'''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
#
########################################################################


_A								=   1_6
_A								=   3_2

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = 16							):
     lowercase_					:       Optional[Any]     				=					AutoTokenizer.from_pretrained('bert-base-cased'							)
     lowercase_					:       Optional[int]     				=					load_dataset('glue'  ,       'mrpc'							)

     def tokenize_function(SCREAMING_SNAKE_CASE_							):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_					:       Optional[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():
          lowercase_					:       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
     lowercase_					:       Optional[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.
          lowercase_					:       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":
               lowercase_					:       Tuple     				=					16
          elif accelerator.mixed_precision != "no":
               lowercase_					:       int     				=					8
          else:
               lowercase_					:       List[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.
     lowercase_					:       Any     				=					DataLoader(
         tokenized_datasets['train']  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       collate_fn=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					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

 _A								=   mocked_dataloaders  # noqa: F811

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,       SCREAMING_SNAKE_CASE_							) == "1":
          lowercase_					:       int     				=					2
     # New Code #
     lowercase_					:       str     				=					int(args.gradient_accumulation_steps							)
     lowercase_					:       int     				=					int(args.local_sgd_steps							)
     # Initialize accelerator
     lowercase_					:       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
     lowercase_					:       List[Any]     				=					config['lr']
     lowercase_					:       List[str]     				=					int(config['num_epochs']							)
     lowercase_					:       int     				=					int(config['seed']							)
     lowercase_					:       str     				=					int(config['batch_size']							)

     lowercase_					:       Optional[int]     				=					evaluate.load('glue'  ,       'mrpc'							)

     set_seed(SCREAMING_SNAKE_CASE_							)
     lowercase_				,lowercase_					:       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)
     lowercase_					:       Tuple     				=					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).
     lowercase_					:       Any     				=					model.to(accelerator.device							)

     # Instantiate optimizer
     lowercase_					:       Union[str, Any]     				=					AdamW(params=model.parameters()  ,       lr=SCREAMING_SNAKE_CASE_							)

     # Instantiate scheduler
     lowercase_					:       str     				=					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.
     lowercase_				,lowercase_				,lowercase_				,lowercase_				,lowercase_					:       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()
          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_							):
                         lowercase_					:       Dict     				=					model(**SCREAMING_SNAKE_CASE_							)
                         lowercase_					:       Tuple     				=					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():
                    lowercase_					:       Any     				=					model(**SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Union[str, Any]     				=					outputs.logits.argmax(dim=-1							)
               lowercase_				,lowercase_					:       Any     				=					accelerator.gather_for_metrics((predictions, batch['labels'])							)
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE_  ,       references=SCREAMING_SNAKE_CASE_  ,       )

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

def _UpperCamelCase   (       ):
     lowercase_					:       Optional[Any]     				=					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.'							)
     lowercase_					:       Dict     				=					parser.parse_args()
     lowercase_					:       List[Any]     				=					{'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_torch_available


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

 _A								=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




