






'''simple docstring'''





from __future__ import annotations

from math import pi






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 if (inductance, frequency, reactance).count(0) != 1:
  raise ValueError("One and only one argument must be 0")
 if inductance < 0:
  raise ValueError("Inductance cannot be negative")
 if frequency < 0:
  raise ValueError("Frequency cannot be negative")
 if reactance < 0:
  raise ValueError("Inductive reactance cannot be negative")
 if inductance == 0:
  return {"inductance": reactance / (2 * pi * frequency)}
 elif frequency == 0:
  return {"frequency": reactance / (2 * pi * inductance)}
 elif reactance == 0:
  return {"reactance": 2 * pi * frequency * inductance}
 else:
  raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
 import doctest

 doctest.testmod()









'''simple docstring'''





import tensorflow as tf

from ...tf_utils import shape_list
class 				lowercase       (							tf.keras.layers.Layer      ):


    '''simple docstring'''

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int=1				,     __lowerCamelCase     :     Tuple=False				,     **__lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     d_embed
     lowerCamelCase__  =     d_proj

     lowerCamelCase__  =     cutoffs + [vocab_size]
     lowerCamelCase__  =     [0] + self.cutoffs
     lowerCamelCase__  =     div_val

     lowerCamelCase__  =     self.cutoffs[0]
     lowerCamelCase__  =     len(self.cutoffs     ) - 1
     lowerCamelCase__  =     self.shortlist_size + self.n_clusters
     lowerCamelCase__  =     keep_order

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




     if self.n_clusters > 0:
      lowerCamelCase__  =     self.add_weight(
          shape=(self.n_clusters, self.d_embed)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name="cluster_weight"     )
      lowerCamelCase__  =     self.add_weight(
          shape=(self.n_clusters,)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name="cluster_bias"     )

     if self.div_val == 1:
      for i in range(len(self.cutoffs     )     ):
       if self.d_proj != self.d_embed:
        lowerCamelCase__  =     self.add_weight(
            shape=(self.d_embed, self.d_proj)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_projs_._{i}'''				,     )
        self.out_projs.append(__lowerCamelCase     )
       else:
        self.out_projs.append(__lowerCamelCase     )
       lowerCamelCase__  =     self.add_weight(
           shape=(self.vocab_size, self.d_embed)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._weight'''				,     )
       lowerCamelCase__  =     self.add_weight(
           shape=(self.vocab_size,)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._bias'''				,     )
       self.out_layers.append((weight, bias)     )
     else:
      for i in range(len(self.cutoffs     )     ):
       lowerCamelCase__  ,	lowerCamelCase__  =     self.cutoff_ends[i], self.cutoff_ends[i + 1]
       lowerCamelCase__  =     self.d_embed // (self.div_val**i)

       lowerCamelCase__  =     self.add_weight(
           shape=(d_emb_i, self.d_proj)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_projs_._{i}'''     )
       self.out_projs.append(__lowerCamelCase     )
       lowerCamelCase__  =     self.add_weight(
           shape=(r_idx - l_idx, d_emb_i)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._weight'''				,     )
       lowerCamelCase__  =     self.add_weight(
           shape=(r_idx - l_idx,)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._bias'''				,     )
       self.out_layers.append((weight, bias)     )
     super().build(__lowerCamelCase     )

    @staticmethod
    def    a__				(					__lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     List[str]=None     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     x
     if proj is not None:
      lowerCamelCase__  =     tf.einsum("ibd,ed->ibe"				,     __lowerCamelCase				,     __lowerCamelCase     )
     return tf.einsum("ibd,nd->ibn"				,     __lowerCamelCase				,     __lowerCamelCase     ) + b

    @staticmethod
    def    a__				(					__lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Union[str, Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     shape_list(__lowerCamelCase     )
     lowerCamelCase__  =     tf.range(lp_size[0]				,     dtype=target.dtype     )
     lowerCamelCase__  =     tf.stack([r, target]				,     1     )
     return tf.gather_nd(__lowerCamelCase				,     __lowerCamelCase     )




    def    a__				(					self     :     Any				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     str=True				,     __lowerCamelCase     :     Tuple=False     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     0
     if self.n_clusters == 0:
      lowerCamelCase__  =     self._logit(__lowerCamelCase				,     self.out_layers[0][0]				,     self.out_layers[0][1]				,     self.out_projs[0]     )
      if target is not None:
       lowerCamelCase__  =     tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__lowerCamelCase				,     logits=__lowerCamelCase     )
      lowerCamelCase__  =     tf.nn.log_softmax(__lowerCamelCase				,     axis=-1     )
     else:
      lowerCamelCase__  =     shape_list(__lowerCamelCase     )
      lowerCamelCase__  =     []
      lowerCamelCase__  =     tf.zeros(hidden_sizes[:2]     )
      for i in range(len(self.cutoffs     )     ):
       lowerCamelCase__  ,	lowerCamelCase__  =     self.cutoff_ends[i], self.cutoff_ends[i + 1]
       if target is not None:
        lowerCamelCase__  =     (target >= l_idx) & (target < r_idx)
        lowerCamelCase__  =     tf.where(__lowerCamelCase     )
        lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     ) - l_idx

       if self.div_val == 1:
        lowerCamelCase__  =     self.out_layers[0][0][l_idx:r_idx]
        lowerCamelCase__  =     self.out_layers[0][1][l_idx:r_idx]
       else:
        lowerCamelCase__  =     self.out_layers[i][0]
        lowerCamelCase__  =     self.out_layers[i][1]

       if i == 0:
        lowerCamelCase__  =     tf.concat([cur_W, self.cluster_weight]				,     0     )
        lowerCamelCase__  =     tf.concat([cur_b, self.cluster_bias]				,     0     )

        lowerCamelCase__  =     self._logit(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     self.out_projs[0]     )
        lowerCamelCase__  =     tf.nn.log_softmax(__lowerCamelCase     )
        out.append(head_logprob[..., : self.cutoffs[0]]     )
        if target is not None:
         lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     )
         lowerCamelCase__  =     self._gather_logprob(__lowerCamelCase				,     __lowerCamelCase     )
       else:
        lowerCamelCase__  =     self._logit(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     self.out_projs[i]     )
        lowerCamelCase__  =     tf.nn.log_softmax(__lowerCamelCase     )
        lowerCamelCase__  =     self.cutoffs[0] + i - 1  # No probability for the head cluster
        lowerCamelCase__  =     head_logprob[..., cluster_prob_idx, None] + tail_logprob
        out.append(__lowerCamelCase     )
        if target is not None:
         lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     )
         lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     )
         lowerCamelCase__  =     self._gather_logprob(__lowerCamelCase				,     __lowerCamelCase     )
         cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
       if target is not None:
        loss += tf.scatter_nd(__lowerCamelCase				,     -cur_logprob				,     shape_list(__lowerCamelCase     )     )
      lowerCamelCase__  =     tf.concat(__lowerCamelCase				,     axis=-1     )

     if target is not None:
      if return_mean:
       lowerCamelCase__  =     tf.reduce_mean(__lowerCamelCase     )
      # Add the training-time loss value to the layer using `self.add_loss()`.
      self.add_loss(__lowerCamelCase     )

      # Log the loss as a metric (we could log arbitrary metrics,
      # including different metrics for training and inference.
      self.add_metric(__lowerCamelCase				,     name=self.name				,     aggregation="mean" if return_mean else ""     )

     return out

