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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__   = 16
lowerCamelCase__   = 32


def 							lowerCAmelCase__    (       a__  ,			a__      )	->Dict:




  '''simple docstring'''

  if args.with_tracking:
    _UpperCamelCase				=  Accelerator(
        cpu=args.cpu  ,			mixed_precision=args.mixed_precision  ,			log_with="all"  ,			project_dir=args.project_dir      )
  else:
    _UpperCamelCase				=  Accelerator(cpu=args.cpu  ,			mixed_precision=args.mixed_precision      )

  if hasattr(args.checkpointing_steps  ,			"isdigit"      ):
    if args.checkpointing_steps == "epoch":
      _UpperCamelCase				=  args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _UpperCamelCase				=  int(args.checkpointing_steps      )
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'      )
  else:
    _UpperCamelCase				=  None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCamelCase				=  config["lr"]
  _UpperCamelCase				=  int(config["num_epochs"]      )
  _UpperCamelCase				=  int(config["seed"]      )
  _UpperCamelCase				=  int(config["batch_size"]      )

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _UpperCamelCase				=  os.path.split(a__      )[-1].split("."      )[0]
    accelerator.init_trackers(a__  ,			a__      )

  _UpperCamelCase				=  AutoTokenizer.from_pretrained("bert-base-cased"      )
  _UpperCamelCase				=  load_dataset("glue"  ,			"mrpc"      )
  _UpperCamelCase				=  evaluate.load("glue"  ,			"mrpc"      )

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

  # If the batch size is too big we use gradient accumulation
  _UpperCamelCase				=  1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _UpperCamelCase				=  batch_size // MAX_GPU_BATCH_SIZE
    _UpperCamelCase				=  MAX_GPU_BATCH_SIZE

  def collate_fn(a__      ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCamelCase				=  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":
      _UpperCamelCase				=  16
    elif accelerator.mixed_precision != "no":
      _UpperCamelCase				=  8
    else:
      _UpperCamelCase				=  None

    return tokenizer.pad(
        a__  ,			padding="longest"  ,			max_length=a__  ,			pad_to_multiple_of=a__  ,			return_tensors="pt"  ,			)

  # Instantiate dataloaders.
  _UpperCamelCase				=  DataLoader(
      tokenized_datasets["train"]  ,			shuffle=a__  ,			collate_fn=a__  ,			batch_size=a__      )
  _UpperCamelCase				=  DataLoader(
      tokenized_datasets["validation"]  ,			shuffle=a__  ,			collate_fn=a__  ,			batch_size=a__      )

  set_seed(a__      )

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

  # 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).
  _UpperCamelCase				=  model.to(accelerator.device      )

  # Instantiate optimizer
  _UpperCamelCase				=  AdamW(params=model.parameters()  ,			lr=a__      )

  # Instantiate scheduler
  _UpperCamelCase				=  get_linear_schedule_with_warmup(
      optimizer=a__  ,			num_warmup_steps=100  ,			num_training_steps=(len(a__      ) * 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.
  _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  accelerator.prepare(
      a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  # We need to keep track of how many total steps we have iterated over
  _UpperCamelCase				=  0
  # We also need to keep track of the stating epoch so files are named properly
  _UpperCamelCase				=  0

  # Potentially load in the weights and states from a previous save
  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      )
      _UpperCamelCase				=  os.path.basename(args.resume_from_checkpoint      )
    else:
      # Get the most recent checkpoint
      _UpperCamelCase				=  [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
      dirs.sort(key=os.path.getctime      )
      _UpperCamelCase				=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _UpperCamelCase				=  os.path.splitext(a__      )[0]

    if "epoch" in training_difference:
      _UpperCamelCase				=  int(training_difference.replace("epoch_"  ,			""      )      ) + 1
      _UpperCamelCase				=  None
    else:
      _UpperCamelCase				=  int(training_difference.replace("step_"  ,			""      )      )
      _UpperCamelCase				=  resume_step // len(a__      )
      resume_step -= starting_epoch * len(a__      )

    # Now we train the model
  for epoch in range(a__  ,			a__      ):
    model.train()
    if args.with_tracking:
      _UpperCamelCase				=  0
    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
      _UpperCamelCase				=  accelerator.skip_first_batches(a__  ,			a__      )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _UpperCamelCase				=  train_dataloader
    for step, batch in enumerate(a__      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device      )
      _UpperCamelCase				=  model(**a__      )
      _UpperCamelCase				=  outputs.loss
      _UpperCamelCase				=  loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(a__      )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(a__  ,			a__      ):
        _UpperCamelCase				=  f'step_{overall_step}'
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _UpperCamelCase				=  os.path.join(args.output_dir  ,			a__      )
          accelerator.save_state(a__      )

    model.eval()
    for step, batch in enumerate(a__      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device      )
      with torch.no_grad():
        _UpperCamelCase				=  model(**a__      )
      _UpperCamelCase				=  outputs.logits.argmax(dim=-1      )
      _UpperCamelCase	,		_UpperCamelCase				=  accelerator.gather_for_metrics((predictions, batch["labels"])      )
      metric.add_batch(
          predictions=a__  ,			references=a__  ,			)

    _UpperCamelCase				=  metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'  ,			a__      )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": eval_metric["accuracy"],
              "f1": eval_metric["f1"],
              "train_loss": total_loss.item() / len(a__      ),
              "epoch": epoch,
          }  ,			step=a__  ,			)

    if checkpointing_steps == "epoch":
      _UpperCamelCase				=  f'epoch_{epoch}'
      if args.output_dir is not None:
        _UpperCamelCase				=  os.path.join(args.output_dir  ,			a__      )
      accelerator.save_state(a__      )

  if args.with_tracking:
    accelerator.end_training()


def 							lowerCAmelCase__    (       )	->Any:




  '''simple docstring'''

  _UpperCamelCase				=  argparse.ArgumentParser(description="Simple example of training script."      )
  parser.add_argument(
      "--mixed_precision"  ,			type=a__  ,			default=a__  ,			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=a__  ,			default=a__  ,			help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."  ,			)
  parser.add_argument(
      "--resume_from_checkpoint"  ,			type=a__  ,			default=a__  ,			help="If the training should continue from a checkpoint folder."  ,			)
  parser.add_argument(
      "--with_tracking"  ,			action="store_true"  ,			help="Whether to load in all available experiment trackers from the environment and use them for logging."  ,			)
  parser.add_argument(
      "--output_dir"  ,			type=a__  ,			default="."  ,			help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."  ,			)
  parser.add_argument(
      "--project_dir"  ,			type=a__  ,			default="logs"  ,			help="Location on where to store experiment tracking logs` and relevent project information"  ,			)
  _UpperCamelCase				=  parser.parse_args()
  _UpperCamelCase				=  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(a__  ,			a__      )


if __name__ == "__main__":
   main()





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase__   = {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__   = ['''ErnieMTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__   = [
       '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ErnieMForMultipleChoice''',
       '''ErnieMForQuestionAnswering''',
       '''ErnieMForSequenceClassification''',
       '''ErnieMForTokenClassification''',
       '''ErnieMModel''',
       '''ErnieMPreTrainedModel''',
       '''ErnieMForInformationExtraction''',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__   = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




