






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,
# specifically showcasing the checkpointing capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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 			=							16
a 			=							32
def    a__    (	A		,					A = 16       ):
   lowercase__    :						int       =			AutoTokenizer.from_pretrained("""bert-base-cased"""       )
   lowercase__    :						Union[str, Any]       =			load_dataset("""glue"""		,					"""mrpc"""       )

   def tokenize_function(A       ):
      # max_length=None => use the model max length (it's actually the default)
      lowercase__    :						Optional[int]       =			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():
      lowercase__    :						List[str]       =			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
   lowercase__    :						List[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.
      lowercase__    :						Optional[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":
         lowercase__    :						Dict       =			16
      elif accelerator.mixed_precision != "no":
         lowercase__    :						Optional[Any]       =			8
      else:
         lowercase__    :						int       =			None

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

   # Instantiate dataloaders.
   lowercase__    :						str       =			DataLoader(
       tokenized_datasets["""train"""]		,					shuffle=A_		,					collate_fn=A_		,					batch_size=A_       )
   lowercase__    :						Dict       =			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

 a 			=							mocked_dataloaders  # noqa: F811
def    a__    (	A		,					A       ):

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

   # New Code #
   # Parse out whether we are saving every epoch or after a certain number of batches
   if hasattr(args.checkpointing_steps		,					"""isdigit"""       ):
      if args.checkpointing_steps == "epoch":
         lowercase__    :						int       =			args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         lowercase__    :						Any       =			int(args.checkpointing_steps       )
      else:
         raise ValueError(
             F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
   else:
      lowercase__    :						str       =			None

   set_seed(A_       )

   lowercase__    :						Union[str, Any]       =			get_dataloaders(A_		,					A_       )
   lowercase__    :						Dict       =			evaluate.load("""glue"""		,					"""mrpc"""       )

   # If the batch size is too big we use gradient accumulation
   lowercase__    :						Dict       =			1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowercase__    :						Any       =			batch_size // MAX_GPU_BATCH_SIZE
      lowercase__    :						int       =			MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase__    :						List[Any]       =			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).
   lowercase__    :						Any       =			model.to(accelerator.device       )

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

   # Instantiate scheduler
   lowercase__    :						str       =			get_linear_schedule_with_warmup(
       optimizer=A_		,					num_warmup_steps=100		,					num_training_steps=(len(A_       ) * 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.
   lowercase__    :						List[Any]       =			accelerator.prepare(
       A_		,					A_		,					A_		,					A_		,					A_       )

   # New Code #
   # We need to keep track of how many total steps we have iterated over
   lowercase__    :						List[str]       =			0
   # We also need to keep track of the stating epoch so files are named properly
   lowercase__    :						Optional[int]       =			0

   # We need to load the checkpoint back in before training here with `load_state`
   # The total number of epochs is adjusted based on where the state is being loaded from,
   # as we assume continuation of the same training script
   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       )
         lowercase__    :						Tuple       =			os.path.basename(args.resume_from_checkpoint       )
      else:
         # Get the most recent checkpoint
         lowercase__    :						Optional[Any]       =			[f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
         dirs.sort(key=os.path.getctime       )
         lowercase__    :						Any       =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      lowercase__    :						Dict       =			os.path.splitext(A_       )[0]

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

      # Now we train the model
   for epoch in range(A_		,					A_       ):
      model.train()
      # New Code #
      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
         lowercase__    :						Any       =			accelerator.skip_first_batches(A_		,					A_       )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowercase__    :						Union[str, Any]       =			train_dataloader
      for step, batch in enumerate(A_       ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device       )
         lowercase__    :						List[Any]       =			model(**A_       )
         lowercase__    :						Any       =			outputs.loss
         lowercase__    :						List[str]       =			loss / gradient_accumulation_steps
         accelerator.backward(A_       )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
         # New Code #
         overall_step += 1

         # New Code #
         # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
         # These are saved to folders named `step_{overall_step}`
         # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
         # If mixed precision was used, will also save a "scalar.bin" file
         if isinstance(A_		,					A_       ):
            lowercase__    :						Union[str, Any]       =			F'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  lowercase__    :						Tuple       =			os.path.join(args.output_dir		,					A_       )
               accelerator.save_state(A_       )

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

      lowercase__    :						List[str]       =			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''		,					A_       )

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `epoch_{epoch}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if checkpointing_steps == "epoch":
         lowercase__    :						Tuple       =			F'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowercase__    :						str       =			os.path.join(args.output_dir		,					A_       )
         accelerator.save_state(A_       )
def    a__    (	):
   lowercase__    :						Optional[int]       =			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."""       )
   parser.add_argument(
       """--checkpointing_steps"""		,					type=A_		,					default=A_		,					help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."""		,					)
   parser.add_argument(
       """--output_dir"""		,					type=A_		,					default="""."""		,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,					)
   parser.add_argument(
       """--resume_from_checkpoint"""		,					type=A_		,					default=A_		,					help="""If the training should continue from a checkpoint folder."""		,					)
   lowercase__    :						Optional[int]       =			parser.parse_args()
   lowercase__    :						Union[str, Any]       =			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(A_		,					A_       )


if __name__ == "__main__":
 main()










"""simple docstring"""


from doctest import testmod
from math import sqrt




def      snake_case    (A_ :int ):







  '''simple docstring'''

  a	:     list[int]							=  []
  if num < 1:
    return facs
  facs.append(1 )
  if num == 1:
    return facs
  facs.append(A_ )
  for i in range(2      ,     int(sqrt(A_ ) ) + 1 ):
    if num % i == 0:  # If i is a factor of num
      facs.append(A_ )
      a	:     Optional[Any]							=  num // i  # num//i is the other factor of num
      if d != i:  # If d and i are distinct
        facs.append(A_ )  # we have found another factor
  facs.sort()
  return facs


if __name__ == "__main__":
   testmod(name='factors_of_a_number', verbose=True)




