






"""simple docstring"""


import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

lowercase_    					=					32



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase = 1_6			):




  """simple docstring"""







  __A						      =			AutoTokenizer.from_pretrained('''bert-base-cased'''			)
  __A						      =			load_dataset('''glue'''		,	'''mrpc'''			)

  def tokenize_function(__UpperCamelCase			):
    # 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(__UpperCamelCase			):
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      __A						      =			1_6
    elif accelerator.mixed_precision != "no":
      __A						      =			8
    else:
      __A						      =			None

    return tokenizer.pad(
        __UpperCamelCase		,	padding='''longest'''		,	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			)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       lowercase_    					=					mocked_dataloaders  # noqa: F811



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,	__UpperCamelCase			) == "1":
    __A						      =			2
  # 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'''			)

  # New Code #
  # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  # half each time. From this, we can calculate the number of gradient accumulation steps needed
  # and modify the Accelerator object as a result
  @find_executable_batch_size(starting_batch_size=int(__UpperCamelCase			)			)
  def inner_training_loop(__UpperCamelCase			):
    # Since we need to modify the outside accelerator object, we need to bring it
    # to the local scope
    nonlocal accelerator

    # We can calculate the number of gradient accumulation steps based on the current
    # batch size vs the starting batch size
    __A						      =			observed_batch_size // batch_size

    # And then set it in the Accelerator directly:
    __A						      =			num_gradient_accumulation_steps

    # Next we need to free all of the stored model references in the Accelerator each time
    accelerator.free_memory()

    # And set the seed so our results are reproducable each reset
    set_seed(__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			)
    __A      ,   __A						      =			get_dataloaders(__UpperCamelCase		,	__UpperCamelCase			)

    # Instantiate scheduler
    __A						      =			get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase		,	num_warmup_steps=1_0_0		,	num_training_steps=(len(__UpperCamelCase			) * num_epochs)		,	)

    # 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			):
        # And perform gradient accumulation
        with accelerator.accumulate(__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
          accelerator.backward(__UpperCamelCase			)
          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 #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __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.'''			)
  __A						      =			parser.parse_args()
  # New Code #
  # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  __A						      =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
  training_function(__UpperCamelCase		,	__UpperCamelCase			)


if __name__ == "__main__":
       main()












"""simple docstring"""


import itertools
from pathlib import Path



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			[tuple(__UpperCamelCase			) for login in logins]

  __A						      =			{char for login in split_logins for char in login}

  for permutation in itertools.permutations(__UpperCamelCase			):
    __A						      =			True
    for login in logins:
      if not (
          permutation.index(login[0]			)
          < permutation.index(login[1]			)
          < permutation.index(login[2]			)
      ):
        __A						      =			False
        break

    if satisfied:
      return int(''''''.join(__UpperCamelCase			)			)

  raise Exception('''Unable to find the secret passcode'''			)



def  lowerCAmelCase    (   __UpperCamelCase = "keylog.txt"			):




  """simple docstring"""







  __A						      =			Path(__UpperCamelCase			).parent.joinpath(__UpperCamelCase			).read_text().splitlines()

  return find_secret_passcode(__UpperCamelCase			)


if __name__ == "__main__":
       print(F'''{solution() = }''')




