


"""simple docstring"""


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["a", "b", "c"]

										# Defaults to last layer if both are None
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(__a					,     __a					,     __a			)
										self.assertEqual(__a					,     ["c"]			)
										self.assertEqual(__a					,     [2]			)

										# Out indices set to match out features
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(["a", "c"]					,     __a					,     __a			)
										self.assertEqual(__a					,     ["a", "c"]			)
										self.assertEqual(__a					,     [0, 2]			)

										# Out features set to match out indices
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(__a					,     [0, 2]					,     __a			)
										self.assertEqual(__a					,     ["a", "c"]			)
										self.assertEqual(__a					,     [0, 2]			)

										# Out features selected from negative indices
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(__a					,     [-3, -1]					,     __a			)
										self.assertEqual(__a					,     ["a", "c"]			)
										self.assertEqual(__a					,     [-3, -1]			)

							def     snake_case       (  self			):
										# Stage names must be set
										with self.assertRaises(__a			):
													verify_out_features_out_indices(["a", "b"]					,     (0, 1)					,     __a			)

										# Out features must be a list
										with self.assertRaises(__a			):
													verify_out_features_out_indices(("a", "b")					,     (0, 1)					,     ["a", "b"]			)

										# Out features must be a subset of stage names
										with self.assertRaises(__a			):
													verify_out_features_out_indices(["a", "b"]					,     (0, 1)					,     ["a"]			)

										# Out indices must be a list or tuple
										with self.assertRaises(__a			):
													verify_out_features_out_indices(__a					,     0					,     ["a", "b"]			)

										# Out indices must be a subset of stage names
										with self.assertRaises(__a			):
													verify_out_features_out_indices(__a					,     (0, 1)					,     ["a"]			)

										# Out features and out indices must be the same length
										with self.assertRaises(__a			):
													verify_out_features_out_indices(["a", "b"]					,     (0,)					,     ["a", "b", "c"]			)

										# Out features should match out indices
										with self.assertRaises(__a			):
													verify_out_features_out_indices(["a", "b"]					,     (0, 2)					,     ["a", "b", "c"]			)

										# Out features and out indices should be in order
										with self.assertRaises(__a			):
													verify_out_features_out_indices(["b", "a"]					,     (0, 1)					,     ["a", "b"]			)

										# Check passes with valid inputs
										verify_out_features_out_indices(["a", "b", "d"]					,     (0, 1, -1)					,     ["a", "b", "c", "d"]			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    BackboneMixin()

										__lowerCAmelCase       					=    ["a", "b", "c"]
										__lowerCAmelCase       					=    ["a", "c"]
										__lowerCAmelCase       					=    [0, 2]

										# Check that the output features and indices are set correctly
										self.assertEqual(backbone.out_features					,     ["a", "c"]			)
										self.assertEqual(backbone.out_indices					,     [0, 2]			)

										# Check out features and indices are updated correctly
										__lowerCAmelCase       					=    ["a", "b"]
										self.assertEqual(backbone.out_features					,     ["a", "b"]			)
										self.assertEqual(backbone.out_indices					,     [0, 1]			)

										__lowerCAmelCase       					=    [-3, -1]
										self.assertEqual(backbone.out_features					,     ["a", "c"]			)
										self.assertEqual(backbone.out_indices					,     [-3, -1]			)








"""simple docstring"""


import tensorflow as tf

from ...tf_utils import shape_list
class 						_UpperCamelCase						(     tf.keras.layers.Layer	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a					,     __a					,     __a					,     __a=1					,     __a=False					,     **__a			):
										super().__init__(**__a			)

										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    d_embed
										__lowerCAmelCase       					=    d_proj

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

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

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

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

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

							@staticmethod
							def     snake_case       (  __a					,     __a			):
										__lowerCAmelCase       					=    shape_list(__a			)
										__lowerCAmelCase       					=    tf.range(lp_size[0]					,     dtype=target.dtype			)
										__lowerCAmelCase       					=    tf.stack([r, target]					,     1			)
										return tf.gather_nd(__a					,     __a			)



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

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

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

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

										if target is not None:
													if return_mean:
																__lowerCAmelCase       					=    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




