




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	:		Any							       =   32







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



       """simple docstring"""

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

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

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

       # Instantiate dataloaders.
       A__       =    DataLoader(
           tokenized_datasets['''train'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)
       A__       =    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	:		Union[str, Any]							       =   mocked_dataloaders  # noqa: F811







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,				lowercase_						) == "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 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)
              A__       =    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).
              A__       =    model.to(accelerator.device						)

              # Instantiate optimizer
              A__       =    AdamW(params=model.parameters() ,				lr=lowercase_						)
              A__ ,							A__       =    get_dataloaders(lowercase_ ,				lowercase_						)

              # Instantiate scheduler
              A__       =    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.
              A__ ,							A__ ,							A__ ,							A__ ,							A__       =    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						)
                            A__       =    model(**lowercase_						)
                            A__       =    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():
                                   A__       =    model(**lowercase_						)
                            A__       =    outputs.logits.argmax(dim=-1						)
                            A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                            metric.add_batch(
                                predictions=lowercase_ ,				references=lowercase_ ,				)

                     A__       =    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 SCREAMING_SNAKE_CASE				(       )   ->       Optional[int]:



       """simple docstring"""

       A__       =    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.'''						)
       A__       =    parser.parse_args()
       A__       =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,				lowercase_						)


if __name__ == "__main__":
  main()








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       "list[int]":



       """simple docstring"""

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

       A__       =    [0] * (upper_limit + 1)

       # Base case: C(0) = C(1) = 1
       A__       =    1
       if upper_limit > 0:
              A__       =    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(lowercase_						):
                     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	:		List[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()


