






'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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							:							int  							=		16
__A							:							Optional[Any]  							=		32






def    lowerCamelCase_     (    lowercase__				,     lowercase__ = 16):
 lowerCamelCase__  =     AutoTokenizer.from_pretrained("bert-base-cased")
 lowerCamelCase__  =     load_dataset("glue"				,     "mrpc")

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

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

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

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

 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

       __A							:							int  							=		mocked_dataloaders  # noqa: F811






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 # For testing only
 if os.environ.get("TESTING_MOCKED_DATALOADERS"				,     __UpperCamelCase) == "1":
  lowerCamelCase__  =     2
 # New Code #
 lowerCamelCase__  =     int(args.gradient_accumulation_steps)
 lowerCamelCase__  =     int(args.local_sgd_steps)
 # Initialize accelerator
 lowerCamelCase__  =     Accelerator(
     cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     gradient_accumulation_steps=__UpperCamelCase)
 if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
  raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)")
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase__  =     config["lr"]
 lowerCamelCase__  =     int(config["num_epochs"])
 lowerCamelCase__  =     int(config["seed"])
 lowerCamelCase__  =     int(config["batch_size"])

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

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

 # 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__  =     model.to(accelerator.device)

 # Instantiate optimizer
 lowerCamelCase__  =     AdamW(params=model.parameters()				,     lr=__UpperCamelCase)

 # Instantiate scheduler
 lowerCamelCase__  =     get_linear_schedule_with_warmup(
     optimizer=__UpperCamelCase				,     num_warmup_steps=100				,     num_training_steps=(len(__UpperCamelCase) * num_epochs)				,     )

 # 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__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     accelerator.prepare(
     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase)

 # Now we train the model
 for epoch in range(__UpperCamelCase):
  model.train()
  with LocalSGD(
      accelerator=__UpperCamelCase				,     model=__UpperCamelCase				,     local_sgd_steps=__UpperCamelCase				,     enabled=local_sgd_steps is not None) as local_sgd:
   for step, batch in enumerate(__UpperCamelCase):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device)
    # New code #
    # We use the new `accumulate` context manager to perform gradient accumulation
    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
    with accelerator.accumulate(__UpperCamelCase):
     lowerCamelCase__  =     model(**__UpperCamelCase)
     lowerCamelCase__  =     output.loss
     accelerator.backward(__UpperCamelCase)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # LocalSGD-specific line
     local_sgd.step()

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

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






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     argparse.ArgumentParser(description="Simple example of training script.")
 parser.add_argument(
     "--mixed_precision"				,     type=__UpperCamelCase				,     default=__UpperCamelCase				,     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."				,     )
 # New Code #
 parser.add_argument(
     "--gradient_accumulation_steps"				,     type=__UpperCamelCase				,     default=1				,     help="The number of minibatches to be ran before gradients are accumulated."				,     )
 parser.add_argument(
     "--local_sgd_steps"				,     type=__UpperCamelCase				,     default=8				,     help="Number of local SGD steps or None to disable local SGD")
 parser.add_argument("--cpu"				,     action="store_true"				,     help="If passed, will train on the CPU.")
 lowerCamelCase__  =     parser.parse_args()
 lowerCamelCase__  =     {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(__UpperCamelCase				,     __UpperCamelCase)


if __name__ == "__main__":
       main()









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float(moles / volume) * nfactor)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((moles * 0.0_821 * temperature) / (volume)))






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((moles * 0.0_821 * temperature) / (pressure)))






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 return round(float((pressure * volume) / (0.0_821 * moles)))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

