

import argparse
import os

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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = 16				)       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    AutoTokenizer.from_pretrained("bert-base-cased"				)
     _lowerCAmelCase   :					Union[str, Any]        =    load_dataset("glue"		,							"mrpc"				)

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

     def collate_fn(SCREAMING_SNAKE_CASE				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          _lowerCAmelCase   :					Tuple        =    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":
               _lowerCAmelCase   :					str        =    16
          elif accelerator.mixed_precision != "no":
               _lowerCAmelCase   :					int        =    8
          else:
               _lowerCAmelCase   :					Any        =    None

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

     # Instantiate dataloaders.
     _lowerCAmelCase   :					str        =    DataLoader(
         tokenized_datasets["train"]		,							shuffle=SCREAMING_SNAKE_CASE		,							collate_fn=SCREAMING_SNAKE_CASE		,							batch_size=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[Any]        =    DataLoader(
         tokenized_datasets["validation"]		,							shuffle=SCREAMING_SNAKE_CASE		,							collate_fn=SCREAMING_SNAKE_CASE		,							batch_size=SCREAMING_SNAKE_CASE				)

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

     """simple docstring"""







     if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							SCREAMING_SNAKE_CASE				) == "1":
          _lowerCAmelCase   :					Optional[Any]        =    2
     # New Code #
     _lowerCAmelCase   :					str        =    int(args.gradient_accumulation_steps				)
     _lowerCAmelCase   :					Dict        =    int(args.local_sgd_steps				)
     # Initialize accelerator
     _lowerCAmelCase   :					Dict        =    Accelerator(
         cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=SCREAMING_SNAKE_CASE				)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _lowerCAmelCase   :					Dict        =    config["lr"]
     _lowerCAmelCase   :					List[str]        =    int(config["num_epochs"]				)
     _lowerCAmelCase   :					List[str]        =    int(config["seed"]				)
     _lowerCAmelCase   :					str        =    int(config["batch_size"]				)

     _lowerCAmelCase   :					List[Any]        =    evaluate.load("glue"		,							"mrpc"				)

     set_seed(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    get_dataloaders(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _lowerCAmelCase   :					Optional[Any]        =    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=SCREAMING_SNAKE_CASE				)

     # 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   :					List[Any]        =    model.to(accelerator.device				)

     # Instantiate optimizer
     _lowerCAmelCase   :					List[Any]        =    AdamW(params=model.parameters()		,							lr=SCREAMING_SNAKE_CASE				)

     # Instantiate scheduler
     _lowerCAmelCase   :					str        =    get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE		,							num_warmup_steps=100		,							num_training_steps=(len(SCREAMING_SNAKE_CASE				) * 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   :					int        =    accelerator.prepare(
         SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE				):
          model.train()
          with LocalSGD(
              accelerator=SCREAMING_SNAKE_CASE		,							model=SCREAMING_SNAKE_CASE		,							local_sgd_steps=SCREAMING_SNAKE_CASE		,							enabled=local_sgd_steps is not None				) as local_sgd:
               for step, batch in enumerate(SCREAMING_SNAKE_CASE				):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device				)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE				):
                         _lowerCAmelCase   :					Tuple        =    model(**SCREAMING_SNAKE_CASE				)
                         _lowerCAmelCase   :					Union[str, Any]        =    output.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE				)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

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

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







def 	_snake_case		(    )       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument(
         "--mixed_precision"		,							type=SCREAMING_SNAKE_CASE		,							default=SCREAMING_SNAKE_CASE		,							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."		,							)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"		,							type=SCREAMING_SNAKE_CASE		,							default=1		,							help="The number of minibatches to be ran before gradients are accumulated."		,							)
     parser.add_argument(
         "--local_sgd_steps"		,							type=SCREAMING_SNAKE_CASE		,							default=8		,							help="Number of local SGD steps or None to disable local SGD"				)
     parser.add_argument("--cpu"		,							action="store_true"		,							help="If passed, will train on the CPU."				)
     _lowerCAmelCase   :					str        =    parser.parse_args()
     _lowerCAmelCase   :					Any        =    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  main()









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

     """simple docstring"""







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

     _lowerCAmelCase   :					Optional[Any]        =    [0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _lowerCAmelCase   :					Optional[int]        =    1
     if upper_limit > 0:
          _lowerCAmelCase   :					Any        =    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(SCREAMING_SNAKE_CASE				):
               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:
      __UpperCAmelCase    				=     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()






