code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  import os
  
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator, DistributedType
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing the checkpointing capability,
  # and builds off the `nlp_example.py` script.
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # To help focus on the differences in the code, building `DataLoaders`
  # was refactored into its own function.
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  __lowerCAmelCase							:			Union[str, Any]				=16
  __lowerCAmelCase							:			int				=32
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[int] = 16 ):
      A__       =    AutoTokenizer.from_pretrained("bert-base-cased" )
      A__       =    load_dataset("glue"		, "mrpc" )
  
      def tokenize_function(_lowerCamelCase      :      int ):
          # max_length=None => use the model max length (it's actually the default)
          A__       =    tokenizer(examples["sentence1"]		, examples["sentence2"]		, truncation=_lowerCamelCase		, max_length=_lowerCamelCase )
          return outputs
  
      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
          A__       =    datasets.map(
              _lowerCamelCase		, batched=_lowerCamelCase		, remove_columns=["idx", "sentence1", "sentence2"]		, )
  
      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      A__       =    tokenized_datasets.rename_column("label"		, "labels" )
  
      def collate_fn(_lowerCamelCase      :      List[str] ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A__       =    1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
              A__       =    16
          elif accelerator.mixed_precision != "no":
              A__       =    8
          else:
              A__       =    None
  
          return tokenizer.pad(
              _lowerCamelCase		, padding="longest"		, max_length=_lowerCamelCase		, pad_to_multiple_of=_lowerCamelCase		, return_tensors="pt"		, )
  
      # Instantiate dataloaders.
      A__       =    DataLoader(
          tokenized_datasets["train"]		, shuffle=_lowerCamelCase		, collate_fn=_lowerCamelCase		, batch_size=_lowerCamelCase )
      A__       =    DataLoader(
          tokenized_datasets["validation"]		, shuffle=_lowerCamelCase		, collate_fn=_lowerCamelCase		, batch_size=_lowerCamelCase )
  
      return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders
  
   __lowerCAmelCase							:			Any				=mocked_dataloaders  # noqa: F811
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      int ):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"		, _lowerCamelCase ) == "1":
          A__       =    2
      # Initialize accelerator
      A__       =    Accelerator(cpu=args.cpu		, mixed_precision=args.mixed_precision )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      A__       =    config["lr"]
      A__       =    int(config["num_epochs"] )
      A__       =    int(config["seed"] )
      A__       =    int(config["batch_size"] )
  
      # New Code #
      # Parse out whether we are saving every epoch or after a certain number of batches
      if hasattr(args.checkpointing_steps		, "isdigit" ):
          if args.checkpointing_steps == "epoch":
              A__       =    args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
              A__       =    int(args.checkpointing_steps )
          else:
              raise ValueError(
                  F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
      else:
          A__       =    None
  
      set_seed(_lowerCamelCase )
  
      A__,			A__       =    get_dataloaders(_lowerCamelCase		, _lowerCamelCase )
      A__       =    evaluate.load("glue"		, "mrpc" )
  
      # If the batch size is too big we use gradient accumulation
      A__       =    1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A__       =    batch_size // MAX_GPU_BATCH_SIZE
          A__       =    MAX_GPU_BATCH_SIZE
  
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      A__       =    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		, return_dict=_lowerCamelCase )
  
      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      A__       =    model.to(accelerator.device )
  
      # Instantiate optimizer
      A__       =    AdamW(params=model.parameters()		, lr=_lowerCamelCase )
  
      # Instantiate scheduler
      A__       =    get_linear_schedule_with_warmup(
          optimizer=_lowerCamelCase		, num_warmup_steps=1_00		, num_training_steps=(len(_lowerCamelCase ) * num_epochs) // gradient_accumulation_steps		, )
  
      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      A__,			A__,			A__,			A__,			A__       =    accelerator.prepare(
          _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )
  
      # New Code #
      # We need to keep track of how many total steps we have iterated over
      A__       =    0
      # We also need to keep track of the stating epoch so files are named properly
      A__       =    0
  
      # We need to load the checkpoint back in before training here with `load_state`
      # The total number of epochs is adjusted based on where the state is being loaded from,
      # as we assume continuation of the same training script
      if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
              accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}" )
              accelerator.load_state(args.resume_from_checkpoint )
              A__       =    os.path.basename(args.resume_from_checkpoint )
          else:
              # Get the most recent checkpoint
              A__       =    [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
              dirs.sort(key=os.path.getctime )
              A__       =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A__       =    os.path.splitext(_lowerCamelCase )[0]
  
          if "epoch" in training_difference:
              A__       =    int(training_difference.replace("epoch_"		, "" ) ) + 1
              A__       =    None
          else:
              A__       =    int(training_difference.replace("step_"		, "" ) )
              A__       =    resume_step // len(_lowerCamelCase )
              resume_step -= starting_epoch * len(_lowerCamelCase )
  
