



"""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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


UpperCAmelCase						:		Dict			= 16
UpperCAmelCase						:		str			= 32


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase = 16							) ->						Dict:







  '''simple docstring'''


  lowercase_								= AutoTokenizer.from_pretrained("""bert-base-cased"""							)
  lowercase_								= load_dataset("""glue""" ,  """mrpc"""							)

  def tokenize_function(__lowerCAmelCase							):
    # max_length=None => use the model max length (it's actually the default)
    lowercase_								= 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():
    lowercase_								= 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
  lowercase_								= tokenized_datasets.rename_column("""label""" ,  """labels"""							)

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

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

  # Instantiate dataloaders.
  lowercase_								= DataLoader(
      tokenized_datasets["""train"""] ,  shuffle=__lowerCAmelCase ,  collate_fn=__lowerCAmelCase ,  batch_size=__lowerCAmelCase							)
  lowercase_								= 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

       UpperCAmelCase						:		List[str]			= mocked_dataloaders  # noqa: F811


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,  __lowerCAmelCase							) == "1":
    lowercase_								= 2
  # Initialize accelerator
  lowercase_								= Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision							)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase_								= config["""lr"""]
  lowercase_								= int(config["""num_epochs"""]							)
  lowercase_								= int(config["""seed"""]							)
  lowercase_								= int(config["""batch_size"""]							)

  lowercase_								= evaluate.load("""glue""" ,  """mrpc"""							)

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

  set_seed(__lowerCAmelCase							)
  lowercase_					,    lowercase_								= get_dataloaders(__lowerCAmelCase ,  __lowerCAmelCase							)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase_								= 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).
  lowercase_								= model.to(accelerator.device							)

  # Instantiate optimizer
  lowercase_								= AdamW(params=model.parameters() ,  lr=__lowerCAmelCase							)

  # Instantiate scheduler
  lowercase_								= 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.
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= accelerator.prepare(
      __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)

  # Now we train the model
  for epoch in range(__lowerCAmelCase							):
    model.train()
    for step, batch in enumerate(__lowerCAmelCase							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      lowercase_								= model(**__lowerCAmelCase							)
      lowercase_								= outputs.loss
      lowercase_								= loss / gradient_accumulation_steps
      accelerator.backward(__lowerCAmelCase							)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    lowercase_								= 0
    for step, batch in enumerate(__lowerCAmelCase							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      with torch.no_grad():
        lowercase_								= model(**__lowerCAmelCase							)
      lowercase_								= outputs.logits.argmax(dim=-1							)
      lowercase_					,    lowercase_								= accelerator.gather((predictions, batch["""labels"""])							)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(__lowerCAmelCase							) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          lowercase_								= predictions[: len(eval_dataloader.dataset							) - samples_seen]
          lowercase_								= references[: len(eval_dataloader.dataset							) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=__lowerCAmelCase ,  references=__lowerCAmelCase ,  )

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


def 			_SCREAMING_SNAKE_CASE						() ->						Any:







  '''simple docstring'''


  lowercase_								= 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."""							)
  lowercase_								= parser.parse_args()
  lowercase_								= {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(__lowerCAmelCase ,  __lowerCAmelCase							)


if __name__ == "__main__":
       main()






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

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

