393 lines
13 KiB
Python
393 lines
13 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 unittest
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from transformers import is_tf_available
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from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
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import tensorflow as tf
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from transformers import (
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FlaubertConfig,
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TFFlaubertForMultipleChoice,
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TFFlaubertForQuestionAnsweringSimple,
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TFFlaubertForSequenceClassification,
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TFFlaubertForTokenClassification,
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TFFlaubertModel,
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TFFlaubertWithLMHeadModel,
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)
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class TFFlaubertModelTester:
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def __init__(
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self,
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parent,
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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_lengths = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.gelu_activation = True
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self.sinusoidal_embeddings = False
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self.causal = False
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self.asm = False
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self.n_langs = 2
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self.vocab_size = 99
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self.n_special = 0
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self.hidden_size = 32
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self.num_hidden_layers = 2
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self.num_attention_heads = 4
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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.summary_type = "last"
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self.use_proj = True
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self.scope = None
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self.bos_token_id = 0
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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 = random_attention_mask([self.batch_size, self.seq_length], dtype=tf.float32)
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input_lengths = None
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if self.use_input_lengths:
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input_lengths = (
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ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
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) # small variation of 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.n_langs)
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sequence_labels = None
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token_labels = None
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is_impossible_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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is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = FlaubertConfig(
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vocab_size=self.vocab_size,
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n_special=self.n_special,
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emb_dim=self.hidden_size,
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n_layers=self.num_hidden_layers,
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n_heads=self.num_attention_heads,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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gelu_activation=self.gelu_activation,
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sinusoidal_embeddings=self.sinusoidal_embeddings,
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asm=self.asm,
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causal=self.causal,
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n_langs=self.n_langs,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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summary_type=self.summary_type,
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use_proj=self.use_proj,
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bos_token_id=self.bos_token_id,
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)
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return (
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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)
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def create_and_check_flaubert_model(
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self,
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFFlaubertModel(config=config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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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_flaubert_lm_head(
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self,
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFFlaubertWithLMHeadModel(config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
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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_flaubert_qa(
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self,
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFFlaubertForQuestionAnsweringSimple(config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths}
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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 create_and_check_flaubert_sequence_classif(
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self,
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFFlaubertForSequenceClassification(config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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def create_and_check_flaubert_for_token_classification(
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self,
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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config.num_labels = self.num_labels
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model = TFFlaubertForTokenClassification(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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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_flaubert_for_multiple_choice(
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self,
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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config.num_choices = self.num_choices
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model = TFFlaubertForMultipleChoice(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 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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"langs": token_type_ids,
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"lengths": input_lengths,
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}
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return config, inputs_dict
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@require_tf
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class TFFlaubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFFlaubertModel,
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TFFlaubertWithLMHeadModel,
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TFFlaubertForSequenceClassification,
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TFFlaubertForQuestionAnsweringSimple,
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TFFlaubertForTokenClassification,
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TFFlaubertForMultipleChoice,
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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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all_generative_model_classes = (
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(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
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) # TODO (PVP): Check other models whether language generation is also applicable
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pipeline_model_mapping = (
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{
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"feature-extraction": TFFlaubertModel,
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"fill-mask": TFFlaubertWithLMHeadModel,
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"question-answering": TFFlaubertForQuestionAnsweringSimple,
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"text-classification": TFFlaubertForSequenceClassification,
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"token-classification": TFFlaubertForTokenClassification,
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"zero-shot": TFFlaubertForSequenceClassification,
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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_head_masking = False
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test_onnx = False
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if (
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pipeline_test_casse_name == "QAPipelineTests"
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and tokenizer_name is not None
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and not tokenizer_name.endswith("Fast")
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):
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# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
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# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
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# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
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return True
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return False
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def setUp(self):
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self.model_tester = TFFlaubertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=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_flaubert_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_flaubert_model(*config_and_inputs)
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def test_flaubert_lm_head(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_flaubert_lm_head(*config_and_inputs)
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def test_flaubert_qa(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_flaubert_qa(*config_and_inputs)
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def test_flaubert_sequence_classif(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_flaubert_sequence_classif(*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_flaubert_for_token_classification(*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_flaubert_for_multiple_choice(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "hf-internal-testing/tiny-random-flaubert"
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model = TFFlaubertModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_tf
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@require_sentencepiece
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@require_tokenizers
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class TFFlaubertModelIntegrationTest(unittest.TestCase):
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@slow
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def test_output_embeds_base_model(self):
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model = TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased")
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input_ids = tf.convert_to_tensor(
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[[0, 158, 735, 2592, 1424, 6727, 82, 1]],
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dtype=tf.int32,
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) # "J'aime flaubert !"
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output = model(input_ids)[0]
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expected_shape = tf.TensorShape((1, 8, 512))
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self.assertEqual(output.shape, expected_shape)
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# compare the actual values for a slice.
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expected_slice = tf.convert_to_tensor(
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[
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[
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[-1.8768773, -1.566555, 0.27072418],
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[-1.6920038, -0.5873505, 1.9329599],
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[-2.9563985, -1.6993835, 1.7972052],
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]
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],
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dtype=tf.float32,
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)
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self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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