"""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
#
########################################################################

lowercase__   			=      16
lowercase__   			=      32







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = 16  )      ->Any:
  a__:	Union[str, Any]     				=      AutoTokenizer.from_pretrained('bert-base-cased'  )
  a__:	Any     				=      load_dataset('glue'						,	'mrpc'  )

  def tokenize_function(_SCREAMING_SNAKE_CASE  ):
    # max_length=None => use the model max length (it's actually the default)
    a__:	Dict     				=      tokenizer(examples['sentence1']						,	examples['sentence2']						,	truncation=_SCREAMING_SNAKE_CASE						,	max_length=_SCREAMING_SNAKE_CASE  )
    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__:	str     				=      datasets.map(
        _SCREAMING_SNAKE_CASE						,	batched=_SCREAMING_SNAKE_CASE						,	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__:	Optional[int]     				=      tokenized_datasets.rename_column('label'						,	'labels'  )

  def collate_fn(_SCREAMING_SNAKE_CASE  ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__:	int     				=      128 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__:	List[str]     				=      16
    elif accelerator.mixed_precision != "no":
      a__:	List[Any]     				=      8
    else:
      a__:	Tuple     				=      None

    return tokenizer.pad(
        _SCREAMING_SNAKE_CASE						,	padding='longest'						,	max_length=_SCREAMING_SNAKE_CASE						,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,	return_tensors='pt'						,	)

  # Instantiate dataloaders.
  a__:	Union[str, Any]     				=      DataLoader(
      tokenized_datasets['train']						,	shuffle=_SCREAMING_SNAKE_CASE						,	collate_fn=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )
  a__:	Dict     				=      DataLoader(
      tokenized_datasets['validation']						,	shuffle=_SCREAMING_SNAKE_CASE						,	collate_fn=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )

  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

 lowercase__   			=      mocked_dataloaders  # noqa: F811







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS'						,	_SCREAMING_SNAKE_CASE  ) == "1":
    a__:	List[str]     				=      2
  # Initialize accelerator
  a__:	Union[str, Any]     				=      Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision  )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__:	int     				=      config['lr']
  a__:	Any     				=      int(config['num_epochs']  )
  a__:	Any     				=      int(config['seed']  )
  a__:	List[str]     				=      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__:	int     				=      args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      a__:	Optional[int]     				=      int(args.checkpointing_steps  )
    else:
      raise ValueError(
          F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'  )
  else:
    a__:	Union[str, Any]     				=      None

  set_seed(_SCREAMING_SNAKE_CASE  )

  a__				,       a__:	int     				=      get_dataloaders(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  a__:	Dict     				=      evaluate.load('glue'						,	'mrpc'  )

  # If the batch size is too big we use gradient accumulation
  a__:	Optional[Any]     				=      1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a__:	str     				=      batch_size // MAX_GPU_BATCH_SIZE
    a__:	Optional[int]     				=      MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a__:	Dict     				=      AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,	return_dict=_SCREAMING_SNAKE_CASE  )

  # 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__:	str     				=      model.to(accelerator.device  )

  # Instantiate optimizer
  a__:	Optional[Any]     				=      AdamW(params=model.parameters()						,	lr=_SCREAMING_SNAKE_CASE  )

  # Instantiate scheduler
  a__:	Optional[int]     				=      get_linear_schedule_with_warmup(
      optimizer=_SCREAMING_SNAKE_CASE						,	num_warmup_steps=100						,	num_training_steps=(len(_SCREAMING_SNAKE_CASE  ) * 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__:	List[Any]     				=      accelerator.prepare(
      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  a__:	str     				=      0
  # We also need to keep track of the stating epoch so files are named properly
  a__:	Any     				=      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__:	Union[str, Any]     				=      os.path.basename(args.resume_from_checkpoint  )
    else:
      # Get the most recent checkpoint
      a__:	Optional[Any]     				=      [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
      dirs.sort(key=os.path.getctime  )
      a__:	int     				=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    a__:	int     				=      os.path.splitext(_SCREAMING_SNAKE_CASE  )[0]

    if "epoch" in training_difference:
      a__:	Union[str, Any]     				=      int(training_difference.replace('epoch_'						,	''  )  ) + 1
      a__:	Dict     				=      None
    else:
      a__:	Optional[int]     				=      int(training_difference.replace('step_'						,	''  )  )
      a__:	Tuple     				=      resume_step // len(_SCREAMING_SNAKE_CASE  )
      resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE  )

