


'''simple docstring'''


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=1024							):




     '''simple docstring'''





     __A  ,__A	:       Union[str, Any]      =							[], []

     __A	:       Optional[int]      =							list(zip(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)							)
     __A  ,__A	:       Dict      =							sorted_examples[0]

     def is_too_big(SCREAMING_SNAKE_CASE							):
          return tok(SCREAMING_SNAKE_CASE       ,					return_tensors="pt"							).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]							):
          __A	:       int      =							new_src + " " + src
          __A	:       Tuple      =							new_tgt + " " + tgt
          if is_too_big(SCREAMING_SNAKE_CASE							) or is_too_big(SCREAMING_SNAKE_CASE							):  # cant fit, finalize example
               finished_src.append(SCREAMING_SNAKE_CASE							)
               finished_tgt.append(SCREAMING_SNAKE_CASE							)
               __A  ,__A	:       str      =							src, tgt
          else:  # can fit, keep adding
               __A  ,__A	:       Union[str, Any]      =							cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(SCREAMING_SNAKE_CASE							)
          finished_tgt.append(SCREAMING_SNAKE_CASE							)
     return finished_src, finished_tgt


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							Path(SCREAMING_SNAKE_CASE							)
     save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE							)
     for split in ["train"]:
          __A  ,__A	:       int      =							data_dir / f"{split}.source", data_dir / f"{split}.target"
          __A	:       Union[str, Any]      =							[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE							).open().readlines()]
          __A	:       Union[str, Any]      =							[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE							).open().readlines()]
          __A  ,__A	:       Dict      =							pack_examples(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
          print(f"packed {split} split from {len(SCREAMING_SNAKE_CASE							)} examples -> {len(SCREAMING_SNAKE_CASE							)}."							)
          Path(save_path / f"{split}.source"							).open("w"							).write("\n".join(SCREAMING_SNAKE_CASE							)							)
          Path(save_path / f"{split}.target"							).open("w"							).write("\n".join(SCREAMING_SNAKE_CASE							)							)
     for split in ["val", "test"]:
          __A  ,__A	:       List[Any]      =							data_dir / f"{split}.source", data_dir / f"{split}.target"
          shutil.copyfile(SCREAMING_SNAKE_CASE       ,					save_path / f"{split}.source"							)
          shutil.copyfile(SCREAMING_SNAKE_CASE       ,					save_path / f"{split}.target"							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       str      =							argparse.ArgumentParser()
     parser.add_argument("--tok_name"       ,					type=SCREAMING_SNAKE_CASE       ,					help="like facebook/bart-large-cnn,t5-base, etc."							)
     parser.add_argument("--max_seq_len"       ,					type=SCREAMING_SNAKE_CASE       ,					default=128							)
     parser.add_argument("--data_dir"       ,					type=SCREAMING_SNAKE_CASE							)
     parser.add_argument("--save_path"       ,					type=SCREAMING_SNAKE_CASE							)
     __A	:       Union[str, Any]      =							parser.parse_args()
     __A	:       Dict      =							AutoTokenizer.from_pretrained(args.tok_name							)
     return pack_data_dir(SCREAMING_SNAKE_CASE       ,					Path(args.data_dir							)       ,					args.max_seq_len       ,					args.save_path							)


if __name__ == "__main__":
      packer_cli()





'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = 16							):




     '''simple docstring'''





     __A	:       Tuple      =							AutoTokenizer.from_pretrained("bert-base-cased"							)
     __A	:       List[str]      =							load_dataset("glue"       ,					"mrpc"							)

     def tokenize_function(SCREAMING_SNAKE_CASE							):
          # max_length=None => use the model max length (it's actually the default)
          __A	:       Any      =							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():
          __A	:       Optional[Any]      =							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
     __A	:       List[Any]      =							tokenized_datasets.rename_column("label"       ,					"labels"							)

     def collate_fn(SCREAMING_SNAKE_CASE							):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A	:       str      =							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":
               __A	:       List[str]      =							16
          elif accelerator.mixed_precision != "no":
               __A	:       Tuple      =							8
          else:
               __A	:       Optional[Any]      =							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.
     __A	:       Optional[int]      =							DataLoader(
         tokenized_datasets["train"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							DataLoader(
         tokenized_datasets["validation"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)

     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

      _UpperCamelCase 							=  mocked_dataloaders  # noqa: F811


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,					SCREAMING_SNAKE_CASE							) == "1":
          __A	:       int      =							2
     # New Code #
     __A	:       List[str]      =							int(args.gradient_accumulation_steps							)
     __A	:       int      =							int(args.local_sgd_steps							)
     # Initialize accelerator
     __A	:       int      =							Accelerator(
         cpu=args.cpu       ,					mixed_precision=args.mixed_precision       ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A	:       Tuple      =							config["lr"]
     __A	:       Union[str, Any]      =							int(config["num_epochs"]							)
     __A	:       Optional[int]      =							int(config["seed"]							)
     __A	:       Optional[Any]      =							int(config["batch_size"]							)

     __A	:       Optional[int]      =							evaluate.load("glue"       ,					"mrpc"							)

     set_seed(SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       Tuple      =							get_dataloaders(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A	:       List[str]      =							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).
     __A	:       Union[str, Any]      =							model.to(accelerator.device							)

     # Instantiate optimizer
     __A	:       str      =							AdamW(params=model.parameters()       ,					lr=SCREAMING_SNAKE_CASE							)

     # Instantiate scheduler
     __A	:       Optional[int]      =							get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE       ,					num_warmup_steps=100       ,					num_training_steps=(len(SCREAMING_SNAKE_CASE							) * 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	:       List[str]      =							accelerator.prepare(
         SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE							):
          model.train()
          with LocalSGD(
              accelerator=SCREAMING_SNAKE_CASE       ,					model=SCREAMING_SNAKE_CASE       ,					local_sgd_steps=SCREAMING_SNAKE_CASE       ,					enabled=local_sgd_steps is not None							) as local_sgd:
               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							)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                         __A	:       List[Any]      =							model(**SCREAMING_SNAKE_CASE							)
                         __A	:       List[str]      =							output.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE							)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          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():
                    __A	:       Tuple      =							model(**SCREAMING_SNAKE_CASE							)
               __A	:       Union[str, Any]      =							outputs.logits.argmax(dim=-1							)
               __A  ,__A	:       Tuple      =							accelerator.gather_for_metrics((predictions, batch["labels"])							)
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE       ,					references=SCREAMING_SNAKE_CASE       ,					)

          __A	:       List[Any]      =							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:"       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							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."       ,					)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1       ,					help="The number of minibatches to be ran before gradients are accumulated."       ,					)
     parser.add_argument(
         "--local_sgd_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=8       ,					help="Number of local SGD steps or None to disable local SGD"							)
     parser.add_argument("--cpu"       ,					action="store_true"       ,					help="If passed, will train on the CPU."							)
     __A	:       List[str]      =							parser.parse_args()
     __A	:       List[str]      =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      main()

