codingutf8 2018 salesforce and huggingface inc 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 hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob typevocabsizeself typevocabsize initializerrangeself initializerrange todo fix the failed tests get tokenizer does not have a padding token error for both fastslow tokenizers ctrlconfig was never used in pipeline tests either because of a missing checkpoint or because a tiny config could not be created cleanup as much as possible gpu memory occupied by pytorch cleanup as much as possible gpu memory occupied by pytorch coding utf 8 2018 salesforce and huggingface inc 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 hidden_act self hidden_act hidden_dropout_prob self hidden_dropout_prob attention_probs_dropout_prob self attention_probs_dropout_prob type_vocab_size self type_vocab_size initializer_range self initializer_range todo fix the failed tests get tokenizer does not have a padding token error for both fast slow tokenizers ctrlconfig was never used in pipeline tests either because of a missing checkpoint or because a tiny config could not be created clean up as much as possible gpu memory occupied by pytorch and it s not used enough to be worth fixing clean up as much as possible gpu memory occupied by pytorch legal the president is legal the president is a good guy and i don t want to lose my job n n i have a
import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
    )


class CTRLModelTester:
    def __init__(
        self,
        parent,
        batch_size=14,
        seq_length=7,
        is_training=True,
        use_token_type_ids=True,
        use_input_mask=True,
        use_labels=True,
        use_mc_token_ids=True,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_labels=3,
        num_choices=4,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_token_type_ids = use_token_type_ids
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.use_mc_token_ids = use_mc_token_ids
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        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.scope = scope
        self.pad_token_id = self.vocab_size - 1

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

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

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

        mc_token_ids = None
        if self.use_mc_token_ids:
            mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)

        sequence_labels = None
        token_labels = None
        choice_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)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)

        return (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )

    def get_config(self):
        return CTRLConfig(
            vocab_size=self.vocab_size,
            n_embd=self.hidden_size,
            n_layer=self.num_hidden_layers,
            n_head=self.num_attention_heads,
            dff=self.intermediate_size,
            
            
            
            n_positions=self.max_position_embeddings,
            
            
            pad_token_id=self.pad_token_id,
        )

    def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
        model = CTRLModel(config=config)
        model.to(torch_device)
        model.eval()

        model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
        model(input_ids, token_type_ids=token_type_ids)
        result = model(input_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(len(result.past_key_values), config.n_layer)

    def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
        model = CTRLLMHeadModel(config)
        model.to(torch_device)
        model.eval()

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

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()

        (
            config,
            input_ids,
            input_mask,
            head_mask,
            token_type_ids,
            mc_token_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs

        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

        return config, inputs_dict

    def create_and_check_ctrl_for_sequence_classification(self, config, input_ids, head_mask, token_type_ids, *args):
        config.num_labels = self.num_labels
        model = CTRLForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
        result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))


@require_torch
class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
    all_generative_model_classes = (CTRLLMHeadModel,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": CTRLModel,
            "text-classification": CTRLForSequenceClassification,
            "text-generation": CTRLLMHeadModel,
            "zero-shot": CTRLForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_pruning = True
    test_resize_embeddings = False
    test_head_masking = False

    
    def is_pipeline_test_to_skip(
        self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
    ):
        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
            
            
            
            return True

        return False

    def setUp(self):
        self.model_tester = CTRLModelTester(self)
        self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)

    def tearDown(self):
        super().tearDown()
        
        gc.collect()
        backend_empty_cache(torch_device)

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

    def test_ctrl_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*config_and_inputs)

    def test_ctrl_lm_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*config_and_inputs)

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

    @unittest.skip("The model doesn't support left padding")  
    def test_left_padding_compatibility(self):
        pass


@require_torch
class CTRLModelLanguageGenerationTest(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        
        gc.collect()
        backend_empty_cache(torch_device)

    @slow
    def test_lm_generate_ctrl(self):
        model = CTRLLMHeadModel.from_pretrained("Salesforce/ctrl")
        model.to(torch_device)
        input_ids = torch.tensor(
            [[11859, 0, 1611, 8]], dtype=torch.long, device=torch_device
        )  
        expected_output_ids = [
            11859,
            0,
            1611,
            8,
            5,
            150,
            26449,
            2,
            19,
            348,
            469,
            3,
            2595,
            48,
            20740,
            246533,
            246533,
            19,
            30,
            5,
        ]  

        output_ids = model.generate(input_ids, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)