




'''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_ 		=      16
UpperCAmelCase_ 		=      32


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							List[Any]				,					a_	:							Any				,					a_	:							Optional[int]				,					a_	:							Optional[int] = 16			):
  __a			=					AutoTokenizer.from_pretrained('bert-base-cased'			)
  __a			=					DatasetDict(
      {
          'train': dataset['train'].select(__UpperCamelCase			),
          'validation': dataset['train'].select(__UpperCamelCase			),
          'test': dataset['validation'],
      }			)

  def tokenize_function(a_	:							Optional[int]			):
    # max_length=None => use the model max length (it's actually the default)
    __a			=					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
  # starting with the main process first:
  with accelerator.main_process_first():
    __a			=					datasets.map(
        __UpperCamelCase				,					batched=__UpperCamelCase				,					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
  __a			=					tokenized_datasets.rename_column('label'				,					'labels'			)

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

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

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

  __a			=					DataLoader(
      tokenized_datasets['test']				,					shuffle=__UpperCamelCase				,					collate_fn=__UpperCamelCase				,					batch_size=__UpperCamelCase			)

  return train_dataloader, eval_dataloader, test_dataloader


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]			):
  # New Code #
  __a			=					[]
  # Download the dataset
  __a			=					load_dataset('glue'				,					'mrpc'			)
  # Create our splits
  __a			=					StratifiedKFold(n_splits=int(args.num_folds			)			)
  # Initialize accelerator
  __a			=					Accelerator(cpu=args.cpu				,					mixed_precision=args.mixed_precision			)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __a			=					config['lr']
  __a			=					int(config['num_epochs']			)
  __a			=					int(config['seed']			)
  __a			=					int(config['batch_size']			)

  __a			=					evaluate.load('glue'				,					'mrpc'			)

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

  set_seed(__UpperCamelCase			)

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

    # 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).
    __a			=					model.to(accelerator.device			)

    # Instantiate optimizer
    __a			=					AdamW(params=model.parameters()				,					lr=__UpperCamelCase			)

    # Instantiate scheduler
    __a			=					get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase				,					num_warmup_steps=100				,					num_training_steps=(len(__UpperCamelCase			) * 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.
    __a	,       __a	,       __a	,       __a	,       __a			=					accelerator.prepare(
        __UpperCamelCase				,					__UpperCamelCase				,					__UpperCamelCase				,					__UpperCamelCase				,					__UpperCamelCase			)

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

      model.eval()
      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():
          __a			=					model(**__UpperCamelCase			)
        __a			=					outputs.logits.argmax(dim=-1			)
        __a	,       __a			=					accelerator.gather_for_metrics((predictions, batch['labels'])			)
        metric.add_batch(
            predictions=__UpperCamelCase				,					references=__UpperCamelCase				,					)

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

    # New Code #
    # We also run predictions on the test set at the very end
    __a			=					[]
    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():
        __a			=					model(**__UpperCamelCase			)
      __a			=					outputs.logits
      __a	,       __a			=					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(__UpperCamelCase				,					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:
  __a			=					torch.cat(__UpperCamelCase				,					dim=0			)
  __a			=					torch.stack(__UpperCamelCase				,					dim=0			).sum(dim=0			).div(int(args.num_folds			)			).argmax(dim=-1			)
  __a			=					metric.compute(predictions=__UpperCamelCase				,					references=__UpperCamelCase			)
  accelerator.print('Average test metrics from all folds:'				,					__UpperCamelCase			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					argparse.ArgumentParser(description='Simple example of training script.'			)
  parser.add_argument(
      '--mixed_precision'				,					type=__UpperCamelCase				,					default=__UpperCamelCase				,					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=__UpperCamelCase				,					default=3				,					help='The number of splits to perform across the dataset'			)
  __a			=					parser.parse_args()
  __a			=					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__UpperCamelCase				,					__UpperCamelCase			)


if __name__ == "__main__":
  main()









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					''
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					[chr(i + 65			) for i in range(26			)]
  # Remove duplicate characters from key
  __a			=					remove_duplicates(key.upper()			)
  __a			=					len(a_			)
  # First fill cipher with key characters
  __a			=					{alphabet[i]: char for i, char in enumerate(a_			)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(a_			)				,					26			):
    __a			=					alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      __a			=					alphabet[i - offset]
    __a			=					char
  return cipher_alphabet


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  return "".join(cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  __a			=					{v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					input('Enter message to encode or decode: '			).strip()
  __a			=					input('Enter keyword: '			).strip()
  __a			=					input('Encipher or decipher? E/D:'			).strip()[0].lower()
  try:
    __a			=					{'e': encipher, 'd': decipher}[option]
  except KeyError:
    raise KeyError('invalid input option'			)
  __a			=					create_cipher_map(a_			)
  print(func(a_				,					a_			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



