



import tensorflow as tf

from ...tf_utils import shape_list






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



	def __init__(						self							:    Dict				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    Any				,	snake_case_							:    Union[str, Any]=1				,	snake_case_							:    Dict=False				,	**snake_case_							:    Dict)->							Optional[Any]:
						super().__init__(**snake_case_)

						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =d_embed
						__lowerCAmelCase	   =d_proj

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

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

						__lowerCAmelCase	   =[]
						__lowerCAmelCase	   =[]



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    Tuple)->							Any:
						if self.n_clusters > 0:
											__lowerCAmelCase	   =self.add_weight(
											    shape=(self.n_clusters, self.d_embed)				,	initializer="""zeros"""				,	trainable=snake_case_				,	name="""cluster_weight""")
											__lowerCAmelCase	   =self.add_weight(
											    shape=(self.n_clusters,)				,	initializer="""zeros"""				,	trainable=snake_case_				,	name="""cluster_bias""")

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

																__lowerCAmelCase	   =self.add_weight(
																    shape=(d_emb_i, self.d_proj)				,	initializer="""zeros"""				,	trainable=snake_case_				,	name=F"""out_projs_._{i}""")
																self.out_projs.append(snake_case_)
																__lowerCAmelCase	   =self.add_weight(
																    shape=(r_idx - l_idx, d_emb_i)				,	initializer="""zeros"""				,	trainable=snake_case_				,	name=F"""out_layers_._{i}_._weight"""				,	)
																__lowerCAmelCase	   =self.add_weight(
																    shape=(r_idx - l_idx,)				,	initializer="""zeros"""				,	trainable=snake_case_				,	name=F"""out_layers_._{i}_._bias"""				,	)
																self.out_layers.append((weight, bias))
						super().build(snake_case_)



	@staticmethod
	def 		UpperCamelCase							(						snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Dict=None)->							Optional[int]:
						__lowerCAmelCase	   =x
						if proj is not None:
											__lowerCAmelCase	   =tf.einsum("""ibd,ed->ibe"""				,	snake_case_				,	snake_case_)
						return tf.einsum("""ibd,nd->ibn"""				,	snake_case_				,	snake_case_) + b



	@staticmethod
	def 		UpperCamelCase							(						snake_case_							:    str				,	snake_case_							:    Union[str, Any])->							Dict:
						__lowerCAmelCase	   =shape_list(snake_case_)
						__lowerCAmelCase	   =tf.range(lp_size[0]				,	dtype=target.dtype)
						__lowerCAmelCase	   =tf.stack([r, target]				,	1)
						return tf.gather_nd(snake_case_				,	snake_case_)



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

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

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

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

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

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

						return out









def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =set({"""(""", """[""", """{"""}	)
					__lowerCAmelCase	   =set({""")""", """]""", """}"""}	)
					__lowerCAmelCase	   ={"""{""": """}""", """[""": """]""", """(""": """)"""}

					for i in range(len(__lowerCamelCase	)	):
										if s[i] in open_brackets:
															stack.append(s[i]	)

										elif s[i] in closed_brackets and (
										    len(__lowerCamelCase	) == 0 or (len(__lowerCamelCase	) > 0 and open_to_closed[stack.pop()] != s[i])
										):
															return False

					return len(__lowerCamelCase	) == 0






def 					__lowerCAmelCase		(  )  ->						List[str]:
					__lowerCAmelCase	   =input("""Enter sequence of brackets: """	)
					if is_balanced(__lowerCamelCase	):
										print(__lowerCamelCase				,   """is balanced"""	)
					else:
										print(__lowerCamelCase				,   """is not balanced"""	)


if __name__ == "__main__":
				main()




