codingutf8 2021 the 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 testing suite for the pytorch rembert model import unittest from transformers import istorchavailable from transformers testingutils import requiretorch slow torchdevice from testconfigurationcommon import configtester from testmodelingcommon import modeltestermixin floatstensor idstensor randomattentionmask from testpipelinemixin import pipelinetestermixin if istorchavailable import torch from transformers import rembertconfig rembertforcausallm rembertformaskedlm rembertformultiplechoice rembertforquestionanswering rembertforsequenceclassification rembertfortokenclassification rembertmodel from transformers models rembert modelingrembert import rembertpretrainedmodelarchivelist class rembertmodeltester def init self parent batchsize13 seqlength7 istrainingtrue useinputmasktrue usetokentypeidstrue uselabelstrue vocabsize99 hiddensize32 inputembeddingsize18 outputembeddingsize43 numhiddenlayers2 numattentionheads4 intermediatesize37 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize16 typesequencelabelsize2 initializerrange0 02 numlabels3 numchoices4 scopenone self parent parent self batchsize batchsize self seqlength seqlength self istraining istraining self useinputmask useinputmask self usetokentypeids usetokentypeids self uselabels uselabels self vocabsize vocabsize self hiddensize hiddensize self inputembeddingsize inputembeddingsize self outputembeddingsize outputembeddingsize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self typesequencelabelsize typesequencelabelsize self initializerrange initializerrange self numlabels numlabels self numchoices numchoices self scope scope def prepareconfigandinputsself inputids idstensorself batchsize self seqlength self vocabsize inputmask none if self useinputmask inputmask randomattentionmaskself batchsize self seqlength tokentypeids none if self usetokentypeids tokentypeids idstensorself batchsize self seqlength self typevocabsize sequencelabels none tokenlabels none choicelabels none if self uselabels sequencelabels idstensorself batchsize self typesequencelabelsize tokenlabels idstensorself batchsize self seqlength self numlabels choicelabels idstensorself batchsize self numchoices config rembertconfig vocabsizeself vocabsize hiddensizeself hiddensize inputembeddingsizeself inputembeddingsize outputembeddingsizeself outputembeddingsize numhiddenlayersself numhiddenlayers numattentionheadsself numattentionheads intermediatesizeself intermediatesize hiddenactself hiddenact hiddendropoutprobself hiddendropoutprob attentionprobsdropoutprobself attentionprobsdropoutprob maxpositionembeddingsself maxpositionembeddings typevocabsizeself typevocabsize isdecoderfalse initializerrangeself initializerrange return config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels def prepareconfigandinputsfordecoderself config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels self prepareconfigandinputs config isdecoder true encoderhiddenstates floatstensorself batchsize self seqlength self hiddensize encoderattentionmask idstensorself batchsize self seqlength vocabsize2 return config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels encoderhiddenstates encoderattentionmask def createandcheckmodel self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels model rembertmodelconfigconfig model totorchdevice model eval result modelinputids attentionmaskinputmask tokentypeidstokentypeids result modelinputids tokentypeidstokentypeids result modelinputids self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckmodelasdecoder self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels encoderhiddenstates encoderattentionmask config addcrossattention true model rembertmodelconfig model totorchdevice model eval result model inputids attentionmaskinputmask tokentypeidstokentypeids encoderhiddenstatesencoderhiddenstates encoderattentionmaskencoderattentionmask result model inputids attentionmaskinputmask tokentypeidstokentypeids encoderhiddenstatesencoderhiddenstates result modelinputids attentionmaskinputmask tokentypeidstokentypeids self parent assertequalresult lasthiddenstate shape self batchsize self seqlength self hiddensize def createandcheckforcausallm self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels encoderhiddenstates encoderattentionmask model rembertforcausallmconfigconfig model totorchdevice model eval result modelinputids attentionmaskinputmask