



"""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
#
########################################################################


lowercase__		:				Optional[int]          =	1_6
lowercase__		:				Union[str, Any]          =	3_2

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Accelerator    ,   lowerCAmelCase__      :				int = 16						)						->   Tuple:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_   :				str								= load_dataset('glue'    ,   'mrpc'						)

  def tokenize_function(lowerCAmelCase__      :				Dict						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_   :				Dict								= tokenizer(examples['sentence1']    ,   examples['sentence2']    ,   truncation=__SCREAMING_SNAKE_CASE    ,   max_length=__SCREAMING_SNAKE_CASE						)
    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_   :				int								= datasets.map(
        __SCREAMING_SNAKE_CASE    ,   batched=__SCREAMING_SNAKE_CASE    ,   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_   :				int								= tokenized_datasets.rename_column('label'    ,   'labels'						)

  def collate_fn(lowerCAmelCase__      :				Dict						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCAmelCase_   :				List[str]								= 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_   :				List[Any]								= 16
    elif accelerator.mixed_precision != "no":
      lowerCAmelCase_   :				int								= 8
    else:
      lowerCAmelCase_   :				Tuple								= None

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

  # Instantiate dataloaders.
  lowerCAmelCase_   :				Any								= DataLoader(
      tokenized_datasets['train']    ,   shuffle=__SCREAMING_SNAKE_CASE    ,   collate_fn=__SCREAMING_SNAKE_CASE    ,   batch_size=__SCREAMING_SNAKE_CASE						)
  lowerCAmelCase_   :				Optional[Any]								= DataLoader(
      tokenized_datasets['validation']    ,   shuffle=__SCREAMING_SNAKE_CASE    ,   collate_fn=__SCREAMING_SNAKE_CASE    ,   batch_size=__SCREAMING_SNAKE_CASE						)

  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

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Dict						)						->   int:

  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,   __SCREAMING_SNAKE_CASE						) == "1":
    lowerCAmelCase_   :				Dict								= 2
  # New Code #
  lowerCAmelCase_   :				Optional[Any]								= int(args.gradient_accumulation_steps						)
  lowerCAmelCase_   :				Dict								= int(args.local_sgd_steps						)
  # Initialize accelerator
  lowerCAmelCase_   :				Tuple								= Accelerator(
      cpu=args.cpu    ,   mixed_precision=args.mixed_precision    ,   gradient_accumulation_steps=__SCREAMING_SNAKE_CASE						)
  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_   :				int								= config['lr']
  lowerCAmelCase_   :				Optional[Any]								= int(config['num_epochs']						)
  lowerCAmelCase_   :				Any								= int(config['seed']						)
  lowerCAmelCase_   :				Optional[int]								= int(config['batch_size']						)

  lowerCAmelCase_   :				List[str]								= evaluate.load('glue'    ,   'mrpc'						)

  set_seed(__SCREAMING_SNAKE_CASE						)
  lowerCAmelCase_   :				Tuple								= get_dataloaders(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase_   :				List[Any]								= AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,   return_dict=__SCREAMING_SNAKE_CASE						)

  # 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_   :				List[str]								= model.to(accelerator.device						)

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

  # Instantiate scheduler
  lowerCAmelCase_   :				Union[str, Any]								= get_linear_schedule_with_warmup(
      optimizer=__SCREAMING_SNAKE_CASE    ,   num_warmup_steps=100    ,   num_training_steps=(len(__SCREAMING_SNAKE_CASE						) * 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_   :				List[Any]								= accelerator.prepare(
      __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)

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

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

    lowerCAmelCase_   :				int								= metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"epoch {epoch}:"    ,   __SCREAMING_SNAKE_CASE						)

def 				UpperCamelCase_    (    )						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= argparse.ArgumentParser(description='Simple example of training script.'						)
  parser.add_argument(
      '--mixed_precision'    ,   type=__SCREAMING_SNAKE_CASE    ,   default=__SCREAMING_SNAKE_CASE    ,   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=__SCREAMING_SNAKE_CASE    ,   default=1    ,   help='The number of minibatches to be ran before gradients are accumulated.'    ,   )
  parser.add_argument(
      '--local_sgd_steps'    ,   type=__SCREAMING_SNAKE_CASE    ,   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_   :				List[Any]								= parser.parse_args()
  lowerCAmelCase_   :				List[Any]								= {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()






"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int | float], int | float]     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int = 1_00     ,			):
     lowercase_	:				Dict													=	x_start
     lowercase_	:				Optional[Any]													=	fnc(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	0.0

     for _ in range(__SCREAMING_SNAKE_CASE      ):
          # Approximates curve as a sequence of linear lines and sums their length
          lowercase_	:				Union[str, Any]													=	(x_end - x_start) / steps + xa
          lowercase_	:				str													=	fnc(__SCREAMING_SNAKE_CASE      )
          length += math.hypot(xa - xa     ,			fxa - fxa      )

          # Increment step
          lowercase_	:				List[Any]													=	xa
          lowercase_	:				Optional[Any]													=	fxa

     return length


if __name__ == "__main__":



       def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
            return math.sin(10 * x      )

       print("f(x) = sin(10 * x)")
       print("The length of the curve from x = -10 to x = 10 is:")
       __SCREAMING_SNAKE_CASE    	=10
       while i <= 10_0000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10

