




'''simple docstring'''

import tensorflow as tf

from ...tf_utils import shape_list






class 		a							(  tf.keras.layers.Layer	):


				def __init__(						self       :   Dict			,							lowercase_       :   int			,							lowercase_       :   List[str]			,							lowercase_       :   int			,							lowercase_       :   str			,							lowercase_       :   List[Any]=1			,							lowercase_       :   str=False			,							**lowercase_       :   List[str]   ):
									super().__init__(**lowercase_   )

									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     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[str]   ):
									if self.n_clusters > 0:
														snake_case_		     =							self.add_weight(
														    shape=(self.n_clusters, self.d_embed)			,							initializer='''zeros'''			,							trainable=lowercase_			,							name='''cluster_weight'''   )
														snake_case_		     =							self.add_weight(
														    shape=(self.n_clusters,)			,							initializer='''zeros'''			,							trainable=lowercase_			,							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=lowercase_			,							name=F"out_projs_._{i}"			,							)
																								self.out_projs.append(lowercase_   )
																			else:
																								self.out_projs.append(lowercase_   )
																			snake_case_		     =							self.add_weight(
																			    shape=(self.vocab_size, self.d_embed)			,							initializer='''zeros'''			,							trainable=lowercase_			,							name=F"out_layers_._{i}_._weight"			,							)
																			snake_case_		     =							self.add_weight(
																			    shape=(self.vocab_size,)			,							initializer='''zeros'''			,							trainable=lowercase_			,							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=lowercase_			,							name=F"out_projs_._{i}"   )
																			self.out_projs.append(lowercase_   )
																			snake_case_		     =							self.add_weight(
																			    shape=(r_idx - l_idx, d_emb_i)			,							initializer='''zeros'''			,							trainable=lowercase_			,							name=F"out_layers_._{i}_._weight"			,							)
																			snake_case_		     =							self.add_weight(
																			    shape=(r_idx - l_idx,)			,							initializer='''zeros'''			,							trainable=lowercase_			,							name=F"out_layers_._{i}_._bias"			,							)
																			self.out_layers.append((weight, bias)   )
									super().build(lowercase_   )


				@staticmethod
				def     A_					(						lowercase_       :   Union[str, Any]			,							lowercase_       :   Any			,							lowercase_       :   int			,							lowercase_       :   Dict=None   ):
									snake_case_		     =							x
									if proj is not None:
														snake_case_		     =							tf.einsum('''ibd,ed->ibe'''			,							lowercase_			,							lowercase_   )
									return tf.einsum('''ibd,nd->ibn'''			,							lowercase_			,							lowercase_   ) + b


				@staticmethod
				def     A_					(						lowercase_       :   List[str]			,							lowercase_       :   Any   ):
									snake_case_		     =							shape_list(lowercase_   )
									snake_case_		     =							tf.range(lp_size[0]			,							dtype=target.dtype   )
									snake_case_		     =							tf.stack([r, target]			,							1   )
									return tf.gather_nd(lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   str			,							lowercase_       :   List[Any]			,							lowercase_       :   Any=True			,							lowercase_       :   Union[str, Any]=False   ):
									snake_case_		     =							0
									if self.n_clusters == 0:
														snake_case_		     =							self._logit(lowercase_			,							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=lowercase_			,							logits=lowercase_   )
														snake_case_		     =							tf.nn.log_softmax(lowercase_			,							axis=-1   )
									else:
														snake_case_		     =							shape_list(lowercase_   )
														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(lowercase_   )
																								snake_case_		     =							tf.boolean_mask(lowercase_			,							lowercase_   ) - 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(lowercase_			,							lowercase_			,							lowercase_			,							self.out_projs[0]   )
																								snake_case_		     =							tf.nn.log_softmax(lowercase_   )
																								out.append(head_logprob[..., : self.cutoffs[0]]   )
																								if target is not None:
																													snake_case_		     =							tf.boolean_mask(lowercase_			,							lowercase_   )
																													snake_case_		     =							self._gather_logprob(lowercase_			,							lowercase_   )
																			else:
																								snake_case_		     =							self._logit(lowercase_			,							lowercase_			,							lowercase_			,							self.out_projs[i]   )
																								snake_case_		     =							tf.nn.log_softmax(lowercase_   )
																								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(lowercase_   )
																								if target is not None:
																													snake_case_		     =							tf.boolean_mask(lowercase_			,							lowercase_   )
																													snake_case_		     =							tf.boolean_mask(lowercase_			,							lowercase_   )
																													snake_case_		     =							self._gather_logprob(lowercase_			,							lowercase_   )
																													cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
																			if target is not None:
																								loss += tf.scatter_nd(lowercase_			,							-cur_logprob			,							shape_list(lowercase_   )   )
														snake_case_		     =							tf.concat(lowercase_			,							axis=-1   )

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

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

									return out







'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