    # Now we train the model
  for epoch in range(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    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__:	List[Any]     				=      accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      a__:	Optional[int]     				=      train_dataloader
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE  ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device  )
      a__:	List[str]     				=      model(**_SCREAMING_SNAKE_CASE  )
      a__:	Any     				=      outputs.loss
      a__:	List[str]     				=      loss / gradient_accumulation_steps
      accelerator.backward(_SCREAMING_SNAKE_CASE  )
      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(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
        a__:	List[str]     				=      F'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            a__:	Dict     				=      os.path.join(args.output_dir						,	_SCREAMING_SNAKE_CASE  )
          accelerator.save_state(_SCREAMING_SNAKE_CASE  )

    model.eval()
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE  ):
      # 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__:	List[Any]     				=      model(**_SCREAMING_SNAKE_CASE  )
      a__:	Tuple     				=      outputs.logits.argmax(dim=-1  )
      a__				,       a__:	Any     				=      accelerator.gather_for_metrics((predictions, batch['labels'])  )
      metric.add_batch(
          predictions=_SCREAMING_SNAKE_CASE						,	references=_SCREAMING_SNAKE_CASE						,	)

    a__:	Tuple     				=      metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'epoch {epoch}:'						,	_SCREAMING_SNAKE_CASE  )

    # 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__:	Dict     				=      F'epoch_{epoch}'
      if args.output_dir is not None:
        a__:	Optional[Any]     				=      os.path.join(args.output_dir						,	_SCREAMING_SNAKE_CASE  )
      accelerator.save_state(_SCREAMING_SNAKE_CASE  )







def 		__a  (      )      ->List[Any]:
  a__:	Optional[Any]     				=      argparse.ArgumentParser(description='Simple example of training script.'  )
  parser.add_argument(
      '--mixed_precision'						,	type=_SCREAMING_SNAKE_CASE						,	default=_SCREAMING_SNAKE_CASE						,	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=_SCREAMING_SNAKE_CASE						,	default=_SCREAMING_SNAKE_CASE						,	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=_SCREAMING_SNAKE_CASE						,	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=_SCREAMING_SNAKE_CASE						,	default=_SCREAMING_SNAKE_CASE						,	help='If the training should continue from a checkpoint folder.'						,	)
  a__:	Any     				=      parser.parse_args()
  a__:	Optional[Any]     				=      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
 main()