      # Now we train the model
      for epoch in range(_lowerCamelCase		, _lowerCamelCase ):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
              # We need to skip steps until we reach the resumed step
              A__       =    accelerator.skip_first_batches(_lowerCamelCase		, _lowerCamelCase )
              overall_step += resume_step
          else:
              # After the first iteration though, we need to go back to the original dataloader
              A__       =    train_dataloader
          for step, batch in enumerate(_lowerCamelCase ):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device )
              A__       =    model(**_lowerCamelCase )
              A__       =    outputs.loss
              A__       =    loss / gradient_accumulation_steps
              accelerator.backward(_lowerCamelCase )
              if step % gradient_accumulation_steps == 0:
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
              # New Code #
              overall_step += 1
  
              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `step_{overall_step}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if isinstance(_lowerCamelCase		, _lowerCamelCase ):
                  A__       =    F"step_{overall_step}"
                  if overall_step % checkpointing_steps == 0:
                      if args.output_dir is not None:
                          A__       =    os.path.join(args.output_dir		, _lowerCamelCase )
                      accelerator.save_state(_lowerCamelCase )
  
          model.eval()
          for step, batch in enumerate(_lowerCamelCase ):
              # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
              batch.to(accelerator.device )
              with torch.no_grad():
                  A__       =    model(**_lowerCamelCase )
              A__       =    outputs.logits.argmax(dim=-1 )
              A__,			A__       =    accelerator.gather_for_metrics((predictions, batch["labels"]) )
              metric.add_batch(
                  predictions=_lowerCamelCase		, references=_lowerCamelCase		, )
  
          A__       =    metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"epoch {epoch}:"		, _lowerCamelCase )
  
          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
              A__       =    F"epoch_{epoch}"
              if args.output_dir is not None:
                  A__       =    os.path.join(args.output_dir		, _lowerCamelCase )
              accelerator.save_state(_lowerCamelCase )
  
  
  
  
  
  def 		UpperCamelCase       (							):
      A__       =    argparse.ArgumentParser(description="Simple example of training script." )
      parser.add_argument(
          "--mixed_precision"		, type=_lowerCamelCase		, default=_lowerCamelCase		, choices=["no", "fp16", "bf16", "fp8"]		, help="Whether to use mixed precision. Choose"
          "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
          "and an Nvidia Ampere GPU."		, )
      parser.add_argument("--cpu"		, action="store_true"		, help="If passed, will train on the CPU." )
      parser.add_argument(
          "--checkpointing_steps"		, type=_lowerCamelCase		, default=_lowerCamelCase		, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		, )
      parser.add_argument(
          "--output_dir"		, type=_lowerCamelCase		, default="."		, help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		, )
      parser.add_argument(
          "--resume_from_checkpoint"		, type=_lowerCamelCase		, default=_lowerCamelCase		, help="If the training should continue from a checkpoint folder."		, )
      A__       =    parser.parse_args()
      A__       =    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(_lowerCamelCase		, _lowerCamelCase )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
    import torch
  
  
  __lowerCAmelCase							:			Optional[int]				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :Optional[int]		, lowercase_   :Any		, lowercase_   :Any=21		, lowercase_   :Any=[]		, lowercase_   :Dict=None							)->							Tuple:
       A__       =    model
       A__       =    trace_batch_nums
       A__       =    abort_after_batch_num
  
       # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
       A__       =    collections.deque([]		, lowercase_							)
       A__       =    []
       A__       =    0
       A__       =    0
       A__       =    False
       A__       =    "                 "
  
       self.analyse_model()
  
