





'''simple docstring'''

import argparse

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)
#
# 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]											=			16
__lowerCamelCase : Union[str, Any]											=			32







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_ = 16							):

    """simple docstring"""






    lowercase         = AutoTokenizer.from_pretrained("bert-base-cased"							)
    lowercase         = load_dataset("glue"		,							"mrpc"							)

    def tokenize_function(lowerCAmelCase_							):
        # max_length=None => use the model max length (it's actually the default)
        lowercase         = tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowerCAmelCase_		,							max_length=lowerCAmelCase_							)
        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(
            lowerCAmelCase_		,							batched=lowerCAmelCase_		,							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(lowerCAmelCase_							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowercase         = 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":
            lowercase         = 16
        elif accelerator.mixed_precision != "no":
            lowercase         = 8
        else:
            lowercase         = None

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

    # Instantiate dataloaders.
    lowercase         = DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowerCAmelCase_		,							collate_fn=lowerCAmelCase_		,							batch_size=lowerCAmelCase_		,							drop_last=lowerCAmelCase_							)
    lowercase         = DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowerCAmelCase_		,							collate_fn=lowerCAmelCase_		,							batch_size=lowerCAmelCase_		,							drop_last=(accelerator.mixed_precision == "fp8")		,							)

    return train_dataloader, eval_dataloader







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    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(lowerCAmelCase_							)
    lowercase   ,							lowercase         = get_dataloaders(lowerCAmelCase_		,							lowerCAmelCase_							)
    # 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=lowerCAmelCase_							)

    # 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=lowerCAmelCase_							)

    # Instantiate scheduler
    lowercase         = get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase_		,							num_warmup_steps=100		,							num_training_steps=(len(lowerCAmelCase_							) * 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(
        lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)

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

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

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







def 							UpperCAmelCase_    (	):

    """simple docstring"""






    lowercase         = argparse.ArgumentParser(description="Simple example of training script."							)
    parser.add_argument(
        "--mixed_precision"		,							type=lowerCAmelCase_		,							default=lowerCAmelCase_		,							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."							)
    lowercase         = parser.parse_args()
    lowercase         = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowerCAmelCase_		,							lowerCAmelCase_							)


if __name__ == "__main__":
   main()











'''simple docstring'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return DownloadCommand(args.model		,							args.cache_dir		,							args.force		,							args.trust_remote_code							)


class       UpperCAmelCase (						_lowercase  ):




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	ArgumentParser				)					-> List[Any]:
						lowercase         = parser.add_parser("download"				)
						download_parser.add_argument(
						    "--cache-dir"							,			type=A__							,			default=A__							,			help="Path to location to store the models"				)
						download_parser.add_argument(
						    "--force"							,			action="store_true"							,			help="Force the model to be download even if already in cache-dir"				)
						download_parser.add_argument(
						    "--trust-remote-code"							,			action="store_true"							,			help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,			)
						download_parser.add_argument("model"							,			type=A__							,			help="Name of the model to download"				)
						download_parser.set_defaults(func=A__				)




		def __init__(self      :	Tuple							,			A__      :	str							,			A__      :	str							,			A__      :	bool							,			A__      :	bool				)					-> str:
						lowercase         = model
						lowercase         = cache
						lowercase         = force
						lowercase         = trust_remote_code





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)
						AutoTokenizer.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)




