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 intermediatesizeself intermediatesize 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 openaigptconfig was never used in pipeline tests either because of a missing checkpoint or because a tiny config could not be created special case for doubleheads model 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 intermediate_size self intermediate_size 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 pvp add double headsmodel when generate function is changed accordingly todo fix the failed tests get tokenizer does not have a padding token error for both fast slow tokenizers openaigptconfig was never used in pipeline tests either because of a missing checkpoint or because a tiny config could not be created special case for doubleheads model the president is the president is a very good man n i m sure he is said the
import unittest

from transformers import is_torch_available
from transformers.testing_utils import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTConfig,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
    )


class OpenAIGPTModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_token_type_ids=True,
        use_labels=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_labels = use_labels
        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)

        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)

        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 = OpenAIGPTConfig(
            vocab_size=self.vocab_size,
            n_embd=self.hidden_size,
            n_layer=self.num_hidden_layers,
            n_head=self.num_attention_heads,
            
            
            
            
            n_positions=self.max_position_embeddings,
            
            
            pad_token_id=self.pad_token_id,
        )

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

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

    def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
        model = OpenAIGPTModel(config=config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
        result = 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))

    def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
        model = OpenAIGPTLMHeadModel(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 create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
        model = OpenAIGPTDoubleHeadsModel(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 create_and_check_openai_gpt_for_sequence_classification(
        self, config, input_ids, head_mask, token_type_ids, *args
    ):
        config.num_labels = self.num_labels
        model = OpenAIGPTForSequenceClassification(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))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            head_mask,
            token_type_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


@require_torch
class OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (
        (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
    )  
    pipeline_model_mapping = (
        {
            "feature-extraction": OpenAIGPTModel,
            "text-classification": OpenAIGPTForSequenceClassification,
            "text-generation": OpenAIGPTLMHeadModel,
            "zero-shot": OpenAIGPTForSequenceClassification,
        }
        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 == "ZeroShotClassificationPipelineTests":
            
            
            
            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__ == "OpenAIGPTDoubleHeadsModel":
                inputs_dict["labels"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
                    dtype=torch.long,
                    device=torch_device,
                )
                inputs_dict["input_ids"] = inputs_dict["labels"]
                inputs_dict["token_type_ids"] = inputs_dict["labels"]
                inputs_dict["mc_token_ids"] = torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.num_choices),
                    dtype=torch.long,
                    device=torch_device,
                )
                inputs_dict["mc_labels"] = torch.zeros(
                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
                )
        return inputs_dict

    def setUp(self):
        self.model_tester = OpenAIGPTModelTester(self)
        self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)

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

    def test_openai_gpt_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)

    def test_openai_gpt_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)

    def test_openai_gpt_double_lm_head_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)

    def test_openai_gpt_classification_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)

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


@require_torch
class OPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_openai_gpt(self):
        model = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
        model.to(torch_device)
        input_ids = torch.tensor([[481, 4735, 544]], dtype=torch.long, device=torch_device)  
        expected_output_ids = [
            481,
            4735,
            544,
            246,
            963,
            870,
            762,
            239,
            244,
            40477,
            244,
            249,
            719,
            881,
            487,
            544,
            240,
            244,
            603,
            481,
        ]  

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