


"""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
#
########################################################################

lowerCamelCase_  :  List[Any]     		=    3_2
def _A		(		lowercase			,       lowercase = 16						):







    """simple docstring"""
    a											=AutoTokenizer.from_pretrained('''bert-base-cased'''						)
    a											=load_dataset('''glue'''			,       '''mrpc'''						)

    def tokenize_function(lowercase						):
        # max_length=None => use the model max length (it's actually the default)
        a											=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
    # starting with the main process first:
    with accelerator.main_process_first():
        a											=datasets.map(
            lowercase			,       batched=lowercase			,       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(lowercase						):
        # 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(
            lowercase			,       padding='''longest'''			,       pad_to_multiple_of=lowercase			,       return_tensors='''pt'''			,       )

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

    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

       lowerCamelCase_  :  str     		=    mocked_dataloaders  # noqa: F811
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,       lowercase						) == "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(lowercase						)						)
    def inner_training_loop(lowercase						):
        # 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(lowercase						)

        # 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=lowercase						)

        # 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=lowercase						)
        a						, a											=get_dataloaders(lowercase			,       lowercase						)

        # Instantiate scheduler
        a											=get_linear_schedule_with_warmup(
            optimizer=lowercase			,       num_warmup_steps=1_00			,       num_training_steps=(len(lowercase						) * 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(
            lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase						)

        # Now we train the model
        for epoch in range(lowercase						):
            model.train()
            for step, batch in enumerate(lowercase						):
                # And perform gradient accumulation
                with accelerator.accumulate(lowercase						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    a											=model(**lowercase						)
                    a											=outputs.loss
                    accelerator.backward(lowercase						)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

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

    # 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 _A		(		):







    """simple docstring"""
    a											=argparse.ArgumentParser(description='''Simple example of training script.'''						)
    parser.add_argument(
        '''--mixed_precision'''			,       type=lowercase			,       default=lowercase			,       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''': 42, '''batch_size''': 2_56}
    training_function(lowercase			,       lowercase						)


if __name__ == "__main__":
       main()



"""simple docstring"""







import itertools
from pathlib import Path
def _A		(		lowercase						):







    """simple docstring"""
    a											=[tuple(lowercase						) for login in logins]

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

    for permutation in itertools.permutations(lowercase						):
        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(lowercase						)						)

    raise Exception('''Unable to find the secret passcode'''						)
def _A		(		lowercase = "keylog.txt"						):







    """simple docstring"""
    a											=Path(lowercase						).parent.joinpath(lowercase						).read_text().splitlines()

    return find_secret_passcode(lowercase						)


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