code: # Copyright The PyTorch Lightning team.
  #
  # 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
  #
  #     http://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.
  """Trainer to automate the training."""
  import inspect
  import logging
  import math
  import os
  import traceback
  import warnings
  from argparse import ArgumentParser, Namespace
  from copy import deepcopy
  from datetime import timedelta
  from pathlib import Path
  from typing import Any, Callable, cast, Dict, Iterable, List, Optional, Tuple, Type, Union
  from weakref import proxy
  
  import torch
  from packaging.version import Version
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  
  import pytorch_lightning as pl
  from pytorch_lightning.accelerators import Accelerator, GPUAccelerator, IPUAccelerator, TPUAccelerator
  from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint, ProgressBarBase
  from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter
  from pytorch_lightning.core.datamodule import LightningDataModule
  from pytorch_lightning.core.optimizer import LightningOptimizer
  from pytorch_lightning.loggers import LightningLoggerBase
  from pytorch_lightning.loggers.base import DummyLogger, LoggerCollection
  from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
  from pytorch_lightning.loops import PredictionLoop, TrainingEpochLoop
  from pytorch_lightning.loops.dataloader.evaluation_loop import EvaluationLoop
  from pytorch_lightning.loops.fit_loop import FitLoop
  from pytorch_lightning.loops.utilities import _parse_loop_limits, _reset_progress
  from pytorch_lightning.plugins import (
      ApexMixedPrecisionPlugin,
      NativeMixedPrecisionPlugin,
      PLUGIN_INPUT,
      PrecisionPlugin,
  )
  from pytorch_lightning.plugins.environments.slurm_environment import SLURMEnvironment
  from pytorch_lightning.profiler import (
      AdvancedProfiler,
      BaseProfiler,
      PassThroughProfiler,
      PyTorchProfiler,
      SimpleProfiler,
      XLAProfiler,
  )
  from pytorch_lightning.strategies import ParallelStrategy, Strategy
  from pytorch_lightning.strategies.ddp_spawn import DDPSpawnStrategy
  from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
  from pytorch_lightning.trainer.configuration_validator import verify_loop_configurations
  from pytorch_lightning.trainer.connectors.accelerator_connector import AcceleratorConnector
  from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
  from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
  from pytorch_lightning.trainer.connectors.data_connector import DataConnector
  from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
  from pytorch_lightning.trainer.connectors.logger_connector.result import _ResultCollection
  from pytorch_lightning.trainer.connectors.signal_connector import SignalConnector
  from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
  from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
  from pytorch_lightning.trainer.states import RunningStage, TrainerFn, TrainerState, TrainerStatus
  from pytorch_lightning.trainer.supporters import CombinedLoader
  from pytorch_lightning.tuner.lr_finder import _LRFinder
  from pytorch_lightning.tuner.tuning import Tuner
  from pytorch_lightning.utilities import (
      _IPU_AVAILABLE,
      _TPU_AVAILABLE,
      AMPType,
      device_parser,
      GradClipAlgorithmType,
      parsing,
  )
  from pytorch_lightning.utilities.apply_func import apply_to_collection
  from pytorch_lightning.utilities.argparse import (
      _defaults_from_env_vars,
      add_argparse_args,
      from_argparse_args,
      parse_argparser,
      parse_env_variables,
  )
  from pytorch_lightning.utilities.auto_restart import _add_capture_metadata_collate
  from pytorch_lightning.utilities.cloud_io import get_filesystem
  from pytorch_lightning.utilities.data import _auto_add_worker_init_fn, has_len_all_ranks
  from pytorch_lightning.utilities.distributed import distributed_available
  from pytorch_lightning.utilities.exceptions import ExitGracefullyException, MisconfigurationException
  from pytorch_lightning.utilities.imports import _fault_tolerant_training
  from pytorch_lightning.utilities.meta import is_on_meta_device, materialize_module
  from pytorch_lightning.utilities.model_helpers import is_overridden
  from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
  from pytorch_lightning.utilities.seed import isolate_rng
  from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
  from pytorch_lightning.utilities.types import (
      _EVALUATE_OUTPUT,
      _PATH,
      _PREDICT_OUTPUT,
      EVAL_DATALOADERS,
      LRSchedulerConfig,
      STEP_OUTPUT,
      TRAIN_DATALOADERS,
  )
  from pytorch_lightning.utilities.warnings import PossibleUserWarning
  
  log = logging.getLogger(__name__)
  # warnings to ignore in trainer
  warnings.filterwarnings(
      "ignore", message="torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead"
  )
  
  
  class Trainer(
      TrainerCallbackHookMixin,  # TODO: Remove in v1.8
      TrainerOptimizersMixin,  # TODO: Remove in v1.8
      TrainerDataLoadingMixin,  # TODO: Remove in v1.8
  ):
      @_defaults_from_env_vars
      def __init__(
          self,
          logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,
          checkpoint_callback: Optional[bool] = None,
          enable_checkpointing: bool = True,
          callbacks: Optional[Union[List[Callback], Callback]] = None,
          default_root_dir: Optional[str] = None,
          gradient_clip_val: Optional[Union[int, float]] = None,
          gradient_clip_algorithm: Optional[str] = None,
          process_position: int = 0,
          num_nodes: int = 1,
          num_processes: Optional[int] = None,
          devices: Optional[Union[List[int], str, int]] = None,
          gpus: Optional[Union[List[int], str, int]] = None,
          auto_select_gpus: bool = False,
          tpu_cores: Optional[Union[List[int], str, int]] = None,
          ipus: Optional[int] = None,
          log_gpu_memory: Optional[str] = None,  # TODO: Remove in 1.7
          progress_bar_refresh_rate: Optional[int] = None,  # TODO: remove in v1.7
          enable_progress_bar: bool = True,
          overfit_batches: Union[int, float] = 0.0,
          track_grad_norm: Union[int, float, str] = -1,
          check_val_every_n_epoch: int = 1,
          fast_dev_run: Union[int, bool] = False,
          accumulate_grad_batches: Optional[Union[int, Dict[int, int]]] = None,
          max_epochs: Optional[int] = None,
          min_epochs: Optional[int] = None,
          max_steps: int = -1,
          min_steps: Optional[int] = None,
          max_time: Optional[Union[str, timedelta, Dict[str, int]]] = None,
          limit_train_batches: Optional[Union[int, float]] = None,
          limit_val_batches: Optional[Union[int, float]] = None,
          limit_test_batches: Optional[Union[int, float]] = None,
          limit_predict_batches: Optional[Union[int, float]] = None,
          val_check_interval: Optional[Union[int, float]] = None,
          flush_logs_every_n_steps: Optional[int] = None,
          log_every_n_steps: int = 50,
          accelerator: Optional[Union[str, Accelerator]] = None,
          strategy: Optional[Union[str, Strategy]] = None,
          sync_batchnorm: bool = False,
          precision: Union[int, str] = 32,
          enable_model_summary: bool = True,
          weights_summary: Optional[str] = "top",
          weights_save_path: Optional[str] = None,  # TODO: Remove in 1.8
          num_sanity_val_steps: int = 2,
          resume_from_checkpoint: Optional[Union[Path, str]] = None,
          profiler: Optional[Union[BaseProfiler, str]] = None,
          benchmark: Optional[bool] = None,
          deterministic: bool = False,
          reload_dataloaders_every_n_epochs: int = 0,
          auto_lr_find: Union[bool, str] = False,
          replace_sampler_ddp: bool = True,
          detect_anomaly: bool = False,
          auto_scale_batch_size: Union[str, bool] = False,
          prepare_data_per_node: Optional[bool] = None,
          plugins: Optional[Union[PLUGIN_INPUT, List[PLUGIN_INPUT]]] = None,
          amp_backend: str = "native",
          amp_level: Optional[str] = None,
          move_metrics_to_cpu: bool = False,
          multiple_trainloader_mode: str = "max_size_cycle",
          stochastic_weight_avg: bool = False,
          terminate_on_nan: Optional[bool] = None,
      ) -> None:
          r"""
          Customize every aspect of training via flags.
  
          Args:
  
              accelerator: Supports passing different accelerator types ("cpu", "gpu", "tpu", "ipu", "auto")
                  as well as custom accelerator instances.
  
                  .. deprecated:: v1.5
                      Passing training strategies (e.g., 'ddp') to ``accelerator`` has been deprecated in v1.5.0
                      and will be removed in v1.7.0. Please use the ``strategy`` argument instead.
  
              accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict.
                  Default: ``None``.
  
              amp_backend: The mixed precision backend to use ("native" or "apex").
                  Default: ``'native''``.
  
              amp_level: The optimization level to use (O1, O2, etc...). By default it will be set to "O2"
                  if ``amp_backend`` is set to "apex".
  
              auto_lr_find: If set to True, will make trainer.tune() run a learning rate finder,
                  trying to optimize initial learning for faster convergence. trainer.tune() method will
                  set the suggested learning rate in self.lr or self.learning_rate in the LightningModule.
                  To use a different key set a string instead of True with the key name.
                  Default: ``False``.
  
              auto_scale_batch_size: If set to True, will `initially` run a batch size
                  finder trying to find the largest batch size that fits into memory.
                  The result will be stored in self.batch_size in the LightningModule.
                  Additionally, can be set to either `power` that estimates the batch size through
                  a power search or `binsearch` that estimates the batch size through a binary search.
                  Default: ``False``.
  
              auto_select_gpus: If enabled and ``gpus`` is an integer, pick available
                  gpus automatically. This is especially useful when
                  GPUs are configured to be in "exclusive mode", such
                  that only one process at a time can access them.
                  Default: ``False``.
  
              benchmark: Sets ``torch.backends.cudnn.benchmark``.
                  Defaults to ``True`` if :paramref:`~pytorch_lightning.trainer.trainer.Trainer.deterministic`
                  is ``False``. Overwrite to manually set a different value. Default: ``None``.
  
              callbacks: Add a callback or list of callbacks.
                  Default: ``None``.
  
              checkpoint_callback: If ``True``, enable checkpointing.
                  Default: ``None``.
  
                  .. deprecated:: v1.5
                      ``checkpoint_callback`` has been deprecated in v1.5 and will be removed in v1.7.
                      Please consider using ``enable_checkpointing`` instead.
  
              enable_checkpointing: If ``True``, enable checkpointing.
                  It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in
                  :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`.
                  Default: ``True``.
  
              check_val_every_n_epoch: Check val every n train epochs.
                  Default: ``1``.
  
  
              default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed.
                  Default: ``os.getcwd()``.
                  Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'
  
              detect_anomaly: Enable anomaly detection for the autograd engine.
                  Default: ``False``.
  
              deterministic: If ``True``, sets whether PyTorch operations must use deterministic algorithms.
                  Default: ``False``.
  
              devices: Will be mapped to either `gpus`, `tpu_cores`, `num_processes` or `ipus`,
                  based on the accelerator type.
  
              fast_dev_run: Runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es)
                  of train, val and test to find any bugs (ie: a sort of unit test).
                  Default: ``False``.
  
              flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps).
  
                  .. deprecated:: v1.5
                      ``flush_logs_every_n_steps`` has been deprecated in v1.5 and will be removed in v1.7.
                      Please configure flushing directly in the logger instead.
  
              gpus: Number of GPUs to train on (int) or which GPUs to train on (list or str) applied per node
                  Default: ``None``.
  
              gradient_clip_val: The value at which to clip gradients. Passing ``gradient_clip_val=None`` disables
                  gradient clipping. If using Automatic Mixed Precision (AMP), the gradients will be unscaled before.
                  Default: ``None``.
  
              gradient_clip_algorithm: The gradient clipping algorithm to use. Pass ``gradient_clip_algorithm="value"``
                  to clip by value, and ``gradient_clip_algorithm="norm"`` to clip by norm. By default it will
                  be set to ``"norm"``.
  
              limit_train_batches: How much of training dataset to check (float = fraction, int = num_batches).
                  Default: ``1.0``.
  
              limit_val_batches: How much of validation dataset to check (float = fraction, int = num_batches).
                  Default: ``1.0``.
  
              limit_test_batches: How much of test dataset to check (float = fraction, int = num_batches).
                  Default: ``1.0``.
  
              limit_predict_batches: How much of prediction dataset to check (float = fraction, int = num_batches).
                  Default: ``1.0``.
  
              logger: Logger (or iterable collection of loggers) for experiment tracking. A ``True`` value uses
                  the default ``TensorBoardLogger``. ``False`` will disable logging. If multiple loggers are
                  provided and the `save_dir` property of that logger is not set, local files (checkpoints,
                  profiler traces, etc.) are saved in ``default_root_dir`` rather than in the ``log_dir`` of any
                  of the individual loggers.
                  Default: ``True``.
  
              log_gpu_memory: None, 'min_max', 'all'. Might slow performance.
  
