import functools
import operator

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


UpperCAmelCase_			    =							logging.get_logger(__name__)

UpperCAmelCase_			    =							{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class  __magic_name__  (		a__    ):





   """simple docstring"""




   lowerCAmelCase      :    Union[str, Any]  			=   '''sew-d'''



   def __init__(	self					:  List[str]		,  _lowercase					:  Union[str, Any]=32		,  _lowercase					:  Any=768		,  _lowercase					:  int=12		,  _lowercase					:  str=12		,  _lowercase					:  Any=3_072		,  _lowercase					:  Tuple=2		,  _lowercase					:  str=512		,  _lowercase					:  Dict=256		,  _lowercase					:  List[str]=True		,  _lowercase					:  Tuple=True		,  _lowercase					:  Dict=("p2c", "c2p")		,  _lowercase					:  List[Any]="layer_norm"		,  _lowercase					:  Union[str, Any]="gelu_python"		,  _lowercase					:  Optional[int]=0.1		,  _lowercase					:  Any=0.1		,  _lowercase					:  Tuple=0.1		,  _lowercase					:  Dict=0.0		,  _lowercase					:  str=0.1		,  _lowercase					:  List[str]=0.02		,  _lowercase					:  Any=1E-7		,  _lowercase					:  Dict=1E-5		,  _lowercase					:  Tuple="group"		,  _lowercase					:  Optional[Any]="gelu"		,  _lowercase					:  int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,  _lowercase					:  Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,  _lowercase					:  int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,  _lowercase					:  Optional[int]=False		,  _lowercase					:  Optional[int]=128		,  _lowercase					:  Union[str, Any]=16		,  _lowercase					:  Tuple=True		,  _lowercase					:  int=0.05		,  _lowercase					:  int=10		,  _lowercase					:  str=2		,  _lowercase					:  Optional[int]=0.0		,  _lowercase					:  List[Any]=10		,  _lowercase					:  Optional[Any]=0		,  _lowercase					:  Any="mean"		,  _lowercase					:  Optional[int]=False		,  _lowercase					:  Optional[Any]=False		,  _lowercase					:  Union[str, Any]=256		,  _lowercase					:  int=0		,  _lowercase					:  Dict=1		,  _lowercase					:  Tuple=2		,  **_lowercase					:  List[str]		,  ):



    """simple docstring"""



    super().__init__(**_A		,  pad_token_id=_A		,  bos_token_id=_A		,  eos_token_id=_A							)
    _UpperCamelCase:     int							= hidden_size
    _UpperCamelCase:     Any							= feat_extract_norm
    _UpperCamelCase:     Optional[int]							= feat_extract_activation
    _UpperCamelCase:     Optional[int]							= list(_A							)
    _UpperCamelCase:     List[Any]							= list(_A							)
    _UpperCamelCase:     Tuple							= list(_A							)
    _UpperCamelCase:     Dict							= conv_bias
    _UpperCamelCase:     Optional[Any]							= num_conv_pos_embeddings
    _UpperCamelCase:     str							= num_conv_pos_embedding_groups
    _UpperCamelCase:     Optional[Any]							= len(self.conv_dim							)
    _UpperCamelCase:     Union[str, Any]							= num_hidden_layers
    _UpperCamelCase:     Optional[int]							= intermediate_size
    _UpperCamelCase:     Dict							= squeeze_factor
    _UpperCamelCase:     Any							= max_position_embeddings
    _UpperCamelCase:     Optional[int]							= position_buckets
    _UpperCamelCase:     Union[str, Any]							= share_att_key
    _UpperCamelCase:     Tuple							= relative_attention
    _UpperCamelCase:     int							= norm_rel_ebd
    _UpperCamelCase:     str							= list(_A							)
    _UpperCamelCase:     str							= hidden_act
    _UpperCamelCase:     Any							= num_attention_heads
    _UpperCamelCase:     List[Any]							= hidden_dropout
    _UpperCamelCase:     Optional[Any]							= attention_dropout
    _UpperCamelCase:     List[str]							= activation_dropout
    _UpperCamelCase:     int							= feat_proj_dropout
    _UpperCamelCase:     Union[str, Any]							= final_dropout
    _UpperCamelCase:     Any							= layer_norm_eps
    _UpperCamelCase:     str							= feature_layer_norm_eps
    _UpperCamelCase:     List[str]							= initializer_range
    _UpperCamelCase:     Any							= vocab_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)`,'''
         f"""but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)"""
         f"""= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

    # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
    _UpperCamelCase:     Tuple							= apply_spec_augment
    _UpperCamelCase:     List[Any]							= mask_time_prob
    _UpperCamelCase:     str							= mask_time_length
    _UpperCamelCase:     Union[str, Any]							= mask_time_min_masks
    _UpperCamelCase:     Any							= mask_feature_prob
    _UpperCamelCase:     List[str]							= mask_feature_length
    _UpperCamelCase:     List[Any]							= mask_feature_min_masks

    # ctc loss
    _UpperCamelCase:     Optional[Any]							= ctc_loss_reduction
    _UpperCamelCase:     List[str]							= ctc_zero_infinity

    # sequence classification
    _UpperCamelCase:     Union[str, Any]							= use_weighted_layer_sum
    _UpperCamelCase:     str							= classifier_proj_size



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



    """simple docstring"""



    return functools.reduce(operator.mul		,  self.conv_stride		,  1							)
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def       lowerCAmelCase_     (		lowercase:							ndarray		)    ->				float:







 '''simple docstring'''
 return np.dot(lowercase  ,					lowercase		)




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  int		,  *,
       _lowercase					:  float = np.inf		,  _lowercase					:  str = "linear"		,  _lowercase					:  float = 0.0		,  ):



    """simple docstring"""



    _UpperCamelCase:     int							= regularization
    _UpperCamelCase:     Optional[int]							= gamma
    if kernel == "linear":
     _UpperCamelCase:     Optional[Any]							= self.__linear
    elif kernel == "rbf":
     if self.gamma == 0:
      raise ValueError('''rbf kernel requires gamma'''							)
     if not isinstance(self.gamma		,  (float, int)							):
      raise ValueError('''gamma must be float or int'''							)
     if not self.gamma > 0:
      raise ValueError('''gamma must be > 0'''							)
     _UpperCamelCase:     Optional[Any]							= self.__rbf
     # in the future, there could be a default value like in sklearn
     # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
     # previously it was 1/(n_features)
    else:
     _UpperCamelCase:     Optional[int]							= f"""Unknown kernel: {kernel}"""
     raise ValueError(_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.dot(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.exp(-(self.gamma * norm_squared(vectora - vectora							))							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  list[ndarray]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= observations
    _UpperCamelCase:     Optional[int]							= classes

    # using Wolfe's Dual to calculate w.
    # Primal problem: minimize 1/2*norm_squared(w)
    #   constraint: yn(w . xn + b) >= 1
    #
    # With l a vector
    # Dual problem: maximize sum_n(ln) -
    #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
    #   constraint: self.C >= ln >= 0
    #           and sum_n(ln*yn) = 0
    # Then we get w using w = sum_n(ln*yn*xn)
    # At the end we can get b ~= mean(yn - w . xn)
    #
    # Since we use kernels, we only need l_star to calculate b
    # and to classify observations

    ((_UpperCamelCase)	,							):     Any							= np.shape(_lowercase							)

    def to_minimize(_lowercase					:  ndarray							) -> float:
     _UpperCamelCase:     Optional[int]							= 0
     ((_UpperCamelCase)	,							):     str							= np.shape(_lowercase							)
     for i in range(_lowercase							):
      for j in range(_lowercase							):
       s += (
           candidate[i]
           * candidate[j]
           * classes[i]
           * classes[j]
           * self.kernel(observations[i]		,  observations[j]							)
       )
     return 1 / 2 * s - sum(_lowercase							)

    _UpperCamelCase:     Optional[Any]							= LinearConstraint(_lowercase		,  0		,  0							)
    _UpperCamelCase:     Optional[int]							= Bounds(0		,  self.regularization							)

    _UpperCamelCase:     Dict							= minimize(
        _lowercase		,  np.ones(_lowercase							)		,  bounds=_lowercase		,  constraints=[ly_contraint]							).x
    _UpperCamelCase:     Union[str, Any]							= l_star

    # calculating mean offset of separation plane to points
    _UpperCamelCase:     List[str]							= 0
    for i in range(_lowercase							):
     for j in range(_lowercase							):
      s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
          observations[i]		,  observations[j]							)
    _UpperCamelCase:     str							= s / n



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= sum(
        self.optimum[n]
        * self.classes[n]
        * self.kernel(self.observations[n]		,  _lowercase							)
        for n in range(len(self.classes							)							)							)
    return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()