
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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    			=					32


def 		_lowerCamelCase       (   _a			,   _a = 1_6  ):
  """simple docstring"""




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

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

  def collate_fn(_a  ):
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCamelCase      =						1_6
    elif accelerator.mixed_precision != "no":
      _lowerCamelCase      =						8
    else:
      _lowerCamelCase      =						None

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

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

  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 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

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

  # New Code #
  # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  # half each time. From this, we can calculate the number of gradient accumulation steps needed
  # and modify the Accelerator object as a result
  @find_executable_batch_size(starting_batch_size=int(_UpperCamelCase  )  )
  def inner_training_loop(_a  ):
    # Since we need to modify the outside accelerator object, we need to bring it
    # to the local scope
    nonlocal accelerator

    # We can calculate the number of gradient accumulation steps based on the current
    # batch size vs the starting batch size
    _lowerCamelCase      =						observed_batch_size // batch_size

    # And then set it in the Accelerator directly:
    _lowerCamelCase      =						num_gradient_accumulation_steps

    # Next we need to free all of the stored model references in the Accelerator each time
    accelerator.free_memory()

    # And set the seed so our results are reproducable each reset
    set_seed(_UpperCamelCase  )

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

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

    # Instantiate optimizer
    _lowerCamelCase      =						AdamW(params=model.parameters()			,   lr=_UpperCamelCase  )
    _lowerCamelCase    ,    _lowerCamelCase      =						get_dataloaders(_UpperCamelCase			,   _UpperCamelCase  )

    # Instantiate scheduler
    _lowerCamelCase      =						get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,   num_warmup_steps=1_0_0			,   num_training_steps=(len(_UpperCamelCase  ) * 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.
    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase      =						accelerator.prepare(
        _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase  )

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

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

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

    # 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 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						argparse.ArgumentParser(description='''Simple example of training script.'''  )
  parser.add_argument(
      '''--mixed_precision'''			,   type=_UpperCamelCase			,   default=_UpperCamelCase			,   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.'''  )
  _lowerCamelCase      =						parser.parse_args()
  # New Code #
  # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  _lowerCamelCase      =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
  training_function(_UpperCamelCase			,   _UpperCamelCase  )


if __name__ == "__main__":
  main()







def  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  _snake_case		=				[0] * length
  _snake_case		=				[[False for i in range(_UpperCamelCase	)] for j in range(_UpperCamelCase	)]
  for i, c in enumerate(_UpperCamelCase	):
    _snake_case		=				i
    for j in range(i + 1	):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        _snake_case		=				True
        _snake_case		=				min(_UpperCamelCase					,			0 if j == 0 else (cut[j - 1] + 1)	)
    _snake_case		=				mincut
  return cut[length - 1]


if __name__ == "__main__":
 __A     						=      input('''Enter the string: ''').strip()
 __A     						=      find_minimum_partitions(s)
 print(f'''Minimum number of partitions required for the \'{s}\' is {ans}''')

