





'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       list:


     '''simple docstring'''



     _UpperCAmelCase     							=   int(__lowercase       )
     if n_element < 1:
          _UpperCAmelCase     							=   ValueError("a should be a positive number"       )
          raise my_error

     _UpperCAmelCase     							=   [1]
     _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   (0, 0, 0)
     _UpperCAmelCase     							=   1
     while index < n_element:
          while hamming_list[i] * 2 <= hamming_list[-1]:
               i += 1
          while hamming_list[j] * 3 <= hamming_list[-1]:
               j += 1
          while hamming_list[k] * 5 <= hamming_list[-1]:
               k += 1
          hamming_list.append(
              min(hamming_list[i] * 2			,     hamming_list[j] * 3			,     hamming_list[k] * 5       )       )
          index += 1
     return hamming_list


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :Any       =				input('''Enter the last number (nth term) of the Hamming Number Series: ''')
  print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
  __SCREAMING_SNAKE_CASE     :Tuple       =				hamming(int(n))
  print('''-----------------------------------------------------''')
  print(F"The list with nth numbers is: {hamming_numbers}")
  print('''-----------------------------------------------------''')










from typing import TYPE_CHECKING

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


lowerCamelCase				:		str     =   {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NezhaForNextSentencePrediction",
     "NezhaForMaskedLM",
     "NezhaForPreTraining",
     "NezhaForMultipleChoice",
     "NezhaForQuestionAnswering",
     "NezhaForSequenceClassification",
     "NezhaForTokenClassification",
     "NezhaModel",
     "NezhaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nezha import (
      NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
      NezhaPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		str     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



