




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


__A     =    16
__A     =    32





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a = 16		)					->		Dict:







  """simple docstring"""





  lowerCamelCase__:				int        =AutoTokenizer.from_pretrained("bert-base-cased"		)
  lowerCamelCase__:				Tuple        =DatasetDict(
      {
          "train": dataset["train"].select(__a		),
          "validation": dataset["train"].select(__a		),
          "test": dataset["validation"],
      }		)

  def tokenize_function(__a		):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase__:				Any        =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():
    lowerCamelCase__:				Tuple        =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
  lowerCamelCase__:				Tuple        =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.
    lowerCamelCase__:				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":
      lowerCamelCase__:				Optional[Any]        =16
    elif accelerator.mixed_precision != "no":
      lowerCamelCase__:				Optional[Any]        =8
    else:
      lowerCamelCase__:				Optional[int]        =None

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

  # Instantiate dataloaders.
  lowerCamelCase__:				Dict        =DataLoader(
      tokenized_datasets["train"]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a		)
  lowerCamelCase__:				Optional[int]        =DataLoader(
      tokenized_datasets["validation"]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a		)

  lowerCamelCase__:				Optional[int]        =DataLoader(
      tokenized_datasets["test"]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a		)

  return train_dataloader, eval_dataloader, test_dataloader





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				str        =[]
  # Download the dataset
  lowerCamelCase__:				Optional[int]        =load_dataset("glue"     ,   "mrpc"		)
  # Create our splits
  lowerCamelCase__:				Optional[int]        =StratifiedKFold(n_splits=int(args.num_folds		)		)
  # Initialize accelerator
  lowerCamelCase__:				List[Any]        =Accelerator(cpu=args.cpu     ,   mixed_precision=args.mixed_precision		)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase__:				Optional[Any]        =config["lr"]
  lowerCamelCase__:				Optional[Any]        =int(config["num_epochs"]		)
  lowerCamelCase__:				Any        =int(config["seed"]		)
  lowerCamelCase__:				Optional[Any]        =int(config["batch_size"]		)

  lowerCamelCase__:				int        =evaluate.load("glue"     ,   "mrpc"		)

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

  set_seed(__a		)

  # New Code #
  # Create our folds:
  lowerCamelCase__:				Dict        =kfold.split(np.zeros(datasets["train"].num_rows		)     ,   datasets["train"]["label"]		)
  lowerCamelCase__:				List[str]        =[]
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(__a		):
    lowerCamelCase__:				Optional[Any]        =get_fold_dataloaders(
        __a     ,   __a     ,   __a     ,   __a     ,   )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__:				Union[str, Any]        =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).
    lowerCamelCase__:				Any        =model.to(accelerator.device		)

    # Instantiate optimizer
    lowerCamelCase__:				str        =AdamW(params=model.parameters()     ,   lr=__a		)

    # Instantiate scheduler
    lowerCamelCase__:				Any        =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.
    lowerCamelCase__:				Optional[Any]        =accelerator.prepare(
        __a     ,   __a     ,   __a     ,   __a     ,   __a		)

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

      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():
          lowerCamelCase__:				Optional[int]        =model(**__a		)
        lowerCamelCase__:				Any        =outputs.logits.argmax(dim=-1		)
        lowerCamelCase__:				int        =accelerator.gather_for_metrics((predictions, batch["labels"])		)
        metric.add_batch(
            predictions=__a     ,   references=__a     ,   )

      lowerCamelCase__:				Dict        =metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""     ,   __a		)

    # New Code #
    # We also run predictions on the test set at the very end
    lowerCamelCase__:				Optional[Any]        =[]
    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():
        lowerCamelCase__:				List[str]        =model(**__a		)
      lowerCamelCase__:				Tuple        =outputs.logits
      lowerCamelCase__:				Optional[Any]        =accelerator.gather_for_metrics((predictions, batch["labels"])		)
      fold_predictions.append(predictions.cpu()		)
      if i == 0:
        # We need all of the test predictions
        test_references.append(references.cpu()		)
        # Use accelerator.print to print only on the main process.
    test_predictions.append(torch.cat(__a     ,   dim=0		)		)
    # We now need to release all our memory and get rid of the current model, optimizer, etc
    accelerator.free_memory()
  # New Code #
  # Finally we check the accuracy of our folded results:
  lowerCamelCase__:				Tuple        =torch.cat(__a     ,   dim=0		)
  lowerCamelCase__:				Tuple        =torch.stack(__a     ,   dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
  lowerCamelCase__:				List[str]        =metric.compute(predictions=__a     ,   references=__a		)
  accelerator.print("Average test metrics from all folds:"     ,   __a		)





def 					lowerCAmelCase_			(	)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =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."		)
  # New Code #
  parser.add_argument("--num_folds"     ,   type=__a     ,   default=3     ,   help="The number of splits to perform across the dataset"		)
  lowerCamelCase__:				Union[str, Any]        =parser.parse_args()
  lowerCamelCase__:				Optional[Any]        ={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(__a     ,   __a		)


if __name__ == "__main__":
     main()








import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =inspect.getfile(accelerate.test_utils)
   lowerCamelCase__:				str        =os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps", "test_metrics.py"])

   from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

   lowerCamelCase__:				str        =test_metrics

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main	,			num_processes=1)

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main)

 @require_single_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



   '''simple docstring'''



   self.test_metrics.main()







 @require_multi_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   print(F"""Found {torch.cuda.device_count()} devices.""")
   lowerCamelCase__:				Optional[Any]        =["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(UpperCAmelCase_	,			env=os.environ.copy())

