






'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  int(_UpperCamelCase				)
   if n_element < 1:
      lowercase_						:     List[str]							     =  ValueError("a should be a positive number"				)
      raise my_error

   lowercase_						:     List[Any]							     =  [1]
   lowercase_     ,		lowercase_     ,		lowercase_						:     int							     =  (0, 0, 0)
   lowercase_						:     Optional[Any]							     =  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__":
 UpperCamelCase__			=      input('Enter the last number (nth term) of the Hamming Number Series: ')
 print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
 UpperCamelCase__			=      hamming(int(n))
 print('-----------------------------------------------------')
 print(f"""The list with nth numbers is: {hamming_numbers}""")
 print('-----------------------------------------------------')












'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__			=      {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FunnelBaseModel',
     'FunnelForMaskedLM',
     'FunnelForMultipleChoice',
     'FunnelForPreTraining',
     'FunnelForQuestionAnswering',
     'FunnelForSequenceClassification',
     'FunnelForTokenClassification',
     'FunnelModel',
     'FunnelPreTrainedModel',
     'load_tf_weights_in_funnel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFunnelBaseModel',
     'TFFunnelForMaskedLM',
     'TFFunnelForMultipleChoice',
     'TFFunnelForPreTraining',
     'TFFunnelForQuestionAnswering',
     'TFFunnelForSequenceClassification',
     'TFFunnelForTokenClassification',
     'TFFunnelModel',
     'TFFunnelPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
 from .tokenization_funnel import FunnelTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_funnel_fast import FunnelTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_funnel import (
      FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      FunnelBaseModel,
      FunnelForMaskedLM,
      FunnelForMultipleChoice,
      FunnelForPreTraining,
      FunnelForQuestionAnswering,
      FunnelForSequenceClassification,
      FunnelForTokenClassification,
      FunnelModel,
      FunnelPreTrainedModel,
      load_tf_weights_in_funnel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_funnel import (
      TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
      TFFunnelPreTrainedModel,
  )

else:
 import sys

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




