# Copyright The PyTorch Lightning 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 functools
from typing import Any, Callable, Dict, List, Optional

from torch.utils.data import DataLoader, Sampler

import flash
from flash.core.adapter import Adapter
from flash.core.data.auto_dataset import BaseAutoDataset
from flash.core.data.io.input import DataKeys
from flash.core.integrations.icevision.transforms import from_icevision_predictions, to_icevision_record
from flash.core.model import Task
from flash.core.utilities.imports import _ICEVISION_AVAILABLE
from flash.core.utilities.url_error import catch_url_error

if _ICEVISION_AVAILABLE:
    from icevision.metrics import COCOMetric
    from icevision.metrics import Metric as IceVisionMetric
else:
    COCOMetric = object


class SimpleCOCOMetric(COCOMetric):
    def finalize(self) -> Dict[str, float]:
        logs = super().finalize()
        return {
            "Precision (IoU=0.50:0.95,area=all)": logs["AP (IoU=0.50:0.95) area=all"],
            "Recall (IoU=0.50:0.95,area=all,maxDets=100)": logs["AR (IoU=0.50:0.95) area=all maxDets=100"],
        }


class IceVisionAdapter(Adapter):
    """The ``IceVisionAdapter`` is an :class:`~flash.core.adapter.Adapter` for integrating with IceVision."""

    required_extras: str = "image"

    def __init__(self, model_type, model, icevision_adapter, backbone, predict_kwargs):
        super().__init__()

        self.model_type = model_type
        self.model = model
        self.icevision_adapter = icevision_adapter
        self.backbone = backbone
        self.predict_kwargs = predict_kwargs

    @classmethod
    @catch_url_error
    def from_task(
        cls,
        task: Task,
        num_classes: int,
        backbone: str,
        head: str,
        predict_kwargs: Dict,
        pretrained: bool = True,
        metrics: Optional["IceVisionMetric"] = None,
        image_size: Optional = None,
        **kwargs,
    ) -> Adapter:
        metadata = task.heads.get(head, with_metadata=True)
        backbones = metadata["metadata"]["backbones"]
        backbone_config = backbones.get(backbone)(pretrained)
        model_type, model, icevision_adapter, backbone = metadata["fn"](
            backbone_config,
            num_classes,
            image_size=image_size,
            **kwargs,
        )
        icevision_adapter = icevision_adapter(model=model, metrics=metrics)
        return cls(model_type, model, icevision_adapter, backbone, predict_kwargs)

    @staticmethod
    def _collate_fn(collate_fn, samples, metadata: Optional[List[Dict[str, Any]]] = None):
        metadata = metadata or [None] * len(samples)
        return {
            DataKeys.INPUT: collate_fn(
                [to_icevision_record({**sample, DataKeys.METADATA: m}) for sample, m in zip(samples, metadata)]
            ),
            DataKeys.METADATA: metadata,
        }

    def process_train_dataset(
        self,
        dataset: BaseAutoDataset,
        trainer: "flash.Trainer",
        batch_size: int,
        num_workers: int,
        pin_memory: bool,
        collate_fn: Optional[Callable] = None,
        shuffle: bool = False,
        drop_last: bool = False,
        sampler: Optional[Sampler] = None,
    ) -> DataLoader:
        data_loader = self.model_type.train_dl(
            dataset,
            batch_size=batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
        )
        data_loader.collate_fn = functools.partial(self._collate_fn, data_loader.collate_fn)
        return data_loader

    def process_val_dataset(
        self,
        dataset: BaseAutoDataset,
        trainer: "flash.Trainer",
        batch_size: int,
        num_workers: int,
        pin_memory: bool,
        collate_fn: Optional[Callable] = None,
        shuffle: bool = False,
        drop_last: bool = False,
        sampler: Optional[Sampler] = None,
    ) -> DataLoader:
        data_loader = self.model_type.valid_dl(
            dataset,
            batch_size=batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
        )
        data_loader.collate_fn = functools.partial(self._collate_fn, data_loader.collate_fn)
        return data_loader

    def process_test_dataset(
        self,
        dataset: BaseAutoDataset,
        trainer: "flash.Trainer",
        batch_size: int,
        num_workers: int,
        pin_memory: bool,
        collate_fn: Optional[Callable] = None,
        shuffle: bool = False,
        drop_last: bool = False,
        sampler: Optional[Sampler] = None,
    ) -> DataLoader:
        data_loader = self.model_type.valid_dl(
            dataset,
            batch_size=batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
        )
        data_loader.collate_fn = functools.partial(self._collate_fn, data_loader.collate_fn)
        return data_loader

    def process_predict_dataset(
        self,
        dataset: BaseAutoDataset,
        batch_size: int = 1,
        num_workers: int = 0,
        pin_memory: bool = False,
        collate_fn: Callable = lambda x: x,
        shuffle: bool = False,
        drop_last: bool = True,
        sampler: Optional[Sampler] = None,
    ) -> DataLoader:
        data_loader = self.model_type.infer_dl(
            dataset,
            batch_size=batch_size,
            num_workers=num_workers,
            pin_memory=pin_memory,
            shuffle=shuffle,
            drop_last=drop_last,
            sampler=sampler,
        )
        data_loader.collate_fn = functools.partial(self._collate_fn, data_loader.collate_fn)
        return data_loader

    def training_step(self, batch, batch_idx) -> Any:
        return self.icevision_adapter.training_step(batch[DataKeys.INPUT], batch_idx)

    def validation_step(self, batch, batch_idx):
        return self.icevision_adapter.validation_step(batch[DataKeys.INPUT], batch_idx)

    def test_step(self, batch, batch_idx):
        return self.icevision_adapter.validation_step(batch[DataKeys.INPUT], batch_idx)

    def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
        batch[DataKeys.PREDS] = self(batch[DataKeys.INPUT])
        return batch

    def forward(self, batch: Any) -> Any:
        return from_icevision_predictions(
            self.model_type.predict_from_dl(self.model, [batch], show_pbar=False, **self.predict_kwargs)
        )

    def training_epoch_end(self, outputs) -> None:
        return self.icevision_adapter.training_epoch_end(outputs)

    def validation_epoch_end(self, outputs) -> None:
        return self.icevision_adapter.validation_epoch_end(outputs)

    def test_epoch_end(self, outputs) -> None:
        return self.icevision_adapter.validation_epoch_end(outputs)
