

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
#
########################################################################


_SCREAMING_SNAKE_CASE  :						List[str]         =    16
_SCREAMING_SNAKE_CASE  :						Any         =    32
def        UpperCAmelCase_     (		_A   ,	_A = 16       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''bert-base-cased'''       )
     SCREAMING_SNAKE_CASE__					=					load_dataset('''glue'''   ,	'''mrpc'''       )

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

     def collate_fn(_A       ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE__					=					1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               SCREAMING_SNAKE_CASE__					=					16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE__					=					8
          else:
               SCREAMING_SNAKE_CASE__					=					None

          return tokenizer.pad(
              _A   ,	padding='''longest'''   ,	max_length=_A   ,	pad_to_multiple_of=_A   ,	return_tensors='''pt'''   ,	)

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE__					=					DataLoader(
         tokenized_datasets['''train''']   ,	shuffle=_A   ,	collate_fn=_A   ,	batch_size=_A       )
     SCREAMING_SNAKE_CASE__					=					DataLoader(
         tokenized_datasets['''validation''']   ,	shuffle=_A   ,	collate_fn=_A   ,	batch_size=_A       )

     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  :						Tuple         =    mocked_dataloaders  # noqa: F811
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,	_A       ) == "1":
          SCREAMING_SNAKE_CASE__					=					2
     # New Code #
     SCREAMING_SNAKE_CASE__					=					int(args.gradient_accumulation_steps       )
     SCREAMING_SNAKE_CASE__					=					int(args.local_sgd_steps       )
     # Initialize accelerator
     SCREAMING_SNAKE_CASE__					=					Accelerator(
         cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	gradient_accumulation_steps=_A       )
     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
     SCREAMING_SNAKE_CASE__					=					config['''lr''']
     SCREAMING_SNAKE_CASE__					=					int(config['''num_epochs''']       )
     SCREAMING_SNAKE_CASE__					=					int(config['''seed''']       )
     SCREAMING_SNAKE_CASE__					=					int(config['''batch_size''']       )

     SCREAMING_SNAKE_CASE__					=					evaluate.load('''glue'''   ,	'''mrpc'''       )

     set_seed(_A       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					get_dataloaders(_A   ,	_A       )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE__					=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,	return_dict=_A       )

     # 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).
     SCREAMING_SNAKE_CASE__					=					model.to(accelerator.device       )

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

     # Instantiate scheduler
     SCREAMING_SNAKE_CASE__					=					get_linear_schedule_with_warmup(
         optimizer=_A   ,	num_warmup_steps=1_00   ,	num_training_steps=(len(_A       ) * 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.
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					accelerator.prepare(
         _A   ,	_A   ,	_A   ,	_A   ,	_A       )

     # Now we train the model
     for epoch in range(_A       ):
          model.train()
          with LocalSGD(
              accelerator=_A   ,	model=_A   ,	local_sgd_steps=_A   ,	enabled=local_sgd_steps is not None       ) as local_sgd:
               for step, batch in enumerate(_A       ):
                    # 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(_A       ):
                         SCREAMING_SNAKE_CASE__					=					model(**_A       )
                         SCREAMING_SNAKE_CASE__					=					output.loss
                         accelerator.backward(_A       )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

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

          SCREAMING_SNAKE_CASE__					=					metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''   ,	_A       )
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser(description='''Simple example of training script.'''       )
     parser.add_argument(
         '''--mixed_precision'''   ,	type=_A   ,	default=_A   ,	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=_A   ,	default=1   ,	help='''The number of minibatches to be ran before gradients are accumulated.'''   ,	)
     parser.add_argument(
         '''--local_sgd_steps'''   ,	type=_A   ,	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.'''       )
     SCREAMING_SNAKE_CASE__					=					parser.parse_args()
     SCREAMING_SNAKE_CASE__					=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_A   ,	_A       )


if __name__ == "__main__":
       main()





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


