





"""simple docstring"""


from doctest import testmod
from math import sqrt





def 				a__      (	_UpperCAmelCase						)     ->	list:

 '''simple docstring'''






 snake_case__     				=					[]
 if num < 1:
  return facs
 facs.append(1						)
 if num == 1:
  return facs
 facs.append(_UpperCAmelCase						)
 for i in range(2						,  int(sqrt(_UpperCAmelCase						)						) + 1						):
  if num % i == 0:  # If i is a factor of num
   facs.append(_UpperCAmelCase						)
   snake_case__     				=					num // i  # num//i is the other factor of num
   if d != i:  # If d and i are distinct
    facs.append(_UpperCAmelCase						)  # we have found another factor
 facs.sort()
 return facs


if __name__ == "__main__":
   testmod(name="""factors_of_a_number""", verbose=True)








"""simple docstring"""


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


a__       :  List[Any]       		=					logging.get_logger(__name__)

a__       :  int       		=					{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}



class   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				int									=  """mpnet"""

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=3_0527 ,       __SCREAMING_SNAKE_CASE=768 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=12 ,       __SCREAMING_SNAKE_CASE=3072 ,       __SCREAMING_SNAKE_CASE="gelu" ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=0.1 ,       __SCREAMING_SNAKE_CASE=512 ,       __SCREAMING_SNAKE_CASE=0.02 ,       __SCREAMING_SNAKE_CASE=1e-12 ,       __SCREAMING_SNAKE_CASE=32 ,       __SCREAMING_SNAKE_CASE=1 ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=2 ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,       bos_token_id=__SCREAMING_SNAKE_CASE ,       eos_token_id=__SCREAMING_SNAKE_CASE ,       **__SCREAMING_SNAKE_CASE  )

        snake_case__     				=					vocab_size
        snake_case__     				=					hidden_size
        snake_case__     				=					num_hidden_layers
        snake_case__     				=					num_attention_heads
        snake_case__     				=					hidden_act
        snake_case__     				=					intermediate_size
        snake_case__     				=					hidden_dropout_prob
        snake_case__     				=					attention_probs_dropout_prob
        snake_case__     				=					max_position_embeddings
        snake_case__     				=					initializer_range
        snake_case__     				=					layer_norm_eps
        snake_case__     				=					relative_attention_num_buckets

