




import tensorflow as tf

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







						'''simple docstring'''





						def __init__(						self							:     Optional[Any]       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[str]       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[Any]=1       ,	__lowercase							:     Optional[Any]=False       ,	**__lowercase							:     Tuple						):






							"""simple docstring"""






							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        snake_case__						(						self							:     int       ,	__lowercase							:     Tuple						):






							"""simple docstring"""






							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        snake_case__						(						__lowercase							:     Optional[int]       ,	__lowercase							:     Any       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     int=None						):






							"""simple docstring"""






							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        snake_case__						(						__lowercase							:     Tuple       ,	__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							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        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     str       ,	__lowercase							:     Tuple       ,	__lowercase							:     Dict=True       ,	__lowercase							:     int=False						):






							"""simple docstring"""






							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






def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     len(_A							)
	for i in range(_A							):
		for j in range(i + 1     ,	_A							):
			if numbers[j] < numbers[i]:
				snake_case_    ,      snake_case_		    =     numbers[j], numbers[i]
	return numbers


if __name__ == "__main__":
		lowercase__				:     int 		=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__				:     Any 		=						[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))
