




def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      int(lowerCAmelCase__				)
     if n_element < 1:
          UpperCAmelCase_      :							Tuple       =      ValueError('''a should be a positive number'''				)
          raise my_error

     UpperCAmelCase_      :							str       =      [1]
     UpperCAmelCase_      :							List[Any]       =      (0, 0, 0)
     UpperCAmelCase_      :							Optional[int]       =      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__":
     __a          =			input('Enter the last number (nth term) of the Hamming Number Series: ')
     print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
     __a          =			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


__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__)