


def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return x if y == 0 else greatest_common_divisor(snake_case__						,   x % y     )




def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return (x * y) // greatest_common_divisor(snake_case__						,   snake_case__     )




def    a__    (		snake_case__					:							int = 20     ):
  _UpperCAmelCase		:							Union[str, Any]							=     1
  for i in range(1						,   n + 1     ):
    _UpperCAmelCase		:							Dict							=     lcm(snake_case__						,   snake_case__     )
  return g


if __name__ == "__main__":
 print(F'{solution() = }')









from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





