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 self keylen seqlength memlen first forward pass create hypothetical next token and extent to nextinputids append to next inputids and tokentypeids causal mask second forward pass select random slice test that outputs are equal for slice todo fix the failed tests xlnet has 2 qa models need to manually set the correct labels for one of them here checking that in autoregressive mode usemems gives the same results xlnet cannot keep gradients in attentions or hidden states overwrite from testmodelingcommon check hidden size every 2nd tensor is from extra stream for first item dummy pad token is appended so need one more for first item dummy pad token is appended so need one more check attn size fmt off fmt on in 1991 the remains of russian tsar nicholas ii and his family except for alexei and maria are discovered the voice of nicholas s young son tsarevich alexei nikolaevich narrates the remainder of the story 1883 western siberia a young grigori rasputin is asked by his father and a group of men to perform magic rasputin has a vision and denounces one of the men as a horse thief although his father initially slaps him for making such an accusation rasputin watches as the man is chased outside and beaten twenty years later rasputin sees a vision of the virgin mary prompting him to become a priest rasputin quickly becomes famous with people even a bishop begging for his blessing fmt off fmt on in 1991 the remains of russian tsar nicholas ii and his family except for alexei and maria are discovered the voice of nicholas s young son tsarevich alexei nikolaevich narrates the remainder of the story 1883 western siberia a young grigori rasputin is asked by his father and a group of men to perform magic rasputin has a vision and denounces one of the men as a horse thief although his father initially slaps him for making such an accusation rasputin watches as the man is chased outside and beaten twenty years later rasputin sees a vision of the virgin mary prompting him to become a priest rasputin quickly becomes famous with people even a bishop begging for his blessing sepcls rasputin is asked to perform magic he is asked to perform a ritual of the virgin mary he is asked to perform a ritual of the virgin mary he is asked to perform 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 self key_len seq_length mem_len previous tokens don t see last token predict last token first forward pass create hypothetical next token and extent to next_input_ids append to next input_ids and token_type_ids causal mask second forward pass select random slice test that outputs are equal for slice todo pvp check other models whether language generation is also applicable todo fix the failed tests xlnet has 2 qa models need to manually set the correct labels for one of them here checking that in auto regressive mode use_mems gives the same results xlnet cannot keep gradients in attentions or hidden states overwrite from test_modeling_common check hidden size every 2nd tensor is from extra stream for first item dummy pad token is appended so need one more for first item dummy pad token is appended so need one more check attn size fmt off fmt on in 1991 the remains of russian tsar nicholas ii and his family except for alexei and maria are discovered the voice of nicholas s young son tsarevich alexei nikolaevich narrates the remainder of the story 1883 western siberia a young grigori rasputin is asked by his father and a group of men to perform magic rasputin has a vision and denounces one of the men as a horse thief although his father initially slaps him for making such an accusation rasputin watches as the man is chased outside and beaten twenty years later rasputin sees a vision of the virgin mary prompting him to become a priest rasputin quickly becomes famous with people even a bishop begging for his blessing fmt off fmt on in 1991 the remains of russian tsar nicholas ii and his family except for alexei and maria are discovered the voice of nicholas s young son tsarevich alexei nikolaevich narrates the remainder of the story 1883 western siberia a young grigori rasputin is asked by his father and a group of men to perform magic rasputin has a vision and denounces one of the men as a horse thief although his father initially slaps him for making such an accusation rasputin watches as the man is chased outside and beaten twenty years later rasputin sees a vision of the virgin mary prompting him to become a priest rasputin quickly becomes famous with people even a bishop begging for his blessing sep cls rasputin is asked to perform magic he is asked to perform a ritual of the virgin mary he is asked to perform a ritual of the virgin mary he is asked to perform
import random
import unittest

from transformers import XLNetConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
    )
    from transformers.models.xlnet.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST


class XLNetModelTester:
    def __init__(
        self,
        parent,
        batch_size=14,
        seq_length=7,
        mem_len=10,
        clamp_len=-1,
        reuse_len=15,
        is_training=True,
        use_labels=True,
        vocab_size=99,
        cutoffs=[10, 50, 80],
        hidden_size=32,
        num_attention_heads=4,
        d_inner=128,
        num_hidden_layers=2,
        type_sequence_label_size=2,
        untie_r=True,
        bi_data=False,
        same_length=False,
        initializer_range=0.05,
        seed=1,
        type_vocab_size=2,
        bos_token_id=1,
        eos_token_id=2,
        pad_token_id=5,
        num_choices=4,
    ):
        self.parent = parent
        self.batch_size = 14
        self.seq_length = 7
        self.mem_len = 10
        
        self.clamp_len = -1
        self.reuse_len = 15
        self.is_training = True
        self.use_labels = True
        self.vocab_size = 99
        self.cutoffs = [10, 50, 80]
        self.hidden_size = 32
        self.num_attention_heads = 4
        self.d_inner = 128
        self.num_hidden_layers = 5
        self.type_sequence_label_size = 2
        self.untie_r = True
        self.bi_data = False
        self.same_length = False
        self.initializer_range = 0.05
        self.seed = 1
        self.type_vocab_size = 2
        self.bos_token_id = 1
        self.eos_token_id = 2
        self.pad_token_id = 5
        self.num_choices = 4

