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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			'wavlm'


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



         super().__init__(**__UpperCAmelCase       ,  pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase		)
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  feat_extract_norm
         UpperCAmelCase__								=  feat_extract_activation
         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  conv_bias
         UpperCAmelCase__								=  num_buckets
         UpperCAmelCase__								=  max_bucket_distance
         UpperCAmelCase__								=  num_conv_pos_embeddings
         UpperCAmelCase__								=  num_conv_pos_embedding_groups
         UpperCAmelCase__								=  len(self.conv_dim		)
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  hidden_dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  feat_proj_dropout
         UpperCAmelCase__								=  final_dropout
         UpperCAmelCase__								=  layerdrop
         UpperCAmelCase__								=  layer_norm_eps
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  num_ctc_classes
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  do_stable_layer_norm
         UpperCAmelCase__								=  use_weighted_layer_sum
         UpperCAmelCase__								=  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
         UpperCAmelCase__								=  apply_spec_augment
         UpperCAmelCase__								=  mask_time_prob
         UpperCAmelCase__								=  mask_time_length
         UpperCAmelCase__								=  mask_time_min_masks
         UpperCAmelCase__								=  mask_feature_prob
         UpperCAmelCase__								=  mask_feature_length

         # parameters for pretraining with codevector quantized representations
         UpperCAmelCase__								=  num_codevectors_per_group
         UpperCAmelCase__								=  num_codevector_groups
         UpperCAmelCase__								=  contrastive_logits_temperature
         UpperCAmelCase__								=  num_negatives
         UpperCAmelCase__								=  codevector_dim
         UpperCAmelCase__								=  proj_codevector_dim
         UpperCAmelCase__								=  diversity_loss_weight

         # ctc loss
         UpperCAmelCase__								=  ctc_loss_reduction
         UpperCAmelCase__								=  ctc_zero_infinity

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

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

         # XVector-specific parameters. Feel free to ignore for other classes.
         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  xvector_output_dim





   @property
   def 			lowercase_      (self     :						Tuple		)						->				Tuple:
         """simple docstring"""



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




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      UpperCamelCase__											=					np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

      # Create two fuzzy sets by defining any membership function
      # (trapmf(), gbellmf(), gaussmf(), etc).
      UpperCamelCase__											=					[0, 2_5, 5_0]
      UpperCamelCase__											=					[2_5, 5_0, 7_5]
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)

      # Compute the different operations using inbuilt functions.
      UpperCamelCase__											=					np.ones(7_5)
      UpperCamelCase__											=					np.zeros((7_5,))
      # 1. Union = max(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_or(X, young, X, middle_aged)[1]
      # 2. Intersection = min(µA(x), µB(x))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, middle_aged)[1]
      # 3. Complement (A) = (1- min(µA(x))
      UpperCamelCase__											=					fuzz.fuzzy_not(young)
      # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
      UpperCamelCase__											=					fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
      # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
      UpperCamelCase__											=					young + middle_aged - (young * middle_aged)
      # 6. Algebraic Product = (µA(x) * µB(x))
      UpperCamelCase__											=					young * middle_aged
      # 7. Bounded Sum = min[1,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
      # 8. Bounded difference = min[0,(µA(x), µB(x))]
      UpperCamelCase__											=					fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

      # max-min composition
      # max-product composition

      # Plot each set A, set B and each operation result using plot() and subplot().
      from matplotlib import pyplot as plt

      plt.figure()

      plt.subplot(4, 3, 1)
      plt.plot(X, young)
      plt.title('Young')
      plt.grid(True)

      plt.subplot(4, 3, 2)
      plt.plot(X, middle_aged)
      plt.title('Middle aged')
      plt.grid(True)

      plt.subplot(4, 3, 3)
      plt.plot(X, union)
      plt.title('union')
      plt.grid(True)

      plt.subplot(4, 3, 4)
      plt.plot(X, intersection)
      plt.title('intersection')
      plt.grid(True)

      plt.subplot(4, 3, 5)
      plt.plot(X, complement_a)
      plt.title('complement_a')
      plt.grid(True)

      plt.subplot(4, 3, 6)
      plt.plot(X, difference)
      plt.title('difference a/b')
      plt.grid(True)

      plt.subplot(4, 3, 7)
      plt.plot(X, alg_sum)
      plt.title('alg_sum')
      plt.grid(True)

      plt.subplot(4, 3, 8)
      plt.plot(X, alg_product)
      plt.title('alg_product')
      plt.grid(True)

      plt.subplot(4, 3, 9)
      plt.plot(X, bdd_sum)
      plt.title('bdd_sum')
      plt.grid(True)

      plt.subplot(4, 3, 1_0)
      plt.plot(X, bdd_difference)
      plt.title('bdd_difference')
      plt.grid(True)

      plt.subplots_adjust(hspace=0.5)
      plt.show()