from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


__a			:	List[Any]							     =		{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__a			:	Dict							     =		[
				    """Constraint""",
				    """ConstraintListState""",
				    """DisjunctiveConstraint""",
				    """PhrasalConstraint""",
				]
				__a			:	int							     =		[
				    """BeamHypotheses""",
				    """BeamScorer""",
				    """BeamSearchScorer""",
				    """ConstrainedBeamSearchScorer""",
				]
				__a			:	List[Any]							     =		[
				    """EpsilonLogitsWarper""",
				    """EtaLogitsWarper""",
				    """ForcedBOSTokenLogitsProcessor""",
				    """ForcedEOSTokenLogitsProcessor""",
				    """HammingDiversityLogitsProcessor""",
				    """InfNanRemoveLogitsProcessor""",
				    """LogitsProcessor""",
				    """LogitsProcessorList""",
				    """LogitsWarper""",
				    """MinLengthLogitsProcessor""",
				    """MinNewTokensLengthLogitsProcessor""",
				    """NoBadWordsLogitsProcessor""",
				    """NoRepeatNGramLogitsProcessor""",
				    """PrefixConstrainedLogitsProcessor""",
				    """RepetitionPenaltyLogitsProcessor""",
				    """SequenceBiasLogitsProcessor""",
				    """EncoderRepetitionPenaltyLogitsProcessor""",
				    """TemperatureLogitsWarper""",
				    """TopKLogitsWarper""",
				    """TopPLogitsWarper""",
				    """TypicalLogitsWarper""",
				    """EncoderNoRepeatNGramLogitsProcessor""",
				    """ExponentialDecayLengthPenalty""",
				    """LogitNormalization""",
				]
				__a			:	Any							     =		[
				    """MaxNewTokensCriteria""",
				    """MaxLengthCriteria""",
				    """MaxTimeCriteria""",
				    """StoppingCriteria""",
				    """StoppingCriteriaList""",
				    """validate_stopping_criteria""",
				]
				__a			:	str							     =		[
				    """GenerationMixin""",
				    """top_k_top_p_filtering""",
				    """GreedySearchEncoderDecoderOutput""",
				    """GreedySearchDecoderOnlyOutput""",
				    """SampleEncoderDecoderOutput""",
				    """SampleDecoderOnlyOutput""",
				    """BeamSearchEncoderDecoderOutput""",
				    """BeamSearchDecoderOnlyOutput""",
				    """BeamSampleEncoderDecoderOutput""",
				    """BeamSampleDecoderOnlyOutput""",
				    """ContrastiveSearchEncoderDecoderOutput""",
				    """ContrastiveSearchDecoderOnlyOutput""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__a			:	List[Any]							     =		[
				    """TFForcedBOSTokenLogitsProcessor""",
				    """TFForcedEOSTokenLogitsProcessor""",
				    """TFLogitsProcessor""",
				    """TFLogitsProcessorList""",
				    """TFLogitsWarper""",
				    """TFMinLengthLogitsProcessor""",
				    """TFNoBadWordsLogitsProcessor""",
				    """TFNoRepeatNGramLogitsProcessor""",
				    """TFRepetitionPenaltyLogitsProcessor""",
				    """TFTemperatureLogitsWarper""",
				    """TFTopKLogitsWarper""",
				    """TFTopPLogitsWarper""",
				    """TFForceTokensLogitsProcessor""",
				    """TFSuppressTokensAtBeginLogitsProcessor""",
				    """TFSuppressTokensLogitsProcessor""",
				]
				__a			:	Any							     =		[
				    """TFGenerationMixin""",
				    """tf_top_k_top_p_filtering""",
				    """TFGreedySearchDecoderOnlyOutput""",
				    """TFGreedySearchEncoderDecoderOutput""",
				    """TFSampleEncoderDecoderOutput""",
				    """TFSampleDecoderOnlyOutput""",
				    """TFBeamSearchEncoderDecoderOutput""",
				    """TFBeamSearchDecoderOnlyOutput""",
				    """TFBeamSampleEncoderDecoderOutput""",
				    """TFBeamSampleDecoderOnlyOutput""",
				    """TFContrastiveSearchEncoderDecoderOutput""",
				    """TFContrastiveSearchDecoderOnlyOutput""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__a			:	str							     =		[
				    """FlaxForcedBOSTokenLogitsProcessor""",
				    """FlaxForcedEOSTokenLogitsProcessor""",
				    """FlaxLogitsProcessor""",
				    """FlaxLogitsProcessorList""",
				    """FlaxLogitsWarper""",
				    """FlaxMinLengthLogitsProcessor""",
				    """FlaxTemperatureLogitsWarper""",
				    """FlaxTopKLogitsWarper""",
				    """FlaxTopPLogitsWarper""",
				]
				__a			:	Tuple							     =		[
				    """FlaxGenerationMixin""",
				    """FlaxGreedySearchOutput""",
				    """FlaxSampleOutput""",
				    """FlaxBeamSearchOutput""",
				]

