





'''simple docstring'''
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


a__							:							Optional[int] =16
a__							:							List[str] =32
def 				lowercase__   (   __lowercase						:  Accelerator				,  __lowercase						:  int = 16				,  __lowercase						:  str = "bert-base-cased"     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  AutoTokenizer.from_pretrained(UpperCamelCase_     )
       __UpperCamelCase       =  load_dataset('glue'				,  'mrpc'     )

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

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       __UpperCamelCase       =  tokenized_datasets.rename_column('label'				,  'labels'     )

       def collate_fn(__lowercase						:  Tuple     ):
              # 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(UpperCamelCase_				,  padding='max_length'				,  max_length=128				,  return_tensors='pt'     )
              return tokenizer.pad(UpperCamelCase_				,  padding='longest'				,  return_tensors='pt'     )

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

       return train_dataloader, eval_dataloader
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      Tuple:



       """simple docstring"""






       __UpperCamelCase       =  Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __UpperCamelCase       =  config['lr']
       __UpperCamelCase       =  int(config['num_epochs']     )
       __UpperCamelCase       =  int(config['seed']     )
       __UpperCamelCase       =  int(config['batch_size']     )
       __UpperCamelCase       =  args.model_name_or_path

       set_seed(UpperCamelCase_     )
       __UpperCamelCase       ,						__UpperCamelCase       =  get_dataloaders(UpperCamelCase_				,  UpperCamelCase_				,  UpperCamelCase_     )

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

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

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

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

       # We need to keep track of how many total steps we have iterated over
       __UpperCamelCase       =  0
       # We also need to keep track of the stating epoch so files are named properly
       __UpperCamelCase       =  0

       # Now we train the model
       __UpperCamelCase       =  evaluate.load('glue'				,  'mrpc'     )
       __UpperCamelCase       =  0
       __UpperCamelCase       =  {}
       for epoch in range(UpperCamelCase_				,  UpperCamelCase_     ):
              model.train()
              for step, batch in enumerate(UpperCamelCase_     ):
                     __UpperCamelCase       =  model(**UpperCamelCase_     )
                     __UpperCamelCase       =  outputs.loss
                     __UpperCamelCase       =  loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

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

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

              if best_performance < eval_metric["accuracy"]:
                     __UpperCamelCase       =  eval_metric['accuracy']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir				,  'all_results.json'     )				,  'w'     ) as f:
                     json.dump(UpperCamelCase_				,  UpperCamelCase_     )
def 				lowercase__   (   ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
       parser.add_argument(
           '--model_name_or_path'				,  type=UpperCamelCase_				,  default='bert-base-cased'				,  help='Path to pretrained model or model identifier from huggingface.co/models.'				,  required=UpperCamelCase_				,  )
       parser.add_argument(
           '--output_dir'				,  type=UpperCamelCase_				,  default='.'				,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'				,  )
       parser.add_argument(
           '--performance_lower_bound'				,  type=UpperCamelCase_				,  default=UpperCamelCase_				,  help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'				,  )
       parser.add_argument(
           '--num_epochs'				,  type=UpperCamelCase_				,  default=3				,  help='Number of train epochs.'				,  )
       __UpperCamelCase       =  parser.parse_args()
       __UpperCamelCase       =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(UpperCamelCase_				,  UpperCamelCase_     )


if __name__ == "__main__":
    main()



"""simple docstring"""





import baseaa




def   _A  (  UpperCamelCase_     :       str)	->						bytes:
   '''simple docstring'''


   return baseaa.baaencode(string.encode("utf-8"))




def   _A  (  UpperCamelCase_     :       bytes)	->						str:
   '''simple docstring'''


   return baseaa.baadecode(UpperCamelCase_).decode("utf-8")


if __name__ == "__main__":
   _a       =							'Hello World!'
   _a       =							baseaa_encode(test)
   print(encoded)

   _a       =							baseaa_decode(encoded)
   print(decoded)




