
def   lowerCamelCase__							(					a      )      ->   list:
   if n_term == "":
      return []
   _A:  list					=       []
   for temp in range(int(a      )      ):
      series.append(f"""1/{temp + 1}""" if series else '''1'''      )
   return series


if __name__ == "__main__":
   UpperCAmelCase__		:     int     			=      input('Enter the last number (nth term) of the Harmonic Series')
   print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
   print(harmonic_series(nth_term))





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__		:     List[str]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    int 				=    '''xlm'''
   __UpperCamelCase		:    Optional[Any] 				=    {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(					self     :							List[Any]			,			lowerCAmelCase_     :							Dict=3_0_1_4_5			,			lowerCAmelCase_     :							Union[str, Any]=2_0_4_8			,			lowerCAmelCase_     :							List[str]=1_2			,			lowerCAmelCase_     :							Optional[Any]=1_6			,			lowerCAmelCase_     :							Union[str, Any]=0.1			,			lowerCAmelCase_     :							List[str]=0.1			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Tuple=False			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							List[str]=True			,			lowerCAmelCase_     :							Tuple=5_1_2			,			lowerCAmelCase_     :							Tuple=2_0_4_8**-0.5			,			lowerCAmelCase_     :							List[str]=1e-12			,			lowerCAmelCase_     :							Any=0.02			,			lowerCAmelCase_     :							int=0			,			lowerCAmelCase_     :							int=1			,			lowerCAmelCase_     :							Dict=2			,			lowerCAmelCase_     :							List[Any]=3			,			lowerCAmelCase_     :							int=5			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							Union[str, Any]="first"			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							List[Any]=None			,			lowerCAmelCase_     :							Any=True			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Tuple=5			,			lowerCAmelCase_     :							Tuple=5			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							Optional[Any]=0			,			lowerCAmelCase_     :							str=2			,			lowerCAmelCase_     :							Dict=0			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""
      _A:  Dict					=       vocab_size
      _A:  Dict					=       emb_dim
      _A:  Optional[Any]					=       n_layers
      _A:  str					=       n_heads
      _A:  Optional[int]					=       dropout
      _A:  Union[str, Any]					=       attention_dropout
      _A:  Any					=       gelu_activation
      _A:  Optional[Any]					=       sinusoidal_embeddings
      _A:  Any					=       causal
      _A:  Optional[int]					=       asm
      _A:  List[str]					=       n_langs
      _A:  int					=       use_lang_emb
      _A:  Any					=       layer_norm_eps
      _A:  Tuple					=       bos_index
      _A:  Any					=       eos_index
      _A:  List[str]					=       pad_index
      _A:  str					=       unk_index
      _A:  str					=       mask_index
      _A:  List[Any]					=       is_encoder
      _A:  List[Any]					=       max_position_embeddings
      _A:  str					=       embed_init_std
      _A:  int					=       init_std
      _A:  List[str]					=       summary_type
      _A:  List[str]					=       summary_use_proj
      _A:  Tuple					=       summary_activation
      _A:  Dict					=       summary_proj_to_labels
      _A:  Tuple					=       summary_first_dropout
      _A:  str					=       start_n_top
      _A:  str					=       end_n_top
      _A:  Optional[Any]					=       mask_token_id
      _A:  Any					=       lang_id

      if "n_words" in kwargs:
         _A:  Union[str, Any]					=       kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_			,			bos_token_id=lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:  int					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:  Tuple					=       {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]     )



