# Copyright 2022 Cerebras Systems.
#
# 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 torch

from modelzoo.common.pytorch.metrics import FBetaScoreMetric
from modelzoo.transformers.data_processing.utils import get_label_id_map
from modelzoo.transformers.pytorch.bert.bert_finetune_models import (
    BertForTokenClassification,
    BertForTokenClassificationLoss,
)
from modelzoo.transformers.pytorch.bert.utils import check_unused_model_params


class BertForTokenClassificationModel(torch.nn.Module):
    def __init__(self, params):
        super().__init__()

        model_params = params["model"].copy()
        num_classes = model_params.pop("num_classes")
        loss_weight = model_params.pop("loss_weight")
        include_padding_in_loss = model_params.pop("include_padding_in_loss")

        classifier_dropout = model_params.pop("encoder_output_dropout_rate")
        dropout_rate = model_params.pop("dropout_rate", 0.0)
        embedding_dropout_rate = model_params.pop(
            "embedding_dropout_rate", dropout_rate
        )

        model_kwargs = {
            "vocab_size": model_params.pop("vocab_size"),
            "hidden_size": model_params.pop("hidden_size"),
            "num_hidden_layers": model_params.pop("num_hidden_layers"),
            "num_heads": model_params.pop("num_heads"),
            "filter_size": model_params.pop("filter_size"),
            "nonlinearity": model_params.pop("encoder_nonlinearity"),
            "pooler_nonlinearity": model_params.pop(
                "pooler_nonlinearity", None
            ),
            "embedding_dropout_rate": embedding_dropout_rate,
            "dropout_rate": dropout_rate,
            "attention_dropout_rate": model_params.pop(
                "attention_dropout_rate", 0.0
            ),
            "max_position_embeddings": model_params.pop(
                "max_position_embeddings"
            ),
            "layer_norm_epsilon": float(model_params.pop("layer_norm_epsilon")),
        }

        self.model = BertForTokenClassification(
            num_classes,
            classifier_dropout=classifier_dropout,
            loss_weight=loss_weight,
            include_padding_in_loss=include_padding_in_loss,
            **model_kwargs,
        )
        self.loss_fn = BertForTokenClassificationLoss(num_classes, loss_weight)

        self.compute_eval_metrics = model_params.pop(
            "compute_eval_metrics", False
        )
        if self.compute_eval_metrics:
            self.label_map_id = get_label_id_map(
                model_params.pop("label_vocab_file")
            )
            # Ignore token labels in eval which dont
            # refer to a token beginning or inside.
            # Labels such as
            # "O", [CLS], [SEP], [PAD], "O", "X"
            # are ignored during eval
            eval_ignore_labels = []
            if self.label_map_id is not None:
                for key, label_id in self.label_map_id.items():
                    if not (key.startswith("B") or key.startswith("I")):
                        eval_ignore_labels.append(label_id)
            self.f1_metric = FBetaScoreMetric(
                num_classes=num_classes,
                beta=1.0,
                average_type="macro",
                ignore_labels=eval_ignore_labels,
                name="eval/f1_score",
            )
        check_unused_model_params(model_params)

    def __call__(self, data):
        logits = self.model(
            input_ids=data["input_ids"],
            attention_mask=data["attention_mask"],
            token_type_ids=data["token_type_ids"],
            loss_mask=data["loss_mask"],
            labels=data["labels"],
        )
        loss = self.loss_fn(logits, data["labels"], data["loss_mask"])
        if not self.model.training and self.compute_eval_metrics:
            labels = data["labels"].clone()
            predictions = logits.argmax(-1).int()

            self.f1_metric(
                labels=labels, predictions=predictions,
            )

        return loss
