# coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.


from __future__ import annotations

import unittest

from transformers import RobertaPreLayerNormConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy
    import tensorflow as tf

    from transformers.models.roberta_prelayernorm.modeling_tf_roberta_prelayernorm import (
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormModel,
    )


# Copied from tests.models.roberta.test_modeling_tf_roberta.TFRobertaModelTester with Roberta->RobertaPreLayerNorm
class TFRobertaPreLayerNormModelTester:
    def __init__(
        self,
        parent,
    ):
        self.parent = parent
        self.batch_size = 13
        self.seq_length = 7
        self.is_training = True
        self.use_input_mask = True
        self.use_token_type_ids = True
        self.use_labels = True
        self.vocab_size = 99
        self.hidden_size = 32
        self.num_hidden_layers = 2
        self.num_attention_heads = 4
        self.intermediate_size = 37
        self.hidden_act = "gelu"
        self.hidden_dropout_prob = 0.1
        self.attention_probs_dropout_prob = 0.1
        self.max_position_embeddings = 512
        self.type_vocab_size = 16
        self.type_sequence_label_size = 2
        self.initializer_range = 0.02
        self.num_labels = 3
        self.num_choices = 4
        self.scope = None

    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 = RobertaPreLayerNormConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_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,
            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 = TFRobertaPreLayerNormModel(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs)

        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_causal_lm_base_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.is_decoder = True

        model = TFRobertaPreLayerNormModel(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs)

        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 = TFRobertaPreLayerNormModel(config=config)
        inputs = {
            "input_ids": 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(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)

        # Also check the case where encoder outputs are not passed
        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_causal_lm_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.is_decoder = True

        model = TFRobertaPreLayerNormForCausalLM(config=config)
        inputs = {
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        prediction_scores = model(inputs)["logits"]
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
        )

    def create_and_check_causal_lm_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 = TFRobertaPreLayerNormForCausalLM(config=config)
        inputs = {
            "input_ids": 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(inputs)

        inputs = [input_ids, input_mask]
        result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)

        prediction_scores = result["logits"]
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
        )

    def create_and_check_causal_lm_model_past(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.is_decoder = True

        model = TFRobertaPreLayerNormForCausalLM(config=config)

        # special to `RobertaPreLayerNormEmbeddings` in `RobertaPreLayerNorm`:
        #   - its `padding_idx` and its effect on `position_ids`
        #     (TFRobertaPreLayerNormEmbeddings.create_position_ids_from_input_ids)
        #   - `1` here is `TFRobertaPreLayerNormEmbeddings.padding_idx`
        input_ids = tf.where(input_ids == 1, 2, input_ids)

        # first forward pass
        outputs = model(input_ids, use_cache=True)
        outputs_use_cache_conf = model(input_ids)
        outputs_no_past = model(input_ids, use_cache=False)

        self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
        self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)

        past_key_values = outputs.past_key_values

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        # append to next input_ids and attn_mask
        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)

        output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
        output_from_past = model(
            next_tokens, past_key_values=past_key_values, output_hidden_states=True
        ).hidden_states[0]

        # select random slice
        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
        output_from_past_slice = output_from_past[:, 0, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)

    def create_and_check_causal_lm_model_past_with_attn_mask(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.is_decoder = True

        model = TFRobertaPreLayerNormForCausalLM(config=config)

        # special to `RobertaPreLayerNormEmbeddings` in `RobertaPreLayerNorm`:
        #   - its `padding_idx` and its effect on `position_ids`
        #     (TFRobertaPreLayerNormEmbeddings.create_position_ids_from_input_ids)
        #   - `1` here is `TFRobertaPreLayerNormEmbeddings.padding_idx`
        # avoid `padding_idx` in the past
        input_ids = tf.where(input_ids == 1, 2, input_ids)

        # create attention mask
        half_seq_length = self.seq_length // 2
        attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
        attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
        attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)

        # first forward pass
        outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

        past_key_values = outputs.past_key_values

        # change a random masked slice from input_ids
        random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
        random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
        vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
        condition = tf.transpose(
            tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
        )
        input_ids = tf.where(condition, random_other_next_tokens, input_ids)
        # avoid `padding_idx` in the past
        input_ids = tf.where(input_ids == 1, 2, input_ids)

        # append to next input_ids and
        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
        attn_mask = tf.concat(
            [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
            axis=1,
        )

        output_from_no_past = model(
            next_input_ids,
            attention_mask=attn_mask,
            output_hidden_states=True,
        ).hidden_states[0]
        output_from_past = model(
            next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
        ).hidden_states[0]

        # select random slice
        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
        output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
        output_from_past_slice = output_from_past[:, 0, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)

    def create_and_check_causal_lm_model_past_large_inputs(
        self,
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ):
        config.is_decoder = True

        model = TFRobertaPreLayerNormForCausalLM(config=config)

