



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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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__      =		16
a__      =		32



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Dict			,    SCREAMING_SNAKE_CASE__    :		List[str]			)					->       str:
 # Initialize accelerator
 if args.with_tracking:
  _snake_case      :					Optional[int]					=   Accelerator(
      cpu=args.cpu			,    mixed_precision=args.mixed_precision			,    log_with="""all"""			,    project_dir=args.project_dir			)
 else:
  _snake_case      :					int					=   Accelerator(cpu=args.cpu			,    mixed_precision=args.mixed_precision			)

 if hasattr(args.checkpointing_steps			,    """isdigit"""			):
  if args.checkpointing_steps == "epoch":
   _snake_case      :					List[str]					=   args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _snake_case      :					Optional[int]					=   int(args.checkpointing_steps			)
  else:
   raise ValueError(
       F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
 else:
  _snake_case      :					str					=   None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _snake_case      :					Tuple					=   config["""lr"""]
 _snake_case      :					List[str]					=   int(config["""num_epochs"""]			)
 _snake_case      :					int					=   int(config["""seed"""]			)
 _snake_case      :					Union[str, Any]					=   int(config["""batch_size"""]			)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _snake_case      :					int					=   os.path.split(SCREAMING_SNAKE_CASE__			)[-1].split("""."""			)[0]
  accelerator.init_trackers(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

 _snake_case      :					Dict					=   AutoTokenizer.from_pretrained("""bert-base-cased"""			)
 _snake_case      :					Any					=   load_dataset("""glue"""			,    """mrpc"""			)
 _snake_case      :					Dict					=   evaluate.load("""glue"""			,    """mrpc"""			)

 def tokenize_function(SCREAMING_SNAKE_CASE__    :		List[str]			):
  # max_length=None => use the model max length (it's actually the default)
  _snake_case      :					Union[str, Any]					=   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():
  _snake_case      :					Union[str, 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
 _snake_case      :					str					=   tokenized_datasets.rename_column("""label"""			,    """labels"""			)

 # If the batch size is too big we use gradient accumulation
 _snake_case      :					Optional[Any]					=   1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  _snake_case      :					Any					=   batch_size // MAX_GPU_BATCH_SIZE
  _snake_case      :					List[str]					=   MAX_GPU_BATCH_SIZE

 def collate_fn(SCREAMING_SNAKE_CASE__    :		Dict			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _snake_case      :					List[Any]					=   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":
   _snake_case      :					Tuple					=   16
  elif accelerator.mixed_precision != "no":
   _snake_case      :					Dict					=   8
  else:
   _snake_case      :					Union[str, 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.
 _snake_case      :					Optional[Any]					=   DataLoader(
     tokenized_datasets["""train"""]			,    shuffle=SCREAMING_SNAKE_CASE__			,    collate_fn=SCREAMING_SNAKE_CASE__			,    batch_size=SCREAMING_SNAKE_CASE__			)
 _snake_case      :					str					=   DataLoader(
     tokenized_datasets["""validation"""]			,    shuffle=SCREAMING_SNAKE_CASE__			,    collate_fn=SCREAMING_SNAKE_CASE__			,    batch_size=SCREAMING_SNAKE_CASE__			)

 set_seed(SCREAMING_SNAKE_CASE__			)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _snake_case      :					int					=   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).
 _snake_case      :					str					=   model.to(accelerator.device			)

 # Instantiate optimizer
 _snake_case      :					Optional[int]					=   AdamW(params=model.parameters()			,    lr=SCREAMING_SNAKE_CASE__			)

 # Instantiate scheduler
 _snake_case      :					List[Any]					=   get_linear_schedule_with_warmup(
     optimizer=SCREAMING_SNAKE_CASE__			,    num_warmup_steps=100			,    num_training_steps=(len(SCREAMING_SNAKE_CASE__			) * num_epochs) // gradient_accumulation_steps			,    )

 # 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.
 _snake_case		,      _snake_case		,      _snake_case		,      _snake_case		,      _snake_case      :					List[str]					=   accelerator.prepare(
     SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

