# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from pathlib import Path
import importlib, warnings
import os, sys, time, numpy as np
import scipy.misc 
if sys.version_info.major == 2: # Python 2.x
  from StringIO import StringIO as BIO
else:                           # Python 3.x
  from io import BytesIO as BIO

if importlib.util.find_spec('tensorflow'):
  import tensorflow as tf

class Logger(object):
  
  def __init__(self, log_dir, logstr, use_tf=False):
    """Create a summary writer logging to log_dir."""
    self.log_dir = Path(log_dir)
    self.model_dir = Path(log_dir) / 'checkpoint'
    self.meta_dir = Path(log_dir) / 'metas'
    self.log_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
    self.model_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
    self.meta_dir.mkdir(mode=0o775, parents=True, exist_ok=True)

    self.use_tf  = bool(use_tf)
    self.tensorboard_dir = self.log_dir / ('tensorboard-{:}'.format(time.strftime( '%d-%h', time.gmtime(time.time()) )))
    #self.tensorboard_dir = self.log_dir / ('tensorboard-{:}'.format(time.strftime( '%d-%h-at-%H:%M:%S', time.gmtime(time.time()) )))
    self.logger_path = self.log_dir / '{:}.log'.format(logstr)
    self.logger_file = open(self.logger_path, 'w')

    if self.use_tf:
      self.tensorboard_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
      self.writer = tf.summary.FileWriter(str(self.tensorboard_dir))
    else:
      self.writer = None

  def __repr__(self):
    return ('{name}(dir={log_dir}, use-tf={use_tf}, writer={writer})'.format(name=self.__class__.__name__, **self.__dict__))

  def path(self, mode):
    if mode == 'meta'   : return self.meta_dir
    elif mode == 'model': return self.model_dir
    elif mode == 'log'  : return self.log_dir
    else: raise TypeError('Unknow mode = {:}'.format(mode))

  def last_info(self):
    return self.log_dir / 'last-info.pth'

  def extract_log(self):
    return self.logger_file

  def close(self):
    self.logger_file.close()
    if self.writer is not None:
      self.writer.close()

  def log(self, string, save=True, stdout=False):
    if stdout:
      sys.stdout.write(string); sys.stdout.flush()
    else:
      print (string)
    if save:
      self.logger_file.write('{:}\n'.format(string))
      self.logger_file.flush()

  def scalar_summary(self, tags, values, step):
    """Log a scalar variable."""
    if not self.use_tf:
      warnings.warn('Do set use-tensorflow installed but call scalar_summary')
    else:
      assert isinstance(tags, list) == isinstance(values, list), 'Type : {:} vs {:}'.format(type(tags), type(values))
      if not isinstance(tags, list):
        tags, values = [tags], [values]
      for tag, value in zip(tags, values):
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
        self.writer.add_summary(summary, step)
        self.writer.flush()

  def image_summary(self, tag, images, step):
    """Log a list of images."""
    if not self.use_tf:
      warnings.warn('Do set use-tensorflow installed but call scalar_summary')
      return

    img_summaries = []
    for i, img in enumerate(images):
      # Write the image to a string
      try:
        s = StringIO()
      except:
        s = BytesIO()
      scipy.misc.toimage(img).save(s, format="png")

      # Create an Image object
      img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
                     height=img.shape[0],
                     width=img.shape[1])
      # Create a Summary value
      img_summaries.append(tf.Summary.Value(tag='{}/{}'.format(tag, i), image=img_sum))

    # Create and write Summary
    summary = tf.Summary(value=img_summaries)
    self.writer.add_summary(summary, step)
    self.writer.flush()
    
  def histo_summary(self, tag, values, step, bins=1000):
    """Log a histogram of the tensor of values."""
    if not self.use_tf: raise ValueError('Do not have tensorflow')
    import tensorflow as tf

    # Create a histogram using numpy
    counts, bin_edges = np.histogram(values, bins=bins)

    # Fill the fields of the histogram proto
    hist = tf.HistogramProto()
    hist.min = float(np.min(values))
    hist.max = float(np.max(values))
    hist.num = int(np.prod(values.shape))
    hist.sum = float(np.sum(values))
    hist.sum_squares = float(np.sum(values**2))

    # Drop the start of the first bin
    bin_edges = bin_edges[1:]

    # Add bin edges and counts
    for edge in bin_edges:
      hist.bucket_limit.append(edge)
    for c in counts:
      hist.bucket.append(c)

    # Create and write Summary
    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
    self.writer.add_summary(summary, step)
    self.writer.flush()
