




import tensorflow as tf

from ...tf_utils import shape_list







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

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a=1		,     _a=False		,     **_a				)						->							Optional[int]:
           super().__init__(**_a				)

           _A							:     List[Any]			=						vocab_size
           _A							:     Union[str, Any]			=						d_embed
           _A							:     str			=						d_proj

           _A							:     List[str]			=						cutoffs + [vocab_size]
           _A							:     Optional[int]			=						[0] + self.cutoffs
           _A							:     Optional[Any]			=						div_val

           _A							:     List[str]			=						self.cutoffs[0]
           _A							:     List[str]			=						len(self.cutoffs				) - 1
           _A							:     Tuple			=						self.shortlist_size + self.n_clusters
           _A							:     List[str]			=						keep_order

           _A							:     Tuple			=						[]
           _A							:     int			=						[]

     def 					a__	(  self		,     _a				)						->							int:
           if self.n_clusters > 0:
                 _A							:     int			=						self.add_weight(
                     shape=(self.n_clusters, self.d_embed)		,     initializer="""zeros"""		,     trainable=_a		,     name="""cluster_weight"""				)
                 _A							:     List[str]			=						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							:     int			=						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							:     str			=						self.add_weight(
                           shape=(self.vocab_size, self.d_embed)		,     initializer="""zeros"""		,     trainable=_a		,     name=F'''out_layers_._{i}_._weight'''		,     )
                       _A							:     Union[str, Any]			=						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			,   _A							:     List[Any]			=						self.cutoff_ends[i], self.cutoff_ends[i + 1]
                       _A							:     Optional[int]			=						self.d_embed // (self.div_val**i)

                       _A							:     Dict			=						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							:     Optional[int]			=						self.add_weight(
                           shape=(r_idx - l_idx, d_emb_i)		,     initializer="""zeros"""		,     trainable=_a		,     name=F'''out_layers_._{i}_._weight'''		,     )
                       _A							:     int			=						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 					a__	(  _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           _A							:     Tuple			=						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 					a__	(  _a		,     _a				)						->							Optional[Any]:
           _A							:     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 					a__	(  self		,     _a		,     _a		,     _a=True		,     _a=False				)						->							Optional[Any]:
           _A							:     Union[str, Any]			=						0
           if self.n_clusters == 0:
                 _A							:     List[Any]			=						self._logit(_a		,     self.out_layers[0][0]		,     self.out_layers[0][1]		,     self.out_projs[0]				)
                 if target is not None:
                       _A							:     int			=						tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_a		,     logits=_a				)
                 _A							:     Union[str, Any]			=						tf.nn.log_softmax(_a		,     axis=-1				)
           else:
                 _A							:     Optional[Any]			=						shape_list(_a				)
                 _A							:     Optional[Any]			=						[]
                 _A							:     Optional[Any]			=						tf.zeros(hidden_sizes[:2]				)
                 for i in range(len(self.cutoffs				)				):
                       _A			,   _A							:     List[str]			=						self.cutoff_ends[i], self.cutoff_ends[i + 1]
                       if target is not None:
                             _A							:     int			=						(target >= l_idx) & (target < r_idx)
                             _A							:     str			=						tf.where(_a				)
                             _A							:     Dict			=						tf.boolean_mask(_a		,     _a				) - l_idx

                       if self.div_val == 1:
                             _A							:     str			=						self.out_layers[0][0][l_idx:r_idx]
                             _A							:     List[str]			=						self.out_layers[0][1][l_idx:r_idx]
                       else:
                             _A							:     List[Any]			=						self.out_layers[i][0]
                             _A							:     int			=						self.out_layers[i][1]

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

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

           if target is not None:
                 if return_mean:
                       _A							:     Tuple			=						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							):







	'''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))
