
'''simple docstring'''







import sys





def 					UpperCAmelCase_		(__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	str			=   len(_lowerCAmelCase )
   _a				:	List[Any]			=   [[0 for x in range(_lowerCAmelCase )] for x in range(_lowerCAmelCase )]
   _a				:	Tuple			=   [[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 ):
         _a				:	Optional[int]			=   a + chain_length - 1

         _a				:	List[str]			=   sys.maxsize
         for c in range(_lowerCAmelCase ,	_lowerCAmelCase ):
            _a				:	Any			=   (
                matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
            )
            if cost < matrix[a][b]:
               _a				:	Any			=   cost
               _a				:	List[Any]			=   c
   return matrix, sol





def 					UpperCAmelCase_		(__a      :    str ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   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 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	int			=   [3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5]
   _a				:	Optional[int]			=   len(_lowerCAmelCase )
   # Size of matrix created from above array will be
   # 30*35 35*15 15*5 5*10 10*20 20*25
   _a				:	List[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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:str             =						{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