if TYPE_CHECKING:
				from .configuration_utils import GenerationConfig
				from .streamers import TextIteratorStreamer, TextStreamer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
								from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
								from .logits_process import (
								    EncoderNoRepeatNGramLogitsProcessor,
								    EncoderRepetitionPenaltyLogitsProcessor,
								    EpsilonLogitsWarper,
								    EtaLogitsWarper,
								    ExponentialDecayLengthPenalty,
								    ForcedBOSTokenLogitsProcessor,
								    ForcedEOSTokenLogitsProcessor,
								    HammingDiversityLogitsProcessor,
								    InfNanRemoveLogitsProcessor,
								    LogitNormalization,
								    LogitsProcessor,
								    LogitsProcessorList,
								    LogitsWarper,
								    MinLengthLogitsProcessor,
								    MinNewTokensLengthLogitsProcessor,
								    NoBadWordsLogitsProcessor,
								    NoRepeatNGramLogitsProcessor,
								    PrefixConstrainedLogitsProcessor,
								    RepetitionPenaltyLogitsProcessor,
								    SequenceBiasLogitsProcessor,
								    TemperatureLogitsWarper,
								    TopKLogitsWarper,
								    TopPLogitsWarper,
								    TypicalLogitsWarper,
								)
								from .stopping_criteria import (
								    MaxLengthCriteria,
								    MaxNewTokensCriteria,
								    MaxTimeCriteria,
								    StoppingCriteria,
								    StoppingCriteriaList,
								    validate_stopping_criteria,
								)
								from .utils import (
								    BeamSampleDecoderOnlyOutput,
								    BeamSampleEncoderDecoderOutput,
								    BeamSearchDecoderOnlyOutput,
								    BeamSearchEncoderDecoderOutput,
								    ContrastiveSearchDecoderOnlyOutput,
								    ContrastiveSearchEncoderDecoderOutput,
								    GenerationMixin,
								    GreedySearchDecoderOnlyOutput,
								    GreedySearchEncoderDecoderOutput,
								    SampleDecoderOnlyOutput,
								    SampleEncoderDecoderOutput,
								    top_k_top_p_filtering,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tf_logits_process import (
								    TFForcedBOSTokenLogitsProcessor,
								    TFForcedEOSTokenLogitsProcessor,
								    TFForceTokensLogitsProcessor,
								    TFLogitsProcessor,
								    TFLogitsProcessorList,
								    TFLogitsWarper,
								    TFMinLengthLogitsProcessor,
								    TFNoBadWordsLogitsProcessor,
								    TFNoRepeatNGramLogitsProcessor,
								    TFRepetitionPenaltyLogitsProcessor,
								    TFSuppressTokensAtBeginLogitsProcessor,
								    TFSuppressTokensLogitsProcessor,
								    TFTemperatureLogitsWarper,
								    TFTopKLogitsWarper,
								    TFTopPLogitsWarper,
								)
								from .tf_utils import (
								    TFBeamSampleDecoderOnlyOutput,
								    TFBeamSampleEncoderDecoderOutput,
								    TFBeamSearchDecoderOnlyOutput,
								    TFBeamSearchEncoderDecoderOutput,
								    TFContrastiveSearchDecoderOnlyOutput,
								    TFContrastiveSearchEncoderDecoderOutput,
								    TFGenerationMixin,
								    TFGreedySearchDecoderOnlyOutput,
								    TFGreedySearchEncoderDecoderOutput,
								    TFSampleDecoderOnlyOutput,
								    TFSampleEncoderDecoderOutput,
								    tf_top_k_top_p_filtering,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .flax_logits_process import (
								    FlaxForcedBOSTokenLogitsProcessor,
								    FlaxForcedEOSTokenLogitsProcessor,
								    FlaxLogitsProcessor,
								    FlaxLogitsProcessorList,
								    FlaxLogitsWarper,
								    FlaxMinLengthLogitsProcessor,
								    FlaxTemperatureLogitsWarper,
								    FlaxTopKLogitsWarper,
								    FlaxTopPLogitsWarper,
								)
								from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
				import sys

				__a			:	List[Any]							     =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
