






from __future__ import annotations

def   snake_case					(UpperCAmelCase__	,    UpperCAmelCase__  )       ->				List[str]:
  UpperCamelCase_      ,UpperCamelCase_:					str          =				0, len(_lowercase  ) - 1
  while start_ind <= end_ind:
    if array[start_ind] == search_item:
      return start_ind
    elif array[end_ind] == search_item:
      return end_ind
    else:
      start_ind += 1
      end_ind -= 1
    # returns -1 if search_item is not found in array
  return -1


if __name__ == "__main__":
   print(double_linear_search(list(range(100)), 40))







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


__A          = logging.get_logger(__name__)

__A          = {
    '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   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_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=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



