




"""simple docstring"""



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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		float:
 return np.dot(lowercase_      ,			lowercase_						)




class      UpperCAmelCase_  :




    def __init__(   self						:	Optional[Any]     ,			*,
        snake_case_						:	float = np.inf     ,			snake_case_						:	str = "linear"     ,			snake_case_						:	float = 0.0     ,			)				->							None:




     '''simple docstring'''

     A__	=    regularization
     A__	=    gamma
     if kernel == "linear":
      A__	=    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__	=    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__	=    F"""Unknown kernel: {kernel}"""
      raise ValueError(snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	ndarray     ,			snake_case_						:	ndarray				)				->							float:




     '''simple docstring'''

     return np.dot(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	ndarray     ,			snake_case_						:	ndarray				)				->							float:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	list[ndarray]     ,			snake_case_						:	ndarray				)				->							None:




     '''simple docstring'''

     A__	=    observations
     A__	=    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__),				)	=    np.shape(snake_case_				)

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

     A__	=    LinearConstraint(snake_case_     ,			0     ,			0				)
     A__	=    Bounds(0     ,			self.regularization				)

     A__	=    minimize(
         snake_case_     ,			np.ones(snake_case_				)     ,			bounds=snake_case_     ,			constraints=[ly_contraint]				).x
     A__	=    l_star

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




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	ndarray				)				->							int:




     '''simple docstring'''

     A__	=    sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]     ,			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"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class      UpperCAmelCase_  (						nn.Module							):




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	int = 16     ,			snake_case_						:	int = 88     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	int = 1     ,			snake_case_						:	float = 0.0     ,			snake_case_						:	int = 32     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	bool = False     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	str = "geglu"     ,			snake_case_						:	Optional[int] = None     ,			)				->							str:




     '''simple docstring'''

     super().__init__()
     A__	=    nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=snake_case_     ,			attention_head_dim=snake_case_     ,			in_channels=snake_case_     ,			num_layers=snake_case_     ,			dropout=snake_case_     ,			norm_num_groups=snake_case_     ,			cross_attention_dim=snake_case_     ,			attention_bias=snake_case_     ,			sample_size=snake_case_     ,			num_vector_embeds=snake_case_     ,			activation_fn=snake_case_     ,			num_embeds_ada_norm=snake_case_     ,			)
             for _ in range(2				)
         ]				)

     # Variables that can be set by a pipeline:

     # The ratio of transformer1 to transformer2's output states to be combined during inference
     A__	=    0.5

     # The shape of `encoder_hidden_states` is expected to be
     # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
     A__	=    [77, 257]

     # Which transformer to use to encode which condition.
     # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
     A__	=    [1, 0]




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	bool = True     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    hidden_states

     A__	=    []
     A__	=    0
     # attention_mask is not used yet
     for i in range(2				):
      # for each of the two transformers, pass the corresponding condition tokens
      A__	=    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      A__	=    self.transformer_index_for_condition[i]
      A__	=    self.transformers[transformer_index](
          snake_case_     ,			encoder_hidden_states=snake_case_     ,			timestep=snake_case_     ,			cross_attention_kwargs=snake_case_     ,			return_dict=snake_case_     ,			)[0]
      encoded_states.append(encoded_state - input_states				)
      tokens_start += self.condition_lengths[i]

     A__	=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
     A__	=    output_states + input_states

     if not return_dict:
      return (output_states,)

     return TransformeraDModelOutput(sample=snake_case_				)