 # We need to keep track of how many total steps we have iterated over
 _snake_case      :					Tuple					=   0
 # We also need to keep track of the stating epoch so files are named properly
 _snake_case      :					Tuple					=   0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
   accelerator.load_state(args.resume_from_checkpoint			)
   _snake_case      :					Optional[int]					=   os.path.basename(args.resume_from_checkpoint			)
  else:
   # Get the most recent checkpoint
   _snake_case      :					List[Any]					=   [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
   dirs.sort(key=os.path.getctime			)
   _snake_case      :					Union[str, Any]					=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _snake_case      :					Union[str, Any]					=   os.path.splitext(SCREAMING_SNAKE_CASE__			)[0]

  if "epoch" in training_difference:
   _snake_case      :					List[str]					=   int(training_difference.replace("""epoch_"""			,    """"""			)			) + 1
   _snake_case      :					Union[str, Any]					=   None
  else:
   _snake_case      :					Optional[Any]					=   int(training_difference.replace("""step_"""			,    """"""			)			)
   _snake_case      :					Any					=   resume_step // len(SCREAMING_SNAKE_CASE__			)
   resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__			)

    # Now we train the model
 for epoch in range(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  model.train()
  if args.with_tracking:
   _snake_case      :					Any					=   0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _snake_case      :					Any					=   accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _snake_case      :					int					=   train_dataloader
  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			)
   _snake_case      :					Optional[int]					=   model(**SCREAMING_SNAKE_CASE__			)
   _snake_case      :					Optional[Any]					=   outputs.loss
   _snake_case      :					Optional[int]					=   loss / gradient_accumulation_steps
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(SCREAMING_SNAKE_CASE__			)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

   if isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
    _snake_case      :					List[Any]					=   F'''step_{overall_step}'''
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _snake_case      :					List[str]					=   os.path.join(args.output_dir			,    SCREAMING_SNAKE_CASE__			)
     accelerator.save_state(SCREAMING_SNAKE_CASE__			)

  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():
    _snake_case      :					List[str]					=   model(**SCREAMING_SNAKE_CASE__			)
   _snake_case      :					Optional[int]					=   outputs.logits.argmax(dim=-1			)
   _snake_case		,      _snake_case      :					Dict					=   accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
   metric.add_batch(
       predictions=SCREAMING_SNAKE_CASE__			,    references=SCREAMING_SNAKE_CASE__			,    )

  _snake_case      :					Tuple					=   metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}:'''			,    SCREAMING_SNAKE_CASE__			)
  if args.with_tracking:
   accelerator.log(
       {
           """accuracy""": eval_metric["""accuracy"""],
           """f1""": eval_metric["""f1"""],
           """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__			),
           """epoch""": epoch,
       }			,    step=SCREAMING_SNAKE_CASE__			,    )

  if checkpointing_steps == "epoch":
   _snake_case      :					Union[str, Any]					=   F'''epoch_{epoch}'''
   if args.output_dir is not None:
    _snake_case      :					Union[str, Any]					=   os.path.join(args.output_dir			,    SCREAMING_SNAKE_CASE__			)
   accelerator.save_state(SCREAMING_SNAKE_CASE__			)

 if args.with_tracking:
  accelerator.end_training()



def  lowercase    (				)					->       str:
 _snake_case      :					Any					=   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."""			,    )
 parser.add_argument("""--cpu"""			,    action="""store_true"""			,    help="""If passed, will train on the CPU."""			)
 parser.add_argument(
     """--checkpointing_steps"""			,    type=SCREAMING_SNAKE_CASE__			,    default=SCREAMING_SNAKE_CASE__			,    help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,    )
 parser.add_argument(
     """--resume_from_checkpoint"""			,    type=SCREAMING_SNAKE_CASE__			,    default=SCREAMING_SNAKE_CASE__			,    help="""If the training should continue from a checkpoint folder."""			,    )
 parser.add_argument(
     """--with_tracking"""			,    action="""store_true"""			,    help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,    )
 parser.add_argument(
     """--output_dir"""			,    type=SCREAMING_SNAKE_CASE__			,    default="""."""			,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,    )
 parser.add_argument(
     """--project_dir"""			,    type=SCREAMING_SNAKE_CASE__			,    default="""logs"""			,    help="""Location on where to store experiment tracking logs` and relevent project information"""			,    )
 _snake_case      :					int					=   parser.parse_args()
 _snake_case      :					List[str]					=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
  main()





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""recursive_lucas_number accepts only integer arguments."""			)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
     n_th_number - 2			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""dynamic_lucas_number accepts only integer arguments."""			)
 _snake_case		,      _snake_case      :					Any					=   2, 1
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case		,      _snake_case      :					Union[str, Any]					=   b, a + b
 return a


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		int(input("""Enter the number of terms in lucas series:\n""").strip())
  print("""Using recursive function to calculate lucas series:""")
  print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
  print("""\nUsing dynamic function to calculate lucas series:""")
  print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))
