import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= '''laion/clap-htsat-unfused'''
    _UpperCamelCase:     Optional[Any]							= tempfile.mkdtemp()



   def   lowerCAmelCase						(	self					:  Optional[int]		,  **_lowercase					:  Dict							):



    """simple docstring"""



    return RobertaTokenizer.from_pretrained(self.checkpoint		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  **_lowercase					:  List[Any]							):



    """simple docstring"""



    return ClapFeatureExtractor.from_pretrained(self.checkpoint		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    shutil.rmtree(self.tmpdirname							)



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.get_tokenizer()
    _UpperCamelCase:     Any							= self.get_feature_extractor()

    _UpperCamelCase:     Dict							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    processor.save_pretrained(self.tmpdirname							)
    _UpperCamelCase:     List[str]							= ClapProcessor.from_pretrained(self.tmpdirname							)

    self.assertEqual(processor.tokenizer.get_vocab()		,  tokenizer.get_vocab()							)
    self.assertIsInstance(processor.tokenizer		,  _lowercase							)

    self.assertEqual(processor.feature_extractor.to_json_string()		,  feature_extractor.to_json_string()							)
    self.assertIsInstance(processor.feature_extractor		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= ClapProcessor(tokenizer=self.get_tokenizer()		,  feature_extractor=self.get_feature_extractor()							)
    processor.save_pretrained(self.tmpdirname							)

    _UpperCamelCase:     Any							= self.get_tokenizer(bos_token='''(BOS)'''		,  eos_token='''(EOS)'''							)
    _UpperCamelCase:     Any							= self.get_feature_extractor(do_normalize=_lowercase		,  padding_value=1.0							)

    _UpperCamelCase:     Dict							= ClapProcessor.from_pretrained(
        self.tmpdirname		,  bos_token='''(BOS)'''		,  eos_token='''(EOS)'''		,  do_normalize=_lowercase		,  padding_value=1.0							)

    self.assertEqual(processor.tokenizer.get_vocab()		,  tokenizer_add_kwargs.get_vocab()							)
    self.assertIsInstance(processor.tokenizer		,  _lowercase							)

    self.assertEqual(processor.feature_extractor.to_json_string()		,  feature_extractor_add_kwargs.to_json_string()							)
    self.assertIsInstance(processor.feature_extractor		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= self.get_feature_extractor()
    _UpperCamelCase:     int							= self.get_tokenizer()

    _UpperCamelCase:     str							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    _UpperCamelCase:     Any							= floats_list((3, 1_000)							)

    _UpperCamelCase:     Any							= feature_extractor(_lowercase		,  return_tensors='''np'''							)
    _UpperCamelCase:     Tuple							= processor(audios=_lowercase		,  return_tensors='''np'''							)

    for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()		,  input_processor[key].sum()		,  delta=1E-2							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= self.get_feature_extractor()
    _UpperCamelCase:     Optional[int]							= self.get_tokenizer()

    _UpperCamelCase:     Optional[int]							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    _UpperCamelCase:     List[Any]							= '''This is a test string'''

    _UpperCamelCase:     int							= processor(text=_lowercase							)

    _UpperCamelCase:     Optional[int]							= tokenizer(_lowercase							)

    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]		,  encoded_processor[key]							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.get_feature_extractor()
    _UpperCamelCase:     Tuple							= self.get_tokenizer()

    _UpperCamelCase:     Any							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    _UpperCamelCase:     int							= processor.batch_decode(_lowercase							)
    _UpperCamelCase:     List[Any]							= tokenizer.batch_decode(_lowercase							)

    self.assertListEqual(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.get_feature_extractor()
    _UpperCamelCase:     List[str]							= self.get_tokenizer()

    _UpperCamelCase:     List[Any]							= ClapProcessor(tokenizer=_lowercase		,  feature_extractor=_lowercase							)

    self.assertListEqual(
        processor.model_input_names[2:]		,  feature_extractor.model_input_names		,  msg='''`processor` and `feature_extractor` model input names do not match'''		,  )
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()