# coding=utf-8
# Copyright 2020 The SqueezeBert authors and 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.


import unittest

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

from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertConfig,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
    )

    class SqueezeBertModelTester(object):
        def __init__(
            self,
            parent,
            batch_size=13,
            seq_length=7,
            is_training=True,
            use_input_mask=True,
            use_token_type_ids=False,
            use_labels=True,
            vocab_size=99,
            hidden_size=32,
            num_hidden_layers=5,
            num_attention_heads=4,
            intermediate_size=64,
            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,
            q_groups=2,
            k_groups=2,
            v_groups=2,
            post_attention_groups=2,
            intermediate_groups=4,
            output_groups=1,
        ):
            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
            self.q_groups = q_groups
            self.k_groups = k_groups
            self.v_groups = v_groups
            self.post_attention_groups = post_attention_groups
            self.intermediate_groups = intermediate_groups
            self.output_groups = output_groups

        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])

            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 = SqueezeBertConfig(
                embedding_size=self.hidden_size,
                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,
                attention_probs_dropout_prob=self.hidden_dropout_prob,
                attention_dropout=self.attention_probs_dropout_prob,
                max_position_embeddings=self.max_position_embeddings,
                initializer_range=self.initializer_range,
                q_groups=self.q_groups,
                k_groups=self.k_groups,
                v_groups=self.v_groups,
                post_attention_groups=self.post_attention_groups,
                intermediate_groups=self.intermediate_groups,
                output_groups=self.output_groups,
                return_dict=True,
            )

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

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

        def create_and_check_squeezebert_for_question_answering(
            self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            model = SqueezeBertForQuestionAnswering(config=config)
            model.to(torch_device)
            model.eval()
            result = model(
                input_ids, attention_mask=input_mask, 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_squeezebert_for_sequence_classification(
            self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_labels = self.num_labels
            model = SqueezeBertForSequenceClassification(config)
            model.to(torch_device)
            model.eval()
            result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))

        def create_and_check_squeezebert_for_token_classification(
            self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_labels = self.num_labels
            model = SqueezeBertForTokenClassification(config=config)
            model.to(torch_device)
            model.eval()

            result = model(input_ids, attention_mask=input_mask, labels=token_labels)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

        def create_and_check_squeezebert_for_multiple_choice(
            self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
        ):
            config.num_choices = self.num_choices
            model = SqueezeBertForMultipleChoice(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_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,
                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, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
            inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict


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

    all_model_classes = (
        (
            SqueezeBertModel,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
        )
        if is_torch_available()
        else None
    )
    test_pruning = False
    test_torchscript = True
    test_resize_embeddings = True
    test_head_masking = False

    def setUp(self):
        self.model_tester = SqueezeBertModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SqueezeBertConfig, dim=37)

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

    def test_squeezebert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_squeezebert_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_squeezebert_for_masked_lm(*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_squeezebert_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_squeezebert_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_squeezebert_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_squeezebert_for_multiple_choice(*config_and_inputs)

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


@require_sentencepiece
@require_tokenizers
@require_torch
class SqueezeBertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_classification_head(self):
        model = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")

        input_ids = torch.tensor([[0, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]])
        output = model(input_ids)[0]
        expected_shape = torch.Size((1, 3))
        self.assertEqual(output.shape, expected_shape)
        expected_tensor = torch.tensor([[0.5075, 0.0682, -0.5881]])
        self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))