    def prepare_config_and_inputs(self):
        input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
        segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
        input_mask = random_attention_mask([self.batch_size, self.seq_length])

        input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
        perm_mask = torch.zeros(
            self.batch_size,
            self.seq_length + 1,
            self.seq_length + 1,
            dtype=torch.float,
            device=torch_device,
        )
        perm_mask[:, :, -1] = 1.0  
        target_mapping = torch.zeros(
            self.batch_size,
            1,
            self.seq_length + 1,
            dtype=torch.float,
            device=torch_device,
        )
        target_mapping[:, 0, -1] = 1.0  

        sequence_labels = None
        lm_labels = None
        is_impossible_labels = None
        token_labels = None
        if self.use_labels:
            lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            is_impossible_labels = ids_tensor([self.batch_size], 2).float()
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = self.get_config()

        return (
            config,
            input_ids_1,
            input_ids_2,
            input_ids_q,
            perm_mask,
            input_mask,
            target_mapping,
            segment_ids,
            lm_labels,
            sequence_labels,
            is_impossible_labels,
            token_labels,
        )

    def get_config(self):
        return XLNetConfig(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            n_head=self.num_attention_heads,
            d_inner=self.d_inner,
            n_layer=self.num_hidden_layers,
            untie_r=self.untie_r,
            mem_len=self.mem_len,
            clamp_len=self.clamp_len,
            same_length=self.same_length,
            reuse_len=self.reuse_len,
            bi_data=self.bi_data,
            initializer_range=self.initializer_range,
            num_labels=self.type_sequence_label_size,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            eos_token_id=self.eos_token_id,
        )

    def set_seed(self):
        random.seed(self.seed)
        torch.manual_seed(self.seed)

    def create_and_check_xlnet_base_model(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1, input_mask=input_mask)
        result = model(input_ids_1, attention_mask=input_mask)
        result = model(input_ids_1, token_type_ids=segment_ids)
        result = model(input_ids_1)

        config.mem_len = 0
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()
        base_model_output = model(input_ids_1)
        self.parent.assertEqual(len(base_model_output), 2)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_use_mems_train(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForSequenceClassification(config)
        model.to(torch_device)
        model.train()

        train_size = input_ids_1.shape[0]

        batch_size = 4
        for i in range(train_size // batch_size + 1):
            input_ids = input_ids_1[i : (i + 1) * batch_size]
            labels = sequence_labels[i : (i + 1) * batch_size]
            outputs = model(input_ids=input_ids, labels=labels, return_dict=True)
            self.parent.assertIsNone(outputs.mems)
            self.parent.assertIsNotNone(outputs.loss)

    def create_and_check_xlnet_model_use_mems(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config=config)
        model.to(torch_device)
        model.eval()

        
        causal_mask = torch.ones(
            input_ids_1.shape[0],
            input_ids_1.shape[1],
            input_ids_1.shape[1],
            dtype=torch.float,
            device=torch_device,
        )
        causal_mask = torch.triu(causal_mask, diagonal=0)
        outputs_cache = model(input_ids_1, use_mems=True, perm_mask=causal_mask)
        outputs_no_cache = model(input_ids_1, use_mems=False, perm_mask=causal_mask)
        outputs_conf = model(input_ids_1)

        self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
        self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)

        output, mems = outputs_cache.to_tuple()

        
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        
        next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)

        
        causal_mask = torch.ones(
            input_ids_1.shape[0],
            input_ids_1.shape[1] + 1,
            input_ids_1.shape[1] + 1,
            dtype=torch.float,
            device=torch_device,
        )
        causal_mask = torch.triu(causal_mask, diagonal=0)
        single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)

        
        output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]

        
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

        
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

    def create_and_check_xlnet_base_model_with_att_output(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetModel(config)
        model.to(torch_device)
        model.eval()

        attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]

        self.parent.assertEqual(len(attentions), config.n_layer)
        self.parent.assertIsInstance(attentions[0], tuple)
        self.parent.assertEqual(len(attentions[0]), 2)
        self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)

    def create_and_check_xlnet_lm_head(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetLMHeadModel(config)
        model.to(torch_device)
        model.eval()

        result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)

        result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)

