# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.


import unittest

from transformers import is_torch_available

from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import require_torch, slow, torch_device


if is_torch_available():
    import torch
    from transformers import (
        RobertaConfig,
        RobertaModel,
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        RobertaForTokenClassification,
    )
    from transformers.modeling_roberta import RobertaEmbeddings, RobertaForMultipleChoice, RobertaForQuestionAnswering
    from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
    from transformers.modeling_utils import create_position_ids_from_input_ids


@require_torch
class RobertaModelTest(ModelTesterMixin, unittest.TestCase):

    all_model_classes = (RobertaForMaskedLM, RobertaModel) if is_torch_available() else ()

    class RobertaModelTester(object):
        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,
            num_hidden_layers=5,
            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.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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

            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 = RobertaConfig(
                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 check_loss_output(self, result):
            self.parent.assertListEqual(list(result["loss"].size()), [])

        def create_and_check_roberta_model(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = RobertaModel(config=config)
            model.to(torch_device)
            model.eval()
            sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
            sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
            sequence_output, pooled_output = model(input_ids)

            result = {
                "sequence_output": sequence_output,
                "pooled_output": pooled_output,
            }
            self.parent.assertListEqual(
                list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
            )
            self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])

        def create_and_check_roberta_for_masked_lm(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = RobertaForMaskedLM(config=config)
            model.to(torch_device)
            model.eval()
            loss, prediction_scores = model(
                input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
            )
            result = {
                "loss": loss,
                "prediction_scores": prediction_scores,
            }
            self.parent.assertListEqual(
                list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size]
            )
            self.check_loss_output(result)

        def create_and_check_roberta_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 = RobertaForTokenClassification(config=config)
            model.to(torch_device)
            model.eval()
            loss, logits = model(
                input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels
            )
            result = {
                "loss": loss,
                "logits": logits,
            }
            self.parent.assertListEqual(
                list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels]
            )
            self.check_loss_output(result)

        def create_and_check_roberta_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 = RobertaForMultipleChoice(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()
            loss, logits = model(
                multiple_choice_inputs_ids,
                attention_mask=multiple_choice_input_mask,
                token_type_ids=multiple_choice_token_type_ids,
                labels=choice_labels,
            )
            result = {
                "loss": loss,
                "logits": logits,
            }
            self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
            self.check_loss_output(result)

        def create_and_check_roberta_for_question_answering(
            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = RobertaForQuestionAnswering(config=config)
            model.to(torch_device)
            model.eval()
            loss, start_logits, end_logits = model(
                input_ids,
                attention_mask=input_mask,
                token_type_ids=token_type_ids,
                start_positions=sequence_labels,
                end_positions=sequence_labels,
            )
            result = {
                "loss": loss,
                "start_logits": start_logits,
                "end_logits": end_logits,
            }
            self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length])
            self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
            self.check_loss_output(result)

        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

    def setUp(self):
        self.model_tester = RobertaModelTest.RobertaModelTester(self)
        self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)

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

    def test_roberta_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_roberta_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_roberta_for_masked_lm(*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_roberta_for_token_classification(*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_roberta_for_multiple_choice(*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_roberta_for_question_answering(*config_and_inputs)

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

    def test_create_position_ids_respects_padding_index(self):
        """ Ensure that the default position ids only assign a sequential . This is a regression
        test for https://github.com/huggingface/transformers/issues/1761

        The position ids should be masked with the embedding object's padding index. Therefore, the
        first available non-padding position index is RobertaEmbeddings.padding_idx + 1
        """
        config = self.model_tester.prepare_config_and_inputs()[0]
        model = RobertaEmbeddings(config=config)

        input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
        expected_positions = torch.as_tensor(
            [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
        )

        position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
        self.assertEqual(position_ids.shape, expected_positions.shape)
        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

    def test_create_position_ids_from_inputs_embeds(self):
        """ Ensure that the default position ids only assign a sequential . This is a regression
        test for https://github.com/huggingface/transformers/issues/1761

        The position ids should be masked with the embedding object's padding index. Therefore, the
        first available non-padding position index is RobertaEmbeddings.padding_idx + 1
        """
        config = self.model_tester.prepare_config_and_inputs()[0]
        embeddings = RobertaEmbeddings(config=config)

        inputs_embeds = torch.Tensor(2, 4, 30)
        expected_single_positions = [
            0 + embeddings.padding_idx + 1,
            1 + embeddings.padding_idx + 1,
            2 + embeddings.padding_idx + 1,
            3 + embeddings.padding_idx + 1,
        ]
        expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
        position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
        self.assertEqual(position_ids.shape, expected_positions.shape)
        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))


class RobertaModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_masked_lm(self):
        model = RobertaForMaskedLM.from_pretrained("roberta-base")

        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = torch.Size((1, 11, 50265))
        self.assertEqual(output.shape, expected_shape)
        # compare the actual values for a slice.
        expected_slice = torch.tensor(
            [[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
        )

        # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
        # roberta.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

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

    @slow
    def test_inference_no_head(self):
        model = RobertaModel.from_pretrained("roberta-base")

        input_ids = torch.tensor([[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 = torch.tensor(
            [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
        )

        # roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
        # roberta.eval()
        # expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach()

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

    @slow
    def test_inference_classification_head(self):
        model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli")

        input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = torch.Size((1, 3))
        self.assertEqual(output.shape, expected_shape)
        expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])

        # roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
        # roberta.eval()
        # expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()

        self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))
