
"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							_lowerCAmelCase  	=			n ** (1 / 3)
							return (val * val * val) == n


if __name__ == "__main__":
	print(perfect_cube(2_7))
	print(perfect_cube(4))








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''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:
	_snake_case          =						[
	    '''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:
	_snake_case          =						[
	    '''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

	_snake_case          =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