        # special to `RobertaPreLayerNormEmbeddings` in `RobertaPreLayerNorm`:
        #   - its `padding_idx` and its effect on `position_ids`
        #     (TFRobertaPreLayerNormEmbeddings.create_position_ids_from_input_ids)
        #   - `1` here is `TFRobertaPreLayerNormEmbeddings.padding_idx`
        # avoid `padding_idx` in the past
        input_ids = tf.where(input_ids == 1, 2, input_ids)

        input_ids = input_ids[:1, :]
        input_mask = input_mask[:1, :]
        self.batch_size = 1

        # first forward pass
        outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
        past_key_values = outputs.past_key_values

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_attn_mask = ids_tensor((self.batch_size, 3), 2)

        # append to next input_ids and
        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
        next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)

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

        self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])

        # select random slice
        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
        output_from_past_slice = output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)

    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.add_cross_attention = True

        model = TFRobertaPreLayerNormForCausalLM(config=config)

        # special to `RobertaPreLayerNormEmbeddings` in `RobertaPreLayerNorm`:
        #   - its `padding_idx` and its effect on `position_ids`
        #     (TFRobertaPreLayerNormEmbeddings.create_position_ids_from_input_ids)
        #   - `1` here is `TFRobertaPreLayerNormEmbeddings.padding_idx`
        # avoid `padding_idx` in the past
        input_ids = tf.where(input_ids == 1, 2, input_ids)

        input_ids = input_ids[:1, :]
        input_mask = input_mask[:1, :]
        encoder_hidden_states = encoder_hidden_states[:1, :, :]
        encoder_attention_mask = encoder_attention_mask[:1, :]
        self.batch_size = 1

        # first forward pass
        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

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_attn_mask = ids_tensor((self.batch_size, 3), 2)

        # append to next input_ids and
        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
        next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-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]

        self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])

        # select random slice
        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
        output_from_past_slice = output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)

    def create_and_check_for_masked_lm(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = TFRobertaPreLayerNormForMaskedLM(config=config)
        result = model([input_ids, input_mask, token_type_ids])
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    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 = TFRobertaPreLayerNormForTokenClassification(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_for_question_answering(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = TFRobertaPreLayerNormForQuestionAnswering(config=config)
        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        result = model(inputs)
        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_multiple_choice(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_choices = self.num_choices
        model = TFRobertaPreLayerNormForMultipleChoice(config=config)
        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
        inputs = {
            "input_ids": multiple_choice_inputs_ids,
            "attention_mask": multiple_choice_input_mask,
            "token_type_ids": multiple_choice_token_type_ids,
        }
        result = model(inputs)
        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_tf
# Copied from tests.models.roberta.test_modeling_tf_roberta.TFRobertaModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,FacebookAI/roberta-base->andreasmadsen/efficient_mlm_m0.15
class TFRobertaPreLayerNormModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormForQuestionAnswering,
        )
        if is_tf_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": TFRobertaPreLayerNormModel,
            "fill-mask": TFRobertaPreLayerNormForMaskedLM,
            "question-answering": TFRobertaPreLayerNormForQuestionAnswering,
            "text-classification": TFRobertaPreLayerNormForSequenceClassification,
            "text-generation": TFRobertaPreLayerNormForCausalLM,
            "token-classification": TFRobertaPreLayerNormForTokenClassification,
            "zero-shot": TFRobertaPreLayerNormForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    test_head_masking = False
    test_onnx = False

    def setUp(self):
        self.model_tester = TFRobertaPreLayerNormModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RobertaPreLayerNormConfig, hidden_size=37)

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

    def test_model(self):
        """Test the base model"""
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_causal_lm_base_model(self):
        """Test the base model of the causal LM model

        is_deocder=True, no cross_attention, no encoder outputs
        """
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)

    def test_model_as_decoder(self):
        """Test the base model as a decoder (of an encoder-decoder architecture)

        is_deocder=True + cross_attention + pass encoder outputs
        """
        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_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_causal_lm(self):
        """Test the causal LM model"""
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)

    def test_causal_lm_model_as_decoder(self):
        """Test the causal LM model as a decoder"""
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)

    def test_causal_lm_model_past(self):
        """Test causal LM model with `past_key_values`"""
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)

    def test_causal_lm_model_past_with_attn_mask(self):
        """Test the causal LM model with `past_key_values` and `attention_mask`"""
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)

    def test_causal_lm_model_past_with_large_inputs(self):
        """Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs(self):
        """Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
        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_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_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_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)

    @slow
    def test_model_from_pretrained(self):
        model_name = "andreasmadsen/efficient_mlm_m0.15"
        model = TFRobertaPreLayerNormModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


@require_tf
@require_sentencepiece
@require_tokenizers
class TFRobertaPreLayerNormModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_masked_lm(self):
        model = TFRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

        input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = [1, 11, 50265]
        self.assertEqual(list(output.numpy().shape), expected_shape)
        # compare the actual values for a slice.
        EXPECTED_SLICE = tf.constant(
            [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]
        )
        self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), EXPECTED_SLICE.numpy(), atol=1e-4))

    @slow
    def test_inference_no_head(self):
        model = TFRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40")

        input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        # compare the actual values for a slice.
        EXPECTED_SLICE = tf.constant(
            [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]
        )
        self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), EXPECTED_SLICE.numpy(), atol=1e-4))
