

'''simple docstring'''

import tensorflow as tf

from ...tf_utils import shape_list






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






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase=1,     _lowercase=False,     **_lowercase       )     ->							List[Any]:
											super().__init__(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				d_embed
											SCREAMING_SNAKE_CASE_				=				d_proj

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

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

											SCREAMING_SNAKE_CASE_				=				[]
											SCREAMING_SNAKE_CASE_				=				[]



				def   a__					(    self,     _lowercase       )     ->							Union[str, Any]:
											if self.n_clusters > 0:
																		SCREAMING_SNAKE_CASE_				=				self.add_weight(
																		    shape=(self.n_clusters, self.d_embed),     initializer='zeros',     trainable=_lowercase,     name='cluster_weight'       )
																		SCREAMING_SNAKE_CASE_				=				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:
																																SCREAMING_SNAKE_CASE_				=				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       )
																									SCREAMING_SNAKE_CASE_				=				self.add_weight(
																									    shape=(self.vocab_size, self.d_embed),     initializer='zeros',     trainable=_lowercase,     name=f"""out_layers_._{i}_._weight""",     )
																									SCREAMING_SNAKE_CASE_				=				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       )       ):
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																									SCREAMING_SNAKE_CASE_				=				self.d_embed // (self.div_val**i)

																									SCREAMING_SNAKE_CASE_				=				self.add_weight(
																									    shape=(d_emb_i, self.d_proj),     initializer='zeros',     trainable=_lowercase,     name=f"""out_projs_._{i}"""       )
																									self.out_projs.append(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				self.add_weight(
																									    shape=(r_idx - l_idx, d_emb_i),     initializer='zeros',     trainable=_lowercase,     name=f"""out_layers_._{i}_._weight""",     )
																									SCREAMING_SNAKE_CASE_				=				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__					(    _lowercase,     _lowercase,     _lowercase,     _lowercase=None       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				x
											if proj is not None:
																		SCREAMING_SNAKE_CASE_				=				tf.einsum('ibd,ed->ibe',     _lowercase,     _lowercase       )
											return tf.einsum('ibd,nd->ibn',     _lowercase,     _lowercase       ) + b



				@staticmethod
				def   a__					(    _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				shape_list(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				tf.range(lp_size[0],     dtype=target.dtype       )
											SCREAMING_SNAKE_CASE_				=				tf.stack([r, target],     1       )
											return tf.gather_nd(_lowercase,     _lowercase       )





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

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

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

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

											if target is not None:
																		if return_mean:
																									SCREAMING_SNAKE_CASE_				=				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'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





