# Path: dreamer/tools/attr_dict.py
# class AttrDict(dict):
#   def __init__(self, *args, **kwargs):
#   def __getattr__(self, name):
#   def __setattr__(self, name, value):
#   def __getitem__(self, name):
#   def __setitem__(self, name, value):
#   def __repr__(self):
#   def get(self, key, default=None):
#   def untouched(self):
#   def unlocked(self):
#   def lock(self):
#   def unlock(self):
#   def summarize(self):
#   def update(self, mapping):
#   def copy(self, _unlocked=False):
#   def save(self, filename):
#   def load(cls, filename):
#   def _format_value(self, value):
# 
# Path: dreamer/tools/chunk_sequence.py
# def chunk_sequence(sequence, chunk_length, randomize=True, num_chunks=None):
#   if 'length' in sequence:
#     length = sequence.pop('length')
#   else:
#     length = tf.shape(nested.flatten(sequence)[0])[0]
#   if randomize:
#     if not num_chunks:
#       num_chunks = tf.maximum(1, length // chunk_length - 1)
#     else:
#       num_chunks = num_chunks + 0 * length
#     used_length = num_chunks * chunk_length
#     max_offset = length - used_length
#     offset = tf.random_uniform((), 0, max_offset + 1, dtype=tf.int32)
#   else:
#     if num_chunks is None:
#       num_chunks = length // chunk_length
#     else:
#       num_chunks = num_chunks + 0 * length
#     used_length = num_chunks * chunk_length
#     offset = 0
#   clipped = nested.map(
#       lambda tensor: tensor[offset: offset + used_length],
#       sequence)
#   chunks = nested.map(
#       lambda tensor: tf.reshape(
#           tensor, [num_chunks, chunk_length] + tensor.shape[1:].as_list()),
#       clipped)
#   return chunks


import functools
import os
import random
import time
import numpy as np
import tensorflow as tf
from scipy.ndimage import interpolation
from dreamer.tools import attr_dict
from dreamer.tools import chunk_sequence
  while True:
    episode = {}
    for key in dtypes:
      dtype, shape = dtypes[key], (length,) + shapes[key][1:]
      if dtype in (np.float32, np.float64):
        episode[key] = random.uniform(0, 1, shape).astype(dtype)
      elif dtype in (np.int32, np.int64, np.uint8):
        episode[key] = random.uniform(0, 255, shape).astype(dtype)
      else:
        raise NotImplementedError('Unsupported dtype {}.'.format(dtype))
    yield episode


def episode_reader(
    filename, resize=None, max_length=None, action_noise=None,
    clip_rewards=False, pcont_scale=None):
  try:
    with tf.gfile.Open(filename, 'rb') as file_:
      episode = np.load(file_)
  except (IOError, ValueError):
    # Try again one second later, in case the file was still being written.
    time.sleep(1)
    with tf.gfile.Open(filename, 'rb') as file_:
      episode = np.load(file_)
  episode = {key: _convert_type(episode[key]) for key in episode.keys()}
  episode['return'] = np.cumsum(episode['reward'])
  if 'reward_mask' not in episode:
    episode['reward_mask'] = np.ones_like(episode['reward'])[..., None]
  if max_length:
    episode = {key: value[:max_length] for key, value in episode.items()}

# Path: dreamer/tools/attr_dict.py
# class AttrDict(dict):
#   def __init__(self, *args, **kwargs):
#   def __getattr__(self, name):
#   def __setattr__(self, name, value):
#   def __getitem__(self, name):
#   def __setitem__(self, name, value):
#   def __repr__(self):
#   def get(self, key, default=None):
#   def untouched(self):
#   def unlocked(self):
#   def lock(self):
#   def unlock(self):
#   def summarize(self):
#   def update(self, mapping):
#   def copy(self, _unlocked=False):
#   def save(self, filename):
#   def load(cls, filename):
#   def _format_value(self, value):
# 
# Path: dreamer/tools/chunk_sequence.py
# def chunk_sequence(sequence, chunk_length, randomize=True, num_chunks=None):
#   if 'length' in sequence:
#     length = sequence.pop('length')
#   else:
#     length = tf.shape(nested.flatten(sequence)[0])[0]
#   if randomize:
#     if not num_chunks:
#       num_chunks = tf.maximum(1, length // chunk_length - 1)
#     else:
#       num_chunks = num_chunks + 0 * length
#     used_length = num_chunks * chunk_length
#     max_offset = length - used_length
#     offset = tf.random_uniform((), 0, max_offset + 1, dtype=tf.int32)
#   else:
#     if num_chunks is None:
#       num_chunks = length // chunk_length
#     else:
#       num_chunks = num_chunks + 0 * length
#     used_length = num_chunks * chunk_length
#     offset = 0
#   clipped = nested.map(
#       lambda tensor: tensor[offset: offset + used_length],
#       sequence)
#   chunks = nested.map(
#       lambda tensor: tf.reshape(
#           tensor, [num_chunks, chunk_length] + tensor.shape[1:].as_list()),
#       clipped)
#   return chunks


# Path: dreamer/tools/numpy_episodes.py
import functools
import os
import random
import time
import numpy as np
import tensorflow as tf
from scipy.ndimage import interpolation
from dreamer.tools import attr_dict
from dreamer.tools import chunk_sequence

  while True:
    episode = {}
    for key in dtypes:
      dtype, shape = dtypes[key], (length,) + shapes[key][1:]
      if dtype in (np.float32, np.float64):
        episode[key] = random.uniform(0, 1, shape).astype(dtype)
      elif dtype in (np.int32, np.int64, np.uint8):
        episode[key] = random.uniform(0, 255, shape).astype(dtype)
      else:
        raise NotImplementedError('Unsupported dtype {}.'.format(dtype))
    yield episode


def episode_reader(
    filename, resize=None, max_length=None, action_noise=None,
    clip_rewards=False, pcont_scale=None):
  try:
    with tf.gfile.Open(filename, 'rb') as file_:
      episode = np.load(file_)
  except (IOError, ValueError):
    # Try again one second later, in case the file was still being written.
    time.sleep(1)
    with tf.gfile.Open(filename, 'rb') as file_:
      episode = np.load(file_)
  episode = {key: _convert_type(episode[key]) for key in episode.keys()}
  episode['return'] = np.cumsum(episode['reward'])
  if 'reward_mask' not in episode:
    episode['reward_mask'] = np.ones_like(episode['reward'])[..., None]
  if max_length:
    episode = {key: value[:max_length] for key, value in episode.items()}

  if resize and resize != 1: