


"""simple docstring"""







import sys




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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

															__SCREAMING_SNAKE_CASE						   =				sys.maxsize
															for c in range(lowerCAmelCase_  ,						lowerCAmelCase_   ):
																				__SCREAMING_SNAKE_CASE						   =				(
																				    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																				)
																				if cost < matrix[a][b]:
																									__SCREAMING_SNAKE_CASE						   =				cost
																									__SCREAMING_SNAKE_CASE						   =				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'''


					__SCREAMING_SNAKE_CASE						   =				[30, 35, 15, 5, 10, 20, 25]
					__SCREAMING_SNAKE_CASE						   =				len(lowerCAmelCase_   )
					# 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						   =				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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

       a_ =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




