

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					:  Tuple             =      16
_SCREAMING_SNAKE_CASE					:  List[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					:  Any             =      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()





import warnings

from .generation import TFGenerationMixin






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  warnings.warn(
      "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
      "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."						,					A__						,					)


