






"""simple docstring"""



import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_UpperCamelCase		:  Optional[int]						=		1_6
_UpperCamelCase		:  str						=		3_2
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Accelerator		,					__snake_case			:  DatasetDict		,					__snake_case			:  List[int]		,					__snake_case			:  List[int]		,					__snake_case			:  int = 16    ):





  '''simple docstring'''





  lowercase							    =       AutoTokenizer.from_pretrained('bert-base-cased'    )
  lowercase							    =       DatasetDict(
      {
          'train': dataset['train'].select(__snake_case    ),
          'validation': dataset['train'].select(__snake_case    ),
          'test': dataset['validation'],
      }    )

  def tokenize_function(__snake_case			:  str    ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase							    =       tokenizer(examples['sentence1']		,					examples['sentence2']		,					truncation=__snake_case		,					max_length=__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():
    lowercase							    =       datasets.map(
        __snake_case		,					batched=__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
  lowercase							    =       tokenized_datasets.rename_column('label'		,					'labels'    )

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

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

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

  lowercase							    =       DataLoader(
      tokenized_datasets['test']		,					shuffle=__snake_case		,					collate_fn=__snake_case		,					batch_size=__snake_case    )

  return train_dataloader, eval_dataloader, test_dataloader
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  # Download the dataset
  lowercase							    =       load_dataset('glue'		,					'mrpc'    )
  # Create our splits
  lowercase							    =       StratifiedKFold(n_splits=int(args.num_folds    )    )
  # Initialize accelerator
  lowercase							    =       Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision    )
  # 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							    =       evaluate.load('glue'		,					'mrpc'    )

  # If the batch size is too big we use gradient accumulation
  lowercase							    =       1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowercase							    =       batch_size // MAX_GPU_BATCH_SIZE
    lowercase							    =       MAX_GPU_BATCH_SIZE

  set_seed(__snake_case    )

  # New Code #
  # Create our folds:
  lowercase							    =       kfold.split(np.zeros(datasets['train'].num_rows    )		,					datasets['train']['label']    )
  lowercase							    =       []
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(__snake_case    ):
    lowercase    ,     lowercase    ,     lowercase							    =       get_fold_dataloaders(
        __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowercase							    =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,					return_dict=__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).
    lowercase							    =       model.to(accelerator.device    )

    # Instantiate optimizer
    lowercase							    =       AdamW(params=model.parameters()		,					lr=__snake_case    )

    # Instantiate scheduler
    lowercase							    =       get_linear_schedule_with_warmup(
        optimizer=__snake_case		,					num_warmup_steps=1_00		,					num_training_steps=(len(__snake_case    ) * num_epochs) // gradient_accumulation_steps		,					)

    # 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(
        __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )

    # Now we train the model
    for epoch in range(__snake_case    ):
      model.train()
      for step, batch in enumerate(__snake_case    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        lowercase							    =       model(**__snake_case    )
        lowercase							    =       outputs.loss
        lowercase							    =       loss / gradient_accumulation_steps
        accelerator.backward(__snake_case    )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

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

      lowercase							    =       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'epoch {epoch}:'		,					__snake_case    )

    # New Code #
    # We also run predictions on the test set at the very end
    lowercase							    =       []
    for step, batch in enumerate(__snake_case    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      with torch.no_grad():
        lowercase							    =       model(**__snake_case    )
      lowercase							    =       outputs.logits
      lowercase    ,     lowercase							    =       accelerator.gather_for_metrics((predictions, batch['labels'])    )
      fold_predictions.append(predictions.cpu()    )
      if i == 0:
        # We need all of the test predictions
        test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
    test_predictions.append(torch.cat(__snake_case		,					dim=0    )    )
    # We now need to release all our memory and get rid of the current model, optimizer, etc
    accelerator.free_memory()
  # New Code #
  # Finally we check the accuracy of our folded results:
  lowercase							    =       torch.cat(__snake_case		,					dim=0    )
  lowercase							    =       torch.stack(__snake_case		,					dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
  lowercase							    =       metric.compute(predictions=__snake_case		,					references=__snake_case    )
  accelerator.print('Average test metrics from all folds:'		,					__snake_case    )
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,					type=__snake_case		,					default=__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.'		,					)
  parser.add_argument('--cpu'		,					action='store_true'		,					help='If passed, will train on the CPU.'    )
  # New Code #
  parser.add_argument('--num_folds'		,					type=__snake_case		,					default=3		,					help='The number of splits to perform across the dataset'    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__snake_case		,					__snake_case    )


if __name__ == "__main__":
    main()










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in table:
    res += inp[i - 1]
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  return data[1:] + data[0]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in range(len(__snake_case    )    ):
    if a[i] == b[i]:
      res += "0"
    else:
      res += "1"
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       int('0b' + data[0] + data[-1]		,					2    )
  lowercase							    =       int('0b' + data[1:3]		,					2    )
  return bin(s[row][col]    )[2:]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Dict		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       message[:4]
  lowercase							    =       message[4:]
  lowercase							    =       apply_table(__snake_case		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  lowercase							    =       apply_sbox(__snake_case		,					temp[:4]    )  # noqa: E741
  lowercase							    =       apply_sbox(__snake_case		,					temp[4:]    )
  lowercase							    =       '0' * (2 - len(__snake_case    )) + l  # noqa: E741
  lowercase							    =       '0' * (2 - len(__snake_case    )) + r
  lowercase							    =       apply_table(l + r		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  return temp + right


if __name__ == "__main__":
    _UpperCamelCase		:  Dict						=		input('Enter 10 bit key: ')
    _UpperCamelCase		:  int						=		input('Enter 8 bit message: ')

    _UpperCamelCase		:  Optional[int]						=		[6, 3, 7, 4, 8, 5, 1_0, 9]
    _UpperCamelCase		:  Optional[Any]						=		[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
    _UpperCamelCase		:  str						=		[2, 4, 3, 1]
    _UpperCamelCase		:  List[Any]						=		[2, 6, 3, 1, 4, 8, 5, 7]
    _UpperCamelCase		:  List[str]						=		[4, 1, 3, 5, 7, 2, 8, 6]
    _UpperCamelCase		:  int						=		[4, 1, 2, 3, 2, 3, 4, 1]
    _UpperCamelCase		:  Optional[Any]						=		[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    _UpperCamelCase		:  Optional[int]						=		[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    _UpperCamelCase		:  str						=		apply_table(key, paa_table)
    _UpperCamelCase		:  Union[str, Any]						=		temp[:5]
    _UpperCamelCase		:  Dict						=		temp[5:]
    _UpperCamelCase		:  List[Any]						=		left_shift(left)
    _UpperCamelCase		:  str						=		left_shift(right)
    _UpperCamelCase		:  int						=		apply_table(left + right, pa_table)
    _UpperCamelCase		:  Optional[int]						=		left_shift(left)
    _UpperCamelCase		:  Any						=		left_shift(right)
    _UpperCamelCase		:  str						=		left_shift(left)
    _UpperCamelCase		:  Tuple						=		left_shift(right)
    _UpperCamelCase		:  Union[str, Any]						=		apply_table(left + right, pa_table)

    # encryption
    _UpperCamelCase		:  Tuple						=		apply_table(message, IP)
    _UpperCamelCase		:  Any						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  List[str]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    _UpperCamelCase		:  List[Any]						=		apply_table(CT, IP)
    _UpperCamelCase		:  Optional[Any]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  str						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)


