





'''simple docstring'''


import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE			=							16
_SCREAMING_SNAKE_CASE			=							32


def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]      ):



    '''simple docstring'''





    if args.with_tracking:
        _lowerCAmelCase		     = Accelerator(
            cpu=args.cpu						,    mixed_precision=args.mixed_precision						,    log_with="all"						,    project_dir=args.project_dir      )
    else:
        _lowerCAmelCase		     = Accelerator(cpu=args.cpu						,    mixed_precision=args.mixed_precision      )

    if hasattr(args.checkpointing_steps						,    "isdigit"      ):
        if args.checkpointing_steps == "epoch":
            _lowerCAmelCase		     = args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            _lowerCAmelCase		     = int(args.checkpointing_steps      )
        else:
            raise ValueError(
                F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
    else:
        _lowerCAmelCase		     = None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowerCAmelCase		     = config["lr"]
    _lowerCAmelCase		     = int(config["num_epochs"]      )
    _lowerCAmelCase		     = int(config["seed"]      )
    _lowerCAmelCase		     = int(config["batch_size"]      )

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        _lowerCAmelCase		     = os.path.split(SCREAMING_SNAKE_CASE_      )[-1].split("."      )[0]
        accelerator.init_trackers(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )

    _lowerCAmelCase		     = AutoTokenizer.from_pretrained("bert-base-cased"      )
    _lowerCAmelCase		     = load_dataset("glue"						,    "mrpc"      )
    _lowerCAmelCase		     = evaluate.load("glue"						,    "mrpc"      )

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

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

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

        return tokenizer.pad(
            SCREAMING_SNAKE_CASE_						,    padding="longest"						,    max_length=SCREAMING_SNAKE_CASE_						,    pad_to_multiple_of=SCREAMING_SNAKE_CASE_						,    return_tensors="pt"						,    )

    # Instantiate dataloaders.
    _lowerCAmelCase		     = DataLoader(
        tokenized_datasets["train"]						,    shuffle=SCREAMING_SNAKE_CASE_						,    collate_fn=SCREAMING_SNAKE_CASE_						,    batch_size=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = DataLoader(
        tokenized_datasets["validation"]						,    shuffle=SCREAMING_SNAKE_CASE_						,    collate_fn=SCREAMING_SNAKE_CASE_						,    batch_size=SCREAMING_SNAKE_CASE_      )

    set_seed(SCREAMING_SNAKE_CASE_      )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCAmelCase		     = AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,    return_dict=SCREAMING_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).
    _lowerCAmelCase		     = model.to(accelerator.device      )

    # Instantiate optimizer
    _lowerCAmelCase		     = AdamW(params=model.parameters()						,    lr=SCREAMING_SNAKE_CASE_      )

    # Instantiate scheduler
    _lowerCAmelCase		     = get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE_						,    num_warmup_steps=100						,    num_training_steps=(len(SCREAMING_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.
    _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = accelerator.prepare(
        SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )

    # We need to keep track of how many total steps we have iterated over
    _lowerCAmelCase		     = 0
    # We also need to keep track of the stating epoch so files are named properly
    _lowerCAmelCase		     = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''      )
            accelerator.load_state(args.resume_from_checkpoint      )
            _lowerCAmelCase		     = os.path.basename(args.resume_from_checkpoint      )
        else:
            # Get the most recent checkpoint
            _lowerCAmelCase		     = [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
            dirs.sort(key=os.path.getctime      )
            _lowerCAmelCase		     = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        _lowerCAmelCase		     = os.path.splitext(SCREAMING_SNAKE_CASE_      )[0]

        if "epoch" in training_difference:
            _lowerCAmelCase		     = int(training_difference.replace("epoch_"						,    ""      )      ) + 1
            _lowerCAmelCase		     = None
        else:
            _lowerCAmelCase		     = int(training_difference.replace("step_"						,    ""      )      )
            _lowerCAmelCase		     = resume_step // len(SCREAMING_SNAKE_CASE_      )
            resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_      )

    # Now we train the model
    for epoch in range(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      ):
        model.train()
        if args.with_tracking:
            _lowerCAmelCase		     = 0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            _lowerCAmelCase		     = accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            _lowerCAmelCase		     = train_dataloader
        for step, batch in enumerate(SCREAMING_SNAKE_CASE_      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            _lowerCAmelCase		     = model(**SCREAMING_SNAKE_CASE_      )
            _lowerCAmelCase		     = outputs.loss
            _lowerCAmelCase		     = loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(SCREAMING_SNAKE_CASE_      )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      ):
                _lowerCAmelCase		     = F'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _lowerCAmelCase		     = os.path.join(args.output_dir						,    SCREAMING_SNAKE_CASE_      )
                    accelerator.save_state(SCREAMING_SNAKE_CASE_      )

