text: # 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.
  """
  TensorBoard Logger
  ------------------
  """
  
  import os
  from argparse import Namespace
  from typing import Any, Dict, Optional, Union
  
  import torch
  from torch.utils.tensorboard import SummaryWriter
  from torch.utils.tensorboard.summary import hparams
  
  from pytorch_lightning import _logger as log
  from pytorch_lightning.core.lightning import LightningModule
  from pytorch_lightning.core.saving import save_hparams_to_yaml
  from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
  from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, rank_zero_only, rank_zero_warn
  from pytorch_lightning.utilities.cloud_io import get_filesystem
  
  if _OMEGACONF_AVAILABLE:
      from omegaconf import Container, OmegaConf
  
  
  class TensorBoardLogger(LightningLoggerBase):
      r"""
      Log to local file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format.
  
      Implemented using :class:`~torch.utils.tensorboard.SummaryWriter`. Logs are saved to
      ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
      preinstalled.
  
      Example:
          >>> from pytorch_lightning import Trainer
          >>> from pytorch_lightning.loggers import TensorBoardLogger
          >>> logger = TensorBoardLogger("tb_logs", name="my_model")
          >>> trainer = Trainer(logger=logger)
  
      Args:
          save_dir: Save directory
          name: Experiment name. Defaults to ``'default'``. If it is the empty string then no per-experiment
              subdirectory is used.
          version: Experiment version. If version is not specified the logger inspects the save
              directory for existing versions, then automatically assigns the next available version.
              If it is a string then it is used as the run-specific subdirectory name,
              otherwise ``'version_${version}'`` is used.
          log_graph: Adds the computational graph to tensorboard. This requires that
              the user has defined the `self.example_input_array` attribute in their
              model.
          default_hp_metric: Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is
              called without a metric (otherwise calls to log_hyperparams without a metric are ignored).
          prefix: A string to put at the beginning of metric keys.
          \**kwargs: Additional arguments like `comment`, `filename_suffix`, etc. used by
              :class:`SummaryWriter` can be passed as keyword arguments in this logger.
  
      """
      NAME_HPARAMS_FILE = 'hparams.yaml'
      LOGGER_JOIN_CHAR = '-'
  
      def __init__(
          self,
          save_dir: str,
          name: Optional[str] = "default",
          version: Optional[Union[int, str]] = None,
          log_graph: bool = False,
          default_hp_metric: bool = True,
          prefix: str = '',
          **kwargs
      ):
          super().__init__()
          self._save_dir = save_dir
          self._name = name or ''
          self._version = version
          self._log_graph = log_graph
          self._default_hp_metric = default_hp_metric
          self._prefix = prefix
          self._fs = get_filesystem(save_dir)
  
          self._experiment = None
          self.hparams = {}
          self._kwargs = kwargs
  
      @property
      def root_dir(self) -> str:
          """
          Parent directory for all tensorboard checkpoint subdirectories.
          If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
          and the checkpoint will be saved in "save_dir/version_dir"
          """
          if self.name is None or len(self.name) == 0:
              return self.save_dir
          else:
              return os.path.join(self.save_dir, self.name)
  
      @property
      def log_dir(self) -> str:
          """
          The directory for this run's tensorboard checkpoint. By default, it is named
          ``'version_${self.version}'`` but it can be overridden by passing a string value
          for the constructor's version parameter instead of ``None`` or an int.
          """
          # create a pseudo standard path ala test-tube
          version = self.version if isinstance(self.version, str) else f"version_{self.version}"
          log_dir = os.path.join(self.root_dir, version)
          return log_dir
  
      @property
      def save_dir(self) -> Optional[str]:
          return self._save_dir
  
      @property
      @rank_zero_experiment
      def experiment(self) -> SummaryWriter:
          r"""
          Actual tensorboard object. To use TensorBoard features in your
          :class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
  
          Example::
  
              self.logger.experiment.some_tensorboard_function()
  
          """
          if self._experiment is not None:
              return self._experiment
  
          assert rank_zero_only.rank == 0, 'tried to init log dirs in non global_rank=0'
          if self.root_dir:
              self._fs.makedirs(self.root_dir, exist_ok=True)
          self._experiment = SummaryWriter(log_dir=self.log_dir, **self._kwargs)
          return self._experiment
  
      @rank_zero_only
      def log_hyperparams(
          self,
          params: Union[Dict[str, Any], Namespace],
          metrics: Optional[Dict[str, Any]] = None,
      ) -> None:
          """
          Record hyperparameters. TensorBoard logs with and without saved hyperparameters
          are incompatible, the hyperparameters are then not displayed in the TensorBoard.
          Please delete or move the previously saved logs to display the new ones with hyperparameters.
  