tokentypeidstokentypeids labelstokenlabels self parent assertequalresult logits shape self batchsize self seqlength self vocabsize def createandcheckformaskedlm self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels model rembertformaskedlmconfigconfig model totorchdevice model eval result modelinputids attentionmaskinputmask tokentypeidstokentypeids labelstokenlabels self parent assertequalresult logits shape self batchsize self seqlength self vocabsize def createandcheckdecodermodelpastlargeinputs self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels encoderhiddenstates encoderattentionmask config isdecoder true config addcrossattention true model rembertforcausallmconfigconfig model totorchdevice model eval first forward pass outputs model inputids attentionmaskinputmask encoderhiddenstatesencoderhiddenstates encoderattentionmaskencoderattentionmask usecachetrue pastkeyvalues outputs pastkeyvalues create hypothetical multiple next token and extent to nextinputids nexttokens idstensorself batchsize 3 config vocabsize nextmask idstensorself batchsize 3 vocabsize2 append to next inputids and nextinputids torch catinputids nexttokens dim1 nextattentionmask torch catinputmask nextmask dim1 outputfromnopast model nextinputids attentionmasknextattentionmask encoderhiddenstatesencoderhiddenstates encoderattentionmaskencoderattentionmask outputhiddenstatestrue hiddenstates0 outputfrompast model nexttokens attentionmasknextattentionmask encoderhiddenstatesencoderhiddenstates encoderattentionmaskencoderattentionmask pastkeyvaluespastkeyvalues outputhiddenstatestrue hiddenstates0 select random slice randomsliceidx idstensor1 outputfrompast shape1 item outputfromnopastslice outputfromnopast 3 randomsliceidx detach outputfrompastslice outputfrompast randomsliceidx detach self parent asserttrueoutputfrompastslice shape1 nexttokens shape1 test that outputs are equal for slice self parent asserttruetorch allcloseoutputfrompastslice outputfromnopastslice atol1e3 def createandcheckforquestionanswering self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels model rembertforquestionansweringconfigconfig model totorchdevice model eval result model inputids attentionmaskinputmask tokentypeidstokentypeids startpositionssequencelabels endpositionssequencelabels self parent assertequalresult startlogits shape self batchsize self seqlength self parent assertequalresult endlogits shape self batchsize self seqlength def createandcheckforsequenceclassification self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels config numlabels self numlabels model rembertforsequenceclassificationconfig model totorchdevice model eval result modelinputids attentionmaskinputmask tokentypeidstokentypeids labelssequencelabels self parent assertequalresult logits shape self batchsize self numlabels def createandcheckfortokenclassification self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels config numlabels self numlabels model rembertfortokenclassificationconfigconfig model totorchdevice model eval result modelinputids attentionmaskinputmask tokentypeidstokentypeids labelstokenlabels self parent assertequalresult logits shape self batchsize self seqlength self numlabels def createandcheckformultiplechoice self config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels config numchoices self numchoices model rembertformultiplechoiceconfigconfig model totorchdevice model eval multiplechoiceinputsids inputids unsqueeze1 expand1 self numchoices 1 contiguous multiplechoicetokentypeids tokentypeids unsqueeze1 expand1 self numchoices 1 contiguous multiplechoiceinputmask inputmask unsqueeze1 expand1 self numchoices 1 contiguous result model multiplechoiceinputsids attentionmaskmultiplechoiceinputmask tokentypeidsmultiplechoicetokentypeids labelschoicelabels self parent assertequalresult logits shape self batchsize self numchoices def prepareconfigandinputsforcommonself configandinputs self prepareconfigandinputs config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels configandinputs inputsdict inputids inputids tokentypeids tokentypeids attentionmask inputmask return config inputsdict requiretorch class rembertmodeltestmodeltestermixin pipelinetestermixin unittest testcase allmodelclasses rembertmodel rembertformaskedlm rembertforcausallm rembertformultiplechoice rembertforquestionanswering rembertforsequenceclassification rembertfortokenclassification if istorchavailable else allgenerativemodelclasses