






"""simple docstring"""






import sys



def       _A    (__a							)				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	len(__a							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	[[0 for x in range(__a							)] for x in range(__a							)]
    SCREAMING_SNAKE_CASE_   :						List[str]							=	[[0 for x in range(__a							)] for x in range(__a							)]

    for chain_length in range(2					,       __a							):
        for a in range(1					,       n - chain_length + 1							):
            SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	a + chain_length - 1

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



def       _A    (__a					,       __a					,       __a							)				-> int:
    """simple docstring"""






    if i == j:
        print('''A''' + str(__a							)					,       end=''' '''							)
    else:
        print('''('''					,       end=''' '''							)
        print_optiomal_solution(__a					,       __a					,       optimal_solution[i][j]							)
        print_optiomal_solution(__a					,       optimal_solution[i][j] + 1					,       __a							)
        print(''')'''					,       end=''' '''							)



def       _A    ()				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	[30, 35, 15, 5, 10, 20, 25]
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(__a							)
    # Size of matrix created from above array will be
    # 30*35 35*15 15*5 5*10 10*20 20*25
    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	matrix_chain_order(__a							)

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


if __name__ == "__main__":
  main()










"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"data2vec-text"
      def __init__(		self  :						Any		,			lowercase_  :						Any=30522		,			lowercase_  :						Any=768		,			lowercase_  :						Union[str, Any]=12		,			lowercase_  :						Dict=12		,			lowercase_  :						List[Any]=3072		,			lowercase_  :						str="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=512		,			lowercase_  :						Optional[int]=2		,			lowercase_  :						int=0.02		,			lowercase_  :						int=1e-12		,			lowercase_  :						Any=1		,			lowercase_  :						Any=0		,			lowercase_  :						List[Any]=2		,			lowercase_  :						Tuple="absolute"		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						int=None		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


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

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




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              SCREAMING_SNAKE_CASE_   :						Any							=	{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ])