                  .. deprecated:: v1.5
                      Deprecated in v1.5.0 and will be removed in v1.7.0
                      Please use the ``DeviceStatsMonitor`` callback directly instead.
  
              log_every_n_steps: How often to log within steps.
                  Default: ``50``.
  
              prepare_data_per_node: If True, each LOCAL_RANK=0 will call prepare data.
                  Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data
  
                  .. deprecated:: v1.5
                      Deprecated in v1.5.0 and will be removed in v1.7.0
                      Please set ``prepare_data_per_node`` in ``LightningDataModule`` and/or
                      ``LightningModule`` directly instead.
  
              process_position: Orders the progress bar when running multiple models on same machine.
  
                  .. deprecated:: v1.5
                      ``process_position`` has been deprecated in v1.5 and will be removed in v1.7.
                      Please pass :class:`~pytorch_lightning.callbacks.progress.TQDMProgressBar` with ``process_position``
                      directly to the Trainer's ``callbacks`` argument instead.
  
              progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar.
                  Ignored when a custom progress bar is passed to :paramref:`~Trainer.callbacks`. Default: None, means
                  a suitable value will be chosen based on the environment (terminal, Google COLAB, etc.).
  
                  .. deprecated:: v1.5
                      ``progress_bar_refresh_rate`` has been deprecated in v1.5 and will be removed in v1.7.
                      Please pass :class:`~pytorch_lightning.callbacks.progress.TQDMProgressBar` with ``refresh_rate``
                      directly to the Trainer's ``callbacks`` argument instead. To disable the progress bar,
                      pass ``enable_progress_bar = False`` to the Trainer.
  
              enable_progress_bar: Whether to enable to progress bar by default.
                  Default: ``False``.
  
              profiler: To profile individual steps during training and assist in identifying bottlenecks.
                  Default: ``None``.
  
              overfit_batches: Overfit a fraction of training data (float) or a set number of batches (int).
                  Default: ``0.0``.
  
              plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
                  Default: ``None``.
  
              precision: Double precision (64), full precision (32), half precision (16) or bfloat16 precision (bf16).
                  Can be used on CPU, GPU, TPUs or IPUs.
                  Default: ``32``.
  
              max_epochs: Stop training once this number of epochs is reached. Disabled by default (None).
                  If both max_epochs and max_steps are not specified, defaults to ``max_epochs = 1000``.
                  To enable infinite training, set ``max_epochs = -1``.
  
              min_epochs: Force training for at least these many epochs. Disabled by default (None).
  
              max_steps: Stop training after this number of steps. Disabled by default (-1). If ``max_steps = -1``
                  and ``max_epochs = None``, will default to ``max_epochs = 1000``. To enable infinite training, set
                  ``max_epochs`` to ``-1``.
  
              min_steps: Force training for at least these number of steps. Disabled by default (``None``).
  
              max_time: Stop training after this amount of time has passed. Disabled by default (``None``).
                  The time duration can be specified in the format DD:HH:MM:SS (days, hours, minutes seconds), as a
                  :class:`datetime.timedelta`, or a dictionary with keys that will be passed to
                  :class:`datetime.timedelta`.
  
              num_nodes: Number of GPU nodes for distributed training.
                  Default: ``1``.
  
              num_processes: Number of processes for distributed training with ``accelerator="cpu"``.
                  Default: ``1``.
  
              num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine.
                  Set it to `-1` to run all batches in all validation dataloaders.
                  Default: ``2``.
  
              reload_dataloaders_every_n_epochs: Set to a non-negative integer to reload dataloaders every n epochs.
                  Default: ``0``.
  
              replace_sampler_ddp: Explicitly enables or disables sampler replacement. If not specified this
                  will toggled automatically when DDP is used. By default it will add ``shuffle=True`` for
                  train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it,
                  you can set ``replace_sampler_ddp=False`` and add your own distributed sampler.
  
              resume_from_checkpoint: Path/URL of the checkpoint from which training is resumed. If there is
                  no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
                  training will start from the beginning of the next epoch.
  
                  .. deprecated:: v1.5
                      ``resume_from_checkpoint`` is deprecated in v1.5 and will be removed in v2.0.
                      Please pass the path to ``Trainer.fit(..., ckpt_path=...)`` instead.
  
              strategy: Supports different training strategies with aliases
                  as well custom training type plugins.
                  Default: ``None``.
  
              sync_batchnorm: Synchronize batch norm layers between process groups/whole world.
                  Default: ``False``.
  
              terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the
                  end of each training batch, if any of the parameters or the loss are NaN or +/-inf.
  
                  .. deprecated:: v1.5
                      Trainer argument ``terminate_on_nan`` was deprecated in v1.5 and will be removed in 1.7.
                      Please use ``detect_anomaly`` instead.
  
              detect_anomaly: Enable anomaly detection for the autograd engine.
                  Default: ``False``.
  
              tpu_cores: How many TPU cores to train on (1 or 8) / Single TPU to train on (1)
                  Default: ``None``.
  
              ipus: How many IPUs to train on.
                  Default: ``None``.
  
              track_grad_norm: -1 no tracking. Otherwise tracks that p-norm. May be set to 'inf' infinity-norm. If using
                  Automatic Mixed Precision (AMP), the gradients will be unscaled before logging them.
                  Default: ``-1``.
  
              val_check_interval: How often to check the validation set. Pass a ``float`` in the range [0.0, 1.0] to check
                  after a fraction of the training epoch. Pass an ``int`` to check after a fixed number of training
                  batches.
                  Default: ``1.0``.
  
              enable_model_summary: Whether to enable model summarization by default.
                  Default: ``True``.
  
              weights_summary: Prints a summary of the weights when training begins.
  
                  .. deprecated:: v1.5
                      ``weights_summary`` has been deprecated in v1.5 and will be removed in v1.7.
                      To disable the summary, pass ``enable_model_summary = False`` to the Trainer.
                      To customize the summary, pass :class:`~pytorch_lightning.callbacks.model_summary.ModelSummary`
                      directly to the Trainer's ``callbacks`` argument.
  
              weights_save_path: Where to save weights if specified. Will override default_root_dir
                  for checkpoints only. Use this if for whatever reason you need the checkpoints
                  stored in a different place than the logs written in `default_root_dir`.
                  Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'
                  Defaults to `default_root_dir`.
  
                  .. deprecated:: v1.6
                      ``weights_save_path`` has been deprecated in v1.6 and will be removed in v1.8. Please pass
                      ``dirpath`` directly to the :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint`
                      callback.
  
              move_metrics_to_cpu: Whether to force internal logged metrics to be moved to cpu.
                  This can save some gpu memory, but can make training slower. Use with attention.
                  Default: ``False``.
  
              multiple_trainloader_mode: How to loop over the datasets when there are multiple train loaders.
                  In 'max_size_cycle' mode, the trainer ends one epoch when the largest dataset is traversed,
                  and smaller datasets reload when running out of their data. In 'min_size' mode, all the datasets
                  reload when reaching the minimum length of datasets.
                  Default: ``"max_size_cycle"``.
  
              stochastic_weight_avg: Whether to use `Stochastic Weight Averaging (SWA)
                  <https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/>`_.
                  Default: ``False``.
  
                  .. deprecated:: v1.5
                      ``stochastic_weight_avg`` has been deprecated in v1.5 and will be removed in v1.7.
                      Please pass :class:`~pytorch_lightning.callbacks.stochastic_weight_avg.StochasticWeightAveraging`
                      directly to the Trainer's ``callbacks`` argument instead.
          """
          super().__init__()
          Trainer._log_api_event("init")
          log.detail(f"{self.__class__.__name__}: Initializing trainer with parameters: {locals()}")
          self.state = TrainerState()
  
          gpu_ids, tpu_cores = self._parse_devices(gpus, auto_select_gpus, tpu_cores)
  
          # init connectors
          self._data_connector = DataConnector(self, multiple_trainloader_mode)
  
          self._accelerator_connector = AcceleratorConnector(
              num_processes=num_processes,
              devices=devices,
              tpu_cores=tpu_cores,
              ipus=ipus,
              accelerator=accelerator,
              strategy=strategy,
              gpus=gpus,
              gpu_ids=gpu_ids,
              num_nodes=num_nodes,
              sync_batchnorm=sync_batchnorm,
              benchmark=benchmark,
              replace_sampler_ddp=replace_sampler_ddp,
              deterministic=deterministic,
              precision=precision,
              amp_type=amp_backend,
              amp_level=amp_level,
              plugins=plugins,
          )
          self._logger_connector = LoggerConnector(self, log_gpu_memory)
          self._callback_connector = CallbackConnector(self)
          self._checkpoint_connector = CheckpointConnector(self, resume_from_checkpoint)
          self._signal_connector = SignalConnector(self)
          self.tuner = Tuner(self)
  
          min_steps, max_steps, min_epochs, max_epochs, max_time = _parse_loop_limits(
              min_steps, max_steps, min_epochs, max_epochs, max_time
          )
          fit_loop = FitLoop(min_epochs=min_epochs, max_epochs=max_epochs)
          training_epoch_loop = TrainingEpochLoop(min_steps=min_steps, max_steps=max_steps)
          fit_loop.connect(epoch_loop=training_epoch_loop)
  
          # default .fit() loop
          self.fit_loop = fit_loop
  
          # default .validate() loop
          self.validate_loop = EvaluationLoop()
  
          # default .test() loop
          self.test_loop = EvaluationLoop()
  
          # default .predict() loop
          self.predict_loop = PredictionLoop()
  
          # set when a checkpoint is loaded via `Trainer.{fit,validate,test,predict}`.
          self._ckpt_path: Optional[str] = None
  
          # .validate(), predict() and .test() set these when they load a checkpoint. They will be removed in favor of
          #  the unified read-only `Trainer.ckpt_path` attribute in v1.8
          self._validated_ckpt_path: Optional[str] = None  # TODO: remove in v1.8
          self._tested_ckpt_path: Optional[str] = None  # TODO: remove in v1.8
          self._predicted_ckpt_path: Optional[str] = None  # TODO: remove in v1.8
  
          # todo: remove in v1.7
          self._weights_summary: Optional[str] = None
  
          # init callbacks
          # Declare attributes to be set in _callback_connector on_trainer_init
          self._callback_connector.on_trainer_init(
              callbacks,
              checkpoint_callback,
              enable_checkpointing,
              enable_progress_bar,
              progress_bar_refresh_rate,
              process_position,
              default_root_dir,
              weights_save_path,
              enable_model_summary,
              weights_summary,
              stochastic_weight_avg,
              max_time,
              accumulate_grad_batches,
          )
  
          # hook
          self._call_callback_hooks("on_init_start")
  
          # init data flags
          self.check_val_every_n_epoch: int
          self._data_connector.on_trainer_init(
              check_val_every_n_epoch,
              reload_dataloaders_every_n_epochs,
              prepare_data_per_node,
          )
  
          if terminate_on_nan is not None:
              rank_zero_deprecation(
                  "Trainer argument `terminate_on_nan` was deprecated in v1.5 and will be removed in 1.7."
                  " Please use `Trainer(detect_anomaly=True)` instead."
              )
              if not isinstance(terminate_on_nan, bool):
                  raise TypeError(f"`terminate_on_nan` should be a bool, got {terminate_on_nan}.")
  
