





'''simple docstring'''






import sys
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				len(lowerCAmelCase    )
      _lowerCAmelCase					=				[[0 for x in range(lowerCAmelCase    )] for x in range(lowerCAmelCase    )]
      _lowerCAmelCase					=				[[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    ):
                  _lowerCAmelCase					=				a + chain_length - 1

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





      """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"""


      _lowerCAmelCase					=				[30, 35, 15, 5, 10, 20, 25]
      _lowerCAmelCase					=				len(lowerCAmelCase    )
      # Size of matrix created from above array will be
      # 30*35 35*15 15*5 5*10 10*20 20*25
      _lowerCAmelCase	,				_lowerCAmelCase					=				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()













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






