





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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


UpperCAmelCase	:	Any		     =       16
UpperCAmelCase	:	str		     =       32
def      _A       (		SCREAMING_SNAKE_CASE     :   Accelerator       ,       SCREAMING_SNAKE_CASE     :   int = 16						):





  """simple docstring"""



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

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

  def collate_fn(SCREAMING_SNAKE_CASE     :   str						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__	:					Optional[Any]										=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__	:					str										=16
    elif accelerator.mixed_precision != "no":
      a__	:					Union[str, Any]										=8
    else:
      a__	:					List[str]										=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__	:					Any										=DataLoader(
      tokenized_datasets["train"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)
  a__	:					int										=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	:	str		     =       mocked_dataloaders  # noqa: F811
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,       SCREAMING_SNAKE_CASE						) == "1":
    a__	:					Tuple										=2
  # Initialize accelerator
  a__	:					int										=Accelerator(cpu=args.cpu       ,       mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__	:					Optional[int]										=config["lr"]
  a__	:					Union[str, Any]										=int(config["num_epochs"]						)
  a__	:					Any										=int(config["seed"]						)
  a__	:					Dict										=int(config["batch_size"]						)

  a__	:					int										=evaluate.load("glue"       ,       "mrpc"						)

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

  set_seed(SCREAMING_SNAKE_CASE						)
  a__					,							a__	:					Optional[int]										=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__	:					List[str]										=model.to(accelerator.device						)

  # Instantiate optimizer
  a__	:					List[Any]										=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) // 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.
  a__					,							a__					,							a__					,							a__					,							a__	:					Optional[int]										=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()
    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						)
      a__	:					Dict										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					List[Any]										=outputs.loss
      a__	:					List[str]										=loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    a__	:					Optional[Any]										=0
    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__	:					Any										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					str										=outputs.logits.argmax(dim=-1						)
      a__					,							a__	:					List[str]										=accelerator.gather((predictions, batch["labels"])						)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(SCREAMING_SNAKE_CASE						) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          a__	:					Optional[Any]										=predictions[: len(eval_dataloader.dataset						) - samples_seen]
          a__	:					Dict										=references[: len(eval_dataloader.dataset						) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=SCREAMING_SNAKE_CASE       ,       references=SCREAMING_SNAKE_CASE       ,       )

    a__	:					Tuple										=metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		):





  """simple docstring"""



  a__	:					List[str]										=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."						)
  a__	:					str										=parser.parse_args()
  a__	:					Optional[int]										={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()












from typing import TYPE_CHECKING

from ....utils import _LazyModule


_UpperCAmelCase		:  Dict	=   {"tokenization_tapex": ["TapexTokenizer"]}


if TYPE_CHECKING:
     from .tokenization_tapex import TapexTokenizer


else:
     import sys

     _UpperCAmelCase		:  Optional[Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure)