          # gradient clipping
          if gradient_clip_val is not None and not isinstance(gradient_clip_val, (int, float)):
              raise TypeError(f"`gradient_clip_val` should be an int or a float. Got {gradient_clip_val}.")
  
          if gradient_clip_algorithm is not None and not GradClipAlgorithmType.supported_type(
              gradient_clip_algorithm.lower()
          ):
              raise MisconfigurationException(
                  f"`gradient_clip_algorithm` {gradient_clip_algorithm} is invalid. "
                  f"Allowed algorithms: {GradClipAlgorithmType.supported_types()}."
              )
  
          # gradient norm tracking
          if track_grad_norm != -1 and not (
              (isinstance(track_grad_norm, (int, float)) or track_grad_norm == "inf") and float(track_grad_norm) > 0
          ):
              raise MisconfigurationException(
                  f"`track_grad_norm` must be a positive number or 'inf' (infinity norm). Got {track_grad_norm}."
              )
  
          self._terminate_on_nan = terminate_on_nan
          self.gradient_clip_val: Union[int, float] = gradient_clip_val
          self.gradient_clip_algorithm = (
              GradClipAlgorithmType(gradient_clip_algorithm.lower())
              if gradient_clip_algorithm is not None
              else gradient_clip_algorithm
          )
          self.track_grad_norm: float = float(track_grad_norm)
  
          self._detect_anomaly: bool = detect_anomaly
          self._setup_on_init(num_sanity_val_steps)
  
          # configure tuner
          self.tuner.on_trainer_init(auto_lr_find, auto_scale_batch_size)
  
          # configure profiler
          self.__init_profiler(profiler)
  
          # init logger flags
          self._loggers: List[LightningLoggerBase]
          self._logger_connector.on_trainer_init(logger, flush_logs_every_n_steps, log_every_n_steps, move_metrics_to_cpu)
  
          # init debugging flags
          self.val_check_interval: Union[int, float]
          self._init_debugging_flags(
              limit_train_batches,
              limit_val_batches,
              limit_test_batches,
              limit_predict_batches,
              val_check_interval,
              overfit_batches,
              fast_dev_run,
          )
  
          # Callback system
          self._call_callback_hooks("on_init_end")
  
      def _init_debugging_flags(
          self,
          limit_train_batches: Optional[Union[int, float]],
          limit_val_batches: Optional[Union[int, float]],
          limit_test_batches: Optional[Union[int, float]],
          limit_predict_batches: Optional[Union[int, float]],
          val_check_interval: Optional[Union[int, float]],
          overfit_batches: Union[int, float],
          fast_dev_run: Union[int, bool],
      ) -> None:
          if isinstance(fast_dev_run, int) and (fast_dev_run < 0):
              raise MisconfigurationException(
                  f"fast_dev_run={fast_dev_run} is not a valid configuration. It should be >= 0."
              )
  
          self.fast_dev_run = fast_dev_run
  
          # set fast_dev_run=True when it is 1, used while logging
          if fast_dev_run == 1:
              self.fast_dev_run = True
  
          if fast_dev_run:
              num_batches = int(fast_dev_run)
              limit_train_batches = num_batches
              limit_val_batches = num_batches
              limit_test_batches = num_batches
              limit_predict_batches = num_batches
              self.fit_loop.max_steps = num_batches
              self.num_sanity_val_steps = 0
              self.fit_loop.max_epochs = 1
              val_check_interval = 1.0
              self.check_val_every_n_epoch = 1
              self.loggers = [DummyLogger()] if self.loggers else []
  
              rank_zero_info(
                  "Running in fast_dev_run mode: will run a full train,"
                  f" val, test and prediction loop using {num_batches} batch(es)."
              )
  
          self.limit_train_batches = _determine_batch_limits(limit_train_batches, "limit_train_batches")
          self.limit_val_batches = _determine_batch_limits(limit_val_batches, "limit_val_batches")
          self.limit_test_batches = _determine_batch_limits(limit_test_batches, "limit_test_batches")
          self.limit_predict_batches = _determine_batch_limits(limit_predict_batches, "limit_predict_batches")
          self.val_check_interval = _determine_batch_limits(val_check_interval, "val_check_interval")
          self.overfit_batches = _determine_batch_limits(overfit_batches, "overfit_batches")
          self._determine_data_use_amount(self.overfit_batches)
  
      def _determine_data_use_amount(self, overfit_batches: float) -> None:
          """Use less data for debugging purposes."""
          if overfit_batches > 0:
              self.limit_train_batches = overfit_batches
              self.limit_val_batches = 0
  
      def _setup_on_init(self, num_sanity_val_steps: int) -> None:
          self._log_device_info()
  
          self.should_stop = False
          self.state = TrainerState()
          self.num_training_batches = float("inf")
          self.train_dataloader = None
  
          if num_sanity_val_steps == -1:
              self.num_sanity_val_steps = float("inf")
          else:
              self.num_sanity_val_steps = num_sanity_val_steps
  
          self.num_sanity_val_batches = []
          self.num_test_batches = []
          self.num_val_batches = []
          self.test_dataloaders = None
          self.val_dataloaders = None
          self._last_train_dl_reload_epoch = float("-inf")
          self._last_val_dl_reload_epoch = float("-inf")
  
          self.num_predict_batches = []
  
      def _call_and_handle_interrupt(self, trainer_fn: Callable, *args: Any, **kwargs: Any) -> Any:
          r"""
          Error handling, intended to be used only for main trainer function entry points (fit, validate, test, predict)
          as all errors should funnel through them
  
          Args:
              trainer_fn: one of (fit, validate, test, predict)
              *args: positional arguments to be passed to the `trainer_fn`
              **kwargs: keyword arguments to be passed to `trainer_fn`
          """
          try:
              if self.strategy.launcher is not None:
                  return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
              else:
                  return trainer_fn(*args, **kwargs)
          # TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7
          except KeyboardInterrupt as exception:
              rank_zero_warn("Detected KeyboardInterrupt, attempting graceful shutdown...")
              # user could press Ctrl+c many times... only shutdown once
              if not self.interrupted:
                  self.state.status = TrainerStatus.INTERRUPTED
                  self._call_callback_hooks("on_keyboard_interrupt")
                  self._call_callback_hooks("on_exception", exception)
          except BaseException as exception:
              self.state.status = TrainerStatus.INTERRUPTED
              if distributed_available() and self.world_size > 1:
                  # try syncing remaining processes, kill otherwise
                  self.strategy.reconciliate_processes(traceback.format_exc())
              self._call_callback_hooks("on_exception", exception)
              self._teardown()
              # teardown might access the stage so we reset it after
              self.state.stage = None
              raise
  
      def fit(
          self,
          model: "pl.LightningModule",
          train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
          val_dataloaders: Optional[EVAL_DATALOADERS] = None,
          datamodule: Optional[LightningDataModule] = None,
          ckpt_path: Optional[str] = None,
      ) -> None:
          r"""
          Runs the full optimization routine.
  
          Args:
              model: Model to fit.
  
              train_dataloaders: A collection of :class:`torch.utils.data.DataLoader` or a
                  :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying training samples.
                  In the case of multiple dataloaders, please see this :ref:`section <multiple-dataloaders>`.
  
              val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
  
              ckpt_path: Path/URL of the checkpoint from which training is resumed. If there is
                  no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
                  training will start from the beginning of the next epoch.
  
              datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
          """
          self.strategy.model = model
          self._call_and_handle_interrupt(
              self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
          )
  
      def _fit_impl(
          self,
          model: "pl.LightningModule",
          train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
          val_dataloaders: Optional[EVAL_DATALOADERS] = None,
          datamodule: Optional[LightningDataModule] = None,
          ckpt_path: Optional[str] = None,
      ) -> None:
          Trainer._log_api_event("fit")
          log.detail(f"{self.__class__.__name__}: trainer fit stage")
  
          self.state.fn = TrainerFn.FITTING
          self.state.status = TrainerStatus.RUNNING
          self.training = True
          self._last_train_dl_reload_epoch = float("-inf")
          self._last_val_dl_reload_epoch = float("-inf")
  
          # if a datamodule comes in as the second arg, then fix it for the user
          if isinstance(train_dataloaders, LightningDataModule):
              datamodule = train_dataloaders
              train_dataloaders = None
          # If you supply a datamodule you can't supply train_dataloader or val_dataloaders
          if (train_dataloaders is not None or val_dataloaders is not None) and datamodule is not None:
              raise MisconfigurationException(
                  "You cannot pass `train_dataloader` or `val_dataloaders` to `trainer.fit(datamodule=...)`"
              )
  
          # links data to the trainer
          self._data_connector.attach_data(
              model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
          )
  
          # TODO: ckpt_path only in v2.0
          ckpt_path = ckpt_path or self.resume_from_checkpoint
          self._ckpt_path = self.__set_ckpt_path(
              ckpt_path, model_provided=True, model_connected=self.lightning_module is not None
          )
          results = self._run(model, ckpt_path=self.ckpt_path)
  
          assert self.state.stopped
          self.training = False
          return results
  
      def validate(
          self,
          model: Optional["pl.LightningModule"] = None,
          dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
          ckpt_path: Optional[str] = None,
          verbose: bool = True,
          datamodule: Optional[LightningDataModule] = None,
      ) -> _EVALUATE_OUTPUT:
          r"""
          Perform one evaluation epoch over the validation set.
  
          Args:
              model: The model to validate.
  
              dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them,
                  or a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying validation samples.
  
              ckpt_path: Either ``best`` or path to the checkpoint you wish to validate.
                  If ``None`` and the model instance was passed, use the current weights.
                  Otherwise, the best model checkpoint from the previous ``trainer.fit`` call will be loaded
                  if a checkpoint callback is configured.
  
              verbose: If True, prints the validation results.
  
              datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
  
          Returns:
              List of dictionaries with metrics logged during the validation phase, e.g., in model- or callback hooks
              like :meth:`~pytorch_lightning.core.lightning.LightningModule.validation_step`,
              :meth:`~pytorch_lightning.core.lightning.LightningModule.validation_epoch_end`, etc.
              The length of the list corresponds to the number of validation dataloaders used.
          """
          self.strategy.model = model or self.lightning_module
          return self._call_and_handle_interrupt(self._validate_impl, model, dataloaders, ckpt_path, verbose, datamodule)
  
      def _validate_impl(
          self,
          model: Optional["pl.LightningModule"] = None,
          dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
          ckpt_path: Optional[str] = None,
          verbose: bool = True,
          datamodule: Optional[LightningDataModule] = None,
      ) -> _EVALUATE_OUTPUT:
          # --------------------
          # SETUP HOOK
          # --------------------
          Trainer._log_api_event("validate")
          log.detail(f"{self.__class__.__name__}: trainer validate stage")
  
          self.state.fn = TrainerFn.VALIDATING
          self.state.status = TrainerStatus.RUNNING
          self.validating = True
  
          # if a datamodule comes in as the second arg, then fix it for the user
          if isinstance(dataloaders, LightningDataModule):
              datamodule = dataloaders
              dataloaders = None
          # If you supply a datamodule you can't supply val_dataloaders
          if dataloaders is not None and datamodule:
              raise MisconfigurationException("You cannot pass both `trainer.validate(dataloaders=..., datamodule=...)`")
  
          model_provided = model is not None
          model = model or self.lightning_module
          if model is None:
              raise MisconfigurationException(
                  "`model` must be provided to `trainer.validate()` when it hasn't been passed in a previous run"
              )
  
          self.validate_loop.verbose = verbose
  
          # links data to the trainer
          self._data_connector.attach_data(model, val_dataloaders=dataloaders, datamodule=datamodule)
  
          self._ckpt_path = self.__set_ckpt_path(
              ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
          )
  
          self._validated_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
  
          # run validate
          results = self._run(model, ckpt_path=self.ckpt_path)
  
          assert self.state.stopped
          self.validating = False
  
          return results
  
      def test(
          self,
          model: Optional["pl.LightningModule"] = None,
          dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
          ckpt_path: Optional[str] = None,
          verbose: bool = True,
          datamodule: Optional[LightningDataModule] = None,
      ) -> _EVALUATE_OUTPUT:
          r"""
          Perform one evaluation epoch over the test set.
          It's separated from fit to make sure you never run on your test set until you want to.
  
          Args:
              model: The model to test.
  
              dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them,
                  or a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying test samples.
  
              ckpt_path: Either ``best`` or path to the checkpoint you wish to test.
                  If ``None`` and the model instance was passed, use the current weights.
                  Otherwise, the best model checkpoint from the previous ``trainer.fit`` call will be loaded
                  if a checkpoint callback is configured.
  
              verbose: If True, prints the test results.
  
              datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
  
          Returns:
              List of dictionaries with metrics logged during the test phase, e.g., in model- or callback hooks
              like :meth:`~pytorch_lightning.core.lightning.LightningModule.test_step`,
              :meth:`~pytorch_lightning.core.lightning.LightningModule.test_epoch_end`, etc.
              The length of the list corresponds to the number of test dataloaders used.
          """
          self.strategy.model = model or self.lightning_module
          return self._call_and_handle_interrupt(self._test_impl, model, dataloaders, ckpt_path, verbose, datamodule)
  
      def _test_impl(
          self,
          model: Optional["pl.LightningModule"] = None,
          dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
          ckpt_path: Optional[str] = None,
          verbose: bool = True,
          datamodule: Optional[LightningDataModule] = None,
      ) -> _EVALUATE_OUTPUT:
          # --------------------
          # SETUP HOOK
          # --------------------
          Trainer._log_api_event("test")
          log.detail(f"{self.__class__.__name__}: trainer test stage")
  
          self.state.fn = TrainerFn.TESTING
          self.state.status = TrainerStatus.RUNNING
          self.testing = True
  
          # if a datamodule comes in as the second arg, then fix it for the user
          if isinstance(dataloaders, LightningDataModule):
              datamodule = dataloaders
              dataloaders = None
          # If you supply a datamodule you can't supply test_dataloaders
          if dataloaders is not None and datamodule:
              raise MisconfigurationException("You cannot pass both `trainer.test(dataloaders=..., datamodule=...)`")
  
          model_provided = model is not None
          model = model or self.lightning_module
          if model is None:
              raise MisconfigurationException(
                  "`model` must be provided to `trainer.test()` when it hasn't been passed in a previous run"
              )
  
          self.test_loop.verbose = verbose
  
          # links data to the trainer
          self._data_connector.attach_data(model, test_dataloaders=dataloaders, datamodule=datamodule)
  
          self._ckpt_path = self.__set_ckpt_path(
              ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
          )
  
          self._tested_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
  
          # run test
          results = self._run(model, ckpt_path=self.ckpt_path)
  
          assert self.state.stopped
          self.testing = False
  
          return results
  
      def predict(
          self,
          model: Optional["pl.LightningModule"] = None,
          dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
          datamodule: Optional[LightningDataModule] = None,
          return_predictions: Optional[bool] = None,
          ckpt_path: Optional[str] = None,
      ) -> Optional[_PREDICT_OUTPUT]:
          r"""
          Run inference on your data.
          This will call the model forward function to compute predictions. Useful to perform distributed
          and batched predictions. Logging is disabled in the predict hooks.
  
