




import functools
import operator

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


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

SCREAMING_SNAKE_CASE__       :      Any  =   {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      """wavlm"""







    def __init__(    self     ,  UpperCamelCase__=32     ,  UpperCamelCase__=768     ,  UpperCamelCase__=12     ,  UpperCamelCase__=12     ,  UpperCamelCase__=3072     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-5     ,  UpperCamelCase__="group"     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=(512, 512, 512, 512, 512, 512, 512)     ,  UpperCamelCase__=(5, 2, 2, 2, 2, 2, 2)     ,  UpperCamelCase__=(10, 3, 3, 3, 3, 2, 2)     ,  UpperCamelCase__=False     ,  UpperCamelCase__=128     ,  UpperCamelCase__=16     ,  UpperCamelCase__=320     ,  UpperCamelCase__=800     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  UpperCamelCase__=0.05     ,  UpperCamelCase__=10     ,  UpperCamelCase__=2     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=10     ,  UpperCamelCase__=320     ,  UpperCamelCase__=2     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=100     ,  UpperCamelCase__=256     ,  UpperCamelCase__=256     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__="mean"     ,  UpperCamelCase__=False     ,  UpperCamelCase__=False     ,  UpperCamelCase__=256     ,  UpperCamelCase__=(512, 512, 512, 512, 1500)     ,  UpperCamelCase__=(5, 3, 3, 1, 1)     ,  UpperCamelCase__=(1, 2, 3, 1, 1)     ,  UpperCamelCase__=512     ,  UpperCamelCase__=80     ,  UpperCamelCase__=0     ,  UpperCamelCase__=1     ,  UpperCamelCase__=2     ,  UpperCamelCase__=False     ,  UpperCamelCase__=3     ,  UpperCamelCase__=2     ,  UpperCamelCase__=3     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     ,  )     ->      Any:
           super().__init__(**UpperCamelCase__     ,  pad_token_id=UpperCamelCase__     ,  bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					hidden_size
           lowerCamelCase   :      Dict			 =					feat_extract_norm
           lowerCamelCase   :      Tuple			 =					feat_extract_activation
           lowerCamelCase   :      Dict			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					conv_bias
           lowerCamelCase   :      Dict			 =					num_buckets
           lowerCamelCase   :      Union[str, Any]			 =					max_bucket_distance
           lowerCamelCase   :      int			 =					num_conv_pos_embeddings
           lowerCamelCase   :      Optional[int]			 =					num_conv_pos_embedding_groups
           lowerCamelCase   :      Dict			 =					len(self.conv_dim     )
           lowerCamelCase   :      Dict			 =					num_hidden_layers
           lowerCamelCase   :      str			 =					intermediate_size
           lowerCamelCase   :      List[str]			 =					hidden_act
           lowerCamelCase   :      Tuple			 =					num_attention_heads
           lowerCamelCase   :      Tuple			 =					hidden_dropout
           lowerCamelCase   :      Dict			 =					attention_dropout
           lowerCamelCase   :      Any			 =					activation_dropout
           lowerCamelCase   :      str			 =					feat_proj_dropout
           lowerCamelCase   :      List[str]			 =					final_dropout
           lowerCamelCase   :      Tuple			 =					layerdrop
           lowerCamelCase   :      List[Any]			 =					layer_norm_eps
           lowerCamelCase   :      Any			 =					initializer_range
           lowerCamelCase   :      Dict			 =					num_ctc_classes
           lowerCamelCase   :      Optional[int]			 =					vocab_size
           lowerCamelCase   :      Tuple			 =					do_stable_layer_norm
           lowerCamelCase   :      Tuple			 =					use_weighted_layer_sum
           lowerCamelCase   :      List[Any]			 =					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
           lowerCamelCase   :      Dict			 =					apply_spec_augment
           lowerCamelCase   :      str			 =					mask_time_prob
           lowerCamelCase   :      List[Any]			 =					mask_time_length
           lowerCamelCase   :      str			 =					mask_time_min_masks
           lowerCamelCase   :      Union[str, Any]			 =					mask_feature_prob
           lowerCamelCase   :      List[str]			 =					mask_feature_length

           # parameters for pretraining with codevector quantized representations
           lowerCamelCase   :      Optional[int]			 =					num_codevectors_per_group
           lowerCamelCase   :      Optional[int]			 =					num_codevector_groups
           lowerCamelCase   :      Union[str, Any]			 =					contrastive_logits_temperature
           lowerCamelCase   :      List[str]			 =					num_negatives
           lowerCamelCase   :      Any			 =					codevector_dim
           lowerCamelCase   :      List[Any]			 =					proj_codevector_dim
           lowerCamelCase   :      Optional[Any]			 =					diversity_loss_weight

           # ctc loss
           lowerCamelCase   :      Union[str, Any]			 =					ctc_loss_reduction
           lowerCamelCase   :      int			 =					ctc_zero_infinity

           # adapter
           lowerCamelCase   :      Union[str, Any]			 =					add_adapter
           lowerCamelCase   :      List[str]			 =					adapter_kernel_size
           lowerCamelCase   :      Dict			 =					adapter_stride
           lowerCamelCase   :      Any			 =					num_adapter_layers
           lowerCamelCase   :      Optional[int]			 =					output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           lowerCamelCase   :      Union[str, Any]			 =					classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           lowerCamelCase   :      Optional[int]			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					list(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					xvector_output_dim







    @property
    def     _lowercase     (    self     )     ->      Optional[Any]:
           return functools.reduce(operator.mul     ,  self.conv_stride     ,  1     )












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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Dict  =   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))






