




"""simple docstring"""



import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

SCREAMING_SNAKE_CASE       = 32







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_ = 16		)		->	Optional[int]:
 A__ =       AutoTokenizer.from_pretrained("bert-base-cased"		)
 A__ =       load_dataset("glue"    ,				"mrpc"		)

 def tokenize_function(lowercase_		):
  # max_length=None => use the model max length (it's actually the default)
  A__ =       tokenizer(examples["sentence1"]    ,				examples["sentence2"]    ,				truncation=lowercase_    ,				max_length=lowercase_		)
  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__ =       datasets.map(
      lowercase_    ,				batched=lowercase_    ,				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__ =       tokenized_datasets.rename_column("label"    ,				"labels"		)

 def collate_fn(lowercase_		):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   A__ =       16
  elif accelerator.mixed_precision != "no":
   A__ =       8
  else:
   A__ =       None

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

 # Instantiate dataloaders.
 A__ =       DataLoader(
     tokenized_datasets["train"]    ,				shuffle=lowercase_    ,				collate_fn=lowercase_    ,				batch_size=lowercase_		)
 A__ =       DataLoader(
     tokenized_datasets["validation"]    ,				shuffle=lowercase_    ,				collate_fn=lowercase_    ,				batch_size=lowercase_		)

 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

      SCREAMING_SNAKE_CASE       = mocked_dataloaders  # noqa: F811







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Tuple:
 # For testing only
 if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,				lowercase_		) == "1":
  A__ =       2
 # Initialize accelerator
 A__ =       Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision		)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 A__ =       config["lr"]
 A__ =       int(config["num_epochs"]		)
 A__ =       int(config["seed"]		)
 A__ =       int(config["batch_size"]		)

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

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(lowercase_		)		)
 def inner_training_loop(lowercase_		):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  A__ =       observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  A__ =       num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(lowercase_		)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  A__ =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,				return_dict=lowercase_		)

  # 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__ =       model.to(accelerator.device		)

  # Instantiate optimizer
  A__ =       AdamW(params=model.parameters()    ,				lr=lowercase_		)
  A__,   A__ =       get_dataloaders(lowercase_    ,				lowercase_		)

  # Instantiate scheduler
  A__ =       get_linear_schedule_with_warmup(
      optimizer=lowercase_    ,				num_warmup_steps=1_00    ,				num_training_steps=(len(lowercase_		) * 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__ =       accelerator.prepare(
      lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)

  # Now we train the model
  for epoch in range(lowercase_		):
   model.train()
   for step, batch in enumerate(lowercase_		):
    # And perform gradient accumulation
    with accelerator.accumulate(lowercase_		):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device		)
     A__ =       model(**lowercase_		)
     A__ =       outputs.loss
     accelerator.backward(lowercase_		)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

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

   A__ =       metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"""epoch {epoch}:"""    ,				lowercase_		)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()







def 						_SCREAMING_SNAKE_CASE     (							)		->	List[Any]:
 A__ =       argparse.ArgumentParser(description="Simple example of training script."		)
 parser.add_argument(
     "--mixed_precision"    ,				type=lowercase_    ,				default=lowercase_    ,				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__ =       parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 A__ =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
 training_function(lowercase_    ,				lowercase_		)


if __name__ == "__main__":
      main()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE       = {"tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"]}


if TYPE_CHECKING:
      from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
      import sys

      SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





