

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__							=			16
lowerCAmelCase__							=			32
def   _lowerCAmelCase(						__A ,							__A = 16      ):
  UpperCAmelCase          =	AutoTokenizer.from_pretrained("bert-base-cased"      )
  UpperCAmelCase          =	load_dataset("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"      )

  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      )

  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__							=			mocked_dataloaders  # noqa: F811
def   _lowerCAmelCase(						__A ,							__A      ):
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS" ,							__A      ) == "1":
    UpperCAmelCase          =	2
  # Initialize accelerator
  UpperCAmelCase          =	Accelerator(cpu=args.cpu ,							mixed_precision=args.mixed_precision      )
  # 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"]      )

  # 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":
      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

  set_seed(__A      )

  UpperCAmelCase   ,						UpperCAmelCase          =	get_dataloaders(__A ,							__A      )
  UpperCAmelCase          =	evaluate.load("glue" ,							"mrpc"      )

  # 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

  # 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      )

  # New Code #
  # 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

  # 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      )
      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()
    # 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
      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
      accelerator.backward(__A      )
      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(__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` (the default).
      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      )

    # 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":
      UpperCAmelCase          =	F"epoch_{epoch}"
      if args.output_dir is not None:
        UpperCAmelCase          =	os.path.join(args.output_dir ,							__A      )
      accelerator.save_state(__A      )
def   _lowerCAmelCase(						):
  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(
      "--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(
      "--resume_from_checkpoint" ,							type=__A ,							default=__A ,							help="If the training should continue from a checkpoint folder." ,							)
  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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["XLMProphetNetTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



