import argparse
import json
import os
import shutil
import sys
import torch
import yaml
from datetime import datetime
from pathlib import Path
from tqdm import tqdm

sys.path.append(str(Path(__file__).parent.parent.parent))  # add utils/ to path
from utils.datasets import LoadImagesAndLabels
from utils.datasets import img2label_paths
from utils.general import colorstr, xywh2xyxy, check_dataset

try:
    import wandb
    from wandb import init, finish
except ImportError:
    wandb = None

WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'


def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
    return from_string[len(prefix):]


def check_wandb_config_file(data_config_file):
    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
    if Path(wandb_config).is_file():
        return wandb_config
    return data_config_file

def get_run_info(run_path):
    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
    run_id = run_path.stem
    project = run_path.parent.stem
    model_artifact_name = 'run_' + run_id + '_model'
    return run_id, project, model_artifact_name

def check_wandb_resume(opt):
    process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None
    if isinstance(opt.resume, str):
        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
            if opt.global_rank not in [-1, 0]: # For resuming DDP runs
                run_id, project, model_artifact_name = get_run_info(opt.resume)
                api = wandb.Api()
                artifact = api.artifact(project + '/' + model_artifact_name + ':latest')
                modeldir = artifact.download()
                opt.weights = str(Path(modeldir) / "last.pt")
            return True
    return None

def process_wandb_config_ddp_mode(opt):
    with open(opt.data) as f:
        data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
    train_dir, val_dir = None, None
    if data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
        api = wandb.Api()
        train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
        train_dir = train_artifact.download()
        train_path = Path(train_dir) / 'data/images/'
        data_dict['train'] = str(train_path)
        
    if data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
        api = wandb.Api()
        val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
        val_dir = val_artifact.download()
        val_path = Path(val_dir) / 'data/images/'
        data_dict['val'] = str(val_path)
    if train_dir or val_dir:
        ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') 
        with open(ddp_data_path, 'w') as f:
            yaml.dump(data_dict, f)
        opt.data = ddp_data_path
    
        

class WandbLogger():
    def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
        # Pre-training routine --
        self.job_type = job_type
        self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
        # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
        if isinstance(opt.resume, str): # checks resume from artifact 
            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
                run_id, project, model_artifact_name = get_run_info(opt.resume)
                model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
                assert wandb, 'install wandb to resume wandb runs'
                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
                self.wandb_run = wandb.init(id=run_id, project=project, resume='allow')
                opt.resume = model_artifact_name
        elif self.wandb:
            self.wandb_run = wandb.init(config=opt,
                                        resume="allow",
                                        project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
                                        name=name,
                                        job_type=job_type,
                                        id=run_id) if not wandb.run else wandb.run      
        if self.wandb_run:
            if self.job_type == 'Training':
                if not opt.resume:
                    wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
                    # Info useful for resuming from artifacts
                    self.wandb_run.config.opt = vars(opt)
                    self.wandb_run.config.data_dict = wandb_data_dict
                self.data_dict = self.setup_training(opt, data_dict)
            if self.job_type == 'Dataset Creation':
                self.data_dict = self.check_and_upload_dataset(opt)
        else:
            print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")


    def check_and_upload_dataset(self, opt):
        assert wandb, 'Install wandb to upload dataset'
        check_dataset(self.data_dict)
        config_path = self.log_dataset_artifact(opt.data,
                                                   opt.single_cls,
                                                   'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
        print("Created dataset config file ", config_path)
        with open(config_path) as f:
            wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader)
        return wandb_data_dict

    def setup_training(self, opt, data_dict):
        self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16  # Logging Constants
        self.bbox_interval = opt.bbox_interval
        if isinstance(opt.resume, str):
            modeldir, _ = self.download_model_artifact(opt)
            if modeldir:
                self.weights = Path(modeldir) / "last.pt"
                config = self.wandb_run.config
                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
                    self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \
                                                                                                       config.opt['hyp']
            data_dict = dict(self.wandb_run.config.data_dict)  # eliminates the need for config file to resume
        if 'val_artifact' not in self.__dict__:  # If --upload_dataset is set, use the existing artifact, don't download
            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
                                                                                           opt.artifact_alias)
            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
                                                                                       opt.artifact_alias)
            self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None
            if self.train_artifact_path is not None:
                train_path = Path(self.train_artifact_path) / 'data/images/'
                data_dict['train'] = str(train_path)
            if self.val_artifact_path is not None:
                val_path = Path(self.val_artifact_path) / 'data/images/'
                data_dict['val'] = str(val_path)
                self.val_table = self.val_artifact.get("val")
                self.map_val_table_path()
        if self.val_artifact is not None:
            self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
            self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
        if opt.bbox_interval == -1:
            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
        return data_dict

