




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		:  Any	=   32



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = 16      ):
  lowercase			:str							=  AutoTokenizer.from_pretrained("bert-base-cased"      )
  lowercase			:Optional[Any]							=  load_dataset("glue",	"mrpc"      )

  def tokenize_function(lowerCamelCase      ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase			:Any							=  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			:Tuple							=  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			:Any							=  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			: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":
      lowercase			:Optional[Any]							=  16
    elif accelerator.mixed_precision != "no":
      lowercase			:Union[str, Any]							=  8
    else:
      lowercase			:Optional[Any]							=  None

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

  # Instantiate dataloaders.
  lowercase			:Tuple							=  DataLoader(
      tokenized_datasets["train"],	shuffle=lowerCamelCase,	collate_fn=lowerCamelCase,	batch_size=lowerCamelCase      )
  lowercase			:Dict							=  DataLoader(
      tokenized_datasets["validation"],	shuffle=lowerCamelCase,	collate_fn=lowerCamelCase,	batch_size=lowerCamelCase      )

  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		:  Any	=   mocked_dataloaders  # noqa: F811



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS",	lowerCamelCase      ) == "1":
    lowercase			:Union[str, Any]							=  2
  # Initialize accelerator
  lowercase			:Any							=  Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision      )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase			:int							=  config["lr"]
  lowercase			:List[str]							=  int(config["num_epochs"]      )
  lowercase			:Union[str, Any]							=  int(config["seed"]      )
  lowercase			:int							=  int(config["batch_size"]      )

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

  # If the batch size is too big we use gradient accumulation
  lowercase			:List[Any]							=  1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowercase			:List[str]							=  batch_size // MAX_GPU_BATCH_SIZE
    lowercase			:Tuple							=  MAX_GPU_BATCH_SIZE

  set_seed(lowerCamelCase      )
  lowercase		,			lowercase			:Any							=  get_dataloaders(lowerCamelCase,	lowerCamelCase      )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase			:Dict							=  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			:Optional[int]							=  model.to(accelerator.device      )

  # Instantiate optimizer
  lowercase			:int							=  AdamW(params=model.parameters(),	lr=lowerCamelCase      )

  # Instantiate scheduler
  lowercase			:int							=  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			:Dict							=  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			:Dict							=  model(**lowerCamelCase      )
      lowercase			:str							=  outputs.loss
      lowercase			:Dict							=  loss / gradient_accumulation_steps
      accelerator.backward(lowerCamelCase      )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    lowercase			:Optional[Any]							=  0
    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			:List[Any]							=  model(**lowerCamelCase      )
      lowercase			:Tuple							=  outputs.logits.argmax(dim=-1      )
      lowercase		,			lowercase			: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(lowerCamelCase      ) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          lowercase			:Dict							=  predictions[: len(eval_dataloader.dataset      ) - samples_seen]
          lowercase			:str							=  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=lowerCamelCase,	references=lowerCamelCase,	)

    lowercase			:Union[str, Any]							=  metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"epoch {epoch}:",	lowerCamelCase      )



def 					UpperCAmelCase__     (   ):
  lowercase			:Union[str, Any]							=  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			:Optional[Any]							=  parser.parse_args()
  lowercase			:List[str]							=  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(lowerCamelCase,	lowerCamelCase      )


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)



