


'''simple docstring'''

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def 					lowercase__				(   __UpperCamelCase					)->				float:
      return np.dot(__UpperCamelCase	,		__UpperCamelCase					)






class        a_		:

  def __init__(   self    ,	*,
      _SCREAMING_SNAKE_CASE = np.inf    ,	_SCREAMING_SNAKE_CASE = "linear"    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	)						->   None:



        """simple docstring"""






        UpperCamelCase       						=      regularization
        UpperCamelCase       						=      gamma
        if kernel == "linear":
              UpperCamelCase       						=      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       						=      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       						=      F"Unknown kernel: {kernel}"
              raise ValueError(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   float:



        """simple docstring"""






        return np.dot(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   float:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      observations
        UpperCamelCase       						=      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)						,)       						=      np.shape(_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      LinearConstraint(_SCREAMING_SNAKE_CASE    ,	0    ,	0   )
        UpperCamelCase       						=      Bounds(0    ,	self.regularization   )

        UpperCamelCase       						=      minimize(
            _SCREAMING_SNAKE_CASE    ,	np.ones(_SCREAMING_SNAKE_CASE   )    ,	bounds=_SCREAMING_SNAKE_CASE    ,	constraints=[ly_contraint]   ).x
        UpperCamelCase       						=      l_star

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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






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


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