    def download_dataset_artifact(self, path, alias):
        if path.startswith(WANDB_ARTIFACT_PREFIX):
            dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
            assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
            datadir = dataset_artifact.download()
            return datadir, dataset_artifact
        return None, None

    def download_model_artifact(self, opt):
        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
            assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
            modeldir = model_artifact.download()
            epochs_trained = model_artifact.metadata.get('epochs_trained')
            total_epochs = model_artifact.metadata.get('total_epochs')
            assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % (
                total_epochs)
            return modeldir, model_artifact
        return None, None

    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
            'original_url': str(path),
            'epochs_trained': epoch + 1,
            'save period': opt.save_period,
            'project': opt.project,
            'total_epochs': opt.epochs,
            'fitness_score': fitness_score
        })
        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
        wandb.log_artifact(model_artifact,
                           aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
        print("Saving model artifact on epoch ", epoch + 1)

    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
        with open(data_file) as f:
            data = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
        names = {k: v for k, v in enumerate(names)}  # to index dictionary
        self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
            data['train']), names, name='train') if data.get('train') else None
        self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
            data['val']), names, name='val') if data.get('val') else None
        if data.get('train'):
            data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
        if data.get('val'):
            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
        path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1))  # updated data.yaml path
        data.pop('download', None)
        with open(path, 'w') as f:
            yaml.dump(data, f)

        if self.job_type == 'Training':  # builds correct artifact pipeline graph
            self.wandb_run.use_artifact(self.val_artifact)
            self.wandb_run.use_artifact(self.train_artifact)
            self.val_artifact.wait()
            self.val_table = self.val_artifact.get('val')
            self.map_val_table_path()
        else:
            self.wandb_run.log_artifact(self.train_artifact)
            self.wandb_run.log_artifact(self.val_artifact)
        return path

    def map_val_table_path(self):
        self.val_table_map = {}
        print("Mapping dataset")
        for i, data in enumerate(tqdm(self.val_table.data)):
            self.val_table_map[data[3]] = data[0]

    def create_dataset_table(self, dataset, class_to_id, name='dataset'):
        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
        artifact = wandb.Artifact(name=name, type="dataset")
        for img_file in tqdm([dataset.path]) if Path(dataset.path).is_dir() else tqdm(dataset.img_files):
            if Path(img_file).is_dir():
                artifact.add_dir(img_file, name='data/images')
                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
                artifact.add_dir(labels_path, name='data/labels')
            else:
                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
                label_file = Path(img2label_paths([img_file])[0])
                artifact.add_file(str(label_file),
                                  name='data/labels/' + label_file.name) if label_file.exists() else None
        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
            height, width = shapes[0]
            labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height])
            box_data, img_classes = [], {}
            for cls, *xyxy in labels[:, 1:].tolist():
                cls = int(cls)
                box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                                 "class_id": cls,
                                 "box_caption": "%s" % (class_to_id[cls]),
                                 "scores": {"acc": 1},
                                 "domain": "pixel"})
                img_classes[cls] = class_to_id[cls]
            boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes),
                           Path(paths).name)
        artifact.add(table, name)
        return artifact

    def log_training_progress(self, predn, path, names):
        if self.val_table and self.result_table:
            class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
            box_data = []
            total_conf = 0
            for *xyxy, conf, cls in predn.tolist():
                if conf >= 0.25:
                    box_data.append(
                        {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                         "class_id": int(cls),
                         "box_caption": "%s %.3f" % (names[cls], conf),
                         "scores": {"class_score": conf},
                         "domain": "pixel"})
                    total_conf = total_conf + conf
            boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
            id = self.val_table_map[Path(path).name]
            self.result_table.add_data(self.current_epoch,
                                       id,
                                       wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
                                       total_conf / max(1, len(box_data))
                                       )

    def log(self, log_dict):
        if self.wandb_run:
            for key, value in log_dict.items():
                self.log_dict[key] = value

    def end_epoch(self, best_result=False):
        if self.wandb_run:
            wandb.log(self.log_dict)
            self.log_dict = {}
            if self.result_artifact:
                train_results = wandb.JoinedTable(self.val_table, self.result_table, "id")
                self.result_artifact.add(train_results, 'result')
                wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch),
                                                                  ('best' if best_result else '')])
                self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")

    def finish_run(self):
        if self.wandb_run:
            if self.log_dict:
                wandb.log(self.log_dict)
            wandb.run.finish()