rembertforcausallm if istorchavailable else pipelinemodelmapping featureextraction rembertmodel fillmask rembertformaskedlm questionanswering rembertforquestionanswering textclassification rembertforsequenceclassification textgeneration rembertforcausallm tokenclassification rembertfortokenclassification zeroshot rembertforsequenceclassification if istorchavailable else def setupself self modeltester rembertmodeltesterself self configtester configtesterself configclassrembertconfig hiddensize37 def testconfigself self configtester runcommontests def testmodelself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckmodelconfigandinputs def testmodelvariousembeddingsself configandinputs self modeltester prepareconfigandinputs for type in absolute relativekey relativekeyquery configandinputs0 positionembeddingtype type self modeltester createandcheckmodelconfigandinputs def testformaskedlmself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckformaskedlmconfigandinputs def testformultiplechoiceself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckformultiplechoiceconfigandinputs def testdecodermodelpastwithlargeinputsself configandinputs self modeltester prepareconfigandinputsfordecoder self modeltester createandcheckdecodermodelpastlargeinputsconfigandinputs def testforquestionansweringself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforquestionansweringconfigandinputs def testforsequenceclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckforsequenceclassificationconfigandinputs def testfortokenclassificationself configandinputs self modeltester prepareconfigandinputs self modeltester createandcheckfortokenclassificationconfigandinputs def testmodelasdecoderself configandinputs self modeltester prepareconfigandinputsfordecoder self modeltester createandcheckmodelasdecoderconfigandinputs def testmodelasdecoderwithdefaultinputmaskself this regression test was failing with pytorch 1 3 config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels encoderhiddenstates encoderattentionmask self modeltester prepareconfigandinputsfordecoder inputmask none self modeltester createandcheckmodelasdecoder config inputids tokentypeids inputmask sequencelabels tokenlabels choicelabels encoderhiddenstates encoderattentionmask slow def testmodelfrompretrainedself for modelname in rembertpretrainedmodelarchivelist 1 model rembertmodel frompretrainedmodelname self assertisnotnonemodel requiretorch class rembertmodelintegrationtestunittest testcase slow def testinferencemodelself test exact values at the last hidden layer model rembertmodel frompretrainedgooglerembert inputids torch tensor312 56498 313 2125 313 segmentids torch tensor0 0 0 1 1 with torch nograd output modelinputids tokentypeidssegmentids outputhiddenstatestrue hiddensize 1152 expectedshape torch size1 5 hiddensize self assertequaloutputlasthiddenstate shape expectedshape expectedimplementation torch tensor 0 0754 0 2022 0 1904 0 3354 0 3692 0 4791 0 2314 0 6729 0 0749 0 0396 0 3105 0 4234 0 1571 0 0525 0 5353 running on the original tf implementation gives slightly different results here not clear why this variations is present todo find reason for discrepancy expectedoriginalimplementation 0 07630594074726105 0 20146065950393677 0 19107051193714142 0 3405614495277405 0 36971670389175415 0 4808273911476135 0 22587086260318756 0 6656315922737122 0 07844287157058716 0 04145475849509239 0 3077218234539032 0 42316967248916626 0 15887849032878876 0 054529931396245956 0 5356100797653198 self asserttruetorch allcloseoutputlasthiddenstate 3 expectedimplementation atol1e4 coding utf 8 2021 the 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 testing suite for the pytorch rembert model first forward pass create hypothetical multiple next token and extent to next_input_ids append to next input_ids and select random slice test that outputs are equal for slice this regression test was failing with pytorch 1 3 test exact values at the last hidden layer running on the original tf implementation gives slightly different results here not clear why this variations is present todo find reason for discrepancy expected_original_implementation 0 07630594074726105 0 20146065950393677 0 19107051193714142 0 3405614495277405 0 36971670389175415 0 4808273911476135 0 22587086260318756 0 6656315922737122 0 07844287157058716 0 04145475849509239 0 3077218234539032 0 42316967248916626 0 15887849032878876 0 054529931396245956 0 5356100797653198
import unittest

