






'''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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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      :       str      			=      16
__UpperCamelCase      :       List[Any]      			=      32



def    __UpperCAmelCase	(				lowercase_:	Accelerator,					lowercase_:	int = 16    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    AutoTokenizer.from_pretrained('bert-base-cased'    )
   __a				     =    load_dataset('glue',					'mrpc'    )

   def tokenize_function(lowercase_:	str    ):
      # max_length=None => use the model max length (it's actually the default)
      __a				     =    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				     =    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				     =    tokenized_datasets.rename_column('label',					'labels'    )

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



def    __UpperCAmelCase	(				lowercase_:	Optional[int],					lowercase_:	Optional[Any]    )  ->			List[str]:

   """simple docstring"""
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS',					SCREAMING_SNAKE_CASE__    ) == "1":
      __a				     =    2
   # New Code #
   __a				     =    int(args.gradient_accumulation_steps    )
   # Initialize accelerator
   __a				     =    Accelerator(
       cpu=args.cpu,					mixed_precision=args.mixed_precision,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE__    )
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'    )
   # 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'    )

   set_seed(SCREAMING_SNAKE_CASE__    )
   __a     ,			__a				     =    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				     =    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				     =    model.to(accelerator.device    )

   # Instantiate optimizer
   __a				     =    AdamW(params=model.parameters(),					lr=SCREAMING_SNAKE_CASE__    )

   # Instantiate scheduler
   __a				     =    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				     =    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    )
         # 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				     =    model(**SCREAMING_SNAKE_CASE__    )
            __a				     =    output.loss
            accelerator.backward(SCREAMING_SNAKE_CASE__    )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      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				     =    model(**SCREAMING_SNAKE_CASE__    )
         __a				     =    outputs.logits.argmax(dim=-1    )
         __a     ,			__a				     =    accelerator.gather_for_metrics((predictions, batch['labels'])    )
         metric.add_batch(
             predictions=SCREAMING_SNAKE_CASE__,					references=SCREAMING_SNAKE_CASE__,					)

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



def    __UpperCAmelCase	(				)  ->			Dict:

   """simple docstring"""
   __a				     =    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('--cpu',					action='store_true',					help='If passed, will train on the CPU.'    )
   __a				     =    parser.parse_args()
   __a				     =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 main()







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Dict      			=      ["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      [
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __UpperCamelCase      :       List[Any]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)