelCase			:List[str]="gelu_new"					,     _UpperCamelCase			:str=0.1					,     _UpperCamelCase			:Dict=0.1					,     _UpperCamelCase			:List[Any]=510					,     _UpperCamelCase			:str=2					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:Optional[int]=65					,     _UpperCamelCase			:Optional[int]=False					,     _UpperCamelCase			:Optional[int]=0.0_2					,     _UpperCamelCase			:Dict=1e-5					,     _UpperCamelCase			:Tuple=1					,     _UpperCamelCase			:Union[str, Any]=0					,     _UpperCamelCase			:Optional[int]=2					,     **_UpperCamelCase			:Dict					,     )->      Any:
         __A      =       vocab_size
         __A      =       max_position_embeddings
         __A      =       hidden_size
         __A      =       num_hidden_layers
         __A      =       num_attention_heads
         __A      =       intermediate_size
         __A      =       hidden_act
         __A      =       hidden_dropout_prob
         __A      =       attention_probs_dropout_prob
         __A      =       initializer_range
         __A      =       type_vocab_size
         __A      =       segment_means_seq_len
         __A      =       num_landmarks
         __A      =       conv_kernel_size
         __A      =       inv_coeff_init_option
         __A      =       layer_norm_eps
         super().__init__(pad_token_id=_UpperCamelCase					,     bos_token_id=_UpperCamelCase					,     eos_token_id=_UpperCamelCase					,     **_UpperCamelCase				)