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

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


if is_torch_available():
    import torch

    from transformers import (
        RemBertConfig,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertModel,
    )
    from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class RemBertModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_token_type_ids=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        input_embedding_size=18,
        output_embedding_size=43,
        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_input_mask = use_input_mask
        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.input_embedding_size = input_embedding_size
        self.output_embedding_size = output_embedding_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

    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)

        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 = RemBertConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            input_embedding_size=self.input_embedding_size,
            output_embedding_size=self.output_embedding_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            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,
            max_position_embeddings=self.max_position_embeddings,
            type_vocab_size=self.type_vocab_size,
            is_decoder=False,
            initializer_range=self.initializer_range,
        )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def prepare_config_and_inputs_for_decoder(self):
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = self.prepare_config_and_inputs()

        config.is_decoder = True
        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

    def create_and_check_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = RemBertModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        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_model_as_decoder(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.add_cross_attention = True
        model = RemBertModel(config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
        )
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_ids,
            encoder_hidden_states=encoder_hidden_states,
        )
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_causal_lm(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        model = RemBertForCausalLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_for_masked_lm(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = RemBertForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_decoder_model_past_large_inputs(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    ):
        config.is_decoder = True
        config.add_cross_attention = True
        model = RemBertForCausalLM(config=config)
        model.to(torch_device)
        model.eval()

        
        outputs = model(
            input_ids,
            attention_mask=input_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values

        
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

        
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

        output_from_no_past = model(
            next_input_ids,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_hidden_states=True,
        )["hidden_states"][0]
        output_from_past = model(
            next_tokens,
            attention_mask=next_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            output_hidden_states=True,
        )["hidden_states"][0]

        
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

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

    def create_and_check_for_question_answering(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = RemBertForQuestionAnswering(config=config)
        model.to(torch_device)
        model.eval()
        result = model(
            input_ids,
            attention_mask=input_mask,
            token_type_ids=token_type_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_for_sequence_classification(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = RemBertForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

    def create_and_check_for_token_classification(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = RemBertForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_for_multiple_choice(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_choices = self.num_choices
        model = RemBertForMultipleChoice(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_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class RemBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            RemBertModel,
            RemBertForMaskedLM,
            RemBertForCausalLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    all_generative_model_classes = (RemBertForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": RemBertModel,
            "fill-mask": RemBertForMaskedLM,
            "question-answering": RemBertForQuestionAnswering,
            "text-classification": RemBertForSequenceClassification,
            "text-generation": RemBertForCausalLM,
            "token-classification": RemBertForTokenClassification,
            "zero-shot": RemBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    def setUp(self):
        self.model_tester = RemBertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RemBertConfig, hidden_size=37)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_model_various_embeddings(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            config_and_inputs[0].position_embedding_type = type
            self.model_tester.create_and_check_model(*config_and_inputs)

    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)

    def test_for_multiple_choice(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_for_question_answering(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)

    def test_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)

    def test_for_token_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)

    def test_model_as_decoder(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)

    def test_model_as_decoder_with_default_input_mask(self):
        
        (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        ) = self.model_tester.prepare_config_and_inputs_for_decoder()

        input_mask = None

        self.model_tester.create_and_check_model_as_decoder(
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

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


@require_torch
class RemBertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_model(self):
        
        model = RemBertModel.from_pretrained("google/rembert")
        input_ids = torch.tensor([[312, 56498, 313, 2125, 313]])
        segment_ids = torch.tensor([[0, 0, 0, 1, 1]])
        with torch.no_grad():
            output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True)

        hidden_size = 1152

        expected_shape = torch.Size((1, 5, hidden_size))
        self.assertEqual(output["last_hidden_state"].shape, expected_shape)

        expected_implementation = torch.tensor(
            [
                [
                    [0.0754, -0.2022, 0.1904],
                    [-0.3354, -0.3692, -0.4791],
                    [-0.2314, -0.6729, -0.0749],
                    [-0.0396, -0.3105, -0.4234],
                    [-0.1571, -0.0525, 0.5353],
                ]
            ]
        )

        
        
        
        
        
        
        
        
        
        

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