



"""simple docstring"""
import re

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					Dict	=	re.compile(R"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"""						)
 if match := re.search(UpperCamelCase			,		UpperCamelCase						):
  return match.string == phone
 return False


if __name__ == "__main__":
      print(indian_phone_validator("+918827897895"))

"""simple docstring"""


import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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__     						=  1_6
UpperCAmelCase__     						=  3_2






def 			__UpperCAmelCase    (	lowercase      ,lowercase = 16						):







  """simple docstring"""
  _UpperCAmelCase         =					AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  _UpperCAmelCase         =					load_dataset("""glue"""      ,"""mrpc"""						)

  def tokenize_function(lowercase						):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCAmelCase         =					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         =					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         =					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         =					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":
      _UpperCAmelCase         =					16
    elif accelerator.mixed_precision != "no":
      _UpperCAmelCase         =					8
    else:
      _UpperCAmelCase         =					None

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

  # Instantiate dataloaders.
  _UpperCAmelCase         =					DataLoader(
      tokenized_datasets["""train"""]      ,shuffle=lowercase      ,collate_fn=lowercase      ,batch_size=lowercase						)
  _UpperCAmelCase         =					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

    UpperCAmelCase__     						=  mocked_dataloaders  # noqa: F811






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,lowercase						) == "1":
    _UpperCAmelCase         =					2
  # Initialize accelerator
  _UpperCAmelCase         =					Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCAmelCase         =					config["""lr"""]
  _UpperCAmelCase         =					int(config["""num_epochs"""]						)
  _UpperCAmelCase         =					int(config["""seed"""]						)
  _UpperCAmelCase         =					int(config["""batch_size"""]						)

  _UpperCAmelCase         =					evaluate.load("""glue"""      ,"""mrpc"""						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowercase						)
  def inner_training_loop(lowercase						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(lowercase						)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _UpperCAmelCase         =					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         =					model.to(accelerator.device						)

    # Instantiate optimizer
    _UpperCAmelCase         =					AdamW(params=model.parameters()      ,lr=lowercase						)
    _UpperCAmelCase       , _UpperCAmelCase         =					get_dataloaders(lowercase      ,lowercase						)

    # Instantiate scheduler
    _UpperCAmelCase         =					get_linear_schedule_with_warmup(
        optimizer=lowercase      ,num_warmup_steps=1_00      ,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         =					accelerator.prepare(
        lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # Now we train the model
    for epoch in range(lowercase						):
      model.train()
      for step, batch in enumerate(lowercase						):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        _UpperCAmelCase         =					model(**lowercase						)
        _UpperCAmelCase         =					outputs.loss
        accelerator.backward(lowercase						)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      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         =					model(**lowercase						)
        _UpperCAmelCase         =					outputs.logits.argmax(dim=-1						)
        _UpperCAmelCase       , _UpperCAmelCase         =					accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
        metric.add_batch(
            predictions=lowercase      ,references=lowercase      ,)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					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."""      ,)
  parser.add_argument("""--cpu"""      ,action="""store_true"""      ,help="""If passed, will train on the CPU."""						)
  _UpperCAmelCase         =					parser.parse_args()
  _UpperCAmelCase         =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase      ,lowercase						)


if __name__ == "__main__":
    main()
