

'''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


UpperCAmelCase					 =	16
UpperCAmelCase					 =	32

def 					__UpperCamelCase				(						lowercase__    :	Accelerator,       lowercase__    :	int = 16,       lowercase__    :	str = "bert-base-cased"			):






     '''simple docstring'''





     __lowercase      		=AutoTokenizer.from_pretrained(lowercase__			)
     __lowercase      		=load_dataset('glue',       'mrpc'			)

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

     # 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(lowercase__    :	List[Any]			):
          # 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(lowercase__,       padding='max_length',       max_length=1_28,       return_tensors='pt'			)
          return tokenizer.pad(lowercase__,       padding='longest',       return_tensors='pt'			)

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

     return train_dataloader, eval_dataloader

def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any],       lowercase__    :	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(lowercase__			)
     __lowercase		,  __lowercase      		=get_dataloaders(lowercase__,       lowercase__,       lowercase__			)

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

     # 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=lowercase__			)

     if accelerator.state.deepspeed_plugin is not None:
          __lowercase      		=accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __lowercase      		=1
     __lowercase      		=(len(lowercase__			) * 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=lowercase__,       num_warmup_steps=0,       num_training_steps=lowercase__,       )
     else:
          __lowercase      		=DummyScheduler(lowercase__,       total_num_steps=lowercase__,       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(
         lowercase__,       lowercase__,       lowercase__,       lowercase__,       lowercase__			)

     # 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

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

               overall_step += 1

          model.eval()
          __lowercase      		=0
          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():
                    __lowercase      		=model(**lowercase__			)
               __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(lowercase__			) - 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=lowercase__,       references=lowercase__,       )

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

          if best_performance < eval_metric["accuracy"]:
               __lowercase      		=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(lowercase__,       lowercase__			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'			)
     parser.add_argument(
         '--model_name_or_path',       type=lowercase__,       default='bert-base-cased',       help='Path to pretrained model or model identifier from huggingface.co/models.',       required=lowercase__,       )
     parser.add_argument(
         '--output_dir',       type=lowercase__,       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=lowercase__,       default=lowercase__,       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=lowercase__,       default=3,       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(lowercase__,       lowercase__			)


if __name__ == "__main__":
       main()





'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     for key in range(len(string.ascii_uppercase			)			):
          __lowercase      		=''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __lowercase      		=string.ascii_uppercase.find(lowercase__			)
                    __lowercase      		=num - key
                    if num < 0:
                         __lowercase      		=num + len(string.ascii_uppercase			)
                    __lowercase      		=translated + string.ascii_uppercase[num]
               else:
                    __lowercase      		=translated + symbol
          print(F'''Decryption using Key #{key}: {translated}'''			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


