# 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
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function





def numpy_episodes(
    train_dir, test_dir, shape, reader=None, loader=None, num_chunks=None,
    preprocess_fn=None, gpu_prefetch=False):
  reader = reader or episode_reader
  loader = loader or cache_loader
  try:
    dtypes, shapes = _read_spec(reader, train_dir)
  except ZeroDivisionError:
    dtypes, shapes = _read_spec(reader, test_dir)
  train = tf.data.Dataset.from_generator(
      functools.partial(loader, reader, train_dir),
      dtypes, shapes)
  test = tf.data.Dataset.from_generator(
      functools.partial(cache_loader, reader, test_dir, shape[0]),
      dtypes, shapes)
  chunking = lambda x: tf.data.Dataset.from_tensor_slices(

# 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

# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function





def numpy_episodes(
    train_dir, test_dir, shape, reader=None, loader=None, num_chunks=None,
    preprocess_fn=None, gpu_prefetch=False):
  reader = reader or episode_reader
  loader = loader or cache_loader
  try:
    dtypes, shapes = _read_spec(reader, train_dir)
  except ZeroDivisionError:
    dtypes, shapes = _read_spec(reader, test_dir)
  train = tf.data.Dataset.from_generator(
      functools.partial(loader, reader, train_dir),
      dtypes, shapes)
  test = tf.data.Dataset.from_generator(
      functools.partial(cache_loader, reader, test_dir, shape[0]),
      dtypes, shapes)
  chunking = lambda x: tf.data.Dataset.from_tensor_slices(

      chunk_sequence.chunk_sequence(x, shape[1], True, num_chunks))