import tensorflow as tf

from ...tf_utils import shape_list






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

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


									'''simple docstring'''

									super().__init__(**__lowercase )

									lowercase       		=		vocab_size
									lowercase       		=		d_embed
									lowercase       		=		d_proj

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

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

									lowercase       		=		[]
									lowercase       		=		[]

						def 							__a		(						self					:   Any				,				__lowerCamelCase					:   Tuple )				->		int:


									'''simple docstring'''

									if self.n_clusters > 0:
												lowercase       		=		self.add_weight(
												    shape=(self.n_clusters, self.d_embed)				,				initializer='''zeros'''				,				trainable=__lowercase				,				name='''cluster_weight''' )
												lowercase       		=		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:
																		lowercase       		=		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 )
															lowercase       		=		self.add_weight(
															    shape=(self.vocab_size, self.d_embed)				,				initializer='''zeros'''				,				trainable=__lowercase				,				name=f'out_layers_._{i}_._weight'				,				)
															lowercase       		=		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 ) ):
															lowercase		,lowercase       		=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
															lowercase       		=		self.d_embed // (self.div_val**i)

															lowercase       		=		self.add_weight(
															    shape=(d_emb_i, self.d_proj)				,				initializer='''zeros'''				,				trainable=__lowercase				,				name=f'out_projs_._{i}' )
															self.out_projs.append(__lowercase )
															lowercase       		=		self.add_weight(
															    shape=(r_idx - l_idx, d_emb_i)				,				initializer='''zeros'''				,				trainable=__lowercase				,				name=f'out_layers_._{i}_._weight'				,				)
															lowercase       		=		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 							__a		(						__lowerCamelCase					:   Tuple				,				__lowerCamelCase					:   Any				,				__lowerCamelCase					:   List[str]				,				__lowerCamelCase					:   str=None )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		x
									if proj is not None:
												lowercase       		=		tf.einsum('''ibd,ed->ibe'''				,				__lowercase				,				__lowercase )
									return tf.einsum('''ibd,nd->ibn'''				,				__lowercase				,				__lowercase ) + b

						@staticmethod
						def 							__a		(						__lowerCamelCase					:   List[Any]				,				__lowerCamelCase					:   List[Any] )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		shape_list(__lowercase )
									lowercase       		=		tf.range(lp_size[0]				,				dtype=target.dtype )
									lowercase       		=		tf.stack([r, target]				,				1 )
									return tf.gather_nd(__lowercase				,				__lowercase )



						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   List[Any]				,				__lowerCamelCase					:   str				,				__lowerCamelCase					:   List[str]=True				,				__lowerCamelCase					:   Optional[Any]=False )				->		Optional[int]:


									'''simple docstring'''

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

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

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

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

									if target is not None:
												if return_mean:
															lowercase       		=		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








'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	Tuple			):






     '''simple docstring'''





     __lowercase      		=[0 for i in range(r + 1			)]
     # nc0 = 1
     __lowercase      		=1
     for i in range(1,       n + 1			):
          # to compute current row from previous row.
          __lowercase      		=min(lowercase__,       lowercase__			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]







print(binomial_coefficient(n=10, r=5))


