




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
#
########################################################################


UpperCamelCase								=					16
UpperCamelCase								=					32



def 							_A       (						lowerCAmelCase_					: Accelerator       ,    lowerCAmelCase_					: int = 16      ):
     """simple docstring"""

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

     def tokenize_function(lowerCAmelCase_					: Any      ):
          # max_length=None => use the model max length (it's actually the default)
          lowerCAmelCase__          =			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():
          lowerCAmelCase__          =			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
     lowerCAmelCase__          =			tokenized_datasets.rename_column("label"       ,    "labels"      )

     def collate_fn(lowerCAmelCase_					: Dict      ):
          # 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(
              lowerCAmelCase_       ,    padding="longest"       ,    max_length=lowerCAmelCase_       ,    pad_to_multiple_of=lowerCAmelCase_       ,    return_tensors="pt"       ,    )

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

     return train_dataloader, eval_dataloader



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			Accelerator(cpu=args.cpu       ,    mixed_precision=args.mixed_precision      )
     # 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"]      )

     lowerCAmelCase__          =			evaluate.load("glue"       ,    "mrpc"      )

     # 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

     set_seed(lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			get_dataloaders(lowerCAmelCase_       ,    lowerCAmelCase_      )
     # 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=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).
     lowerCAmelCase__          =			model.to(accelerator.device      )
     # Instantiate optimizer
     lowerCAmelCase__          =			AdamW(params=model.parameters()       ,    lr=lowerCAmelCase_      )

     # Instantiate scheduler
     lowerCAmelCase__          =			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.

     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			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      )
               lowerCAmelCase__          =			model(**lowerCAmelCase_      )
               lowerCAmelCase__          =			outputs.loss
               lowerCAmelCase__          =			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():
                    lowerCAmelCase__          =			model(**lowerCAmelCase_      )
               lowerCAmelCase__          =			outputs.logits.argmax(dim=-1      )
               lowerCAmelCase__     ,		lowerCAmelCase__          =			accelerator.gather_for_metrics((predictions, batch["labels"])      )
               metric.add_batch(
                   predictions=lowerCAmelCase_       ,    references=lowerCAmelCase_       ,    )

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



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			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."      )
     lowerCAmelCase__          =			parser.parse_args()
     lowerCAmelCase__          =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowerCAmelCase_       ,    lowerCAmelCase_      )


if __name__ == "__main__":
     main()






from statistics import mean, stdev



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			min(lowerCAmelCase_      )
     lowerCAmelCase__          =			max(lowerCAmelCase_      )
     # normalize data
     return [round((x - x_min) / (x_max - x_min)       ,    lowerCAmelCase_      ) for x in data]






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			mean(lowerCAmelCase_      )
     lowerCAmelCase__          =			stdev(lowerCAmelCase_      )
     # standardize data
     return [round((x - mu) / (sigma)       ,    lowerCAmelCase_      ) for x in data]