          Args:
              model: The model to predict with.
  
              dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them,
                  or a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying prediction samples.
  
              datamodule: The datamodule with a predict_dataloader method that returns one or more dataloaders.
  
              return_predictions: Whether to return predictions.
                  ``True`` by default except when an accelerator that spawns processes is used (not supported).
  
              ckpt_path: Either ``best`` or path to the checkpoint you wish to predict.
                  If ``None`` and the model instance was passed, use the current weights.
                  Otherwise, the best model checkpoint from the previous ``trainer.fit`` call will be loaded
                  if a checkpoint callback is configured.
  
          Returns:
              Returns a list of dictionaries, one for each provided dataloader containing their respective predictions.
          """
          self.strategy.model = model or self.lightning_module
          return self._call_and_handle_interrupt(
              self._predict_impl, model, dataloaders, datamodule, return_predictions, ckpt_path
          )
  
      def _predict_impl(
          self,
          model: Optional["pl.LightningModule"] = None,
          dataloaders: Optional[Union[EVAL_DATALOADERS, LightningDataModule]] = None,
          datamodule: Optional[LightningDataModule] = None,
          return_predictions: Optional[bool] = None,
          ckpt_path: Optional[str] = None,
      ) -> Optional[_PREDICT_OUTPUT]:
          # --------------------
          # SETUP HOOK
          # --------------------
          Trainer._log_api_event("predict")
          log.detail(f"{self.__class__.__name__}: trainer predict stage")
  
          self.state.fn = TrainerFn.PREDICTING
          self.state.status = TrainerStatus.RUNNING
          self.predicting = True
  
          self.predict_loop.return_predictions = return_predictions
  
          # if a datamodule comes in as the second arg, then fix it for the user
          if isinstance(dataloaders, LightningDataModule):
              datamodule = dataloaders
              dataloaders = None
          if dataloaders is not None and datamodule:
              raise MisconfigurationException("You cannot pass both `trainer.predict(dataloaders=..., datamodule=...)`")
  
          model_provided = model is not None
          model = model or self.lightning_module
          if model is None:
              raise MisconfigurationException(
                  "`model` must be provided to `trainer.predict()` when it hasn't been passed in a previous run"
              )
  
          # links data to the trainer
          self._data_connector.attach_data(model, predict_dataloaders=dataloaders, datamodule=datamodule)
  
          self._ckpt_path = self.__set_ckpt_path(
              ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
          )
  
          self._predicted_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
  
          results = self._run(model, ckpt_path=self.ckpt_path)
  
          assert self.state.stopped
          self.predicting = False
  
          return results
  
      def tune(
          self,
          model: "pl.LightningModule",
          train_dataloaders: Optional[Union[TRAIN_DATALOADERS, LightningDataModule]] = None,
          val_dataloaders: Optional[EVAL_DATALOADERS] = None,
          datamodule: Optional[LightningDataModule] = None,
          scale_batch_size_kwargs: Optional[Dict[str, Any]] = None,
          lr_find_kwargs: Optional[Dict[str, Any]] = None,
      ) -> Dict[str, Optional[Union[int, _LRFinder]]]:
          r"""
          Runs routines to tune hyperparameters before training.
  
          Args:
              model: Model to tune.
  
              train_dataloaders: A collection of :class:`torch.utils.data.DataLoader` or a
                  :class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying training samples.
                  In the case of multiple dataloaders, please see this :ref:`section <multiple-dataloaders>`.
  
              val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
  
              datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
  
              scale_batch_size_kwargs: Arguments for :func:`~pytorch_lightning.tuner.batch_size_scaling.scale_batch_size`
  
              lr_find_kwargs: Arguments for :func:`~pytorch_lightning.tuner.lr_finder.lr_find`
          """
          Trainer._log_api_event("tune")
  
          self.state.fn = TrainerFn.TUNING
          self.state.status = TrainerStatus.RUNNING
          self.tuning = True
  
          # if a datamodule comes in as the second arg, then fix it for the user
          if isinstance(train_dataloaders, LightningDataModule):
              datamodule = train_dataloaders
              train_dataloaders = None
          # If you supply a datamodule you can't supply train_dataloader or val_dataloaders
          if (train_dataloaders is not None or val_dataloaders is not None) and datamodule is not None:
              raise MisconfigurationException(
                  "You cannot pass `train_dataloader` or `val_dataloaders` to `trainer.tune(datamodule=...)`"
              )
  
          # links data to the trainer
          self._data_connector.attach_data(
              model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
          )
  
          with isolate_rng():
              result = self.tuner._tune(
                  model, scale_batch_size_kwargs=scale_batch_size_kwargs, lr_find_kwargs=lr_find_kwargs
              )
  
          assert self.state.stopped
          self.tuning = False
  
          return result
  
      def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None) -> None:
          # restore modules after setup
          self._checkpoint_connector.resume_start(checkpoint_path)
          self._checkpoint_connector.restore_model()
          self._checkpoint_connector.restore_datamodule()
          if self.state.fn == TrainerFn.FITTING:
              # restore callback states
              self._checkpoint_connector.restore_callbacks()
  
      def _run(
          self, model: "pl.LightningModule", ckpt_path: Optional[str] = None
      ) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
          # clean hparams
          if hasattr(model, "hparams"):
              parsing.clean_namespace(model.hparams)
  
          # attach model to the training type plugin
          self.strategy.connect(model)
  
          self._callback_connector._attach_model_callbacks()
          self._callback_connector._attach_model_logging_functions()
  
          verify_loop_configurations(self)
  
          # hook
          log.detail(f"{self.__class__.__name__}: preparing data")
          self._data_connector.prepare_data()
  
          # ----------------------------
          # SET UP TRAINING
          # ----------------------------
          self._call_callback_hooks("on_before_accelerator_backend_setup")
          log.detail(f"{self.__class__.__name__}: setting up strategy environment")
          self.strategy.setup_environment()
          self.__setup_profiler()
  
          self._call_setup_hook()  # allow user to setup lightning_module in accelerator environment
  
          # check if we should delay restoring checkpoint till later
          if not self.strategy.restore_checkpoint_after_setup:
              log.detail(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}")
              self._restore_modules_and_callbacks(ckpt_path)
  
          log.detail(f"{self.__class__.__name__}: configuring sharded model")
          self._call_configure_sharded_model()  # allow user to setup in model sharded environment
  
          # ----------------------------
          # INSPECT THE CORE LOOPS
          # ----------------------------
          fr"""
               Lightning internal flow looks like this:
          {Trainer.fit} or {Trainer.test} or {Trainer.predict}  ||
                                  |                             ||
                           spawn processes                      ||
                   {self.strategy.setup_environment}            ||
                                  |                             ||
                          setup accelerator                     ||
                             and strategy                       ||  LIGHTNING
                                  |                             ||
                          {self._run_stage}                     ||  FLOW
                                  |                             ||
                          {self._run_train}                     ||  DIRECTION
                       or {self._run_evaluate}                  ||
                       or {self._run_predict}                   ||
                                  |                             ||
                               results                          \/
          This is used to guide readers to the core loops: train, test, predict.
          {self._run_predict} is the simplest to understand, use `Go to Definition` to read it :)
          """
  
          # ----------------------------
          # TRAIN
          # ----------------------------
  
          # reset logger connector
          self._logger_connector.reset_results()
          self._logger_connector.reset_metrics()
  
          # strategy will configure model and move it to the device
          self.strategy.setup(self)
  
          # hook
          if self.state.fn == TrainerFn.FITTING:
              self._call_callback_hooks("on_fit_start")
              self._call_lightning_module_hook("on_fit_start")
  
          self._log_hyperparams()
  
          if self.strategy.restore_checkpoint_after_setup:
              log.detail(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}")
              self._restore_modules_and_callbacks(ckpt_path)
  
          # restore optimizers, etc.
          log.detail(f"{self.__class__.__name__}: restoring training state")
          self._checkpoint_connector.restore_training_state()
  
          self._checkpoint_connector.resume_end()
  
          results = self._run_stage()
  
          log.detail(f"{self.__class__.__name__}: trainer tearing down")
          self._teardown()
  
          # ----------------------------
          # POST-Training CLEAN UP
          # ----------------------------
          # hook
          if self.state.fn == TrainerFn.FITTING:
              self._call_callback_hooks("on_fit_end")
              self._call_lightning_module_hook("on_fit_end")
  
          log.detail(f"{self.__class__.__name__}: calling teardown hooks")
          self._call_teardown_hook()
  
          self.state.status = TrainerStatus.FINISHED
          self.state.stage = None
  
          return results
  
      def _log_hyperparams(self) -> None:
          if not self.loggers:
              return
          # log hyper-parameters
          hparams_initial = None
  
          # save exp to get started (this is where the first experiment logs are written)
          datamodule_log_hyperparams = self.datamodule._log_hyperparams if self.datamodule is not None else False
  
          if self.lightning_module._log_hyperparams and datamodule_log_hyperparams:
              datamodule_hparams = self.datamodule.hparams_initial
              lightning_hparams = self.lightning_module.hparams_initial
              inconsistent_keys = []
              for key in lightning_hparams.keys() & datamodule_hparams.keys():
                  lm_val, dm_val = lightning_hparams[key], datamodule_hparams[key]
                  if type(lm_val) != type(dm_val):
                      inconsistent_keys.append(key)
                  elif isinstance(lm_val, torch.Tensor) and id(lm_val) != id(dm_val):
                      inconsistent_keys.append(key)
                  elif lm_val != dm_val:
                      inconsistent_keys.append(key)
              if inconsistent_keys:
                  raise MisconfigurationException(
                      f"Error while merging hparams: the keys {inconsistent_keys} are present "
                      "in both the LightningModule's and LightningDataModule's hparams "
                      "but have different values."
                  )
              hparams_initial = {**lightning_hparams, **datamodule_hparams}
          elif self.lightning_module._log_hyperparams:
              hparams_initial = self.lightning_module.hparams_initial
          elif datamodule_log_hyperparams:
              hparams_initial = self.datamodule.hparams_initial
  
          for logger in self.loggers:
              if hparams_initial is not None:
                  logger.log_hyperparams(hparams_initial)
              logger.log_graph(self.lightning_module)
              logger.save()
  
      def _teardown(self):
          """This is the Trainer's internal teardown, unrelated to the `teardown` hooks in LightningModule and
          Callback; those are handled by :meth:`_call_teardown_hook`."""
          self.strategy.post_dispatch(self)
          self.strategy.teardown()
          loop = self._active_loop
          # loop should never be `None` here but it can because we don't know the trainer stage with `ddp_spawn`
          if loop is not None:
              loop.teardown()
          self._logger_connector.teardown()
          self._signal_connector.teardown()
  
      def run_stage(self) -> None:
          rank_zero_deprecation(
              "`Trainer.run_stage` is deprecated in v1.6 and will be removed in v1.8. Use"
              " `Trainer.{fit,validate,test,predict}` instead."
          )
          return self._run_stage()
  
      def _run_stage(self):
          self.strategy.barrier("run-stage")
          self.strategy.dispatch(self)
  
          if self.evaluating:
              return self._run_evaluate()
          if self.predicting:
              return self._run_predict()
          return self._run_train()
  
      def _pre_training_routine(self):
          # wait for all to join if on distributed
          self.strategy.barrier("setup_training")
  
          # register signals
          self._signal_connector.register_signal_handlers()
  
          # --------------------------
          # Pre-train
          # --------------------------
          self._call_callback_hooks("on_pretrain_routine_start")
          self._call_lightning_module_hook("on_pretrain_routine_start")
  
          self._call_callback_hooks("on_pretrain_routine_end")
          self._call_lightning_module_hook("on_pretrain_routine_end")
  
      def _run_train(self) -> None:
          self._pre_training_routine()
  
          with isolate_rng():
              self._run_sanity_check()
  
          # enable train mode
          self.model.train()
          torch.set_grad_enabled(True)
  
          self.fit_loop.trainer = self
          with torch.autograd.set_detect_anomaly(self._detect_anomaly):
              self.fit_loop.run()
  
      def _run_evaluate(self) -> _EVALUATE_OUTPUT:
          assert self.evaluating
  
          # reload dataloaders
          self._evaluation_loop._reload_evaluation_dataloaders()
  
          # reset trainer on this loop and all child loops in case user connected a custom loop
          self._evaluation_loop.trainer = self
  
          with self.profiler.profile(f"run_{self.state.stage}_evaluation"), torch.no_grad():
              eval_loop_results = self._evaluation_loop.run()
  
