






'''simple docstring'''
import functools
import operator

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


UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{
    '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						(		snake_case			):
    __lowerCamelCase:   Union[str, Any]		       =    'wavlm'




    def __init__(			self  :       Dict  ,     a  :       Optional[Any]=3_2  ,     a  :       List[str]=7_6_8  ,     a  :       Tuple=1_2  ,     a  :       List[str]=1_2  ,     a  :       Optional[int]=3_0_7_2  ,     a  :       Dict="gelu"  ,     a  :       Any=0.1  ,     a  :       List[str]=0.1  ,     a  :       Union[str, Any]=0.1  ,     a  :       str=0.0  ,     a  :       Tuple=0.1  ,     a  :       Dict=0.1  ,     a  :       Union[str, Any]=0.02  ,     a  :       Dict=1e-5  ,     a  :       Optional[int]="group"  ,     a  :       int="gelu"  ,     a  :       Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,     a  :       Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)  ,     a  :       int=(1_0, 3, 3, 3, 3, 2, 2)  ,     a  :       Optional[int]=False  ,     a  :       Tuple=1_2_8  ,     a  :       List[str]=1_6  ,     a  :       Optional[Any]=3_2_0  ,     a  :       Tuple=8_0_0  ,     a  :       Dict=False  ,     a  :       Optional[Any]=True  ,     a  :       Tuple=0.05  ,     a  :       int=1_0  ,     a  :       List[Any]=2  ,     a  :       Tuple=0.0  ,     a  :       int=1_0  ,     a  :       List[Any]=3_2_0  ,     a  :       Dict=2  ,     a  :       List[Any]=0.1  ,     a  :       Dict=1_0_0  ,     a  :       Dict=2_5_6  ,     a  :       Any=2_5_6  ,     a  :       Optional[Any]=0.1  ,     a  :       Tuple="mean"  ,     a  :       str=False  ,     a  :       Tuple=False  ,     a  :       Optional[Any]=2_5_6  ,     a  :       str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)  ,     a  :       int=(5, 3, 3, 1, 1)  ,     a  :       Tuple=(1, 2, 3, 1, 1)  ,     a  :       int=5_1_2  ,     a  :       int=8_0  ,     a  :       List[str]=0  ,     a  :       Any=1  ,     a  :       Dict=2  ,     a  :       Tuple=False  ,     a  :       str=3  ,     a  :       Any=2  ,     a  :       List[Any]=3  ,     a  :       List[Any]=None  ,     **a  :       Optional[Any]  ,     ):






       '''simple docstring'''






       super().__init__(**a  ,     pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a			)
       lowercase_	:    Tuple           =     hidden_size
       lowercase_	:    int           =     feat_extract_norm
       lowercase_	:    Optional[Any]           =     feat_extract_activation
       lowercase_	:    Optional[Any]           =     list(a			)
       lowercase_	:    Tuple           =     list(a			)
       lowercase_	:    List[str]           =     list(a			)
       lowercase_	:    str           =     conv_bias
       lowercase_	:    Dict           =     num_buckets
       lowercase_	:    Dict           =     max_bucket_distance
       lowercase_	:    List[str]           =     num_conv_pos_embeddings
       lowercase_	:    Any           =     num_conv_pos_embedding_groups
       lowercase_	:    Dict           =     len(self.conv_dim			)
       lowercase_	:    List[str]           =     num_hidden_layers
       lowercase_	:    Optional[int]           =     intermediate_size
       lowercase_	:    Optional[Any]           =     hidden_act
       lowercase_	:    Optional[Any]           =     num_attention_heads
       lowercase_	:    Union[str, Any]           =     hidden_dropout
       lowercase_	:    Dict           =     attention_dropout
       lowercase_	:    List[Any]           =     activation_dropout
       lowercase_	:    Union[str, Any]           =     feat_proj_dropout
       lowercase_	:    str           =     final_dropout
       lowercase_	:    str           =     layerdrop
       lowercase_	:    List[str]           =     layer_norm_eps
       lowercase_	:    Any           =     initializer_range
       lowercase_	:    Dict           =     num_ctc_classes
       lowercase_	:    List[Any]           =     vocab_size
       lowercase_	:    Any           =     do_stable_layer_norm
       lowercase_	:    Optional[int]           =     use_weighted_layer_sum
       lowercase_	:    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
       lowercase_	:    Any           =     apply_spec_augment
       lowercase_	:    int           =     mask_time_prob
       lowercase_	:    Optional[int]           =     mask_time_length
       lowercase_	:    Tuple           =     mask_time_min_masks
       lowercase_	:    Any           =     mask_feature_prob
       lowercase_	:    List[Any]           =     mask_feature_length

       # parameters for pretraining with codevector quantized representations
       lowercase_	:    Optional[int]           =     num_codevectors_per_group
       lowercase_	:    int           =     num_codevector_groups
       lowercase_	:    List[Any]           =     contrastive_logits_temperature
       lowercase_	:    Optional[int]           =     num_negatives
       lowercase_	:    Any           =     codevector_dim
       lowercase_	:    List[Any]           =     proj_codevector_dim
       lowercase_	:    Tuple           =     diversity_loss_weight

       # ctc loss
       lowercase_	:    Dict           =     ctc_loss_reduction
       lowercase_	:    Union[str, Any]           =     ctc_zero_infinity

       # adapter
       lowercase_	:    Dict           =     add_adapter
       lowercase_	:    Any           =     adapter_kernel_size
       lowercase_	:    str           =     adapter_stride
       lowercase_	:    Tuple           =     num_adapter_layers
       lowercase_	:    Tuple           =     output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       lowercase_	:    Optional[Any]           =     classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       lowercase_	:    Optional[int]           =     list(a			)
       lowercase_	:    Union[str, Any]           =     list(a			)
       lowercase_	:    str           =     list(a			)
       lowercase_	:    List[str]           =     xvector_output_dim





    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






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












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase = 200							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [1, 2, 5, 10, 20, 50, 100, 200]
   lowercase_	:    str           =     [0] * (pence + 1)
   lowercase_	:    Dict           =     1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(_UpperCamelCase						,		pence + 1						,		1							):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(200) == 73682




