"""simple docstring"""
from __future__ import annotations







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	)      ->list[float | int]:
  if not all((nth_term, start_term_a, common_ratio_r)  ):
    return []
  a__:	list[float | int]     				=      []
  a__:	Dict     				=      1
  a__:	Tuple     				=      common_ratio_r
  for _ in range(int(_SCREAMING_SNAKE_CASE  )  ):
    if not series:
      series.append(_SCREAMING_SNAKE_CASE  )
    else:
      power += 1
      series.append(float(start_term_a * multiple  )  )
      a__:	Tuple     				=      pow(float(_SCREAMING_SNAKE_CASE  )						,	_SCREAMING_SNAKE_CASE  )
  return series


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowercase__   			=      float(input('Enter the last number (n term) of the Geometric Series'))
 lowercase__   			=      float(input('Enter the starting term (a) of the Geometric Series'))
 lowercase__   			=      float(
     input('Enter the common ratio between two terms (r) of the Geometric Series')
 )
 print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
 print(geometric_series(nth_term, start_term_a, common_ratio_r))






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase			:					Union[str, Any]         =							logging.get_logger(__name__)

UpperCamelCase			:					List[Any]         =							{
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Tuple								=			"hubert"
					def __init__(						self       :						List[Any]	,      _lowerCAmelCase       :						Any=3_2	,      _lowerCAmelCase       :						Optional[Any]=7_6_8	,      _lowerCAmelCase       :						str=1_2	,      _lowerCAmelCase       :						List[Any]=1_2	,      _lowerCAmelCase       :						Optional[int]=3_0_7_2	,      _lowerCAmelCase       :						int="gelu"	,      _lowerCAmelCase       :						List[str]=0.1	,      _lowerCAmelCase       :						Union[str, Any]=0.1	,      _lowerCAmelCase       :						Optional[Any]=0.1	,      _lowerCAmelCase       :						Optional[int]=True	,      _lowerCAmelCase       :						Any=0.0	,      _lowerCAmelCase       :						Tuple=0.1	,      _lowerCAmelCase       :						Optional[int]=0.1	,      _lowerCAmelCase       :						str=0.02	,      _lowerCAmelCase       :						str=1E-5	,      _lowerCAmelCase       :						Dict="group"	,      _lowerCAmelCase       :						Tuple="gelu"	,      _lowerCAmelCase       :						List[str]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,      _lowerCAmelCase       :						List[str]=(5, 2, 2, 2, 2, 2, 2)	,      _lowerCAmelCase       :						Union[str, Any]=(1_0, 3, 3, 3, 3, 2, 2)	,      _lowerCAmelCase       :						Any=False	,      _lowerCAmelCase       :						Union[str, Any]=1_2_8	,      _lowerCAmelCase       :						Any=1_6	,      _lowerCAmelCase       :						Tuple=False	,      _lowerCAmelCase       :						Dict=True	,      _lowerCAmelCase       :						Union[str, Any]=0.05	,      _lowerCAmelCase       :						Optional[int]=1_0	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Any=0.0	,      _lowerCAmelCase       :						Tuple=1_0	,      _lowerCAmelCase       :						int=0	,      _lowerCAmelCase       :						Tuple="sum"	,      _lowerCAmelCase       :						Tuple=False	,      _lowerCAmelCase       :						str=False	,      _lowerCAmelCase       :						Tuple=2_5_6	,      _lowerCAmelCase       :						List[str]=0	,      _lowerCAmelCase       :						int=1	,      _lowerCAmelCase       :						int=2	,      **_lowerCAmelCase       :						Optional[Any]	,      )							->					Dict:



											'''simple docstring'''





											super().__init__(**_lowerCAmelCase	,      pad_token_id=_lowerCAmelCase	,      bos_token_id=_lowerCAmelCase	,      eos_token_id=_lowerCAmelCase  )
											__magic_name__					=      hidden_size
											__magic_name__					=      feat_extract_norm
											__magic_name__					=      feat_extract_activation
											__magic_name__					=      list(_lowerCAmelCase  )
											__magic_name__					=      list(_lowerCAmelCase  )
											__magic_name__					=      list(_lowerCAmelCase  )
											__magic_name__					=      conv_bias
											__magic_name__					=      num_conv_pos_embeddings
											__magic_name__					=      num_conv_pos_embedding_groups
											__magic_name__					=      len(self.conv_dim  )
											__magic_name__					=      num_hidden_layers
											__magic_name__					=      intermediate_size
											__magic_name__					=      hidden_act
											__magic_name__					=      num_attention_heads
											__magic_name__					=      hidden_dropout
											__magic_name__					=      attention_dropout
											__magic_name__					=      activation_dropout
											__magic_name__					=      feat_proj_layer_norm
											__magic_name__					=      feat_proj_dropout
											__magic_name__					=      final_dropout
											__magic_name__					=      layerdrop
											__magic_name__					=      layer_norm_eps
											__magic_name__					=      initializer_range
											__magic_name__					=      vocab_size
											__magic_name__					=      do_stable_layer_norm
											__magic_name__					=      use_weighted_layer_sum
											__magic_name__					=      classifier_proj_size

											if (
											    (len(self.conv_stride  ) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
											    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
											):
																	raise ValueError(
																	    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
																	    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
																	    F''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
																	    F''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											__magic_name__					=      apply_spec_augment
											__magic_name__					=      mask_time_prob
											__magic_name__					=      mask_time_length
											__magic_name__					=      mask_time_min_masks
											__magic_name__					=      mask_feature_prob
											__magic_name__					=      mask_feature_length
											__magic_name__					=      mask_feature_min_masks

											# ctc loss
											__magic_name__					=      ctc_loss_reduction
											__magic_name__					=      ctc_zero_infinity







					@property
					def 	a			(						self       :						Optional[int]  )							->					Any:



											'''simple docstring'''





											return functools.reduce(operator.mul	,      self.conv_stride	,      1  )

