"""simple docstring"""





from typing import TYPE_CHECKING

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


__A	=					{
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A	=					[
				    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DebertaForMaskedLM',
				    'DebertaForQuestionAnswering',
				    'DebertaForSequenceClassification',
				    'DebertaForTokenClassification',
				    'DebertaModel',
				    'DebertaPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A	=					[
				    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDebertaForMaskedLM',
				    'TFDebertaForQuestionAnswering',
				    'TFDebertaForSequenceClassification',
				    'TFDebertaForTokenClassification',
				    'TFDebertaModel',
				    'TFDebertaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

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



"""simple docstring"""




def       a_ (	lowercase__ :int = 10**9     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      2

			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			while perimeter <= max_perimeter:
						perimeters_sum += perimeter

						prev_value += 2 * value
						value += prev_value

						__lowerCamelCase          =      2 * value + 2 if i % 2 == 0 else 2 * value - 2
						i += 1

			return perimeters_sum


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




