

import sys

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int )   ->       Tuple:
				_UpperCAmelCase       :		Union[str, Any] 		=    len(lowerCAmelCase )
				_UpperCAmelCase       :		Union[str, Any] 		=    [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
				_UpperCAmelCase       :		Any 		=    [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]

				for chain_length in range(2  ,      lowerCAmelCase ):
								for a in range(1  ,      n - chain_length + 1 ):
												_UpperCAmelCase       :		str 		=    a + chain_length - 1

												_UpperCAmelCase       :		int 		=    sys.maxsize
												for c in range(lowerCAmelCase  ,      lowerCAmelCase ):
																_UpperCAmelCase       :		Optional[int] 		=    (
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				_UpperCAmelCase       :		Dict 		=    cost
																				_UpperCAmelCase       :		Tuple 		=    c
				return matrix, sol

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Any )   ->       int:
				if i == j:
								print("A" + str(lowerCAmelCase )  ,      end=" " )
				else:
								print("("  ,      end=" " )
								print_optiomal_solution(lowerCAmelCase  ,      lowerCAmelCase  ,      optimal_solution[i][j] )
								print_optiomal_solution(lowerCAmelCase  ,      optimal_solution[i][j] + 1  ,      lowerCAmelCase )
								print(")"  ,      end=" " )

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		Tuple 		=    [30, 35, 15, 5, 10, 20, 25]
				_UpperCAmelCase       :		str 		=    len(lowerCAmelCase )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				_UpperCAmelCase       :		Any 		=    matrix_chain_order(lowerCAmelCase )

				print("No. of Operation required: " + str(matrix[1][n - 1] ) )
				print_optiomal_solution(lowerCAmelCase  ,      1  ,      n - 1 )


if __name__ == "__main__":
					main()





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


