# Path: dreamer/tools/count_dataset.py
# def count_dataset(directory):
#   directory = os.path.expanduser(directory)
#   if not tf.gfile.Exists(directory):
#     message = "Data set directory '{}' does not exist."
#     raise ValueError(message.format(directory))
#   pattern = os.path.join(directory, '*.npz')
#   def func():
#     filenames = tf.gfile.Glob(pattern)
#     episodes = len(filenames)
#     episodes = np.array(episodes, dtype=np.int32)
#     return episodes
#   return tf.py_func(func, [], tf.int32)
# 
# Path: dreamer/tools/gif_summary.py
# def gif_summary(name, tensor, max_outputs, fps, collections=None, family=None):
#   tensor = tf.convert_to_tensor(tensor)
#   if tensor.dtype in (tf.float32, tf.float64):
#     tensor = tf.cast(255.0 * tensor, tf.uint8)
#   with summary_op_util.summary_scope(
#       name, family, values=[tensor]) as (tag, scope):
#     val = tf.py_func(
#         py_gif_summary,
#         [tag, tensor, max_outputs, fps],
#         tf.string,
#         stateful=False,
#         name=scope)
#     summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
#   return val
# 
# Path: dreamer/tools/image_strip_summary.py
# def image_strip_summary(name, images, max_length=100, max_batch=10):
#   if max_batch:
#     images = images[:max_batch]
#   if max_length:
#     images = images[:, :max_length]
#   if images.dtype == tf.uint8:
#     images = tf.cast(images, tf.float32) / 255.0
#   length, width = tf.shape(images)[1], tf.shape(images)[3]
#   channels = images.shape[-1].value
#   images = tf.transpose(images, [0, 2, 1, 3, 4])
#   images = tf.reshape(images, [1, -1, length * width, channels])
#   images = tf.clip_by_value(images, 0., 1.)
#   return tf.summary.image(name, images)
# 
# Path: dreamer/tools/shape.py
# def shape(tensor):
#   static = tensor.get_shape().as_list()
#   dynamic = tf.unstack(tf.shape(tensor))
#   assert len(static) == len(dynamic)
#   combined = [d if s is None else s for s, d in zip(static, dynamic)]
#   return combined


import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from dreamer.tools import count_dataset
from dreamer.tools import gif_summary
from dreamer.tools import image_strip_summary
from dreamer.tools import shape as shapelib
    fig.canvas.draw()
    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
    image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
    plt.close(fig)
    return image
  image = tf.py_func(body_fn, (lines,), tf.uint8)
  image = image[None]
  summary = tf.summary.image(name, image)
  return summary


def data_summaries(
    data, postprocess_fn, histograms=False, max_batch=6, name='data'):
  summaries = []
  with tf.variable_scope(name):
    if histograms:
      for key, value in data.items():
        if key in ('image',):
          continue
        summaries.append(tf.summary.histogram(key, data[key]))
    image = data['image'][:max_batch]
    if postprocess_fn:
      image = postprocess_fn(image)
    summaries.append(image_strip_summary.image_strip_summary('image', image))
  return summaries


def dataset_summaries(directory, name='dataset'):
  summaries = []
  with tf.variable_scope(name):

# Path: dreamer/tools/count_dataset.py
# def count_dataset(directory):
#   directory = os.path.expanduser(directory)
#   if not tf.gfile.Exists(directory):
#     message = "Data set directory '{}' does not exist."
#     raise ValueError(message.format(directory))
#   pattern = os.path.join(directory, '*.npz')
#   def func():
#     filenames = tf.gfile.Glob(pattern)
#     episodes = len(filenames)
#     episodes = np.array(episodes, dtype=np.int32)
#     return episodes
#   return tf.py_func(func, [], tf.int32)
# 
# Path: dreamer/tools/gif_summary.py
# def gif_summary(name, tensor, max_outputs, fps, collections=None, family=None):
#   tensor = tf.convert_to_tensor(tensor)
#   if tensor.dtype in (tf.float32, tf.float64):
#     tensor = tf.cast(255.0 * tensor, tf.uint8)
#   with summary_op_util.summary_scope(
#       name, family, values=[tensor]) as (tag, scope):
#     val = tf.py_func(
#         py_gif_summary,
#         [tag, tensor, max_outputs, fps],
#         tf.string,
#         stateful=False,
#         name=scope)
#     summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
#   return val
# 
# Path: dreamer/tools/image_strip_summary.py
# def image_strip_summary(name, images, max_length=100, max_batch=10):
#   if max_batch:
#     images = images[:max_batch]
#   if max_length:
#     images = images[:, :max_length]
#   if images.dtype == tf.uint8:
#     images = tf.cast(images, tf.float32) / 255.0
#   length, width = tf.shape(images)[1], tf.shape(images)[3]
#   channels = images.shape[-1].value
#   images = tf.transpose(images, [0, 2, 1, 3, 4])
#   images = tf.reshape(images, [1, -1, length * width, channels])
#   images = tf.clip_by_value(images, 0., 1.)
#   return tf.summary.image(name, images)
# 
# Path: dreamer/tools/shape.py
# def shape(tensor):
#   static = tensor.get_shape().as_list()
#   dynamic = tf.unstack(tf.shape(tensor))
#   assert len(static) == len(dynamic)
#   combined = [d if s is None else s for s, d in zip(static, dynamic)]
#   return combined


# Path: dreamer/tools/summary.py
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from dreamer.tools import count_dataset
from dreamer.tools import gif_summary
from dreamer.tools import image_strip_summary
from dreamer.tools import shape as shapelib

    fig.canvas.draw()
    image = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
    image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
    plt.close(fig)
    return image
  image = tf.py_func(body_fn, (lines,), tf.uint8)
  image = image[None]
  summary = tf.summary.image(name, image)
  return summary


def data_summaries(
    data, postprocess_fn, histograms=False, max_batch=6, name='data'):
  summaries = []
  with tf.variable_scope(name):
    if histograms:
      for key, value in data.items():
        if key in ('image',):
          continue
        summaries.append(tf.summary.histogram(key, data[key]))
    image = data['image'][:max_batch]
    if postprocess_fn:
      image = postprocess_fn(image)
    summaries.append(image_strip_summary.image_strip_summary('image', image))
  return summaries


def dataset_summaries(directory, name='dataset'):
  summaries = []
  with tf.variable_scope(name):

    episodes = count_dataset.count_dataset(directory)