          # remove the tensors from the eval results
          for result in eval_loop_results:
              if isinstance(result, dict):
                  for k, v in result.items():
                      if isinstance(v, torch.Tensor):
                          result[k] = v.cpu().item()
  
          return eval_loop_results
  
      def _run_predict(self) -> Optional[_PREDICT_OUTPUT]:
          self.reset_predict_dataloader(self.lightning_module)
          # reset trainer on this loop and all child loops in case user connected a custom loop
          self.predict_loop.trainer = self
          with torch.no_grad():
              return self.predict_loop.run()
  
      def _run_sanity_check(self) -> None:
          val_loop = self.fit_loop.epoch_loop.val_loop
  
          should_sanity_check = (
              self.enable_validation
              and self.num_sanity_val_steps > 0
              # do not sanity check if restarting because it would mess up the loaded state
              and not val_loop.restarting
          )
  
          # run tiny validation (if validation defined)
          # to make sure program won't crash during val
          if should_sanity_check:
              stage = self.state.stage
              self.sanity_checking = True
  
              # reset logger connector
              self._logger_connector.reset_results()
              self._logger_connector.reset_metrics()
  
              self._call_callback_hooks("on_sanity_check_start")
  
              # reload dataloaders
              val_loop._reload_evaluation_dataloaders()
              self.num_sanity_val_batches = [
                  min(self.num_sanity_val_steps, val_batches) for val_batches in self.num_val_batches
              ]
  
              # run eval step
              with torch.no_grad():
                  val_loop.run()
  
              self._call_callback_hooks("on_sanity_check_end")
  
              # reset logger connector
              self._logger_connector.reset_results()
              self._logger_connector.reset_metrics()
  
              # reset the progress tracking state after sanity checking. we don't need to set the state before
              # because sanity check only runs when we are not restarting
              _reset_progress(val_loop)
  
              # restore the previous stage when the sanity check if finished
              self.state.stage = stage
  
      def __set_ckpt_path(self, ckpt_path: Optional[str], model_provided: bool, model_connected: bool) -> Optional[str]:
          # fault-tolerance takes precedence
          from pytorch_lightning.callbacks.fault_tolerance import _FaultToleranceCheckpoint
  
          ft_checkpoints = [cb for cb in self.callbacks if isinstance(cb, _FaultToleranceCheckpoint)]
          if ft_checkpoints:
              ft_ckpt_path = ft_checkpoints[0].ckpt_path
              fs = get_filesystem(ft_ckpt_path)
              if fs.exists(ft_ckpt_path):
                  return ft_ckpt_path
  
          if model_provided and ckpt_path is None:
              # use passed model to function without loading weights
              return
  
          fn = self.state.fn.value
  
          if model_connected and ckpt_path is None:
              rank_zero_warn(
                  f"`.{fn}(ckpt_path=None)` was called without a model."
                  " The best model of the previous `fit` call will be used."
                  f" You can pass `{fn}(ckpt_path='best')` to use and best model"
                  " checkpoint and avoid this warning or"
                  " `ckpt_path=trainer.checkpoint_callback.last_model_path` to use the last model."
              )
              ckpt_path = "best"
  
          if ckpt_path == "best":
              if len(self.checkpoint_callbacks) > 1:
                  rank_zero_warn(
                      f'`.{fn}(ckpt_path="best")` is called with Trainer configured with multiple `ModelCheckpoint`'
                      " callbacks. It will use the best checkpoint path from first checkpoint callback."
                  )
  
              if not self.checkpoint_callback:
                  raise MisconfigurationException(
                      f'`.{fn}(ckpt_path="best")` is set but `ModelCheckpoint` is not configured.'
                  )
  
              if not self.checkpoint_callback.best_model_path:
                  if self.fast_dev_run:
                      raise MisconfigurationException(
                          f'You cannot execute `.{fn}(ckpt_path="best")` with `fast_dev_run=True`.'
                          f" Please pass an exact checkpoint path to `.{fn}(ckpt_path=...)`"
                      )
                  raise MisconfigurationException(
                      f'`.{fn}(ckpt_path="best")` is set but `ModelCheckpoint` is not configured to save the best model.'
                  )
              # load best weights
              ckpt_path = self.checkpoint_callback.best_model_path
  
          if not ckpt_path:
              raise MisconfigurationException(
                  f"`.{fn}()` found no path for the best weights: {ckpt_path!r}. Please"
                  f" specify a path for a checkpoint `.{fn}(ckpt_path=PATH)`"
              )
          return ckpt_path
  
      def _call_setup_hook(self) -> None:
          fn = self.state.fn._setup_fn
  
          self.strategy.barrier("pre_setup")
  
          if self.datamodule is not None:
              self.datamodule.setup(stage=fn)
          self._call_callback_hooks("setup", stage=fn)
          self._call_lightning_module_hook("setup", stage=fn)
  
          self.strategy.barrier("post_setup")
  
      def _call_configure_sharded_model(self) -> None:
          with self.strategy.model_sharded_context():
              self._handle_meta_model()
              self._call_lightning_module_hook("configure_sharded_model")
              self._call_callback_hooks("on_configure_sharded_model")
  
      def _handle_meta_model(self) -> None:
          if not is_on_meta_device(self.lightning_module):
              return
  
          if isinstance(self.strategy, DDPSpawnStrategy):
              raise MisconfigurationException("LightningModule on meta device isn't supported with spawn.")
  
          materialize_module(self.lightning_module)
          # the trainer reference is lost during materialization
          self.lightning_module.trainer = proxy(self)
  
      def _call_teardown_hook(self) -> None:
          fn = self.state.fn._setup_fn
  
          if self.datamodule is not None:
              self.datamodule.teardown(stage=fn)
  
          self._call_callback_hooks("teardown", stage=fn)
          self._call_lightning_module_hook("teardown", stage=fn)
  
          self.lightning_module._current_fx_name = None
          # these could have become stale if metrics are defined in `setup`
          self.lightning_module._metric_attributes = None
  
          # todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
          # It might be related to xla tensors blocked when moving the cpu kill loggers.
          for logger in self.loggers:
              logger.finalize("success")
  
          # summarize profile results
          self.profiler.describe()
  
      def call_hook(
          self, hook_name: str, *args: Any, pl_module: Optional["pl.LightningModule"] = None, **kwargs: Any
      ) -> Any:
          r"""
          .. deprecated:: v1.6
              The Trainer's `call_hook` method was deprecated in v1.6 and will be removed in v1.8.
          """
          rank_zero_deprecation("The Trainer's `call_hook` method was deprecated in v1.6 and will be removed in v1.8.")
          pl_module = self.lightning_module or pl_module
          if pl_module:
              prev_fx_name = pl_module._current_fx_name
              pl_module._current_fx_name = hook_name
  
          # always profile hooks
          with self.profiler.profile(hook_name):
  
              # first call trainer hook
              callback_fx = getattr(self, hook_name, None)
              if callable(callback_fx):
                  callback_fx(*args, **kwargs)
  
              # next call hook in lightningModule
              output = None
              model_fx = getattr(pl_module, hook_name, None)
              if callable(model_fx):
                  output = model_fx(*args, **kwargs)
  
              # call the strategy hook
              if hook_name not in ("setup", "teardown", "on_train_start") and hasattr(self.strategy, hook_name):
                  strategy_hook = getattr(self.strategy, hook_name)
                  strategy_output = strategy_hook(*args, **kwargs)
                  output = strategy_output if output is None else output
  
          if pl_module:
              # restore current_fx when nested context
              pl_module._current_fx_name = prev_fx_name
  
          return output
  
      def _call_lightning_module_hook(
          self,
          hook_name: str,
          *args: Any,
          pl_module: Optional["pl.LightningModule"] = None,
          **kwargs: Any,
      ) -> Any:
          pl_module = pl_module or self.lightning_module
  
          if pl_module is None:
              raise TypeError("No Lightning Module is available to call hooks on")
  
          fn = getattr(pl_module, hook_name)
          if not callable(fn):
              return
  
          prev_fx_name = pl_module._current_fx_name
          pl_module._current_fx_name = hook_name
  
          with self.profiler.profile(f"[LightningModule]{pl_module.__class__.__name__}.{hook_name}"):
              output = fn(*args, **kwargs)
  
          # restore current_fx when nested context
          pl_module._current_fx_name = prev_fx_name
  
          return output
  
      def _call_callback_hooks(
          self,
          hook_name: str,
          *args: Any,
          **kwargs: Any,
      ) -> None:
          log.detail(f"{self.__class__.__name__}: calling callback hook: {hook_name}")
          # TODO: remove if block in v1.8
          if hook_name in ("on_init_start", "on_init_end"):
              # these `Callback` hooks are the only ones that do not take a lightning module.
              # we also don't profile bc profiler hasn't been set yet
              for callback in self.callbacks:
                  fn = getattr(callback, hook_name)
                  if callable(fn):
                      fn(self, *args, **kwargs)
              return
  
          pl_module = self.lightning_module
          if pl_module:
              prev_fx_name = pl_module._current_fx_name
              pl_module._current_fx_name = hook_name
  
          # TODO: remove if block in v1.7
          if hook_name == "on_train_batch_start":
              with self.profiler.profile(hook_name):
                  self._on_train_batch_start(*args, **kwargs)
          elif hook_name == "on_train_batch_end":
              with self.profiler.profile(hook_name):
                  self._on_train_batch_end(*args, **kwargs)
          else:
              for callback in self.callbacks:
                  fn = getattr(callback, hook_name)
                  if callable(fn):
                      with self.profiler.profile(f"[Callback]{callback.state_key}.{hook_name}"):
                          fn(self, self.lightning_module, *args, **kwargs)
  
          if pl_module:
              # restore current_fx when nested context
              pl_module._current_fx_name = prev_fx_name
  
      # TODO: Delete this in v1.7 (deprecations: #9816 and #11148)
      def _on_train_batch_start(self, batch, batch_idx, dataloader_idx=0):
          r"""Called when the training batch begins. This function is needed because of two different deprecations affecting
          the original function in TrainerCallbackHookMixin: #9816 and #11148.
          """
          for callback in self.callbacks:
              if is_param_in_hook_signature(callback.on_train_batch_start, "dataloader_idx", explicit=True):
                  callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx, 0)
              else:
                  callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx)
  
      # TODO: Delete this in v1.7 (deprecations: #9816 and #11148)
      def _on_train_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx=0):
          r"""Called when the training batch ends. This function is needed because of two different deprecations affecting
          the original function in TrainerCallbackHookMixin: #9816 and #11148.
          """
          for callback in self.callbacks:
              if is_param_in_hook_signature(callback.on_train_batch_end, "dataloader_idx", explicit=True):
                  callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx, 0)
              else:
                  callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx)
  
      def _call_callbacks_state_dict(self) -> Dict[str, dict]:
          """Called when saving a model checkpoint, calls and returns every callback's `state_dict`, keyed by
          `Callback.state_key`."""
          callback_state_dicts = {}
          for callback in self.callbacks:
              state_dict = callback.state_dict()
              if state_dict:
                  callback_state_dicts[callback.state_key] = state_dict
          return callback_state_dicts
  
      def _call_callbacks_on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          """Called when saving a model checkpoint, calls every callback's `on_save_checkpoint` hook.
  
          Will be removed in v1.8: If state is returned, we insert the callback state into
          ``checkpoint["callbacks"][Callback.state_key]``. It overrides ``state_dict`` if already present.
          """
          for callback in self.callbacks:
              # TODO: Add profiling for on_save_checkpoint hook
              state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
              if state:
                  # TODO: Add deprecation warning if state is returned (see reference PR #11887)
                  checkpoint["callbacks"][callback.state_key] = state
  
      def _call_callbacks_on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          """Called when loading a model checkpoint.
  
