


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


__UpperCamelCase       :       Union[str, Any]						       =				16
__UpperCamelCase       :       Dict						       =				32

def 			_a  (				SCREAMING_SNAKE_CASE	:				Accelerator    , SCREAMING_SNAKE_CASE	:				int = 16     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 UpperCamelCase__					:    str          =   load_dataset('''glue'''    , '''mrpc'''     )

 def tokenize_function(SCREAMING_SNAKE_CASE	:				List[str]     ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCamelCase__					:    List[str]          =   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():
  UpperCamelCase__					:    List[Any]          =   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
 UpperCamelCase__					:    Dict          =   tokenized_datasets.rename_column('''label'''    , '''labels'''     )

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    , SCREAMING_SNAKE_CASE_     ) == "1":
  UpperCamelCase__					:    Dict          =   2
 # New Code #
 UpperCamelCase__					:    Optional[Any]          =   int(args.gradient_accumulation_steps     )
 UpperCamelCase__					:    str          =   int(args.local_sgd_steps     )
 # Initialize accelerator
 UpperCamelCase__					:    Optional[Any]          =   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
 UpperCamelCase__					:    Optional[int]          =   config["lr"]
 UpperCamelCase__					:    Optional[int]          =   int(config['''num_epochs''']     )
 UpperCamelCase__					:    Optional[int]          =   int(config['''seed''']     )
 UpperCamelCase__					:    Dict          =   int(config['''batch_size''']     )

 UpperCamelCase__					:    Tuple          =   evaluate.load('''glue'''    , '''mrpc'''     )

 set_seed(SCREAMING_SNAKE_CASE_     )
 UpperCamelCase__					:    Dict          =   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)
 UpperCamelCase__					:    int          =   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).
 UpperCamelCase__					:    Any          =   model.to(accelerator.device     )

 # Instantiate optimizer
 UpperCamelCase__					:    Tuple          =   AdamW(params=model.parameters()    , lr=SCREAMING_SNAKE_CASE_     )

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

  UpperCamelCase__					:    str          =   metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"    , SCREAMING_SNAKE_CASE_     )

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   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.'''     )
 UpperCamelCase__					:    Tuple          =   parser.parse_args()
 UpperCamelCase__					:    Any          =   {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(SCREAMING_SNAKE_CASE_    , SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       main()






from typing import TYPE_CHECKING

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


__UpperCamelCase       :       Dict						       =				{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       str						       =				["MobileViTFeatureExtractor"]
       __UpperCamelCase       :       Optional[Any]						       =				["MobileViTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Union[str, Any]						       =				[
           "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileViTForImageClassification",
           "MobileViTForSemanticSegmentation",
           "MobileViTModel",
           "MobileViTPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMobileViTForImageClassification",
           "TFMobileViTForSemanticSegmentation",
           "TFMobileViTModel",
           "TFMobileViTPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilevit import MobileViTFeatureExtractor
              from .image_processing_mobilevit import MobileViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilevit import (
                  MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileViTForImageClassification,
                  MobileViTForSemanticSegmentation,
                  MobileViTModel,
                  MobileViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mobilevit import (
                  TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMobileViTForImageClassification,
                  TFMobileViTForSemanticSegmentation,
                  TFMobileViTModel,
                  TFMobileViTPreTrainedModel,
              )


else:
       import sys

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


