# Path: compiler_opt/rl/compilation_runner.py
# class RewardStat:
# class DataClassJSONEncoder(json.JSONEncoder):
# class ProcessKilledError(Exception):
# class ProcessCancellationToken:
# class WorkerCancellationManager:
# class CompilationRunner:
#   def default(self, o):
# def _postprocessing_sequence_example(
#     sequence_example: tf.train.SequenceExample, moving_average_reward: float,
#     policy_reward: float) -> tf.train.SequenceExample:
# def get_command_line_for_bundle(cmd_file: str,
#                                 ir_file: str,
#                                 thinlto: Optional[str] = None) -> List[str]:
#   def __init__(self):
#   def __init__(self):
#   def signal(self):
#   def wait(self):
# def kill_process_ignore_exceptions(p: subprocess.Popen):
#   def __init__(self):
#   def register_process(self, p: subprocess.Popen):
#   def signal(self):
#   def unregister_process(self, p: subprocess.Popen):
# def start_cancellable_process(
#     cmdline: List[str],
#     timeout: float,
#     cancellation_manager: Optional[WorkerCancellationManager],
#     want_output: bool = False) -> Optional[bytes]:
#   def init_pool():
#   def _get_pool():
#   def __init__(self,
#                clang_path: str,
#                llvm_size_path: str,
#                launcher_path: Optional[str] = None,
#                moving_average_decay_rate: float = 1):
#   def _get_cancellation_manager(
#       self, cancellation_token: Optional[ProcessCancellationToken]
#   ) -> Optional[WorkerCancellationManager]:
#     def signaler():
#   def collect_data(
#       self,
#       file_paths: Tuple[str, ...],
#       tf_policy_path: str,
#       reward_stat: Optional[Dict[str, RewardStat]],
#       cancellation_token: Optional[ProcessCancellationToken] = None
#   ) -> Tuple[List[str], Dict[str, RewardStat], List[float]]:
#   def _compile_fn(
#       self, file_paths: Tuple[str, ...], tf_policy_path: str, reward_only: bool,
#       cancellation_manager: Optional[WorkerCancellationManager]
#   ) -> Dict[str, Tuple[tf.train.SequenceExample, float]]:
#   _POOL: concurrent.futures.ThreadPoolExecutor = None


import io
import os
import tempfile
import tensorflow as tf
from typing import Tuple, Dict, Optional
from compiler_opt.rl import compilation_runner
"""Module for collect data of inlining-for-size."""





# TODO(mtrofin): maybe the deadline is a requirement for plugins (such as the
# inliner) and the data collector expects and uses it to define its own? This
# would serve as an extra hint to the developer of a new plugin to make sure
# their long-running tasks have timeouts.
_DEADLINE_IN_SECONDS = 60

_DEFAULT_IDENTIFIER = 'default'


class InliningRunner(compilation_runner.CompilationRunner):
  """Class for collecting data for inlining-for-size.

  Usage:
  inliner = InliningRunner(
                clang_path, llvm_size_path, launcher_path,
                moving_average_decay_rate)
  serialized_sequence_example, default_reward, moving_average_reward,
  policy_reward = inliner.collect_data(
      ir_path, tf_policy_path, default_reward, moving_average_reward)
  """

  def _compile_fn(
      self, file_paths: Tuple[str, str], tf_policy_path: str, reward_only: bool,
      cancellation_manager: Optional[

# Path: compiler_opt/rl/compilation_runner.py
# class RewardStat:
# class DataClassJSONEncoder(json.JSONEncoder):
# class ProcessKilledError(Exception):
# class ProcessCancellationToken:
# class WorkerCancellationManager:
# class CompilationRunner:
#   def default(self, o):
# def _postprocessing_sequence_example(
#     sequence_example: tf.train.SequenceExample, moving_average_reward: float,
#     policy_reward: float) -> tf.train.SequenceExample:
# def get_command_line_for_bundle(cmd_file: str,
#                                 ir_file: str,
#                                 thinlto: Optional[str] = None) -> List[str]:
#   def __init__(self):
#   def __init__(self):
#   def signal(self):
#   def wait(self):
# def kill_process_ignore_exceptions(p: subprocess.Popen):
#   def __init__(self):
#   def register_process(self, p: subprocess.Popen):
#   def signal(self):
#   def unregister_process(self, p: subprocess.Popen):
# def start_cancellable_process(
#     cmdline: List[str],
#     timeout: float,
#     cancellation_manager: Optional[WorkerCancellationManager],
#     want_output: bool = False) -> Optional[bytes]:
#   def init_pool():
#   def _get_pool():
#   def __init__(self,
#                clang_path: str,
#                llvm_size_path: str,
#                launcher_path: Optional[str] = None,
#                moving_average_decay_rate: float = 1):
#   def _get_cancellation_manager(
#       self, cancellation_token: Optional[ProcessCancellationToken]
#   ) -> Optional[WorkerCancellationManager]:
#     def signaler():
#   def collect_data(
#       self,
#       file_paths: Tuple[str, ...],
#       tf_policy_path: str,
#       reward_stat: Optional[Dict[str, RewardStat]],
#       cancellation_token: Optional[ProcessCancellationToken] = None
#   ) -> Tuple[List[str], Dict[str, RewardStat], List[float]]:
#   def _compile_fn(
#       self, file_paths: Tuple[str, ...], tf_policy_path: str, reward_only: bool,
#       cancellation_manager: Optional[WorkerCancellationManager]
#   ) -> Dict[str, Tuple[tf.train.SequenceExample, float]]:
#   _POOL: concurrent.futures.ThreadPoolExecutor = None


# Path: compiler_opt/rl/inlining/inlining_runner.py
import io
import os
import tempfile
import tensorflow as tf
from typing import Tuple, Dict, Optional
from compiler_opt.rl import compilation_runner

"""Module for collect data of inlining-for-size."""





# TODO(mtrofin): maybe the deadline is a requirement for plugins (such as the
# inliner) and the data collector expects and uses it to define its own? This
# would serve as an extra hint to the developer of a new plugin to make sure
# their long-running tasks have timeouts.
_DEADLINE_IN_SECONDS = 60

_DEFAULT_IDENTIFIER = 'default'


class InliningRunner(compilation_runner.CompilationRunner):
  """Class for collecting data for inlining-for-size.

  Usage:
  inliner = InliningRunner(
                clang_path, llvm_size_path, launcher_path,
                moving_average_decay_rate)
  serialized_sequence_example, default_reward, moving_average_reward,
  policy_reward = inliner.collect_data(
      ir_path, tf_policy_path, default_reward, moving_average_reward)
  """

  def _compile_fn(
      self, file_paths: Tuple[str, str], tf_policy_path: str, reward_only: bool,
      cancellation_manager: Optional[

          compilation_runner.WorkerCancellationManager]