          Calls every callback's `on_load_checkpoint` hook. We have a dedicated function for this rather than using
          `_call_callback_hooks` because we have special logic for getting callback_states.
          """
          callback_states: Dict[Union[Type, str], Dict] = checkpoint.get("callbacks")
  
          if callback_states is None:
              return
  
          is_legacy_ckpt = Version(checkpoint["pytorch-lightning_version"]) < Version("1.5.0dev")
          current_callbacks_keys = {cb._legacy_state_key if is_legacy_ckpt else cb.state_key for cb in self.callbacks}
          difference = callback_states.keys() - current_callbacks_keys
          if difference:
              rank_zero_warn(
                  "Be aware that when using `ckpt_path`,"
                  " callbacks used to create the checkpoint need to be provided during `Trainer` instantiation."
                  f" Please add the following callbacks: {list(difference)}.",
              )
  
          for callback in self.callbacks:
              state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
              if state:
                  state = deepcopy(state)
                  # TODO: Add profiling for on_load_checkpoint hook
                  callback.on_load_checkpoint(self, self.lightning_module, state)
  
      def _call_callbacks_load_state_dict(self, checkpoint: Dict[str, Any]) -> None:
          """Called when loading a model checkpoint, calls every callback's `load_state_dict`."""
          callback_states: Dict[Union[Type, str], Dict] = checkpoint.get("callbacks")
  
          if callback_states is None:
              return
  
          for callback in self.callbacks:
              state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
              if state:
                  state = deepcopy(state)
                  callback.load_state_dict(state)
  
      def _call_strategy_hook(
          self,
          hook_name: str,
          *args: Any,
          **kwargs: Any,
      ) -> Any:
          pl_module = self.lightning_module
          prev_fx_name = pl_module._current_fx_name
          pl_module._current_fx_name = hook_name
  
          fn = getattr(self.strategy, hook_name)
          if not callable(fn):
              return
  
          with self.profiler.profile(f"[Strategy]{self.strategy.__class__.__name__}.{hook_name}"):
              output = fn(*args, **kwargs)
  
          # restore current_fx when nested context
          pl_module._current_fx_name = prev_fx_name
  
          return output
  
      @staticmethod
      def _parse_devices(
          gpus: Optional[Union[List[int], str, int]],
          auto_select_gpus: bool,
          tpu_cores: Optional[Union[List[int], str, int]],
      ) -> Tuple[Optional[List[int]], Optional[Union[List[int], int]]]:
          return device_parser._parse_devices(gpus, auto_select_gpus, tpu_cores)
  
      @staticmethod
      def _log_api_event(event: str) -> None:
          torch._C._log_api_usage_once("lightning.trainer." + event)
  
      def __init_profiler(self, profiler: Optional[Union[BaseProfiler, str]]) -> None:
          if isinstance(profiler, str):
              PROFILERS = {
                  "simple": SimpleProfiler,
                  "advanced": AdvancedProfiler,
                  "pytorch": PyTorchProfiler,
                  "xla": XLAProfiler,
              }
              profiler = profiler.lower()
              if profiler not in PROFILERS:
                  raise MisconfigurationException(
                      "When passing string value for the `profiler` parameter of `Trainer`,"
                      f" it can only be one of {list(PROFILERS.keys())}"
                  )
              profiler_class = PROFILERS[profiler]
              profiler = profiler_class()
          self.profiler: BaseProfiler = profiler or PassThroughProfiler()
  
      def __setup_profiler(self) -> None:
          local_rank = self.local_rank if self.world_size > 1 else None
          self.profiler._lightning_module = proxy(self.lightning_module)
          self.profiler.setup(stage=self.state.fn._setup_fn, local_rank=local_rank, log_dir=self.log_dir)
  
      def _log_device_info(self) -> None:
          rank_zero_info(
              f"GPU available: {torch.cuda.is_available()}, used: {isinstance(self.accelerator, GPUAccelerator)}"
          )
  
          num_tpu_cores = (
              self.tpu_cores if self.tpu_cores is not None and isinstance(self.accelerator, TPUAccelerator) else 0
          )
          rank_zero_info(f"TPU available: {_TPU_AVAILABLE}, using: {num_tpu_cores} TPU cores")
  
          num_ipus = self.ipus if self.ipus is not None else 0
          rank_zero_info(f"IPU available: {_IPU_AVAILABLE}, using: {num_ipus} IPUs")
  
          if torch.cuda.is_available() and not isinstance(self.accelerator, GPUAccelerator):
              rank_zero_warn(
                  "GPU available but not used. Set `accelerator` and `devices` using"
                  f" `Trainer(accelerator='gpu', devices={GPUAccelerator.auto_device_count()})`.",
                  category=PossibleUserWarning,
              )
  
          if _TPU_AVAILABLE and not isinstance(self.accelerator, TPUAccelerator):
              rank_zero_warn(
                  "TPU available but not used. Set `accelerator` and `devices` using"
                  f" `Trainer(accelerator='tpu', devices={TPUAccelerator.auto_device_count()})`."
              )
  
          if _IPU_AVAILABLE and not isinstance(self.accelerator, IPUAccelerator):
              rank_zero_warn(
                  "IPU available but not used. Set `accelerator` and `devices` using"
                  f" `Trainer(accelerator='ipu', devices={IPUAccelerator.auto_device_count()})`."
              )
  
      """
      Data loading methods
      """
  
      def reset_train_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
          """Resets the train dataloader and initialises required variables (number of batches, when to validate,
          etc.).
  
          Args:
              model: The ``LightningModule`` if calling this outside of the trainer scope.
          """
          source = self._data_connector._train_dataloader_source
          pl_module = self.lightning_module or model
          has_step = is_overridden("training_step", pl_module)
          enable_training = self.limit_train_batches > 0
          if not (source.is_defined() and has_step and enable_training):
              return
  
          self.train_dataloader = self._data_connector._request_dataloader(RunningStage.TRAINING, model=model)
  
          if self.overfit_batches > 0:
              self.train_dataloader = self._data_connector._resolve_overfit_batches(self.train_dataloader)
  
          # automatically add samplers
          self.train_dataloader = apply_to_collection(
              self.train_dataloader,
              (DataLoader, CombinedLoader),
              self._data_connector._prepare_dataloader,
              mode=RunningStage.TRAINING,
          )
          loaders = (
              self.train_dataloader.loaders
              if isinstance(self.train_dataloader, CombinedLoader)
              else self.train_dataloader
          )
  
          # check the workers recursively
          apply_to_collection(loaders, DataLoader, self._data_connector._worker_check, "train_dataloader")
  
          # add worker_init_fn for correct seeding in worker processes
          apply_to_collection(loaders, DataLoader, _auto_add_worker_init_fn, rank=self.global_rank)
  
          # add collate_fn to collect metadata for fault tolerant training
          if _fault_tolerant_training():
              apply_to_collection(loaders, DataLoader, _add_capture_metadata_collate)
  
          # wrap the sequence of train loaders to a CombinedLoader object for computing the num_training_batches
          if not isinstance(self.train_dataloader, CombinedLoader):
              self.train_dataloader = CombinedLoader(loaders, self._data_connector.multiple_trainloader_mode)
  
          module = model or self.lightning_module or self.datamodule
          self.num_training_batches = (
              len(self.train_dataloader)
              if has_len_all_ranks(self.train_dataloader, self.strategy, module)
              else float("inf")
          )
  
          if isinstance(self.limit_train_batches, int):
              self.num_training_batches = min(self.num_training_batches, int(self.limit_train_batches))
          elif self.num_training_batches != float("inf"):
              self.num_training_batches = int(self.num_training_batches * self.limit_train_batches)
          elif self.limit_train_batches != 1.0:
              raise MisconfigurationException(
                  "When using an IterableDataset for `limit_train_batches`,"
                  " `Trainer(limit_train_batches)` must be `1.0` or an int. An int k specifies"
                  " `num_training_batches` to use."
              )
  
          if isinstance(self.val_check_interval, int):
              self.val_check_batch = self.val_check_interval
              if self.val_check_batch > self.num_training_batches:
                  raise ValueError(
                      f"`val_check_interval` ({self.val_check_interval}) must be less than or equal "
                      f"to the number of the training batches ({self.num_training_batches}). "
                      "If you want to disable validation set `limit_val_batches` to 0.0 instead."
                  )
          else:
              if not has_len_all_ranks(self.train_dataloader, self.strategy, module):
                  if self.val_check_interval == 1.0:
                      self.val_check_batch = float("inf")
                  else:
                      raise MisconfigurationException(
                          "When using an IterableDataset for `train_dataloader`,"
                          " `Trainer(val_check_interval)` must be `1.0` or an int. An int k specifies"
                          " checking validation every k training batches."
                      )
              else:
                  self.val_check_batch = int(self.num_training_batches * self.val_check_interval)
                  self.val_check_batch = max(1, self.val_check_batch)
  
          if self.loggers and self.num_training_batches < self.log_every_n_steps:
              rank_zero_warn(
                  f"The number of training samples ({self.num_training_batches}) is smaller than the logging interval"
                  f" Trainer(log_every_n_steps={self.log_every_n_steps}). Set a lower value for log_every_n_steps if"
                  " you want to see logs for the training epoch.",
                  category=PossibleUserWarning,
              )
  
          # store epoch of dataloader reset for reload_dataloaders_every_n_epochs
          self._last_train_dl_reload_epoch = self.current_epoch
  
      def reset_val_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
          """Resets the validation dataloader and determines the number of batches.
  
          Args:
              model: The ``LightningModule`` if called outside of the trainer scope.
          """
          source = self._data_connector._val_dataloader_source
          pl_module = self.lightning_module or model
          has_step = is_overridden("validation_step", pl_module)
          enable_validation = self.limit_val_batches > 0
          if source.is_defined() and has_step and enable_validation:
              self.num_val_batches, self.val_dataloaders = self._data_connector._reset_eval_dataloader(
                  RunningStage.VALIDATING, model=pl_module
              )
  
              # store epoch of dataloader reset for reload_dataloaders_every_n_epochs
              self._last_val_dl_reload_epoch = self.current_epoch
  
      def reset_test_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
          """Resets the test dataloader and determines the number of batches.
  
          Args:
              model: The ``LightningModule`` if called outside of the trainer scope.
          """
          source = self._data_connector._test_dataloader_source
          pl_module = self.lightning_module or model
          has_step = is_overridden("test_step", pl_module)
          enable_testing = self.limit_test_batches > 0
          if source.is_defined() and has_step and enable_testing:
              self.num_test_batches, self.test_dataloaders = self._data_connector._reset_eval_dataloader(
                  RunningStage.TESTING, model=pl_module
              )
  
      def reset_predict_dataloader(self, model: Optional["pl.LightningModule"] = None) -> None:
          """Resets the predict dataloader and determines the number of batches.
  
          Args:
              model: The ``LightningModule`` if called outside of the trainer scope.
          """
          source = self._data_connector._predict_dataloader_source
          pl_module = self.lightning_module or model
          enable_prediction = self.limit_predict_batches > 0
          if source.is_defined() and enable_prediction:
              self.num_predict_batches, self.predict_dataloaders = self._data_connector._reset_eval_dataloader(
                  RunningStage.PREDICTING, model=pl_module
              )
  
      def reset_train_val_dataloaders(self, model: Optional["pl.LightningModule"] = None) -> None:
          """Resets train and val dataloaders if none are attached to the trainer.
  
          The val dataloader must be initialized before training loop starts, as the training loop
          inspects the val dataloader to determine whether to run the evaluation loop.
          Args:
              model: The ``LightningModule`` if called outside of the trainer scope.
          """
          if self.train_dataloader is None:
              self.reset_train_dataloader(model=model)
          if self.val_dataloaders is None:
              self.reset_val_dataloader(model=model)
  
      """
      Accelerator properties
      """
  
      @property
      def accelerator(self) -> Accelerator:
          return self.strategy.accelerator
  
      @property
      def strategy(self) -> Strategy:
          return self._accelerator_connector.strategy
  
      @property
      def training_type_plugin(self) -> Strategy:
          rank_zero_deprecation(
              "`Trainer.training_type_plugin` is deprecated in v1.6 and will be removed in v1.8. Use"
              " `Trainer.strategy` instead."
          )
          return self.strategy
  
      @property
      def precision_plugin(self) -> PrecisionPlugin:
          return self.strategy.precision_plugin
  
      @property
      def global_rank(self) -> int:
          return self.strategy.global_rank
  
      @property
      def local_rank(self) -> int:
          # some training types define a local rank
          return getattr(self.strategy, "local_rank", 0)
  
      @property
      def node_rank(self) -> int:
          # some training types define a node rank
          return getattr(self.strategy, "node_rank", 0)
  
      @property
      def world_size(self) -> int:
          # some training types define a world size
          return getattr(self.strategy, "world_size", 1)
  
      @property
      def should_rank_save_checkpoint(self) -> bool:
          rank_zero_deprecation(
              "`Trainer.should_rank_save_checkpoint` is deprecated in v1.6 and will be removed in v1.8.", stacklevel=5
          )
          strategy = self.strategy
          return (
              isinstance(strategy, pl.strategies.TPUSpawnStrategy) and strategy.local_rank == 0 or strategy.is_global_zero
          )
  
      @property
      def num_nodes(self) -> int:
          return getattr(self.strategy, "num_nodes", 1)
  
      @property
      def device_ids(self) -> List[int]:
          """List of device indexes per node."""
          devices = getattr(self.strategy, "parallel_devices", [self.strategy.root_device])
          device_ids = []
          for idx, device in enumerate(devices):
              if isinstance(device, torch.device):
                  device_ids.append(device.index or idx)
              elif isinstance(device, int):
                  device_ids.append(device)
          return device_ids
  
      @property
      def num_devices(self) -> int:
          """Number of devices the trainer uses per node."""
          return len(self.device_ids)
  
      @property
      def num_processes(self) -> int:
          return self._accelerator_connector.num_processes
  
      @property
      def root_gpu(self) -> Optional[int]:
          rank_zero_deprecation(
              "`Trainer.root_gpu` is deprecated in v1.6 and will be removed in v1.8. "
              "Please use `Trainer.strategy.root_device.index` instead."
          )
          return self.strategy.root_device.index if isinstance(self.accelerator, GPUAccelerator) else None
  
