


"""simple docstring"""






def 			A__		(					_UpperCAmelCase    :      int	)							->	int:

	'''simple docstring'''


	return n * (2 * n - 1)



def 			A__		(					_UpperCAmelCase    :      int	)							->	bool:

	'''simple docstring'''


	snake_case__  :						Optional[int]		=      (1 + 24 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0



def 			A__		(					_UpperCAmelCase    :      int = 1_44	)							->	int:

	'''simple docstring'''


	snake_case__  :						Optional[int]		=      start
	snake_case__  :						Tuple		=      hexagonal_num(_UpperCAmelCase	)
	while not is_pentagonal(_UpperCAmelCase	):
		n += 1
		snake_case__  :						List[str]		=      hexagonal_num(_UpperCAmelCase	)
	return num


if __name__ == "__main__":
					print(f"{solution()} = ")









"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaubertForMultipleChoice""",
					    """FlaubertForQuestionAnswering""",
					    """FlaubertForQuestionAnsweringSimple""",
					    """FlaubertForSequenceClassification""",
					    """FlaubertForTokenClassification""",
					    """FlaubertModel""",
					    """FlaubertWithLMHeadModel""",
					    """FlaubertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFFlaubertForMultipleChoice""",
					    """TFFlaubertForQuestionAnsweringSimple""",
					    """TFFlaubertForSequenceClassification""",
					    """TFFlaubertForTokenClassification""",
					    """TFFlaubertModel""",
					    """TFFlaubertPreTrainedModel""",
					    """TFFlaubertWithLMHeadModel""",
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

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





