

'''simple docstring'''







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowerCamelCase		       =    TypeVar('''T''')
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       return (position - 1) // 2
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       return (2 * position) + 1
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       return (2 * position) + 2


class 					A__      (      Generic[T]			):



    def __init__(					self       )						->      str:





           '''simple docstring'''
           A_						=      []
           A_						=      {}
           A_						=      0



    def __len__(					self       )						->      int:





           '''simple docstring'''
           return self.elements



    def __repr__(					self       )						->      List[Any]:





           '''simple docstring'''
           return str(self.heap       )



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           return self.elements == 0



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           self.heap.append((elem, weight)       )
           A_						=      self.elements
           self.elements += 1
           self._bubble_up(UpperCAmelCase__       )



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           # Remove and return the element with lowest weight (highest priority)
           if self.elements > 1:
                  self._swap_nodes(0		,				self.elements - 1       )
           A_						=      self.heap.pop()
           del self.position_map[elem]
           self.elements -= 1
           if self.elements > 0:
                  A_						=      self.heap[0]
                  self._bubble_down(UpperCAmelCase__       )
           return elem



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      self.position_map[elem]
           A_						=      (elem, weight)
           if position > 0:
                  A_						=      get_parent_position(UpperCAmelCase__       )
                  A_						=      self.heap[parent_position]
                  if parent_weight > weight:
                         self._bubble_up(UpperCAmelCase__       )
                  else:
                         self._bubble_down(UpperCAmelCase__       )
           else:
                  self._bubble_down(UpperCAmelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           A_						=      self.position_map[elem]
           if curr_pos == 0:
                  return None
           A_						=      get_parent_position(UpperCAmelCase__       )
           A_						=      self.heap[curr_pos]
           A_						=      self.heap[parent_position]
           if parent_weight > weight:
                  self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__       )
                  return self._bubble_up(UpperCAmelCase__       )
           return None



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      int:





           '''simple docstring'''
           A_						=      self.position_map[elem]
           A_						=      self.heap[curr_pos]
           A_						=      get_child_left_position(UpperCAmelCase__       )
           A_						=      get_child_right_position(UpperCAmelCase__       )
           if child_left_position < self.elements and child_right_position < self.elements:
                  A_						=      self.heap[child_left_position]
                  A_						=      self.heap[child_right_position]
                  if child_right_weight < child_left_weight and child_right_weight < weight:
                         self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__       )
                         return self._bubble_down(UpperCAmelCase__       )
           if child_left_position < self.elements:
                  A_						=      self.heap[child_left_position]
                  if child_left_weight < weight:
                         self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__       )
                         return self._bubble_down(UpperCAmelCase__       )
           else:
                  return None
           if child_right_position < self.elements:
                  A_						=      self.heap[child_right_position]
                  if child_right_weight < weight:
                         self._swap_nodes(UpperCAmelCase__		,				UpperCAmelCase__       )
                         return self._bubble_down(UpperCAmelCase__       )
           return None







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      self.heap[nodea_pos][0]
           A_						=      self.heap[nodea_pos][0]
           A_						=      (
               self.heap[nodea_pos],
               self.heap[nodea_pos],
           )
           A_						=      nodea_pos
           A_						=      nodea_pos


class 					A__      (      Generic[T]			):



    def __init__(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      {}
           A_						=      0



    def __repr__(					self       )						->      int:





           '''simple docstring'''
           return str(self.connections       )



    def __len__(					self       )						->      List[str]:





           '''simple docstring'''
           return self.nodes



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           # Add a node in the graph if it is not in the graph
           if node not in self.connections:
                  A_						=      {}
                  self.nodes += 1







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           self.add_node(UpperCAmelCase__       )
           self.add_node(UpperCAmelCase__       )
           A_						=      weight
           A_						=      weight

def       UpperCAmelCase__    (      UpperCAmelCase__,       )			->			tuple[dict[T, int], dict[T, T | None]]:
       A_						=      {node: maxsize for node in graph.connections}
       A_						=      {node: None for node in graph.connections}

       A_						=      MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCAmelCase__,       UpperCAmelCase__				)

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       A_						=      priority_queue.extract_min()
       A_						=      0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     A_						=      dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCAmelCase__,       dist[neighbour]				)
                     A_						=      node

    # running prim's algorithm
       while not priority_queue.is_empty():
              A_						=      priority_queue.extract_min()
              for neighbour in graph.connections[node]:
                     if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                            A_						=      dist[node] + graph.connections[node][neighbour]
                            priority_queue.update_key(UpperCAmelCase__,       dist[neighbour]				)
                            A_						=      node
       return dist, parent



'''simple docstring'''







import tensorflow as tf

from ...tf_utils import shape_list


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



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=1		,				UpperCamelCase__=False		,				**UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )

           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 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           if self.n_clusters > 0:
                  A_						=      self.add_weight(
                      shape=(self.n_clusters, self.d_embed)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name="""cluster_weight"""       )
                  A_						=      self.add_weight(
                      shape=(self.n_clusters,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				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=UpperCamelCase__		,				name=f'''out_projs_._{i}'''		,				)
                                self.out_projs.append(UpperCamelCase__       )
                         else:
                                self.out_projs.append(UpperCamelCase__       )
                         A_						=      self.add_weight(
                             shape=(self.vocab_size, self.d_embed)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._weight'''		,				)
                         A_						=      self.add_weight(
                             shape=(self.vocab_size,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				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=UpperCamelCase__		,				name=f'''out_projs_._{i}'''       )
                         self.out_projs.append(UpperCamelCase__       )
                         A_						=      self.add_weight(
                             shape=(r_idx - l_idx, d_emb_i)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._weight'''		,				)
                         A_						=      self.add_weight(
                             shape=(r_idx - l_idx,)		,				initializer="""zeros"""		,				trainable=UpperCamelCase__		,				name=f'''out_layers_._{i}_._bias'''		,				)
                         self.out_layers.append((weight, bias)       )
           super().build(UpperCamelCase__       )



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      List[Any]:





           '''simple docstring'''
           A_						=      x
           if proj is not None:
                  A_						=      tf.einsum("""ibd,ed->ibe"""		,				UpperCamelCase__		,				UpperCamelCase__       )
           return tf.einsum("""ibd,nd->ibn"""		,				UpperCamelCase__		,				UpperCamelCase__       ) + b



    @staticmethod
    def 					snake_case_			(					UpperCamelCase__		,				UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           A_						=      shape_list(UpperCamelCase__       )
           A_						=      tf.range(lp_size[0]		,				dtype=target.dtype       )
           A_						=      tf.stack([r, target]		,				1       )
           return tf.gather_nd(UpperCamelCase__		,				UpperCamelCase__       )







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=True		,				UpperCamelCase__=False       )						->      Optional[int]:





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

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

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

           return out
