




from math import ceil







def lowerCamelCase__       (      _lowercase = 1001				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      1

     for i in range(1			,						int(ceil(n / 2.0				)				)				):
          UpperCAmelCase_      :							List[Any]       =      2 * i + 1
          UpperCAmelCase_      :							Optional[int]       =      2 * i
          UpperCAmelCase_      :							Any       =      total + 4 * odd**2 - 6 * even

     return total


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          print(solution())
     else:
          try:
               __a          =			int(sys.argv[1])
               print(solution(n))
          except ValueError:
               print('Invalid entry - please enter a number')





from typing import TYPE_CHECKING

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


__a          =			{
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			['BloomTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BloomForCausalLM',
         'BloomModel',
         'BloomPreTrainedModel',
         'BloomForSequenceClassification',
         'BloomForTokenClassification',
         'BloomForQuestionAnswering',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

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