       self.register_forward_hook()
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :Any=None							)->							Optional[Any]:
       if frame is not None:
           self.expand_frame(lowercase_							)
       self.frames.append("\n".join(self.frame							)							)
       A__       =    []  # start a new frame
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :Dict							)->							List[str]:
       self.frame.append(lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Dict:
       print("\n".join(self.frames							)							)
       A__       =    []
   def   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
       A__       =    []
   def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[Any]:
       print(F"\nDetected inf/nan during batch_number={self.batch_number}"							)
       print(F"Last {len(self.frames							)} forward frames:"							)
       print(F"{'abs min':8} {'abs max':8} metadata"							)
       print("\n".join(self.frames							)							)
       print("\n\n"							)
       A__       =    []
   def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
       # extract the fully qualified module names, to be able to report at run time. e.g.:
       # encoder.block.2.layer.0.SelfAttention.o
       #
       # for shared weights only the first shared module name will be registered
       A__       =    {m: name for name, m in self.model.named_modules()}
       # self.longest_module_name = max(len(v) for v in self.module_names.values())
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Any							)->							str:
       if torch.is_tensor(lowercase_							):
           self.expand_frame(get_abs_min_max(lowercase_		, lowercase_							)							)
           if detect_overflow(lowercase_		, lowercase_							):
               A__       =    True
       elif var is None:
           self.expand_frame(F"{'None':>17} {ctx}"							)
       else:
           self.expand_frame(F"{'not a tensor':>17} {ctx}"							)
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							str:
       self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"							)
       self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"							)
   def   UpperCAmelCase_							(       self   :int							)->							int:
       self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"							)
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Dict		, lowercase_   :Dict		, lowercase_   :str							)->							int:
       self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"							)
  
       # params
       for name, p in module.named_parameters(recurse=lowercase_							):
           self.analyse_variable(lowercase_		, lowercase_							)
  
       # inputs
       if isinstance(lowercase_		, lowercase_							):
           for i, x in enumerate(lowercase_							):
               self.analyse_variable(lowercase_		, F"input[{i}]"							)
       else:
           self.analyse_variable(lowercase_		, "input"							)
  
       # outputs
       if isinstance(lowercase_		, lowercase_							):
           for i, x in enumerate(lowercase_							):
               # possibly a tuple of tuples
               if isinstance(lowercase_		, lowercase_							):
                   for j, y in enumerate(lowercase_							):
                       self.analyse_variable(lowercase_		, F"output[{i}][{j}]"							)
               else:
                   self.analyse_variable(lowercase_		, F"output[{i}]"							)
       else:
           self.analyse_variable(lowercase_		, "output"							)
  
       self.save_frame()
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       self.model.apply(self._register_forward_hook							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]							)->							Tuple:
       module.register_forward_hook(self.forward_hook							)
  
  
  
   def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :int		, lowercase_   :Optional[Any]		, lowercase_   :Dict							)->							Union[str, Any]:
       # - input is a tuple of packed inputs (could be non-Tensors)
       # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
       A__       =    False
  
       A__       =    True if self.batch_number in self.trace_batch_nums else False
       if trace_mode:
           self.reset_saved_frames()
  
       if self.total_calls == 0:
           self.batch_start_frame()
       self.total_calls += 1
  
       # count batch numbers - the very first forward hook of the batch will be called when the
       # batch completes - i.e. it gets called very last - we know this batch has finished
       if module == self.model:
           self.batch_number += 1
           A__       =    True
  
       self.create_frame(lowercase_		, lowercase_		, lowercase_							)
  
       # if last_frame_of_batch:
       #     self.batch_end_frame()
  
       if trace_mode:
           self.trace_frames()
  
       if last_frame_of_batch:
           self.batch_start_frame()
  
       if self.detected_overflow and not trace_mode:
           self.dump_saved_frames()
  
           # now we can abort, as it's pointless to continue running
           raise ValueError(
               "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
               "Please scroll up above this traceback to see the activation values prior to this event."							)
  
       # abort after certain batch if requested to do so
       if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
           raise ValueError(
               F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
               F" `abort_after_batch_num={self.abort_after_batch_num}` arg"							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      int ):
      A__       =    var.abs()
      return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Optional[int] ):
      A__       =    False
      if torch.isnan(_lowerCamelCase ).any().item():
          A__       =    True
          print(F"{ctx} has nans" )
      if torch.isinf(_lowerCamelCase ).any().item():
          A__       =    True
          print(F"{ctx} has infs" )
  
      # if needed to monitor large elements can enable the following
      if 0:  # and detected:
          A__       =    var[torch.ge(var.abs()		, 1_00 )]
          if naaa.numel() > 0:
              print(F"{ctx}:  n100={naaa.numel()}" )
          A__       =    var[torch.ge(var.abs()		, 10_00 )]
          if naaaa.numel() > 0:
              print(F"{ctx}: n1000={naaaa.numel()}" )
          A__       =    var[torch.ge(var.abs()		, 1_00_00 )]
          if naaaaa.numel() > 0:
              print(F"{ctx}: n10000={naaaaa.numel()}" )
  
      if 0:
          print(F"min={var.min():9.2e} max={var.max():9.2e}" )
  
      if 0:
          print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})" )
  
      return detected
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """underflow_overflow"""
   __lowercase     	=       """tpu_metrics_debug"""
  
  
  
  