codingutf8 2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license todo fix the failed tests qapipelinetests fails for a few models when the slower tokenizer are used the slower tokenizers were never used for pipeline tests before the pipeline testing rework todo check and possibly fix the qapipelinetests with slower tokenizer flaubert has 2 qa models need to manually set the correct labels for one of them here flaubertformultiplechoice behaves incorrectly in jit environments coding utf 8 2020 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license small variation of seq_length todo fix the failed tests qapipelinetests fails for a few models when the slower tokenizer are used the slower tokenizers were never used for pipeline tests before the pipeline testing rework todo check and possibly fix the qapipelinetests with slower tokenizer flaubert has 2 qa models need to manually set the correct labels for one of them here flaubertformultiplechoice behaves incorrectly in jit environments
import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertWithLMHeadModel,
    )
    from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class FlaubertModelTester(object):
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_lengths=True,
        use_token_type_ids=True,
        use_labels=True,
        gelu_activation=True,
        sinusoidal_embeddings=False,
        causal=False,
        asm=False,
        n_langs=2,
        vocab_size=99,
        n_special=0,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=12,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_labels=3,
        num_choices=4,
        summary_type="last",
        use_proj=None,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_lengths = use_input_lengths
        self.use_token_type_ids = use_token_type_ids
        self.use_labels = use_labels
        self.gelu_activation = gelu_activation
        self.sinusoidal_embeddings = sinusoidal_embeddings
        self.causal = causal
        self.asm = asm
        self.n_langs = n_langs
        self.vocab_size = vocab_size
        self.n_special = n_special
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.summary_type = summary_type
        self.use_proj = use_proj
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_mask = random_attention_mask([self.batch_size, self.seq_length])

        input_lengths = None
        if self.use_input_lengths:
            input_lengths = (
                ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
            )  

        token_type_ids = None
        if self.use_token_type_ids:
            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)

        sequence_labels = None
        token_labels = None
        is_impossible_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
            is_impossible_labels = ids_tensor([self.batch_size], 2).float()
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )

    def get_config(self):
        return FlaubertConfig(
            vocab_size=self.vocab_size,
            n_special=self.n_special,
            emb_dim=self.hidden_size,
            n_layers=self.num_hidden_layers,
            n_heads=self.num_attention_heads,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            gelu_activation=self.gelu_activation,
            sinusoidal_embeddings=self.sinusoidal_embeddings,
            asm=self.asm,
            causal=self.causal,
            n_langs=self.n_langs,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
            summary_type=self.summary_type,
            use_proj=self.use_proj,
        )

    def create_and_check_flaubert_model(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = FlaubertModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
        result = model(input_ids, langs=token_type_ids)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_flaubert_lm_head(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = FlaubertWithLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_flaubert_simple_qa(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = FlaubertForQuestionAnsweringSimple(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids)

        result = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))

    def create_and_check_flaubert_qa(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = FlaubertForQuestionAnswering(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids)

        result_with_labels = model(
            input_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
            p_mask=input_mask,
        )

        result_with_labels = model(
            input_ids,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
        )

        (total_loss,) = result_with_labels.to_tuple()

        result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)

        (total_loss,) = result_with_labels.to_tuple()

        self.parent.assertEqual(result_with_labels.loss.shape, ())
        self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
        self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
        self.parent.assertEqual(
            result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
        )
        self.parent.assertEqual(
            result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
        )
        self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))

    def create_and_check_flaubert_sequence_classif(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        model = FlaubertForSequenceClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids)
        result = model(input_ids, labels=sequence_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    def create_and_check_flaubert_token_classif(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        config.num_labels = self.num_labels
        model = FlaubertForTokenClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_flaubert_multiple_choice(
        self,
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    ):
        config.num_choices = self.num_choices
        model = FlaubertForMultipleChoice(config=config)
        model.to(torch_device)
        model.eval()
        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
        result = model(
            multiple_choice_inputs_ids,
            attention_mask=multiple_choice_input_mask,
            token_type_ids=multiple_choice_token_type_ids,
            labels=choice_labels,
        )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        ) = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "lengths": input_lengths,
            "attention_mask": input_mask,
        }
        return config, inputs_dict


@require_torch
class FlaubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            FlaubertModel,
            FlaubertWithLMHeadModel,
            FlaubertForQuestionAnswering,
            FlaubertForQuestionAnsweringSimple,
            FlaubertForSequenceClassification,
            FlaubertForTokenClassification,
            FlaubertForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": FlaubertModel,
            "fill-mask": FlaubertWithLMHeadModel,
            "question-answering": FlaubertForQuestionAnsweringSimple,
            "text-classification": FlaubertForSequenceClassification,
            "token-classification": FlaubertForTokenClassification,
            "zero-shot": FlaubertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast")
        ):
            
            
            
            return True

        return False

    
    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

        if return_labels:
            if model_class.__name__ == "FlaubertForQuestionAnswering":
                inputs_dict["start_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
                inputs_dict["end_positions"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )

        return inputs_dict

    def setUp(self):
        self.model_tester = FlaubertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_flaubert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*config_and_inputs)

    def test_flaubert_lm_head(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)

    def test_flaubert_simple_qa(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_simple_qa(*config_and_inputs)

    def test_flaubert_qa(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*config_and_inputs)

    def test_flaubert_sequence_classif(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*config_and_inputs)

    def test_flaubert_token_classif(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_token_classif(*config_and_inputs)

    def test_flaubert_multiple_choice(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_multiple_choice(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = FlaubertModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @slow
    @require_torch_accelerator
    def test_torchscript_device_change(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            
            if model_class == FlaubertForMultipleChoice:
                return

            config.torchscript = True
            model = model_class(config=config)

            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            traced_model = torch.jit.trace(
                model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
            )

            with tempfile.TemporaryDirectory() as tmp:
                torch.jit.save(traced_model, os.path.join(tmp, "traced_model.pt"))
                loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device)
                loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))


@require_torch
class FlaubertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_no_head_absolute_embedding(self):
        model = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased")
        input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
        with torch.no_grad():
            output = model(input_ids)[0]
        expected_shape = torch.Size((1, 11, 768))
        self.assertEqual(output.shape, expected_shape)
        expected_slice = torch.tensor(
            [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]
        )

        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))