        _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)

        self.parent.assertEqual(result1.loss.shape, ())
        self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result1.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

        self.parent.assertEqual(result2.loss.shape, ())
        self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result2.mems],
            [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_qa(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForQuestionAnswering(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)

        result_with_labels = model(
            input_ids_1,
            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_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
            cls_index=sequence_labels,
            is_impossible=is_impossible_labels,
        )

        total_loss, mems = result_with_labels.to_tuple()

        result_with_labels = model(
            input_ids_1,
            start_positions=sequence_labels,
            end_positions=sequence_labels,
        )

        total_loss, mems = 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,))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_token_classif(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForTokenClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)
        result = model(input_ids_1, labels=token_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def create_and_check_xlnet_sequence_classif(
        self,
        config,
        input_ids_1,
        input_ids_2,
        input_ids_q,
        perm_mask,
        input_mask,
        target_mapping,
        segment_ids,
        lm_labels,
        sequence_labels,
        is_impossible_labels,
        token_labels,
    ):
        model = XLNetForSequenceClassification(config)
        model.to(torch_device)
        model.eval()

        result = model(input_ids_1)
        result = model(input_ids_1, labels=sequence_labels)

        self.parent.assertEqual(result.loss.shape, ())
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
        self.parent.assertListEqual(
            [mem.shape for mem in result.mems],
            [(self.seq_length, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids_1,
            input_ids_2,
            input_ids_q,
            perm_mask,
            input_mask,
            target_mapping,
            segment_ids,
            lm_labels,
            sequence_labels,
            is_impossible_labels,
            token_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids_1}
        return config, inputs_dict


@require_torch
class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            XLNetModel,
            XLNetLMHeadModel,
            XLNetForTokenClassification,
            XLNetForSequenceClassification,
            XLNetForQuestionAnswering,
            XLNetForQuestionAnsweringSimple,
            XLNetForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (
        (XLNetLMHeadModel,) if is_torch_available() else ()
    )  
    pipeline_model_mapping = (
        {
            "feature-extraction": XLNetModel,
            "question-answering": XLNetForQuestionAnsweringSimple,
            "text-classification": XLNetForSequenceClassification,
            "text-generation": XLNetLMHeadModel,
            "token-classification": XLNetForTokenClassification,
            "zero-shot": XLNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    fx_compatible = False
    test_pruning = 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 == "QAPipelineTests" 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__ == "XLNetForQuestionAnswering":
                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 = XLNetModelTester(self)
        self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)

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

    def test_xlnet_base_model(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)

    def test_xlnet_base_model_use_mems(self):
        
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_model_use_mems(*config_and_inputs)

    def test_seq_classification_use_mems_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_use_mems_train(*config_and_inputs)

    def test_xlnet_base_model_with_att_output(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)

    def test_xlnet_lm_head(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)

    def test_xlnet_sequence_classif(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)

    def test_xlnet_token_classif(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)

    def test_xlnet_qa(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)

    def test_retain_grad_hidden_states_attentions(self):
        
        return

    
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)

        for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
            if hasattr(module, param) and getattr(module, param) is not None:
                weight = getattr(module, param)
                weight.data.fill_(3)

    def _check_hidden_states_for_generate(
        self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(hidden_states, tuple)
        self.assertListEqual(
            [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
            [True] * len(hidden_states),
        )
        self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

        for idx, iter_hidden_states in enumerate(hidden_states):
            
            for i, layer_hidden_states in enumerate(iter_hidden_states):
                
                if i % 2 != 0:
                    seq_len = 1
                else:
                    
                    seq_len = (min_length + 1) if idx == 0 else min_length

                expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
                self.assertEqual(layer_hidden_states.shape, expected_shape)

    def _check_attentions_for_generate(
        self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(attentions, tuple)
        self.assertListEqual(
            [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
        )
        self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

        for idx, attentions_item in enumerate(attentions):
            for iter_attentions in attentions_item:
                tgt_len = min_length

                
                if idx == 0:
                    tgt_len += 1

                src_len = min_length + idx + 1

                expected_shape = (
                    batch_size * num_beam_groups,
                    config.num_attention_heads,
                    tgt_len,
                    src_len,
                )
                
                self.assertListEqual(
                    [layer_attention.shape for layer_attention in iter_attentions],
                    [expected_shape] * len(iter_attentions),
                )

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


@require_torch
class XLNetModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_xlnet_base_cased(self):
        model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
        model.to(torch_device)
        
        input_ids = torch.tensor(
            [
                [
                    67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3,
                ]
            ],
            dtype=torch.long,
            device=torch_device,
        )
        
        
        
        
        
        
        
        
        
        
        

        
        expected_output_ids = [
            67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153, 27, 442, 37, 45, 668, 21, 24, 256, 20, 416, 22, 2771, 4901, 9, 12943, 4354, 153, 51, 24, 3004, 21, 28142, 23, 65, 20, 18, 416, 34, 24, 2958, 22947, 9, 1177, 45, 668, 3097, 13768, 23, 103, 28, 441, 148, 48, 20522, 19, 12943, 4354, 153, 12860, 34, 18, 326, 27, 17492, 684, 21, 6709, 9, 8585, 123, 266, 19, 12943, 4354, 153, 6872, 24, 3004, 20, 18, 9225, 2198, 19, 12717, 103, 22, 401, 24, 6348, 9, 12943, 4354, 153, 1068, 2768, 2286, 19, 33, 104, 19, 176, 24, 9313, 19, 20086, 28, 45, 10292, 9, 4, 3, 19, 12943, 4354, 153, 27, 442, 22, 2771, 4901, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771, 24, 11335, 20, 18, 9225, 2198, 9, 69, 27, 442, 22, 2771,
        ]
        
        
        
        
        
        
        
        
        
        
        

        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)