import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   ['''image_processor''', '''tokenizer''']
   lowerCAmelCase      :    Optional[int]  			=   '''ViltImageProcessor'''
   lowerCAmelCase      :    List[Any]  			=   ('''BertTokenizer''', '''BertTokenizerFast''')



   def __init__(	self					:  int		,  _lowercase					:  Tuple=None		,  _lowercase					:  Any=None		,  **_lowercase					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= None
    if "feature_extractor" in kwargs:
     warnings.warn(
         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
         ''' instead.'''		,  _lowercase		,  )
     _UpperCamelCase:     Dict							= kwargs.pop('''feature_extractor'''							)

    _UpperCamelCase:     Any							= image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError('''You need to specify an `image_processor`.'''							)
    if tokenizer is None:
     raise ValueError('''You need to specify a `tokenizer`.'''							)

    super().__init__(_lowercase		,  _lowercase							)
    _UpperCamelCase:     Dict							= self.image_processor



   def __call__(	self					:  int		,  _lowercase					:  Dict		,  _lowercase					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,  _lowercase					:  bool = True		,  _lowercase					:  Union[bool, str, PaddingStrategy] = False		,  _lowercase					:  Union[bool, str, TruncationStrategy] = None		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  int = 0		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Optional[bool] = None		,  _lowercase					:  Optional[bool] = None		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = True		,  _lowercase					:  Optional[Union[str, TensorType]] = None		,  **_lowercase					:  Dict		,  ):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.tokenizer(
        text=_lowercase		,  add_special_tokens=_lowercase		,  padding=_lowercase		,  truncation=_lowercase		,  max_length=_lowercase		,  stride=_lowercase		,  pad_to_multiple_of=_lowercase		,  return_token_type_ids=_lowercase		,  return_attention_mask=_lowercase		,  return_overflowing_tokens=_lowercase		,  return_special_tokens_mask=_lowercase		,  return_offsets_mapping=_lowercase		,  return_length=_lowercase		,  verbose=_lowercase		,  return_tensors=_lowercase		,  **_lowercase		,  )
    # add pixel_values + pixel_mask
    _UpperCamelCase:     int							= self.image_processor(_lowercase		,  return_tensors=_lowercase							)
    encoding.update(_lowercase							)

    return encoding



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



    """simple docstring"""



    return self.tokenizer.batch_decode(*_lowercase		,  **_lowercase							)



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



    """simple docstring"""



    return self.tokenizer.decode(*_lowercase		,  **_lowercase							)



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= self.tokenizer.model_input_names
    _UpperCamelCase:     Optional[int]							= self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



   @property
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,  _lowercase		,  )
    return self.image_processor_class



   @property
   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,  _lowercase		,  )
    return self.image_processor
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()