old_contents: # coding=utf-8
  # Copyright 2020 The TF-Agents Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # 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.
  
  """Common utilities for TF-Agents Environments."""
  
  from __future__ import absolute_import
  from __future__ import division
  # Using Type Annotations.
  from __future__ import print_function
  
  from typing import Optional, Union
  
  from tf_agents.environments import py_environment
  from tf_agents.environments import tf_environment
  from tf_agents.environments import tf_py_environment
  from tf_agents.policies import random_py_policy
  from tf_agents.specs import array_spec
  from tf_agents.typing import types
  
  
  def get_tf_env(
      environment: Union[py_environment.PyEnvironment,
                         tf_environment.TFEnvironment]
  ) -> tf_environment.TFEnvironment:
    """Ensures output is a tf_environment, wrapping py_environments if needed."""
    if environment is None:
      raise ValueError('`environment` cannot be None')
    if isinstance(environment, py_environment.PyEnvironment):
      tf_env = tf_py_environment.TFPyEnvironment(environment)
    elif isinstance(environment, tf_environment.TFEnvironment):
      tf_env = environment
    else:
      raise ValueError(
          '`environment` %s must be an instance of '
          '`tf_environment.TFEnvironment` or `py_environment.PyEnvironment`.' %
          environment)
    return tf_env
  
  
  def validate_py_environment(
      environment: py_environment.PyEnvironment,
      episodes: int = 5,
      seed: Optional[types.Seed] = None,
      observation_and_action_constraint_splitter: Optional[
          types.Splitter] = None):
    """Validates the environment follows the defined specs."""
    time_step_spec = environment.time_step_spec()
    action_spec = environment.action_spec()
  
    random_policy = random_py_policy.RandomPyPolicy(
        time_step_spec=time_step_spec, action_spec=action_spec,
        seed=seed,
        observation_and_action_constraint_splitter=(
          observation_and_action_constraint_splitter))
  
    episode_count = 0
    time_step = environment.reset()
  
    while episode_count < episodes:
      if not array_spec.check_arrays_nest(time_step, time_step_spec):
        raise ValueError(
            'Given `time_step`: %r does not match expected `time_step_spec`: %r' %
            (time_step, time_step_spec))
  
      action = random_policy.action(time_step).action
      time_step = environment.step(action)
  
      if time_step.is_last():
        episode_count += 1
        time_step = environment.reset()
  
new_contents: # coding=utf-8
  # Copyright 2020 The TF-Agents Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # 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.
  
  """Common utilities for TF-Agents Environments."""
  
  from __future__ import absolute_import
  from __future__ import division
  # Using Type Annotations.
  from __future__ import print_function
  
  from typing import Optional, Union
  
  from tf_agents.environments import py_environment
  from tf_agents.environments import tf_environment
  from tf_agents.environments import tf_py_environment
  from tf_agents.policies import random_py_policy
  from tf_agents.specs import array_spec
  from tf_agents.typing import types
  
  
  def get_tf_env(
      environment: Union[py_environment.PyEnvironment,
                         tf_environment.TFEnvironment]
  ) -> tf_environment.TFEnvironment:
    """Ensures output is a tf_environment, wrapping py_environments if needed."""
    if environment is None:
      raise ValueError('`environment` cannot be None')
    if isinstance(environment, py_environment.PyEnvironment):
      tf_env = tf_py_environment.TFPyEnvironment(environment)
    elif isinstance(environment, tf_environment.TFEnvironment):
      tf_env = environment
    else:
      raise ValueError(
          '`environment` %s must be an instance of '
          '`tf_environment.TFEnvironment` or `py_environment.PyEnvironment`.' %
          environment)
    return tf_env
  
  
  def validate_py_environment(
      environment: py_environment.PyEnvironment,
      episodes: int = 5,
      observation_and_action_constraint_splitter: Optional[
          types.Splitter] = None):
    """Validates the environment follows the defined specs."""
    time_step_spec = environment.time_step_spec()
    action_spec = environment.action_spec()
  
    random_policy = random_py_policy.RandomPyPolicy(
        time_step_spec=time_step_spec, 
        action_spec=action_spec,
        observation_and_action_constraint_splitter=(
          observation_and_action_constraint_splitter))
  
    episode_count = 0
    time_step = environment.reset()
  
    while episode_count < episodes:
      if not array_spec.check_arrays_nest(time_step, time_step_spec):
        raise ValueError(
            'Given `time_step`: %r does not match expected `time_step_spec`: %r' %
            (time_step, time_step_spec))
  
      action = random_policy.action(time_step).action
      time_step = environment.step(action)
  
      if time_step.is_last():
        episode_count += 1
        time_step = environment.reset()
  
subject: Remove extraneous parameters