





"""simple docstring"""







import math

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


a__      :	Tuple       				=      logging.get_logger(__name__)

a__      :	List[Any]       				=      {
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class __magic_name__   (       _UpperCamelCase		):
       UpperCamelCase    :						str      =	"data2vec-audio"
       def __init__(     self   ,  __magic_name__=3_2   ,  __magic_name__=7_6_8   ,  __magic_name__=1_2   ,  __magic_name__=1_2   ,  __magic_name__=3_0_7_2   ,  __magic_name__="gelu"   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=0.0   ,  __magic_name__=0.1   ,  __magic_name__=0.1   ,  __magic_name__=0.02   ,  __magic_name__=1e-5   ,  __magic_name__="gelu"   ,  __magic_name__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,  __magic_name__=(5, 2, 2, 2, 2, 2, 2)   ,  __magic_name__=(1_0, 3, 3, 3, 3, 2, 2)   ,  __magic_name__=False   ,  __magic_name__=1_6   ,  __magic_name__=1_9   ,  __magic_name__=5   ,  __magic_name__=0.05   ,  __magic_name__=1_0   ,  __magic_name__=2   ,  __magic_name__=0.0   ,  __magic_name__=1_0   ,  __magic_name__=0   ,  __magic_name__="sum"   ,  __magic_name__=False   ,  __magic_name__=False   ,  __magic_name__=2_5_6   ,  __magic_name__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)   ,  __magic_name__=(5, 3, 3, 1, 1)   ,  __magic_name__=(1, 2, 3, 1, 1)   ,  __magic_name__=5_1_2   ,  __magic_name__=0   ,  __magic_name__=1   ,  __magic_name__=2   ,  __magic_name__=False   ,  __magic_name__=3   ,  __magic_name__=2   ,  __magic_name__=3   ,  __magic_name__=None   ,  **__magic_name__   ,  ):





         """simple docstring"""


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

         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
         _lowerCAmelCase	      =					mask_time_prob
         _lowerCAmelCase	      =					mask_time_length
         _lowerCAmelCase	      =					mask_time_min_masks
         _lowerCAmelCase	      =					mask_feature_prob
         _lowerCAmelCase	      =					mask_feature_length
         _lowerCAmelCase	      =					mask_feature_min_masks

         # ctc loss
         _lowerCAmelCase	      =					ctc_loss_reduction
         _lowerCAmelCase	      =					ctc_zero_infinity

         # adapter
         _lowerCAmelCase	      =					add_adapter
         _lowerCAmelCase	      =					adapter_kernel_size
         _lowerCAmelCase	      =					adapter_stride
         _lowerCAmelCase	      =					num_adapter_layers
         _lowerCAmelCase	      =					output_hidden_size or hidden_size

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         _lowerCAmelCase	      =					classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         _lowerCAmelCase	      =					list(a_	)
         _lowerCAmelCase	      =					list(a_	)
         _lowerCAmelCase	      =					list(a_	)
         _lowerCAmelCase	      =					xvector_output_dim
       @property
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         return math.prod(self.conv_stride	)






'''simple docstring'''




from __future__ import annotations


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str							, SCREAMING_SNAKE_CASE   : int | float | str    ):
  if nth_term == "":
    return [""]
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						[]
  for temp in range(int(SCREAMING_SNAKE_CASE    )    ):
    series.append(f'''1 / {pow(temp + 1							, int(SCREAMING_SNAKE_CASE    )    )}''' if series else '1'    )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  _a					:			List[Any]							=    int(input('Enter the last number (nth term) of the P-Series'))
  _a					:			Tuple							=    int(input('Enter the power for  P-Series'))
  print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
  print(p_series(nth_term, power))


