# Path: envlogger/step_data.py
# class StepData(NamedTuple):
# 
# Path: envlogger/backends/in_memory_backend.py
# class InMemoryBackendWriter(backend_writer.BackendWriter):
# class InMemoryBackendReader(backend_reader.BackendReader):
#   def __init__(self, **base_kwargs):
#   def _record_step(self, data: step_data.StepData,
#                    is_new_episode: bool) -> None:
#   def set_episode_metadata(self, data: Any) -> None:
#   def close(self) -> None:
#   def __init__(self, in_memory_backend_writer: InMemoryBackendWriter):
#   def _copy(self) -> 'InMemoryBackendReader':
#   def close(self) -> None:
#   def _get_nth_step(self, i: int) -> step_data.StepData:
#   def _get_nth_episode_info(self,
#                             i: int,
#                             include_metadata: bool = False
#                            ) -> episode_info.EpisodeInfo:
#   def _get_num_steps(self) -> int:
#   def _get_num_episodes(self) -> int:
#   def metadata(self):
# 
# Path: envlogger/backends/schedulers.py
# def n_step_scheduler(step_interval: int) -> Scheduler:
#   def f(unused_data: step_data.StepData) -> bool:
# def bernoulli_step_scheduler(keep_probability: float) -> Scheduler:
#   def f(unused_data: step_data.StepData) -> bool:
# def n_episode_scheduler(episode_interval: int) -> Scheduler:
#   def f(data: step_data.StepData) -> bool:
# def bernoulli_episode_scheduler(keep_probability: float) -> Scheduler:
#   def f(data: step_data.StepData) -> bool:
# def list_step_scheduler(
#     desired_steps: Union[List[int], np.ndarray]) -> Scheduler:
#   def f(unused_data: step_data.StepData) -> bool:
# def list_episode_scheduler(
#     desired_episodes: Union[List[int], np.ndarray]) -> Scheduler:
#   def f(data: step_data.StepData) -> bool:
# 
# Path: envlogger/testing/catch_env.py
# _ACTIONS = (-1, 0, 1)  # Left, no-op, right.
# class Catch(dm_env.Environment):
#   def __init__(self, rows=10, columns=5, seed=1):
#   def reset(self):
#   def step(self, action):
#   def observation_spec(self):
#   def action_spec(self):
#   def _observation(self):


import operator
import numpy as np
from typing import List, Optional, Tuple
from absl import logging
from absl.testing import absltest
from absl.testing import parameterized
from envlogger import step_data
from envlogger.backends import in_memory_backend
from envlogger.backends import schedulers
from envlogger.testing import catch_env
                      num_steps_per_episode=10):
    num_steps = num_episodes * num_steps_per_episode
    self.assertLen(actual_steps, num_steps)
    self.assertLen(expected_steps, num_steps)
    for actual_step, expected_step in zip(actual_steps, expected_steps):
      np.testing.assert_equal(actual_step, expected_step)

  def test_steps_round_trip(self):
    num_episodes = 3
    expected_steps, backend = _collect_episode_data(num_episodes)
    actual_steps = list(in_memory_backend.InMemoryBackendReader(backend).steps)
    self._validate_steps(actual_steps, expected_steps, num_episodes)

  def test_episodes_round_trip(self):
    num_episodes = 3
    num_steps_per_episode = 10
    expected_steps, backend = _collect_episode_data(num_episodes)
    for episode_index, episode in enumerate(
        in_memory_backend.InMemoryBackendReader(backend).episodes):
      episode_actual_steps = list(episode)
      episode_expected_steps = expected_steps[episode_index *
                                              num_steps_per_episode:
                                              (episode_index + 1) *
                                              num_steps_per_episode]
      self._validate_steps(
          episode_actual_steps, episode_expected_steps, num_episodes=1)

