# coding=utf-8
# Copyright 2018 XXX 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, require_torch_and_cuda, slow, torch_device


if is_torch_available():
    from transformers import (
        XxxConfig,
        XxxModel,
        XxxForMaskedLM,
        XxxForQuestionAnswering,
        XxxForSequenceClassification,
        XxxForTokenClassification,
        AutoModelForMaskedLM,
        AutoTokenizer,
    )
    from transformers.file_utils import cached_property

    #


class XxxModelTester:
    """You can also import this e.g from .test_modeling_bart import BartModelTester """

    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 = XxxConfig(
            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_xxx_model(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = XxxModel(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_xxx_for_masked_lm(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = XxxForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        loss, prediction_scores = model(
            input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_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_xxx_for_question_answering(
        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = XxxForQuestionAnswering(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 create_and_check_xxx_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 = XxxForSequenceClassification(config)
        model.to(torch_device)
        model.eval()
        loss, logits = model(
            input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels
        )
        result = {
            "loss": loss,
            "logits": logits,
        }
        self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
        self.check_loss_output(result)

    def create_and_check_xxx_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 = XxxForTokenClassification(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 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 XxxModelTest(ModelTesterMixin, unittest.TestCase):

    all_model_classes = (
        (XxxModel, XxxForMaskedLM, XxxForQuestionAnswering, XxxForSequenceClassification, XxxForTokenClassification)
        if is_torch_available()
        else ()
    )

    def setUp(self):
        self.model_tester = XxxModelTester(self)
        self.config_tester = ConfigTester(self, config_class=XxxConfig, hidden_size=37)

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

    def test_xxx_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_xxx_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_xxx_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_xxx_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_xxx_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_xxx_for_token_classification(*config_and_inputs)

    @slow
    def test_lm_outputs_same_as_reference_model(self):
        """Write something that could help someone fixing this here."""
        checkpoint_path = "XXX/bart-large"
        model = self.big_model
        tokenizer = AutoTokenizer.from_pretrained(
            checkpoint_path
        )  # same with AutoTokenizer (see tokenization_auto.py). This is not mandatory
        # MODIFY THIS DEPENDING ON YOUR MODELS RELEVANT TASK.
        batch = tokenizer(["I went to the <mask> yesterday"]).to(torch_device)
        desired_mask_result = tokenizer.decode("store")  # update this
        logits = model(**batch).logits
        masked_index = (batch.input_ids == self.tokenizer.mask_token_id).nonzero()
        assert model.num_parameters() == 175e9  # a joke
        mask_entry_logits = logits[0, masked_index.item(), :]
        probs = mask_entry_logits.softmax(dim=0)
        _, predictions = probs.topk(1)
        self.assertEqual(tokenizer.decode(predictions), desired_mask_result)

    @cached_property
    def big_model(self):
        """Cached property means this code will only be executed once."""
        checkpoint_path = "XXX/bart-large"
        model = AutoModelForMaskedLM.from_pretrained(checkpoint_path).to(
            torch_device
        )  # test whether AutoModel can determine your model_class from checkpoint name
        if torch_device == "cuda":
            model.half()

    # optional: do more testing! This will save you time later!
    @slow
    def test_that_XXX_can_be_used_in_a_pipeline(self):
        """We can use self.big_model here without calling __init__ again."""
        pass

    def test_XXX_loss_doesnt_change_if_you_add_padding(self):
        pass

    def test_XXX_bad_args(self):
        pass

    def test_XXX_backward_pass_reduces_loss(self):
        """Test loss/gradients same as reference implementation, for example."""
        pass

    @require_torch_and_cuda
    def test_large_inputs_in_fp16_dont_cause_overflow(self):
        pass