      @property
      def tpu_cores(self) -> int:
          return self._accelerator_connector.tpu_cores
  
      @property
      def ipus(self) -> int:
          return self._accelerator_connector.num_ipus
  
      @property
      def num_gpus(self) -> int:
          rank_zero_deprecation(
              "`Trainer.num_gpus` was deprecated in v1.6 and will be removed in v1.8."
              " Please use `Trainer.num_devices` instead."
          )
          return self.num_devices if isinstance(self.accelerator, GPUAccelerator) else 0
  
      @property
      def devices(self) -> int:
          rank_zero_deprecation(
              "`Trainer.devices` was deprecated in v1.6 and will be removed in v1.8."
              " Please use `Trainer.num_devices` or `Trainer.device_ids` to get device information instead."
          )
          return self.num_devices
  
      @property
      def data_parallel_device_ids(self) -> Optional[List[int]]:
          return (
              self._accelerator_connector.parallel_device_ids if self._accelerator_connector.parallel_device_ids else None
          )
  
      @property
      def lightning_module(self) -> "pl.LightningModule":
          # TODO: this is actually an optional return
          return self.strategy.lightning_module
  
      @property
      def optimizers(self) -> List[Optimizer]:
          return self.strategy.optimizers
  
      @optimizers.setter
      def optimizers(self, new_optims: Optional[List[Optimizer]]) -> None:
          self.strategy.optimizers = new_optims
  
      @property
      def lightning_optimizers(self) -> Dict[int, LightningOptimizer]:
          rank_zero_deprecation(
              "`Trainer.lightning_optimizers` is deprecated in v1.6 and will be removed in v1.8", stacklevel=5
          )
          return self.strategy._lightning_optimizers
  
      @property
      def lr_scheduler_configs(self) -> List[LRSchedulerConfig]:
          return self.strategy.lr_scheduler_configs
  
      @property
      def lr_schedulers(self) -> List[Dict[str, Any]]:
          rank_zero_deprecation(
              "`Trainer.lr_schedulers` is deprecated in v1.6 and will be removed in v1.8."
              " You can use `trainer.lr_scheduler_configs` instead which contains dataclasses instead of dictionaries.",
              stacklevel=5,
          )
          from dataclasses import asdict
  
          return [asdict(config) for config in self.strategy.lr_scheduler_configs]
  
      @property
      def optimizer_frequencies(self) -> List[int]:
          return self.strategy.optimizer_frequencies
  
      @optimizer_frequencies.setter
      def optimizer_frequencies(self, new_freqs: List[int]) -> None:
          self.strategy.optimizer_frequencies = new_freqs
  
      @property
      def amp_backend(self) -> Optional[AMPType]:
          if isinstance(self.precision_plugin, ApexMixedPrecisionPlugin):
              return AMPType.APEX
          if isinstance(self.precision_plugin, NativeMixedPrecisionPlugin):
              return AMPType.NATIVE
          return None
  
      @property
      def precision(self) -> Union[str, int]:
          return self.strategy.precision_plugin.precision
  
      @property
      def scaler(self) -> Optional[Any]:
          return getattr(self.precision_plugin, "scaler", None)
  
      @property
      def gpus(self) -> Optional[Union[List[int], str, int]]:
          return self._accelerator_connector.gpus
  
      @property
      def model(self) -> torch.nn.Module:
          """The LightningModule, but possibly wrapped into DataParallel or DistributedDataParallel.
  
          To access the pure LightningModule, use
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.lightning_module` instead.
          """
          return self.strategy.model
  
      @model.setter
      def model(self, model: torch.nn.Module) -> None:
          """Setter for the model, pass-through to accelerator and plugin where the model reference is stored. Used
          by the Tuner to reset the state of Trainer and Accelerator.
  
          Args:
              model: The LightningModule, possibly wrapped into DataParallel or DistributedDataParallel, depending
                  on the backend.
          """
          self.strategy.model = model
  
      """
      General properties
      """
  
      @property
      def log_dir(self) -> Optional[str]:
          if len(self.loggers) == 1:
              if isinstance(self.logger, TensorBoardLogger):
                  dirpath = self.logger.log_dir
              else:
                  dirpath = self.logger.save_dir
          else:
              dirpath = self.default_root_dir
  
          dirpath = self.strategy.broadcast(dirpath)
          return dirpath
  
      @property
      def use_amp(self) -> bool:
          rank_zero_deprecation(
              "`Trainer.use_amp` is deprecated in v1.6.0 and will be removed in v1.8.0."
              " Please use `Trainer.amp_backend` instead."
          )
          return self.precision == 16
  
      @property
      def is_global_zero(self) -> bool:
          return self.strategy.is_global_zero
  
      @property
      def slurm_job_id(self) -> Optional[int]:
          rank_zero_deprecation("Method `slurm_job_id` is deprecated in v1.6.0 and will be removed in v1.7.0.")
          return SLURMEnvironment.job_id()
  
      @property
      def distributed_sampler_kwargs(self) -> Optional[dict]:
          if isinstance(self.strategy, ParallelStrategy):
              return self.strategy.distributed_sampler_kwargs
  
      @property
      def data_parallel(self) -> bool:
          return isinstance(self.strategy, ParallelStrategy)
  
      @property
      def progress_bar_dict(self) -> dict:
          """Read-only for progress bar metrics."""
          rank_zero_deprecation(
              "`trainer.progress_bar_dict` is deprecated in v1.5 and will be removed in v1.7."
              " Use `ProgressBarBase.get_metrics` instead."
          )
          ref_model = self.lightning_module
          ref_model = cast(pl.LightningModule, ref_model)
          if self.progress_bar_callback:
              return self.progress_bar_callback.get_metrics(self, ref_model)
          return self.progress_bar_metrics
  
      @property
      def enable_validation(self) -> bool:
          """Check if we should run validation during training."""
          return (
              self._data_connector._val_dataloader_source.is_defined()
              and is_overridden("validation_step", self.lightning_module)
              and self.limit_val_batches > 0
          )
  
      @property
      def default_root_dir(self) -> str:
          """The default location to save artifacts of loggers, checkpoints etc.
  
          It is used as a fallback if logger or checkpoint callback do not define specific save paths.
          """
          if get_filesystem(self._default_root_dir).protocol == "file":
              return os.path.normpath(self._default_root_dir)
          return self._default_root_dir
  
      @property
      def weights_save_path(self) -> str:
          """
          The default root location to save weights (checkpoints), e.g., when the
          :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` does not define a file path.
  
          .. deprecated:: v1.6
              `Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.
          """
          rank_zero_deprecation("`Trainer.weights_save_path` has been deprecated in v1.6 and will be removed in v1.8.")
          return self._weights_save_path_internal
  
      # TODO: Remove _weights_save_path_internal in v1.8
      @property
      def _weights_save_path_internal(self) -> str:
          """This is an internal implementation of weights_save_path which allows weights_save_path to be used
          internally by the framework without emitting a deprecation warning.
  
          To be removed in v1.8.
          """
          if get_filesystem(self._weights_save_path).protocol == "file":
              return os.path.normpath(self._weights_save_path)
          return self._weights_save_path
  
      @property
      def early_stopping_callback(self) -> Optional[EarlyStopping]:
          """The first :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback in the
          Trainer.callbacks list, or ``None`` if it doesn't exist."""
          callbacks = self.early_stopping_callbacks
          return callbacks[0] if len(callbacks) > 0 else None
  
      @property
      def early_stopping_callbacks(self) -> List[EarlyStopping]:
          """A list of all instances of :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` found in
          the Trainer.callbacks list."""
          return [c for c in self.callbacks if isinstance(c, EarlyStopping)]
  
      @property
      def prediction_writer_callbacks(self) -> List[BasePredictionWriter]:
          """A list of all instances of :class:`~pytorch_lightning.callbacks.prediction_writer.BasePredictionWriter`
          found in the Trainer.callbacks list."""
          return [cb for cb in self.callbacks if isinstance(cb, BasePredictionWriter)]
  
      @property
      def checkpoint_callback(self) -> Optional[ModelCheckpoint]:
          """The first :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` callback in the
          Trainer.callbacks list, or ``None`` if it doesn't exist."""
          callbacks = self.checkpoint_callbacks
          return callbacks[0] if len(callbacks) > 0 else None
  
      @property
      def checkpoint_callbacks(self) -> List[ModelCheckpoint]:
          """A list of all instances of :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` found
          in the Trainer.callbacks list."""
          return [c for c in self.callbacks if isinstance(c, ModelCheckpoint)]
  
      @property
      def progress_bar_callback(self) -> Optional[ProgressBarBase]:
          """An instance of :class:`~pytorch_lightning.callbacks.progress.base.ProgressBarBase` found in the
          Trainer.callbacks list, or ``None`` if one doesn't exist."""
          for c in self.callbacks:
              if isinstance(c, ProgressBarBase):
                  return c
          return None
  
      @property
      def resume_from_checkpoint(self) -> Optional[Union[str, Path]]:
          resume_from_checkpoint = self._checkpoint_connector.resume_from_checkpoint_fit_path
          if resume_from_checkpoint is not None:
              rank_zero_deprecation(
                  "`trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v2.0."
                  " Specify the fit checkpoint path with `trainer.fit(ckpt_path=)` instead.",
                  stacklevel=5,
              )
  
          return resume_from_checkpoint
  
      @property
      def ckpt_path(self) -> Optional[str]:
          """Set to the path/URL of a checkpoint loaded via :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`,
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`,
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.test`, or
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`. ``None`` otherwise."""
          return self._ckpt_path
  
      @property
      def validated_ckpt_path(self) -> Optional[str]:
          rank_zero_deprecation(
              "The `Trainer.validated_ckpt_path` attribute was deprecated in v1.6 and will be removed in v1.8. The"
              " path of a checkpoint loaded via `Trainer.{fit,validate,test,predict}` should be accessed via"
              " `Trainer.ckpt_path` instead.",
              stacklevel=5,
          )
          return self._validated_ckpt_path
  
      @validated_ckpt_path.setter
      def validated_ckpt_path(self, ckpt_path: Optional[str]) -> None:
          rank_zero_deprecation(
              "The `Trainer.validated_ckpt_path` attribute was deprecated in v1.6 and will be removed in v1.8. The"
              " path of a checkpoint loaded via `Trainer.{fit,validate,test,predict}` should be accessed via the"
              " read-only `Trainer.ckpt_path`.",
              stacklevel=5,
          )
          self._validated_ckpt_path = ckpt_path
  
      @property
      def tested_ckpt_path(self) -> Optional[str]:
          rank_zero_deprecation(
              "The `Trainer.tested_ckpt_path` attribute was deprecated in v1.6 and will be removed in v1.8. The"
              " path of a checkpoint loaded via `Trainer.{fit,validate,test,predict}` should be accessed via"
              " `Trainer.ckpt_path` instead.",
              stacklevel=5,
          )
          return self._tested_ckpt_path
  
      @tested_ckpt_path.setter
      def tested_ckpt_path(self, ckpt_path: Optional[str]) -> None:
          rank_zero_deprecation(
              "The `Trainer.tested_ckpt_path` attribute was deprecated in v1.6 and will be removed in v1.8. The"
              " path of a checkpoint loaded via `Trainer.{fit,validate,test,predict}` should be accessed via the"
              " read-only `Trainer.ckpt_path` instead.",
              stacklevel=5,
          )
          self._tested_ckpt_path = ckpt_path
  
      @property
      def predicted_ckpt_path(self) -> Optional[str]:
          rank_zero_deprecation(
              "The `Trainer.predicted_ckpt_path` attribute was deprecated in v1.6 and will be removed in v1.8. The"
              " path of a checkpoint loaded via `Trainer.{fit,validate,test,predict}` should be accessed via"
              " `Trainer.ckpt_path` instead.",
              stacklevel=5,
          )
          return self._predicted_ckpt_path
  
      @predicted_ckpt_path.setter
      def predicted_ckpt_path(self, ckpt_path: Optional[str]) -> None:
          rank_zero_deprecation(
              "The `Trainer.predicted_ckpt_path` attribute was deprecated in v1.6 and will be removed in v1.8. The"
              " path of a checkpoint loaded via `Trainer.{fit,validate,test,predict}` should be accessed via the"
              " read-only `Trainer.ckpt_path` instead.",
              stacklevel=5,
          )
          self._predicted_ckpt_path = ckpt_path
  
      def save_checkpoint(
          self, filepath: _PATH, weights_only: bool = False, storage_options: Optional[Any] = None
      ) -> None:
          r"""
          Runs routine to create a checkpoint.
  
