


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class      UpperCamelCase					(		nn.Module     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_ = 16   ,							UpperCamelCase_ = 88   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = 1   ,							UpperCamelCase_ = 0.0   ,							UpperCamelCase_ = 32   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = False   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = "geglu"   ,							UpperCamelCase_ = None   ,							):
        super().__init__()
        lowercase_       :List[str]     =       nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=UpperCamelCase_   ,							attention_head_dim=UpperCamelCase_   ,							in_channels=UpperCamelCase_   ,							num_layers=UpperCamelCase_   ,							dropout=UpperCamelCase_   ,							norm_num_groups=UpperCamelCase_   ,							cross_attention_dim=UpperCamelCase_   ,							attention_bias=UpperCamelCase_   ,							sample_size=UpperCamelCase_   ,							num_vector_embeds=UpperCamelCase_   ,							activation_fn=UpperCamelCase_   ,							num_embeds_ada_norm=UpperCamelCase_   ,							)
                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
        lowercase_       :Any     =       0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        lowercase_       :Optional[int]     =       [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])`
        lowercase_       :str     =       [1, 0]




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_ = True   ,							):
        lowercase_       :Optional[int]     =       hidden_states

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

        lowercase_       :Dict     =       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        lowercase_       :Any     =       output_states + input_states

        if not return_dict:
             return (output_states,)

        return TransformeraDModelOutput(sample=UpperCamelCase_		)





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




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :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'''		)
             lowercase_       :Union[str, 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:
             lowercase_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, Any]     =       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

        ((lowercase_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

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

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

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




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Union[str, Any]     =       sum(
            self.optimum[n]
            * self.classes[n]
            * self.kernel(self.observations[n]   ,							UpperCamelCase_		)
            for n in range(len(self.classes		)		)		)
        return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

