

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


lowerCAmelCase     :   Any						     =  16
lowerCAmelCase     :   Dict						     =  32

def    A_  (							a					,   a = 1_6  ):

      """simple docstring"""



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

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

      def collate_fn(a  ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_				:  str           =     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE_				:  int           =     1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     8
            else:
                  SCREAMING_SNAKE_CASE_				:  Dict           =     None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     DataLoader(
          tokenized_datasets['train']					,   shuffle=a					,   collate_fn=a					,   batch_size=a  )
      SCREAMING_SNAKE_CASE_				:  int           =     DataLoader(
          tokenized_datasets['validation']					,   shuffle=a					,   collate_fn=a					,   batch_size=a  )

      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     :   Tuple						     =  mocked_dataloaders  # noqa: F811

def    A_  (							a					,   a  ):

      """simple docstring"""



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

      SCREAMING_SNAKE_CASE_				:  Any           =     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=a  )
      def inner_training_loop(a  ):
            # 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(a  )

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

            # 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).
            SCREAMING_SNAKE_CASE_				:  int           =     model.to(accelerator.device  )

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AdamW(params=model.parameters()					,   lr=a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     get_dataloaders(a					,   a  )

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     get_linear_schedule_with_warmup(
                optimizer=a					,   num_warmup_steps=1_0_0					,   num_training_steps=(len(a  ) * 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.
            SCREAMING_SNAKE_CASE_				:  str           =     accelerator.prepare(
                a					,   a					,   a					,   a					,   a  )

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

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

                  SCREAMING_SNAKE_CASE_				:  Dict           =     metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"epoch {epoch}:"					,   a  )

    # 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    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     argparse.ArgumentParser(description='Simple example of training script.'  )
      parser.add_argument(
          '--mixed_precision'					,   type=a					,   default=a					,   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.'  )
      SCREAMING_SNAKE_CASE_				:  str           =     parser.parse_args()
      SCREAMING_SNAKE_CASE_				:  List[str]           =     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
      training_function(a					,   a  )


if __name__ == "__main__":
 main()









def    A_  (							a  ):

      """simple docstring"""



      if upper_limit < 0:
            raise ValueError('Limit for the Catalan sequence must be ≥ 0'  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     [0] * (upper_limit + 1)

      # Base case: C(0) = C(1) = 1
      SCREAMING_SNAKE_CASE_				:  Any           =     1
      if upper_limit > 0:
            SCREAMING_SNAKE_CASE_				:  int           =     1

      # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
      for i in range(2					,   upper_limit + 1  ):
            for j in range(a  ):
                  catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

      return catalan_list


if __name__ == "__main__":
    print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
    print('\n*** Enter -1 at any time to quit ***')
    print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
    try:
        while True:
            lowerCAmelCase     :   Union[str, Any]						     =  int(input().strip())
            if N < 0:
                print('\n********* Goodbye!! ************')
                break
            else:
                print(F'The Catalan numbers from 0 through {N} are:')
                print(catalan_numbers(N))
                print('Try another upper limit for the sequence: ', end='')
    except (NameError, ValueError):
        print('\n********* Invalid input, goodbye! ************\n')

    import doctest

    doctest.testmod()






