





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
#
########################################################################


_lowerCamelCase			       =      16
_lowerCamelCase			       =      32




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ = 16      ):
       _lowerCamelCase							:     int	=		AutoTokenizer.from_pretrained('''bert-base-cased'''      )
       _lowerCamelCase							:     Optional[int]	=		load_dataset('''glue''' ,    '''mrpc'''      )

       def tokenize_function(lowercase_      ):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCamelCase							:     List[Any]	=		tokenizer(examples['''sentence1'''] ,    examples['''sentence2'''] ,    truncation=lowercase_ ,    max_length=lowercase_      )
              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							:     Union[str, Any]	=		datasets.map(
                  lowercase_ ,    batched=lowercase_ ,    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							:     Optional[int]	=		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							:     Optional[Any]	=		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":
                     _lowerCamelCase							:     Any	=		16
              elif accelerator.mixed_precision != "no":
                     _lowerCamelCase							:     Any	=		8
              else:
                     _lowerCamelCase							:     str	=		None

              return tokenizer.pad(
                  lowercase_ ,    padding='''longest''' ,    max_length=lowercase_ ,    pad_to_multiple_of=lowercase_ ,    return_tensors='''pt''' ,    )

       # Instantiate dataloaders.
       _lowerCamelCase							:     List[str]	=		DataLoader(
           tokenized_datasets['''train'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )
       _lowerCamelCase							:     Dict	=		DataLoader(
           tokenized_datasets['''validation'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )

       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    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,    lowercase_      ) == "1":
              _lowerCamelCase							:     Any	=		2
       # New Code #
       _lowerCamelCase							:     Optional[Any]	=		int(args.gradient_accumulation_steps      )
       _lowerCamelCase							:     Optional[int]	=		int(args.local_sgd_steps      )
       # Initialize accelerator
       _lowerCamelCase							:     List[str]	=		Accelerator(
           cpu=args.cpu ,    mixed_precision=args.mixed_precision ,    gradient_accumulation_steps=lowercase_      )
       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							:     Dict	=		config['''lr''']
       _lowerCamelCase							:     str	=		int(config['''num_epochs''']      )
       _lowerCamelCase							:     Dict	=		int(config['''seed''']      )
       _lowerCamelCase							:     List[str]	=		int(config['''batch_size''']      )

       _lowerCamelCase							:     Tuple	=		evaluate.load('''glue''' ,    '''mrpc'''      )

       set_seed(lowercase_      )
       _lowerCamelCase							:     int	=		get_dataloaders(lowercase_ ,    lowercase_      )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCamelCase							:     Dict	=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,    return_dict=lowercase_      )

       # 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							:     int	=		model.to(accelerator.device      )

       # Instantiate optimizer
       _lowerCamelCase							:     List[Any]	=		AdamW(params=model.parameters() ,    lr=lowercase_      )

       # Instantiate scheduler
       _lowerCamelCase							:     List[Any]	=		get_linear_schedule_with_warmup(
           optimizer=lowercase_ ,    num_warmup_steps=1_00 ,    num_training_steps=(len(lowercase_      ) * 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							:     Any	=		accelerator.prepare(
           lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      )

       # Now we train the model
       for epoch in range(lowercase_      ):
              model.train()
              with LocalSGD(
                  accelerator=lowercase_ ,    model=lowercase_ ,    local_sgd_steps=lowercase_ ,    enabled=local_sgd_steps is not None      ) as local_sgd:
                     for step, batch in enumerate(lowercase_      ):
                            # 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(lowercase_      ):
                                   _lowerCamelCase							:     Union[str, Any]	=		model(**lowercase_      )
                                   _lowerCamelCase							:     Optional[Any]	=		output.loss
                                   accelerator.backward(lowercase_      )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

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

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




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Dict	=		argparse.ArgumentParser(description='''Simple example of training script.'''      )
       parser.add_argument(
           '''--mixed_precision''' ,    type=lowercase_ ,    default=lowercase_ ,    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=lowercase_ ,    default=1 ,    help='''The number of minibatches to be ran before gradients are accumulated.''' ,    )
       parser.add_argument(
           '''--local_sgd_steps''' ,    type=lowercase_ ,    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							:     int	=		parser.parse_args()
       _lowerCamelCase							:     Tuple	=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,    lowercase_      )


if __name__ == "__main__":
       main()








def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float(moles / volume      ) * nfactor      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (volume)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((pressure * volume) / (0.0_8_2_1 * moles)      )      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

