


'''simple docstring'''
import sys


def 			a      (			__a   )  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :Optional[int]												= len(lowerCAmelCase_   )
					UpperCamelCase__   :Tuple												= [[0 for x in range(lowerCAmelCase_   )] for x in range(lowerCAmelCase_   )]
					UpperCamelCase__   :List[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__   :Union[str, Any]												= a + chain_length - 1

															UpperCamelCase__   :Optional[Any]												= sys.maxsize
															for c in range(lowerCAmelCase_ ,					lowerCAmelCase_   ):
																				UpperCamelCase__   :Union[str, Any]												= (
																				    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																				)
																				if cost < matrix[a][b]:
																									UpperCamelCase__   :int												= cost
																									UpperCamelCase__   :Tuple												= c
					return matrix, sol


def 			a      (			__a ,					__a ,					__a   )  ->	str:



					'''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 			a      (			)  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :Union[str, Any]												= [30, 35, 15, 5, 10, 20, 25]
					UpperCamelCase__   :Dict												= len(lowerCAmelCase_   )
					# Size of matrix created from above array will be
					# 30*35 35*15 15*5 5*10 10*20 20*25
					UpperCamelCase__					,				UpperCamelCase__   :Tuple												= 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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
