


import functools
import operator

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


SCREAMING_SNAKE_CASE       :		Optional[Any]         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		List[Any]         =				{
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	Dict									="""unispeech"""
   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_=0.0   ,							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_=False   ,							UpperCamelCase_=True   ,							UpperCamelCase_=0.05   ,							UpperCamelCase_=10   ,							UpperCamelCase_=2   ,							UpperCamelCase_=0.0   ,							UpperCamelCase_=10   ,							UpperCamelCase_=0   ,							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_=80   ,							UpperCamelCase_=0   ,							UpperCamelCase_=1   ,							UpperCamelCase_=2   ,							UpperCamelCase_=0.5   ,							**UpperCamelCase_   ,							):
        super().__init__(**UpperCamelCase_   ,							pad_token_id=UpperCamelCase_   ,							bos_token_id=UpperCamelCase_   ,							eos_token_id=UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       hidden_size
        lowercase_       :Any     =       feat_extract_norm
        lowercase_       :int     =       feat_extract_activation
        lowercase_       :Optional[Any]     =       list(UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       list(UpperCamelCase_		)
        lowercase_       :List[Any]     =       list(UpperCamelCase_		)
        lowercase_       :List[str]     =       conv_bias
        lowercase_       :str     =       num_conv_pos_embeddings
        lowercase_       :int     =       num_conv_pos_embedding_groups
        lowercase_       :Optional[Any]     =       len(self.conv_dim		)
        lowercase_       :Tuple     =       num_hidden_layers
        lowercase_       :Tuple     =       intermediate_size
        lowercase_       :Tuple     =       hidden_act
        lowercase_       :List[Any]     =       num_attention_heads
        lowercase_       :Optional[int]     =       hidden_dropout
        lowercase_       :Optional[Any]     =       attention_dropout
        lowercase_       :Optional[Any]     =       activation_dropout
        lowercase_       :Any     =       feat_proj_dropout
        lowercase_       :Any     =       final_dropout
        lowercase_       :Optional[int]     =       layerdrop
        lowercase_       :Tuple     =       layer_norm_eps
        lowercase_       :Optional[int]     =       initializer_range
        lowercase_       :int     =       num_ctc_classes
        lowercase_       :int     =       vocab_size
        lowercase_       :List[Any]     =       do_stable_layer_norm
        lowercase_       :Optional[int]     =       use_weighted_layer_sum
        lowercase_       :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
        lowercase_       :str     =       apply_spec_augment
        lowercase_       :Dict     =       mask_time_prob
        lowercase_       :Union[str, Any]     =       mask_time_length
        lowercase_       :Dict     =       mask_time_min_masks
        lowercase_       :List[Any]     =       mask_feature_prob
        lowercase_       :int     =       mask_feature_length
        lowercase_       :int     =       mask_feature_min_masks

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

        # ctc loss
        lowercase_       :Tuple     =       ctc_loss_reduction
        lowercase_       :Dict     =       ctc_zero_infinity

        # pretraining loss
        lowercase_       :Union[str, Any]     =       replace_prob




   @property
   def 		UpperCamelCase  (     self		):
        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     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :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'''		)
             lowercase_       :Union[str, 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:
             lowercase_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, Any]     =       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

        ((lowercase_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

        def to_minimize(UpperCamelCase_		) -> float:
             lowercase_       :Dict     =       0
             ((lowercase_)    ,      )       :Tuple     =       np.shape(UpperCamelCase_		)
             for i in range(UpperCamelCase_		):
                  for j in range(UpperCamelCase_		):
                       s += (
                           candidate[i]
                           * candidate[j]
                           * classes[i]
                           * classes[j]
                           * self.kernel(observations[i]   ,							observations[j]		)
                       )
             return 1 / 2 * s - sum(UpperCamelCase_		)

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

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




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Union[str, Any]     =       sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]   ,							UpperCamelCase_		)
            for n in range(len(self.classes		)		)		)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