          Args:
              params: a dictionary-like container with the hyperparameters
              metrics: Dictionary with metric names as keys and measured quantities as values
          """
  
          params = self._convert_params(params)
  
          # store params to output
          if _OMEGACONF_AVAILABLE and isinstance(params, Container):
              self.hparams = OmegaConf.merge(self.hparams, params)
          else:
              self.hparams.update(params)
  
          # format params into the suitable for tensorboard
          params = self._flatten_dict(params)
          params = self._sanitize_params(params)
  
          if metrics is None:
              if self._default_hp_metric:
                  metrics = {"hp_metric": -1}
          elif not isinstance(metrics, dict):
              metrics = {"hp_metric": metrics}
  
          if metrics:
              self.log_metrics(metrics, 0)
              exp, ssi, sei = hparams(params, metrics)
              writer = self.experiment._get_file_writer()
              writer.add_summary(exp)
              writer.add_summary(ssi)
              writer.add_summary(sei)
  
      @rank_zero_only
      def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
          assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'
  
          metrics = self._add_prefix(metrics)
  
          for k, v in metrics.items():
              if isinstance(v, torch.Tensor):
                  v = v.item()
  
              if isinstance(v, dict):
                  self.experiment.add_scalars(k, v, step)
              else:
                  try:
                      self.experiment.add_scalar(k, v, step)
                  # todo: specify the possible exception
                  except Exception as ex:
                      m = f'\n you tried to log {v} which is not currently supported. Try a dict or a scalar/tensor.'
                      type(ex)(ex.message + m)
  
      @rank_zero_only
      def log_graph(self, model: LightningModule, input_array=None):
          if self._log_graph:
              if input_array is None:
                  input_array = model.example_input_array
  
              if input_array is not None:
                  input_array = model.transfer_batch_to_device(input_array, model.device)
                  self.experiment.add_graph(model, input_array)
              else:
                  rank_zero_warn(
                      'Could not log computational graph since the'
                      ' `model.example_input_array` attribute is not set'
                      ' or `input_array` was not given', UserWarning
                  )
  
      @rank_zero_only
      def save(self) -> None:
          super().save()
          dir_path = self.log_dir
          if not self._fs.isdir(dir_path):
              dir_path = self.save_dir
  
          # prepare the file path
          hparams_file = os.path.join(dir_path, self.NAME_HPARAMS_FILE)
  
          # save the metatags file if it doesn't exist
          if not self._fs.isfile(hparams_file):
              save_hparams_to_yaml(hparams_file, self.hparams)
  
      @rank_zero_only
      def finalize(self, status: str) -> None:
          self.experiment.flush()
          self.experiment.close()
          self.save()
  
      @property
      def name(self) -> str:
          return self._name
  
      @property
      def version(self) -> int:
          if self._version is None:
              self._version = self._get_next_version()
          return self._version
  
      def _get_next_version(self):
          root_dir = os.path.join(self.save_dir, self.name)
  
          if not self._fs.isdir(root_dir):
              log.warning('Missing logger folder: %s', root_dir)
              return 0
  
          existing_versions = []
          for listing in self._fs.listdir(root_dir):
              d = listing["name"]
              bn = os.path.basename(d)
              if self._fs.isdir(d) and bn.startswith("version_"):
                  dir_ver = bn.split("_")[1].replace('/', '')
                  existing_versions.append(int(dir_ver))
          if len(existing_versions) == 0:
              return 0
  
          return max(existing_versions) + 1
  
      def __getstate__(self):
          state = self.__dict__.copy()
          state["_experiment"] = None
          return state
  