          Args:
              filepath: Path where checkpoint is saved.
              weights_only: If ``True``, will only save the model weights.
              storage_options: parameter for how to save to storage, passed to ``CheckpointIO`` plugin
  
          """
          self._checkpoint_connector.save_checkpoint(filepath, weights_only=weights_only, storage_options=storage_options)
  
      """
      Parsing properties
      """
  
      @classmethod
      def default_attributes(cls) -> dict:
          init_signature = inspect.signature(cls)
          return {k: v.default for k, v in init_signature.parameters.items()}
  
      @classmethod
      def get_deprecated_arg_names(cls) -> List:
          """Returns a list with deprecated Trainer arguments."""
          depr_arg_names = []
          for name, val in cls.__dict__.items():
              if name.startswith("DEPRECATED") and isinstance(val, (tuple, list)):
                  depr_arg_names.extend(val)
          return depr_arg_names
  
      @classmethod
      def from_argparse_args(cls: Any, args: Union[Namespace, ArgumentParser], **kwargs) -> Any:
          return from_argparse_args(cls, args, **kwargs)
  
      @classmethod
      def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:
          return parse_argparser(cls, arg_parser)
  
      @classmethod
      def match_env_arguments(cls) -> Namespace:
          return parse_env_variables(cls)
  
      @classmethod
      def add_argparse_args(cls, parent_parser: ArgumentParser, **kwargs) -> ArgumentParser:
          return add_argparse_args(cls, parent_parser, **kwargs)
  
      """
      State properties
      """
  
      @property
      def interrupted(self) -> bool:
          return self.state.status == TrainerStatus.INTERRUPTED
  
      @property
      def training(self) -> bool:
          return self.state.stage == RunningStage.TRAINING
  
      @training.setter
      def training(self, val: bool) -> None:
          if val:
              self.state.stage = RunningStage.TRAINING
          elif self.training:
              self.state.stage = None
  
      @property
      def testing(self) -> bool:
          return self.state.stage == RunningStage.TESTING
  
      @testing.setter
      def testing(self, val: bool) -> None:
          if val:
              self.state.stage = RunningStage.TESTING
          elif self.testing:
              self.state.stage = None
  
      @property
      def predicting(self) -> bool:
          return self.state.stage == RunningStage.PREDICTING
  
      @predicting.setter
      def predicting(self, val: bool) -> None:
          if val:
              self.state.stage = RunningStage.PREDICTING
          elif self.predicting:
              self.state.stage = None
  
      @property
      def tuning(self) -> bool:
          return self.state.stage == RunningStage.TUNING
  
      @tuning.setter
      def tuning(self, val: bool) -> None:
          if val:
              self.state.stage = RunningStage.TUNING
          elif self.tuning:
              self.state.stage = None
  
      @property
      def validating(self) -> bool:
          return self.state.stage == RunningStage.VALIDATING
  
      @validating.setter
      def validating(self, val: bool) -> None:
          if val:
              self.state.stage = RunningStage.VALIDATING
          elif self.validating:
              self.state.stage = None
  
      @property
      def evaluating(self) -> bool:
          return self.state.stage and self.state.stage.evaluating
  
      @property
      def sanity_checking(self) -> bool:
          return self.state.stage == RunningStage.SANITY_CHECKING
  
      @sanity_checking.setter
      def sanity_checking(self, val: bool) -> None:
          if val:
              self.state.stage = RunningStage.SANITY_CHECKING
          elif self.sanity_checking:
              self.state.stage = None
  
      """
      Loop properties
      """
  
      @property
      def global_step(self) -> int:
          """The number of optimizer steps taken (does not reset each epoch).
  
          This includes multiple optimizers and TBPTT steps (if enabled).
          """
          return self.fit_loop.epoch_loop.global_step
  
      @property
      def current_epoch(self) -> int:
          """The current epoch, updated after the epoch end hooks are run."""
          return self.fit_loop.epoch_progress.current.completed
  
      @property
      def max_epochs(self) -> int:
          return self.fit_loop.max_epochs
  
      @property
      def min_epochs(self) -> int:
          return self.fit_loop.min_epochs
  
      @property
      def max_steps(self) -> int:
          return self.fit_loop.max_steps
  
      @property
      def min_steps(self) -> Optional[int]:
          return self.fit_loop.min_steps
  
      @property
      def is_last_batch(self) -> bool:
          return self.fit_loop.epoch_loop.batch_progress.is_last_batch
  
      @property
      def fit_loop(self) -> FitLoop:
          return self._fit_loop
  
      @fit_loop.setter
      def fit_loop(self, loop: FitLoop):
          """Attach a custom fit loop to this Trainer.
  
          It will run with
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`.
          """
          loop.trainer = self
          self._fit_loop = loop
  
      @property
      def validate_loop(self) -> EvaluationLoop:
          return self._validate_loop
  
      @validate_loop.setter
      def validate_loop(self, loop: EvaluationLoop):
          """Attach a custom validation loop to this Trainer.
  
          It will run with
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`. Note that this loop is different from the one
          running during training inside the :meth:`pytorch_lightning.trainer.trainer.Trainer.fit` call.
          """
          loop.trainer = self
          self._validate_loop = loop
  
      @property
      def test_loop(self) -> EvaluationLoop:
          return self._test_loop
  
      @test_loop.setter
      def test_loop(self, loop: EvaluationLoop):
          """Attach a custom test loop to this Trainer.
  
          It will run with
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.test`.
          """
          loop.trainer = self
          self._test_loop = loop
  
      @property
      def predict_loop(self) -> PredictionLoop:
          return self._predict_loop
  
      @predict_loop.setter
      def predict_loop(self, loop: PredictionLoop):
          """Attach a custom prediction loop to this Trainer.
  
          It will run with
          :meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`.
          """
          loop.trainer = self
          self._predict_loop = loop
  
      @property
      def verbose_evaluate(self) -> bool:
          rank_zero_deprecation(
              "The `Trainer.verbose_evaluate` property has been deprecated and will be removed in v1.8. The current value"
              " returned is the union of the validate and test loop values. You can choose which one to access with"
              " `trainer.{validate,test}_loop.verbose`.",
              stacklevel=5,
          )
          return self.validate_loop.verbose or self.test_loop.verbose
  
      @verbose_evaluate.setter
      def verbose_evaluate(self, verbose: bool) -> None:
          rank_zero_deprecation(
              "The `Trainer.verbose_evaluate` property has been deprecated and will be removed in v1.8. This will set"
              " the value for both trainer.{validate,test}_loop.verbose`.",
              stacklevel=5,
          )
          self.validate_loop.verbose = verbose
          self.test_loop.verbose = verbose
  
      @property
      def _evaluation_loop(self) -> EvaluationLoop:
          if self.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
              return self.fit_loop.epoch_loop.val_loop
          if self.state.fn == TrainerFn.VALIDATING:
              return self.validate_loop
          if self.state.fn == TrainerFn.TESTING:
              return self.test_loop
          raise RuntimeError("The `Trainer._evaluation_loop` property isn't defined. Accessed outside of scope")
  
      @property
      def _active_loop(self) -> Optional[Union[FitLoop, EvaluationLoop, PredictionLoop]]:
          if self.training:
              return self.fit_loop
          if self.sanity_checking or self.evaluating:
              return self._evaluation_loop
          if self.predicting:
              return self.predict_loop
  
      """
      Logging properties
      """
  
      @property
      def logger(self) -> Optional[LightningLoggerBase]:
          if len(self.loggers) == 0:
              return None
          if len(self.loggers) == 1:
              return self.loggers[0]
          else:
              rank_zero_warn(
                  "Using trainer.logger when Trainer is configured to use multiple loggers."
                  " This behavior will change in v1.8 when LoggerCollection is removed, and"
                  " trainer.logger will return the first logger in trainer.loggers"
              )
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore")
                  return LoggerCollection(self.loggers)
  
      @logger.setter
      def logger(self, logger: Optional[LightningLoggerBase]) -> None:
          if not logger:
              self.loggers = []
          elif isinstance(logger, LoggerCollection):
              self.loggers = list(logger)
          else:
              self.loggers = [logger]
  
      @property
      def loggers(self) -> List[LightningLoggerBase]:
          return self._loggers
  
      @loggers.setter
      def loggers(self, loggers: Optional[List[LightningLoggerBase]]) -> None:
          self._loggers = loggers if loggers else []
  
      @property
      def callback_metrics(self) -> dict:
          return self._logger_connector.callback_metrics
  
      @property
      def logged_metrics(self) -> dict:
          return self._logger_connector.logged_metrics
  
      @property
      def progress_bar_metrics(self) -> dict:
          return self._logger_connector.progress_bar_metrics
  
      @property
      def _results(self) -> Optional[_ResultCollection]:
          active_loop = self._active_loop
          if active_loop is not None:
              return active_loop._results
  
      def _exit_gracefully_on_signal(self) -> None:
          if not _fault_tolerant_training() or not self._should_terminate_gracefully():
              return
          raise ExitGracefullyException(0)
  
      def _should_terminate_gracefully(self) -> bool:
          value = torch.tensor(int(self._terminate_gracefully), device=self.strategy.root_device)
          return self.strategy.reduce(value, reduce_op="sum") > 0
  
      @property
      def weights_summary(self) -> Optional[str]:
          rank_zero_deprecation("`Trainer.weights_summary` is deprecated in v1.5 and will be removed in v1.7.")
          return self._weights_summary
  
      @weights_summary.setter
      def weights_summary(self, val: Optional[str]) -> None:
          rank_zero_deprecation("Setting `Trainer.weights_summary` is deprecated in v1.5 and will be removed in v1.7.")
          self._weights_summary = val
  
      """
      Other
      """
  
      @property
      def estimated_stepping_batches(self) -> Union[int, float]:
          r"""
          Estimated stepping batches for the complete training inferred from DataLoaders, gradient
          accumulation factor and distributed setup.
  
          Examples::
  
              def configure_optimizers(self):
                  optimizer = ...
                  scheduler = torch.optim.lr_scheduler.OneCycleLR(
                      optimizer, max_lr=1e-3, total_steps=self.trainer.estimated_stepping_batches
                  )
                  return [optimizer], [scheduler]
  
          """
          accumulation_scheduler = self.accumulation_scheduler
  
          if accumulation_scheduler.epochs != [0]:
              raise MisconfigurationException(
                  "Estimated stepping batches cannot be computed with different"
                  " `accumulate_grad_batches` at different epochs."
              )
  
          # infinite training
          if self.max_epochs == -1 and self.max_steps == -1:
              return float("inf")
  
          if self.train_dataloader is None:
              rank_zero_info("Loading `train_dataloader` to estimate number of stepping batches.")
              self.reset_train_dataloader()
  
          total_batches = self.num_training_batches
  
          # iterable dataset
          if total_batches == float("inf"):
              return self.max_steps
  
          self.accumulate_grad_batches = accumulation_scheduler.get_accumulate_grad_batches(self.current_epoch)
          effective_batch_size = self.accumulate_grad_batches
          max_estimated_steps = math.ceil(total_batches / effective_batch_size) * max(self.max_epochs, 1)
  
          max_estimated_steps = min(max_estimated_steps, self.max_steps) if self.max_steps != -1 else max_estimated_steps
          return max_estimated_steps
  
      @property
      def terminate_on_nan(self) -> bool:
          rank_zero_deprecation("`Trainer.terminate_on_nan` is deprecated in v1.5 and will be removed in 1.7.")
          return self._terminate_on_nan
  
      @terminate_on_nan.setter
      def terminate_on_nan(self, val: bool) -> None:
          rank_zero_deprecation(
              f"Setting `Trainer.terminate_on_nan = {val}` is deprecated in v1.5 and will be removed in 1.7."
              f" Please set `Trainer(detect_anomaly={val})` instead."
          )
          self._terminate_on_nan = val  # : 212
  
  
  def _determine_batch_limits(batches: Optional[Union[int, float]], name: str) -> Union[int, float]:
      if batches is None:
          # batches is optional to know if the user passed a value so that we can show the above info messages only to the
          # users that set a value explicitly
          return 1.0
  
      # differentiating based on the type can be error-prone for users. show a message describing the chosen behaviour
      if isinstance(batches, int) and batches == 1:
          if name == "limit_train_batches":
              message = "1 batch per epoch will be used."
          elif name == "val_check_interval":
              message = "validation will run after every batch."
          else:
              message = "1 batch will be used."
          rank_zero_info(f"`Trainer({name}=1)` was configured so {message}")
      elif isinstance(batches, float) and batches == 1.0:
          if name == "limit_train_batches":
              message = "100% of the batches per epoch will be used."
          elif name == "val_check_interval":
              message = "validation will run at the end of the training epoch."
          else:
              message = "100% of the batches will be used."
          rank_zero_info(f"`Trainer({name}=1.0)` was configured so {message}.")
  
      if 0 <= batches <= 1:
          return batches
      if batches > 1 and batches % 1.0 == 0:
          return int(batches)
      raise MisconfigurationException(
          f"You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int."
      )
  
apis: torch.set_grad_enabled torch.no_grad torch.cuda.is_available torch.autograd.set_detect_anomaly torch._C._log_api_usage_once