

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
#
########################################################################

__a						:Tuple            = 32

def 			__snake_case	(   __UpperCamelCase  :   Accelerator							,__UpperCamelCase  :   int = 16						):



      """simple docstring"""

      A_	     =							AutoTokenizer.from_pretrained("bert-base-cased"						)
      A_	     =							load_dataset("glue"							,"mrpc"						)

      def tokenize_function(__UpperCamelCase  :   int						):
            # max_length=None => use the model max length (it's actually the default)
            A_	     =							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():
            A_	     =							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
      A_	     =							tokenized_datasets.rename_column("label"							,"labels"						)

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

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

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

      __a						:Tuple            = mocked_dataloaders  # noqa: F811

def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   int						):



      """simple docstring"""

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

      A_	     =							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(__UpperCamelCase  :   Optional[int]						):
            # 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
            A_	     =							observed_batch_size // batch_size

            # And then set it in the Accelerator directly:
            A_	     =							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)
            A_	     =							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).
            A_	     =							model.to(accelerator.device						)

            # Instantiate optimizer
            A_	     =							AdamW(params=model.parameters()							,lr=__UpperCamelCase						)
            A_     ,    A_	     =							get_dataloaders(__UpperCamelCase							,__UpperCamelCase						)

            # Instantiate scheduler
            A_	     =							get_linear_schedule_with_warmup(
                optimizer=__UpperCamelCase							,num_warmup_steps=100							,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.
            A_     ,    A_     ,    A_     ,    A_     ,    A_	     =							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						)
                              A_	     =							model(**__UpperCamelCase						)
                              A_	     =							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():
                              A_	     =							model(**__UpperCamelCase						)
                        A_	     =							outputs.logits.argmax(dim=-1						)
                        A_     ,    A_	     =							accelerator.gather_for_metrics((predictions, batch["labels"])						)
                        metric.add_batch(
                            predictions=__UpperCamelCase							,references=__UpperCamelCase							,)

                  A_	     =							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 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							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."						)
      A_	     =							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
      A_	     =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
      training_function(__UpperCamelCase							,__UpperCamelCase						)


if __name__ == "__main__":
      main()


def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

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


if __name__ == "__main__":
      __a						:Union[str, Any]            = input('Enter the string: ').strip()
      __a						:Tuple            = find_minimum_partitions(s)
      print(F"Minimum number of partitions required for the '{s}' is {ans}")