



import tensorflow as tf

from ...tf_utils import shape_list







class  snake_case_     (tf.keras.layers.Layer   ):
					def __init__(   self     :Optional[Any]    ,__snake_case     :Any    ,__snake_case     :int    ,__snake_case     :List[str]    ,__snake_case     :Optional[int]    ,__snake_case     :List[str]=1    ,__snake_case     :Dict=False    ,**__snake_case     :Optional[Any]   )      ->					Any:
												super().__init__(**_A   )

												a__    =  vocab_size
												a__    =  d_embed
												a__    =  d_proj

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

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

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

																										a__    =  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__    =  self.add_weight(
																										    shape=(r_idx - l_idx, d_emb_i)    ,initializer='zeros'    ,trainable=_A    ,name=F'out_layers_._{i}_._weight'    ,)
																										a__    =  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 				lowerCamelCase__(   __snake_case     :int    ,__snake_case     :Union[str, Any]    ,__snake_case     :List[Any]    ,__snake_case     :Any=None   )      ->					Optional[int]:
												a__    =  x
												if proj is not None:
																			a__    =  tf.einsum('ibd,ed->ibe'    ,_A    ,_A   )
												return tf.einsum('ibd,nd->ibn'    ,_A    ,_A   ) + b
					@staticmethod
					def 				lowerCamelCase__(   __snake_case     :int    ,__snake_case     :int   )      ->					Tuple:
												a__    =  shape_list(_A   )
												a__    =  tf.range(lp_size[0]    ,dtype=target.dtype   )
												a__    =  tf.stack([r, target]    ,1   )
												return tf.gather_nd(_A    ,_A   )



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

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

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

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

												if target is not None:
																			if return_mean:
																										a__    =  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








from math import pi






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):
							return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
						print(arc_length(90, 10))




