




from __future__ import annotations

lowerCAmelCase_		      = 8.988E9  # units = N * m^s * C^-2






def 					snake_case(				__magic_name__ ,		__magic_name__ ,		__magic_name__ ,		__magic_name__    ) ->							dict[str, float]:





     '''simple docstring'''


     lowercase					:   List[Any]								= abs(chargea * chargea    )

     if (force, chargea, chargea, distance).count(0    ) != 1:
          raise ValueError('''One and only one argument must be 0'''    )
     if distance < 0:
          raise ValueError('''Distance cannot be negative'''    )
     if force == 0:
          lowercase					:   Any								= COULOMBS_CONSTANT * charge_product / (distance**2)
          return {"force": force}
     elif chargea == 0:
          lowercase					:   Tuple								= abs(__A    ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
          return {"charge1": chargea}
     elif chargea == 0:
          lowercase					:   str								= abs(__A    ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
          return {"charge2": chargea}
     elif distance == 0:
          lowercase					:   Optional[Any]								= (COULOMBS_CONSTANT * charge_product / abs(__A    )) ** 0.5
          return {"distance": distance}
     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 						__UpperCAmelCase      (					tf.keras.layers.Layer						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=False    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_  )

								_snake_case   						=		vocab_size
								_snake_case   						=		d_embed
								_snake_case   						=		d_proj

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

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

								_snake_case   						=		[]
								_snake_case   						=		[]


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if self.n_clusters > 0:
									_snake_case   						=		self.add_weight(
									    shape=(self.n_clusters, self.d_embed)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name='cluster_weight'  )
									_snake_case   						=		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:
											_snake_case   						=		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_  )
										_snake_case   						=		self.add_weight(
										    shape=(self.vocab_size, self.d_embed)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._weight'    ,   )
										_snake_case   						=		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  )  ):
										_snake_case						, _snake_case   						=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
										_snake_case   						=		self.d_embed // (self.div_val**i)

										_snake_case   						=		self.add_weight(
										    shape=(d_emb_i, self.d_proj)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_projs_._{i}'  )
										self.out_projs.append(lowerCAmelCase_  )
										_snake_case   						=		self.add_weight(
										    shape=(r_idx - l_idx, d_emb_i)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._weight'    ,   )
										_snake_case   						=		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  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None  ):





								"""simple docstring"""






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


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






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

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

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

											_snake_case   						=		self._logit(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self.out_projs[0]  )
											_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_  )
											out.append(head_logprob[..., : self.cutoffs[0]]  )
											if target is not None:
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		self._gather_logprob(lowerCAmelCase_    ,   lowerCAmelCase_  )
										else:
											_snake_case   						=		self._logit(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self.out_projs[i]  )
											_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_  )
											_snake_case   						=		self.cutoffs[0] + i - 1  # No probability for the head cluster
											_snake_case   						=		head_logprob[..., cluster_prob_idx, None] + tail_logprob
											out.append(lowerCAmelCase_  )
											if target is not None:
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		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_  )  )
									_snake_case   						=		tf.concat(lowerCAmelCase_    ,   axis=-1  )

								if target is not None:
									if return_mean:
										_snake_case   						=		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

