



"""simple docstring"""
from __future__ import annotations

def       _snake_case						(						UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		):
 if (stress, tangential_force, area).count(0						) != 1:
  raise ValueError("""You cannot supply more or less than 2 values"""						)
 elif stress < 0:
  raise ValueError("""Stress cannot be negative"""						)
 elif tangential_force < 0:
  raise ValueError("""Tangential Force cannot be negative"""						)
 elif area < 0:
  raise ValueError("""Area cannot be negative"""						)
 elif stress == 0:
  return (
      "stress",
      tangential_force / area,
  )
 elif tangential_force == 0:
  return (
      "tangential_force",
      stress * area,
  )
 else:
  return (
      "area",
      tangential_force / stress,
  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_									=   1_6
lowercase_									=   3_2
def      a      (			A__	:   Accelerator      ,   A__	:   int = 16      ,   A__	:   str = "bert-base-cased"     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =AutoTokenizer.from_pretrained(A__     )
      _lowercase	       =load_dataset('glue'      ,   'mrpc'     )

      def tokenize_function(A__	:   Optional[int]     ):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase	       =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
      _lowercase	       =datasets.map(
          A__      ,   batched=A__      ,   remove_columns=['idx', 'sentence1', 'sentence2']      ,   load_from_cache_file=A__     )

      # 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(A__	:   List[str]     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(A__      ,   padding='max_length'      ,   max_length=128      ,   return_tensors='pt'     )
            return tokenizer.pad(A__      ,   padding='longest'      ,   return_tensors='pt'     )

      # Instantiate dataloaders.
      _lowercase	       =DataLoader(
          tokenized_datasets['train']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__     )
      _lowercase	       =DataLoader(
          tokenized_datasets['validation']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__     )

      return train_dataloader, eval_dataloader
def      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[int]      ,   A__	:   List[str]      ,   A__	:   Dict     )    ->      Dict:





      """simple docstring"""



      model.eval()
      _lowercase	       =0
      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():
                  _lowercase	       =model(**A__     )
            _lowercase	       =outputs.logits.argmax(dim=-1     )
            # It is slightly faster to call this once, than multiple times
            _lowercase       ,       _lowercase	       =accelerator.gather(
                (predictions, batch['labels'])     )  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(A__     ) - 1:
                        _lowercase	       =predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                        _lowercase	       =references[: len(eval_dataloader.dataset     ) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=A__      ,   references=A__      ,   )

      _lowercase	       =metric.compute()
      return eval_metric["accuracy"]
def      a      (			A__	:   str      ,   A__	:   Optional[Any]     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =Accelerator()

      # 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	       =args.model_name_or_path

      set_seed(A__     )
      _lowercase       ,       _lowercase	       =get_dataloaders(A__      ,   A__      ,   A__     )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _lowercase	       =AutoModelForSequenceClassification.from_pretrained(A__      ,   return_dict=A__     )

      # Instantiate optimizer
      _lowercase	       =(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      _lowercase	       =optimizer_cls(params=model.parameters()      ,   lr=A__     )

      if accelerator.state.deepspeed_plugin is not None:
            _lowercase	       =accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            _lowercase	       =1
      _lowercase	       =(len(A__     ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            _lowercase	       =get_linear_schedule_with_warmup(
                optimizer=A__      ,   num_warmup_steps=0      ,   num_training_steps=A__      ,   )
      else:
            _lowercase	       =DummyScheduler(A__      ,   total_num_steps=A__      ,   warmup_num_steps=0     )

      # 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(
          A__      ,   A__      ,   A__      ,   A__      ,   A__     )

      # We need to keep track of how many total steps we have iterated over
      _lowercase	       =0
      # We also need to keep track of the stating epoch so files are named properly
      _lowercase	       =0
      _lowercase	       =evaluate.load('glue'      ,   'mrpc'     )
      _lowercase	       =num_epochs

      if args.partial_train_epoch is not None:
            _lowercase	       =args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint     )
            _lowercase	       =args.resume_from_checkpoint.split('epoch_'     )[1]
            _lowercase	       =''
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            _lowercase	       =int(A__     ) + 1
            _lowercase	       =evaluation_loop(A__      ,   A__      ,   A__      ,   A__     )
            accelerator.print('resumed checkpoint performance:'      ,   A__     )
            accelerator.print('resumed checkpoint\'s scheduler\'s lr:'      ,   lr_scheduler.get_lr()[0]     )
            accelerator.print('resumed optimizers\'s lr:'      ,   optimizer.param_groups[0]['lr']     )
            with open(os.path.join(args.output_dir      ,   F'''state_{starting_epoch-1}.json'''     )      ,   'r'     ) as f:
                  _lowercase	       =json.load(A__     )
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      _lowercase	       ={}
      for epoch in range(A__      ,   A__     ):
            model.train()
            for step, batch in enumerate(A__     ):
                  _lowercase	       =model(**A__     )
                  _lowercase	       =outputs.loss
                  _lowercase	       =loss / gradient_accumulation_steps
                  accelerator.backward(A__     )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            _lowercase	       =F'''epoch_{epoch}'''
            _lowercase	       =os.path.join(args.output_dir      ,   A__     )
            accelerator.save_state(A__     )
            _lowercase	       =evaluation_loop(A__      ,   A__      ,   A__      ,   A__     )
            _lowercase	       =accuracy
            _lowercase	       =lr_scheduler.get_lr()[0]
            _lowercase	       =optimizer.param_groups[0]['lr']
            _lowercase	       =epoch
            _lowercase	       =overall_step
            accelerator.print(F'''epoch {epoch}:'''      ,   A__     )

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir      ,   F'''state_{epoch}.json'''     )      ,   'w'     ) as f:
                        json.dump(A__      ,   A__     )
def      a      (			)    ->      Tuple:





      """simple docstring"""



      _lowercase	       =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
      parser.add_argument(
          '--model_name_or_path'      ,   type=A__      ,   default='bert-base-cased'      ,   help='Path to pretrained model or model identifier from huggingface.co/models.'      ,   required=A__      ,   )
      parser.add_argument(
          '--output_dir'      ,   type=A__      ,   default='.'      ,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,   )
      parser.add_argument(
          '--resume_from_checkpoint'      ,   type=A__      ,   default=A__      ,   help='If the training should continue from a checkpoint folder.'      ,   )
      parser.add_argument(
          '--partial_train_epoch'      ,   type=A__      ,   default=A__      ,   help='If passed, the training will stop after this number of epochs.'      ,   )
      parser.add_argument(
          '--num_epochs'      ,   type=A__      ,   default=2      ,   help='Number of train epochs.'      ,   )
      _lowercase	       =parser.parse_args()
      _lowercase	       ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

      training_function(A__      ,   A__     )


if __name__ == "__main__":
  main()