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

        _lowerCAmelCase		     = metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''						,    SCREAMING_SNAKE_CASE_      )
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(SCREAMING_SNAKE_CASE_      ),
                    "epoch": epoch,
                }						,    step=SCREAMING_SNAKE_CASE_						,    )

        if checkpointing_steps == "epoch":
            _lowerCAmelCase		     = F'''epoch_{epoch}'''
            if args.output_dir is not None:
                _lowerCAmelCase		     = os.path.join(args.output_dir						,    SCREAMING_SNAKE_CASE_      )
            accelerator.save_state(SCREAMING_SNAKE_CASE_      )

    if args.with_tracking:
        accelerator.end_training()


def 		__a():



    '''simple docstring'''





    _lowerCAmelCase		     = argparse.ArgumentParser(description="Simple example of training script."      )
    parser.add_argument(
        "--mixed_precision"						,    type=SCREAMING_SNAKE_CASE_						,    default=SCREAMING_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."      )
    parser.add_argument(
        "--checkpointing_steps"						,    type=SCREAMING_SNAKE_CASE_						,    default=SCREAMING_SNAKE_CASE_						,    help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."						,    )
    parser.add_argument(
        "--resume_from_checkpoint"						,    type=SCREAMING_SNAKE_CASE_						,    default=SCREAMING_SNAKE_CASE_						,    help="If the training should continue from a checkpoint folder."						,    )
    parser.add_argument(
        "--with_tracking"						,    action="store_true"						,    help="Whether to load in all available experiment trackers from the environment and use them for logging."						,    )
    parser.add_argument(
        "--output_dir"						,    type=SCREAMING_SNAKE_CASE_						,    default="."						,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."						,    )
    parser.add_argument(
        "--project_dir"						,    type=SCREAMING_SNAKE_CASE_						,    default="logs"						,    help="Location on where to store experiment tracking logs` and relevent project information"						,    )
    _lowerCAmelCase		     = parser.parse_args()
    _lowerCAmelCase		     = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       main()









'''simple docstring'''


import math


def 		__a(SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    if number == 0:
        _lowerCAmelCase		     = "Zero"
        return words
    else:
        _lowerCAmelCase		     = math.logaa(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = digits + 1
        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "One"
        _lowerCAmelCase		     = "Two"
        _lowerCAmelCase		     = "Three"
        _lowerCAmelCase		     = "Four"
        _lowerCAmelCase		     = "Five"
        _lowerCAmelCase		     = "Six"
        _lowerCAmelCase		     = "Seven"
        _lowerCAmelCase		     = "Eight"
        _lowerCAmelCase		     = "Nine"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "Twenty"
        _lowerCAmelCase		     = "Thirty"
        _lowerCAmelCase		     = "Forty"
        _lowerCAmelCase		     = "Fifty"
        _lowerCAmelCase		     = "Sixty"
        _lowerCAmelCase		     = "Seventy"
        _lowerCAmelCase		     = "Eighty"
        _lowerCAmelCase		     = "Ninety"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Ten"
        _lowerCAmelCase		     = "Eleven"
        _lowerCAmelCase		     = "Twelve"
        _lowerCAmelCase		     = "Thirteen"
        _lowerCAmelCase		     = "Fourteen"
        _lowerCAmelCase		     = "Fifteen"
        _lowerCAmelCase		     = "Sixteen"
        _lowerCAmelCase		     = "Seventeen"
        _lowerCAmelCase		     = "Eighteen"
        _lowerCAmelCase		     = "Nineteen"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Hundred,"
        _lowerCAmelCase		     = "Thousand,"
        _lowerCAmelCase		     = "Lakh,"
        _lowerCAmelCase		     = "Crore,"

        _lowerCAmelCase		     = number
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = 0
        _lowerCAmelCase		     = int(SCREAMING_SNAKE_CASE_      )
        while counter < digits:
            _lowerCAmelCase		     = temp_num % 10
            if counter % 2 == 0:
                _lowerCAmelCase		     = ""
                if counter in placevalue and current != 0:
                    _lowerCAmelCase		     = placevalue[counter]
                if counter == 2:
                    _lowerCAmelCase		     = singles[current] + addition + words
                elif counter == 0:
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words

                else:
                    _lowerCAmelCase		     = doubles[current] + addition + words

            else:
                if counter == 1:
                    if current == 1:
                        _lowerCAmelCase		     = teens[number % 10] + words
                    else:
                        _lowerCAmelCase		     = ""
                        if counter in placevalue:
                            _lowerCAmelCase		     = placevalue[counter]
                        _lowerCAmelCase		     = doubles[current] + addition + words
                else:
                    _lowerCAmelCase		     = ""
                    if counter in placevalue:
                        if current == 0 and ((temp_num % 100) // 10) == 0:
                            _lowerCAmelCase		     = ""
                        else:
                            _lowerCAmelCase		     = placevalue[counter]
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words
            counter += 1
            _lowerCAmelCase		     = temp_num // 10
    return words


if __name__ == "__main__":
       import doctest

       doctest.testmod()


