





"""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
#
########################################################################


lowerCamelCase__						= 16
lowerCamelCase__						= 32




def        lowercase__     (       lowercase_		,lowercase_ = 16      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							AutoTokenizer.from_pretrained("bert-base-cased"      )
      _UpperCamelCase   :						Tuple    =							load_dataset("glue"		,"mrpc"      )

      def tokenize_function(lowercase_      ):
            # max_length=None => use the model max length (it's actually the default)
            _UpperCamelCase   :						int    =							tokenizer(examples["sentence1"]		,examples["sentence2"]		,truncation=lowercase_		,max_length=lowercase_      )
            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   :						Union[str, Any]    =							datasets.map(
                lowercase_		,batched=lowercase_		,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   :						Optional[Any]    =							tokenized_datasets.rename_column("label"		,"labels"      )

      def collate_fn(lowercase_      ):
            # 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   :						Tuple    =							16
            elif accelerator.mixed_precision != "no":
                  _UpperCamelCase   :						str    =							8
            else:
                  _UpperCamelCase   :						Union[str, Any]    =							None

            return tokenizer.pad(
                lowercase_		,padding="longest"		,max_length=lowercase_		,pad_to_multiple_of=lowercase_		,return_tensors="pt"		,)

      # Instantiate dataloaders.
      _UpperCamelCase   :						Optional[Any]    =							DataLoader(
          tokenized_datasets["train"]		,shuffle=lowercase_		,collate_fn=lowercase_		,batch_size=lowercase_      )
      _UpperCamelCase   :						Optional[int]    =							DataLoader(
          tokenized_datasets["validation"]		,shuffle=lowercase_		,collate_fn=lowercase_		,batch_size=lowercase_      )

      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

  lowerCamelCase__						= mocked_dataloaders  # noqa: F811




def        lowercase__     (       lowercase_		,lowercase_      )       ->		List[Any]:




      """simple docstring"""

      if os.environ.get("TESTING_MOCKED_DATALOADERS"		,lowercase_      ) == "1":
            _UpperCamelCase   :						List[str]    =							2
      # New Code #
      _UpperCamelCase   :						Tuple    =							int(args.gradient_accumulation_steps      )
      _UpperCamelCase   :						Tuple    =							int(args.local_sgd_steps      )
      # Initialize accelerator
      _UpperCamelCase   :						List[Any]    =							Accelerator(
          cpu=args.cpu		,mixed_precision=args.mixed_precision		,gradient_accumulation_steps=lowercase_      )
      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   :						int    =							config["lr"]
      _UpperCamelCase   :						Dict    =							int(config["num_epochs"]      )
      _UpperCamelCase   :						Tuple    =							int(config["seed"]      )
      _UpperCamelCase   :						List[str]    =							int(config["batch_size"]      )

      _UpperCamelCase   :						Optional[int]    =							evaluate.load("glue"		,"mrpc"      )

      set_seed(lowercase_      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							get_dataloaders(lowercase_		,lowercase_      )
      # 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=lowercase_      )

      # 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   :						Dict    =							model.to(accelerator.device      )

      # Instantiate optimizer
      _UpperCamelCase   :						Tuple    =							AdamW(params=model.parameters()		,lr=lowercase_      )

      # Instantiate scheduler
      _UpperCamelCase   :						Optional[Any]    =							get_linear_schedule_with_warmup(
          optimizer=lowercase_		,num_warmup_steps=100		,num_training_steps=(len(lowercase_      ) * 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,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Tuple    =							accelerator.prepare(
          lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )

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

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

            _UpperCamelCase   :						Optional[int]    =							metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''		,lowercase_      )




def        lowercase__     (       )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						int    =							argparse.ArgumentParser(description="Simple example of training script."      )
      parser.add_argument(
          "--mixed_precision"		,type=lowercase_		,default=lowercase_		,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=lowercase_		,default=1		,help="The number of minibatches to be ran before gradients are accumulated."		,)
      parser.add_argument(
          "--local_sgd_steps"		,type=lowercase_		,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   :						List[str]    =							parser.parse_args()
      _UpperCamelCase   :						Union[str, Any]    =							{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(lowercase_		,lowercase_      )


if __name__ == "__main__":
  main()







"""simple docstring"""


import re




def        lowercase__     (       lowercase_      )       ->		bool:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							re.compile(
          r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$"      )

      return bool(re.search(lowercase_		,lowercase_      )      )


if __name__ == "__main__":
  lowerCamelCase__						= "0094702343221"

  print(is_sri_lankan_phone_number(phone))