  def test_scheduler(self):
    num_episodes = 2
    step_interval = 2

# Path: envlogger/step_data.py
# class StepData(NamedTuple):
# 
# Path: envlogger/backends/in_memory_backend.py
# class InMemoryBackendWriter(backend_writer.BackendWriter):
# class InMemoryBackendReader(backend_reader.BackendReader):
#   def __init__(self, **base_kwargs):
#   def _record_step(self, data: step_data.StepData,
#                    is_new_episode: bool) -> None:
#   def set_episode_metadata(self, data: Any) -> None:
#   def close(self) -> None:
#   def __init__(self, in_memory_backend_writer: InMemoryBackendWriter):
#   def _copy(self) -> 'InMemoryBackendReader':
#   def close(self) -> None:
#   def _get_nth_step(self, i: int) -> step_data.StepData:
#   def _get_nth_episode_info(self,
#                             i: int,
#                             include_metadata: bool = False
#                            ) -> episode_info.EpisodeInfo:
#   def _get_num_steps(self) -> int:
#   def _get_num_episodes(self) -> int:
#   def metadata(self):
# 
# Path: envlogger/backends/schedulers.py
# def n_step_scheduler(step_interval: int) -> Scheduler:
#   def f(unused_data: step_data.StepData) -> bool:
# def bernoulli_step_scheduler(keep_probability: float) -> Scheduler:
#   def f(unused_data: step_data.StepData) -> bool:
# def n_episode_scheduler(episode_interval: int) -> Scheduler:
#   def f(data: step_data.StepData) -> bool:
# def bernoulli_episode_scheduler(keep_probability: float) -> Scheduler:
#   def f(data: step_data.StepData) -> bool:
# def list_step_scheduler(
#     desired_steps: Union[List[int], np.ndarray]) -> Scheduler:
#   def f(unused_data: step_data.StepData) -> bool:
# def list_episode_scheduler(
#     desired_episodes: Union[List[int], np.ndarray]) -> Scheduler:
#   def f(data: step_data.StepData) -> bool:
# 
# Path: envlogger/testing/catch_env.py
# _ACTIONS = (-1, 0, 1)  # Left, no-op, right.
# class Catch(dm_env.Environment):
#   def __init__(self, rows=10, columns=5, seed=1):
#   def reset(self):
#   def step(self, action):
#   def observation_spec(self):
#   def action_spec(self):
#   def _observation(self):


# Path: envlogger/backends/in_memory_backend_test.py
import operator
import numpy as np
from typing import List, Optional, Tuple
from absl import logging
from absl.testing import absltest
from absl.testing import parameterized
from envlogger import step_data
from envlogger.backends import in_memory_backend
from envlogger.backends import schedulers
from envlogger.testing import catch_env

                      num_steps_per_episode=10):
    num_steps = num_episodes * num_steps_per_episode
    self.assertLen(actual_steps, num_steps)
    self.assertLen(expected_steps, num_steps)
    for actual_step, expected_step in zip(actual_steps, expected_steps):
      np.testing.assert_equal(actual_step, expected_step)

  def test_steps_round_trip(self):
    num_episodes = 3
    expected_steps, backend = _collect_episode_data(num_episodes)
    actual_steps = list(in_memory_backend.InMemoryBackendReader(backend).steps)
    self._validate_steps(actual_steps, expected_steps, num_episodes)

  def test_episodes_round_trip(self):
    num_episodes = 3
    num_steps_per_episode = 10
    expected_steps, backend = _collect_episode_data(num_episodes)
    for episode_index, episode in enumerate(
        in_memory_backend.InMemoryBackendReader(backend).episodes):
      episode_actual_steps = list(episode)
      episode_expected_steps = expected_steps[episode_index *
                                              num_steps_per_episode:
                                              (episode_index + 1) *
                                              num_steps_per_episode]
      self._validate_steps(
          episode_actual_steps, episode_expected_steps, num_episodes=1)

  def test_scheduler(self):
    num_episodes = 2
    step_interval = 2

    scheduler = schedulers.n_step_scheduler(step_interval=step_interval)