426 lines
17 KiB
Python
426 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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import tempfile
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import unittest
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from transformers import ConvBertConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import (
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TFConvBertForMaskedLM,
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TFConvBertForMultipleChoice,
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TFConvBertForQuestionAnswering,
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TFConvBertForSequenceClassification,
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TFConvBertForTokenClassification,
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TFConvBertModel,
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)
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from transformers.modeling_tf_utils import keras
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class TFConvBertModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 384
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self.num_hidden_layers = 2
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.embedding_size = 128
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self.head_ratio = 2
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self.conv_kernel_size = 9
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self.num_groups = 1
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self.scope = None
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = ConvBertConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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return_dict=True,
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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFConvBertModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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inputs = [input_ids, input_mask]
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result = model(inputs)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFConvBertForMaskedLM(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_for_sequence_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = TFConvBertForSequenceClassification(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_for_multiple_choice(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = TFConvBertForMultipleChoice(config=config)
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multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
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multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
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multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
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inputs = {
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"input_ids": multiple_choice_inputs_ids,
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"attention_mask": multiple_choice_input_mask,
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"token_type_ids": multiple_choice_token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_token_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = TFConvBertForTokenClassification(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_question_answering(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFConvBertForQuestionAnswering(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_tf
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class TFConvBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFConvBertModel,
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TFConvBertForMaskedLM,
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TFConvBertForQuestionAnswering,
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TFConvBertForSequenceClassification,
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TFConvBertForTokenClassification,
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TFConvBertForMultipleChoice,
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)
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if is_tf_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"feature-extraction": TFConvBertModel,
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"fill-mask": TFConvBertForMaskedLM,
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"question-answering": TFConvBertForQuestionAnswering,
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"text-classification": TFConvBertForSequenceClassification,
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"token-classification": TFConvBertForTokenClassification,
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"zero-shot": TFConvBertForSequenceClassification,
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}
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if is_tf_available()
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else {}
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)
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test_pruning = False
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFConvBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ConvBertConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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@slow
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def test_saved_model_creation_extended(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = True
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if hasattr(config, "use_cache"):
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config.use_cache = True
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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for model_class in self.all_model_classes:
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class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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num_out = len(model(class_inputs_dict))
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname, saved_model=True)
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saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
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model = keras.models.load_model(saved_model_dir)
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outputs = model(class_inputs_dict)
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if self.is_encoder_decoder:
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output_hidden_states = outputs["encoder_hidden_states"]
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output_attentions = outputs["encoder_attentions"]
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else:
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output_hidden_states = outputs["hidden_states"]
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output_attentions = outputs["attentions"]
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self.assertEqual(len(outputs), num_out)
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(output_hidden_states), expected_num_layers)
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self.assertListEqual(
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list(output_hidden_states[0].shape[-2:]),
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[self.model_tester.seq_length, self.model_tester.hidden_size],
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)
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self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(output_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
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)
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@slow
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def test_model_from_pretrained(self):
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model = TFConvBertModel.from_pretrained("YituTech/conv-bert-base")
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self.assertIsNotNone(model)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
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decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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def check_decoder_attentions_output(outputs):
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out_len = len(outputs)
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self.assertEqual(out_len % 2, 0)
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decoder_attentions = outputs.decoder_attentions
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length],
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)
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def check_encoder_attentions_output(outputs):
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attentions = [
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t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
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]
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
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)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = False
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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out_len = len(outputs)
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self.assertEqual(config.output_hidden_states, False)
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check_encoder_attentions_output(outputs)
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if self.is_encoder_decoder:
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(config.output_hidden_states, False)
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check_decoder_attentions_output(outputs)
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# Check that output attentions can also be changed via the config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(config.output_hidden_states, False)
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check_encoder_attentions_output(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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config.output_hidden_states = True
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model = model_class(config)
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outputs = model(self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
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self.assertEqual(model.config.output_hidden_states, True)
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check_encoder_attentions_output(outputs)
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@require_tf
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class TFConvBertModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_masked_lm(self):
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model = TFConvBertModel.from_pretrained("YituTech/conv-bert-base")
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input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
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output = model(input_ids)[0]
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expected_shape = [1, 6, 768]
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self.assertEqual(output.shape, expected_shape)
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expected_slice = tf.constant(
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[
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[
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[-0.03475493, -0.4686034, -0.30638832],
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[0.22637248, -0.26988646, -0.7423424],
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[0.10324868, -0.45013508, -0.58280784],
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]
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]
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)
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tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
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