





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer





class  A__    :




   """simple docstring"""



   def 							__lowercase		(					self					,  lowercase)			->						str:







          '''simple docstring'''

          raise NotImplementedError()

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          raise NotImplementedError()





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase = False					,  **lowercase)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[str] =				tokenizer
          a__						:	Union[str, Any] =				skip_prompt
          a__						:	Dict =				decode_kwargs

          # variables used in the streaming process
          a__						:	Optional[int] =				[]
          a__						:	Optional[Any] =				0
          a__						:	Dict =				True

   def 							__lowercase		(					self					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

          if len(value.shape) > 1 and value.shape[0] > 1:
                 raise ValueError('TextStreamer only supports batch size 1')
          elif len(value.shape) > 1:
                 a__						:	Any =				value[0]

          if self.skip_prompt and self.next_tokens_are_prompt:
                 a__						:	str =				False
                 return

          # Add the new token to the cache and decodes the entire thing.
          self.token_cache.extend(value.tolist())
          a__						:	Any =				self.tokenizer.decode(self.token_cache					,  **self.decode_kwargs)

          # After the symbol for a new line, we flush the cache.
          if text.endswith('\n'):
                 a__						:	Union[str, Any] =				text[self.print_len :]
                 a__						:	Union[str, Any] =				[]
                 a__						:	Union[str, Any] =				0
          # If the last token is a CJK character, we print the characters.
          elif len(lowercase) > 0 and self._is_chinese_char(ord(text[-1])):
                 a__						:	Dict =				text[self.print_len :]
                 self.print_len += len(lowercase)
          # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
          # which may change with the subsequent token -- there are probably smarter ways to do this!)
          else:
                 a__						:	List[Any] =				text[self.print_len : text.rfind(' ') + 1]
                 self.print_len += len(lowercase)

          self.on_finalized_text(lowercase)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          if len(self.token_cache) > 0:
                 a__						:	Dict =				self.tokenizer.decode(self.token_cache					,  **self.decode_kwargs)
                 a__						:	str =				text[self.print_len :]
                 a__						:	List[Any] =				[]
                 a__						:	Any =				0
          else:
                 a__						:	Optional[int] =				''

          a__						:	Dict =				True
          self.on_finalized_text(lowercase					,  stream_end=lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase = False)			->						Any:







          '''simple docstring'''

          print(lowercase					,  flush=lowercase					,  end='' if not stream_end else None)

   def 							__lowercase		(					self					,  lowercase)			->						List[str]:







          '''simple docstring'''

          if (
              (cp >= 0x4_e00 and cp <= 0x9_fff)
              or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
              or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
              or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
              or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
              or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
              or (cp >= 0xf_900 and cp <= 0xf_aff)
              or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
          ):  #
                 return True

          return False





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase = False					,  lowercase = None					,  **lowercase)			->						int:







          '''simple docstring'''

          super().__init__(lowercase					,  lowercase					,  **lowercase)
          a__						:	Tuple =				Queue()
          a__						:	List[str] =				None
          a__						:	Union[str, Any] =				timeout

   def 							__lowercase		(					self					,  lowercase					,  lowercase = False)			->						Optional[int]:







          '''simple docstring'''

          self.text_queue.put(lowercase					,  timeout=self.timeout)
          if stream_end:
                 self.text_queue.put(self.stop_signal					,  timeout=self.timeout)

   def __iter__(					self)			->						Optional[int]:







          '''simple docstring'''

          return self

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.text_queue.get(timeout=self.timeout)
          if value == self.stop_signal:
                 raise StopIteration()
          else:
                 return value









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



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



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







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	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')
                 a__						:	str =				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:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				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

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				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)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				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])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	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()


