


import tensorflow as tf

from ...tf_utils import shape_list


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




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






									"""simple docstring"""






									super().__init__(**__A						)

									a__ :int						  =    vocab_size
									a__ :List[Any]						  =    d_embed
									a__ :str						  =    d_proj

									a__ :Optional[Any]						  =    cutoffs + [vocab_size]
									a__ :Dict						  =    [0] + self.cutoffs
									a__ :Optional[int]						  =    div_val

									a__ :int						  =    self.cutoffs[0]
									a__ :List[Any]						  =    len(self.cutoffs						) - 1
									a__ :str						  =    self.shortlist_size + self.n_clusters
									a__ :Optional[Any]						  =    keep_order

									a__ :int						  =    []
									a__ :Union[str, Any]						  =    []




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									if self.n_clusters > 0:
												a__ :Optional[Any]						  =    self.add_weight(
												    shape=(self.n_clusters, self.d_embed)   ,     initializer="zeros"   ,     trainable=__A   ,     name="cluster_weight"						)
												a__ :Tuple						  =    self.add_weight(
												    shape=(self.n_clusters,)   ,     initializer="zeros"   ,     trainable=__A   ,     name="cluster_bias"						)

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

															a__ :List[Any]						  =    self.add_weight(
															    shape=(d_emb_i, self.d_proj)   ,     initializer="zeros"   ,     trainable=__A   ,     name=F'''out_projs_._{i}'''						)
															self.out_projs.append(__A						)
															a__ :List[Any]						  =    self.add_weight(
															    shape=(r_idx - l_idx, d_emb_i)   ,     initializer="zeros"   ,     trainable=__A   ,     name=F'''out_layers_._{i}_._weight'''   ,     )
															a__ :List[Any]						  =    self.add_weight(
															    shape=(r_idx - l_idx,)   ,     initializer="zeros"   ,     trainable=__A   ,     name=F'''out_layers_._{i}_._bias'''   ,     )
															self.out_layers.append((weight, bias)						)
									super().build(__A						)




						@staticmethod
						def  _snake_case					(	__A						:   str   ,     __A						:   Tuple   ,     __A						:   Union[str, Any]   ,     __A						:   List[str]=None						)      ->Any:






									"""simple docstring"""






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




						@staticmethod
						def  _snake_case					(	__A						:   Tuple   ,     __A						:   int						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :List[Any]						  =    shape_list(__A						)
									a__ :List[str]						  =    tf.range(lp_size[0]   ,     dtype=target.dtype						)
									a__ :Optional[Any]						  =    tf.stack([r, target]   ,     1						)
									return tf.gather_nd(__A   ,     __A						)






						def  _snake_case					(	self						:   Any   ,     __A						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   Optional[Any]=True   ,     __A						:   Dict=False						)      ->Tuple:






									"""simple docstring"""






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

															if self.div_val == 1:
																		a__ :Any						  =    self.out_layers[0][0][l_idx:r_idx]
																		a__ :Optional[int]						  =    self.out_layers[0][1][l_idx:r_idx]
															else:
																		a__ :Union[str, Any]						  =    self.out_layers[i][0]
																		a__ :List[str]						  =    self.out_layers[i][1]

															if i == 0:
																		a__ :Optional[Any]						  =    tf.concat([cur_W, self.cluster_weight]   ,     0						)
																		a__ :Optional[int]						  =    tf.concat([cur_b, self.cluster_bias]   ,     0						)

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

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

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

									return out






def 				lowerCamelCase__	(      a	:   int = 100				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    n * (n + 1) * (2 * n + 1) / 6
			a__ :Tuple						  =    (n * (n + 1) / 2) ** 2
			return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
		print(f'''{solution() = }''')


