
"""simple docstring"""





import tensorflow as tf

from ...tf_utils import shape_list



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




			"""simple docstring"""







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







								"""simple docstring"""
								super().__init__(**_snake_case						)

								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    :       Optional[Any]							, _snake_case    :       Union[str, Any]						)   ->			Any:







								"""simple docstring"""
								if self.n_clusters > 0:
													A_								=    self.add_weight(
													    shape=(self.n_clusters, self.d_embed)							, initializer="zeros"							, trainable=_snake_case							, name="cluster_weight"						)
													A_								=    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:
																							A_								=    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						)
																		A_								=    self.add_weight(
																		    shape=(self.vocab_size, self.d_embed)							, initializer="zeros"							, trainable=_snake_case							, name=F'out_layers_._{i}_._weight'							, )
																		A_								=    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						)						):
																		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=_snake_case							, name=F'out_projs_._{i}'						)
																		self.out_projs.append(_snake_case						)
																		A_								=    self.add_weight(
																		    shape=(r_idx - l_idx, d_emb_i)							, initializer="zeros"							, trainable=_snake_case							, name=F'out_layers_._{i}_._weight'							, )
																		A_								=    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   lowerCamelCase__  (					_snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       Any=None						)   ->			Optional[int]:







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




			@staticmethod
			def   lowerCamelCase__  (					_snake_case    :       List[Any]							, _snake_case    :       Any						)   ->			str:







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






			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Dict							, _snake_case    :       Optional[int]							, _snake_case    :       Dict=True							, _snake_case    :       Optional[int]=False						)   ->			Any:







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

								if target is not None:
													if return_mean:
																		A_								=    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






"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




