code: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import csv
  import json
  import logging
  import numpy as np
  import os
  import yaml
  
  from ray.tune.log_sync import get_syncer
  from ray.tune.result import NODE_IP, TRAINING_ITERATION, TIME_TOTAL_S, \
      TIMESTEPS_TOTAL
  
  logger = logging.getLogger(__name__)
  
  try:
      import tensorflow as tf
  except ImportError:
      tf = None
      logger.warning("Couldn't import TensorFlow - "
                     "disabling TensorBoard logging.")
  
  
  class Logger(object):
      """Logging interface for ray.tune; specialized implementations follow.
  
      By default, the UnifiedLogger implementation is used which logs results in
      multiple formats (TensorBoard, rllab/viskit, plain json) at once.
      """
  
      def __init__(self, config, logdir, upload_uri=None):
          self.config = config
          self.logdir = logdir
          self.uri = upload_uri
          self._init()
  
      def _init(self):
          pass
  
      def on_result(self, result):
          """Given a result, appends it to the existing log."""
  
          raise NotImplementedError
  
      def close(self):
          """Releases all resources used by this logger."""
  
          pass
  
      def flush(self):
          """Flushes all disk writes to storage."""
  
          pass
  
  
  class UnifiedLogger(Logger):
      """Unified result logger for TensorBoard, rllab/viskit, plain json.
  
      This class also periodically syncs output to the given upload uri."""
  
      def _init(self):
          self._loggers = []
          for cls in [_JsonLogger, _TFLogger, _VisKitLogger]:
              if cls is _TFLogger and tf is None:
                  logger.info("TF not installed - "
                              "cannot log with {}...".format(cls))
                  continue
              self._loggers.append(cls(self.config, self.logdir, self.uri))
          self._log_syncer = get_syncer(self.logdir, self.uri)
  
      def on_result(self, result):
          for logger in self._loggers:
              logger.on_result(result)
          self._log_syncer.set_worker_ip(result.get(NODE_IP))
          self._log_syncer.sync_if_needed()
  
      def close(self):
          for logger in self._loggers:
              logger.close()
          self._log_syncer.sync_now(force=True)
  
      def flush(self):
          for logger in self._loggers:
              logger.flush()
          self._log_syncer.sync_now(force=True)
          self._log_syncer.wait()
  
  
  class NoopLogger(Logger):
      def on_result(self, result):
          pass
  
  
  class _JsonLogger(Logger):
      def _init(self):
          config_out = os.path.join(self.logdir, "params.json")
          with open(config_out, "w") as f:
              json.dump(self.config, f, sort_keys=True, cls=_SafeFallbackEncoder)
          local_file = os.path.join(self.logdir, "result.json")
          self.local_out = open(local_file, "w")
  
      def on_result(self, result):
          json.dump(result, self, cls=_SafeFallbackEncoder)
          self.write("\n")
  
      def write(self, b):
          self.local_out.write(b)
          self.local_out.flush()
  
      def close(self):
          self.local_out.close()
  
  
  def to_tf_values(result, path):
      values = []
      for attr, value in result.items():
          if value is not None:
              if type(value) in [int, float, np.float32, np.float64, np.int32]:
                  values.append(
                      tf.Summary.Value(
                          tag="/".join(path + [attr]), simple_value=value))
              elif type(value) is dict:
                  values.extend(to_tf_values(value, path + [attr]))
      return values
  
  
  class _TFLogger(Logger):
      def _init(self):
          self._file_writer = tf.summary.FileWriter(self.logdir)
  
      def on_result(self, result):
          tmp = result.copy()
          for k in [
                  "config", "pid", "timestamp", TIME_TOTAL_S, TRAINING_ITERATION
          ]:
              del tmp[k]  # not useful to tf log these
          values = to_tf_values(tmp, ["ray", "tune"])
          train_stats = tf.Summary(value=values)
          t = result.get(TIMESTEPS_TOTAL) or result[TRAINING_ITERATION]
          self._file_writer.add_summary(train_stats, t)
          iteration_value = to_tf_values({
              "training_iteration": result[TRAINING_ITERATION]
          }, ["ray", "tune"])
          iteration_stats = tf.Summary(value=iteration_value)
          self._file_writer.add_summary(iteration_stats, t)
          self._file_writer.flush()
  
      def flush(self):
          self._file_writer.flush()
  
      def close(self):
          self._file_writer.close()
  
  
  class _VisKitLogger(Logger):
      def _init(self):
          """CSV outputted with Headers as first set of results."""
          # Note that we assume params.json was already created by JsonLogger
          self._file = open(os.path.join(self.logdir, "progress.csv"), "w")
          self._csv_out = None
  
      def on_result(self, result):
          if self._csv_out is None:
              self._csv_out = csv.DictWriter(self._file, result.keys())
              self._csv_out.writeheader()
          self._csv_out.writerow(result.copy())
  
      def close(self):
          self._file.close()
  
  
  class _SafeFallbackEncoder(json.JSONEncoder):
      def __init__(self, nan_str="null", **kwargs):
          super(_SafeFallbackEncoder, self).__init__(**kwargs)
          self.nan_str = nan_str
  
      def default(self, value):
          try:
              if np.isnan(value):
                  return None
              if np.issubdtype(value, float):
                  return float(value)
              if np.issubdtype(value, int):
                  return int(value)
          except Exception:
              return str(value)  # give up, just stringify it (ok for logs)
  
  
  def pretty_print(result):
      result = result.copy()
      result.update(config=None)  # drop config from pretty print
      out = {}
      for k, v in result.items():
          if v is not None:
              out[k] = v
  
      cleaned = json.dumps(out, cls=_SafeFallbackEncoder)
      return yaml.safe_dump(json.loads(cleaned), default_flow_style=False)
  
apis: tensorflow.summary.FileWriter numpy.issubdtype tensorflow.Summary numpy.isnan