



'''simple docstring'''





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


lowercase_			=  16
lowercase_			=  32




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     if args.with_tracking:
          _a     =  Accelerator(
              cpu=args.cpu  ,     mixed_precision=args.mixed_precision  ,     log_with='''all'''  ,     project_dir=args.project_dir)
     else:
          _a     =  Accelerator(cpu=args.cpu  ,     mixed_precision=args.mixed_precision)

     if hasattr(args.checkpointing_steps  ,     '''isdigit'''):
          if args.checkpointing_steps == "epoch":
               _a     =  args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _a     =  int(args.checkpointing_steps)
          else:
               raise ValueError(
                   F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''')
     else:
          _a     =  None
     # 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'''])

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

     _a     =  AutoTokenizer.from_pretrained('''bert-base-cased''')
     _a     =  load_dataset('''glue'''  ,     '''mrpc''')
     _a     =  evaluate.load('''glue'''  ,     '''mrpc''')

     def tokenize_function(__A):
          # max_length=None => use the model max length (it's actually the default)
          _a     =  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():
          _a     =  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
     _a     =  tokenized_datasets.rename_column('''label'''  ,     '''labels''')

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

     def collate_fn(__A):
          # 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(
              __A  ,     padding='''longest'''  ,     max_length=__A  ,     pad_to_multiple_of=__A  ,     return_tensors='''pt'''  ,     )

     # Instantiate dataloaders.
     _a     =  DataLoader(
         tokenized_datasets['''train''']  ,     shuffle=__A  ,     collate_fn=__A  ,     batch_size=__A)
     _a     =  DataLoader(
         tokenized_datasets['''validation''']  ,     shuffle=__A  ,     collate_fn=__A  ,     batch_size=__A)

     set_seed(__A)

     # 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=__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).
     _a     =  model.to(accelerator.device)

     # Instantiate optimizer
     _a     =  AdamW(params=model.parameters()  ,     lr=__A)

     # Instantiate scheduler
     _a     =  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.
     _a  ,   _a  ,   _a  ,   _a  ,   _a     =  accelerator.prepare(
         __A  ,     __A  ,     __A  ,     __A  ,     __A)

     # We need to keep track of how many total steps we have iterated over
     _a     =  0
     # We also need to keep track of the stating epoch so files are named properly
     _a     =  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)
               _a     =  os.path.basename(args.resume_from_checkpoint)
          else:
               # Get the most recent checkpoint
               _a     =  [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
               dirs.sort(key=os.path.getctime)
               _a     =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _a     =  os.path.splitext(__A)[0]

          if "epoch" in training_difference:
               _a     =  int(training_difference.replace('''epoch_'''  ,     '''''')) + 1
               _a     =  None
          else:
               _a     =  int(training_difference.replace('''step_'''  ,     ''''''))
               _a     =  resume_step // len(__A)
               resume_step -= starting_epoch * len(__A)

    # Now we train the model
     for epoch in range(__A  ,     __A):
          model.train()
          if args.with_tracking:
               _a     =  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
               _a     =  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
               _a     =  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)
               _a     =  model(**__A)
               _a     =  outputs.loss
               _a     =  loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__A)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__A  ,     __A):
                    _a     =  F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              _a     =  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`.
               batch.to(accelerator.device)
               with torch.no_grad():
                    _a     =  model(**__A)
               _a     =  outputs.logits.argmax(dim=-1)
               _a  ,   _a     =  accelerator.gather_for_metrics((predictions, batch['''labels''']))
               metric.add_batch(
                   predictions=__A  ,     references=__A  ,     )

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

          if checkpointing_steps == "epoch":
               _a     =  F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    _a     =  os.path.join(args.output_dir  ,     __A)
               accelerator.save_state(__A)

     if args.with_tracking:
          accelerator.end_training()




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  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(
         '''--resume_from_checkpoint'''  ,     type=__A  ,     default=__A  ,     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=__A  ,     default='''.'''  ,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,     )
     parser.add_argument(
         '''--project_dir'''  ,     type=__A  ,     default='''logs'''  ,     help='''Location on where to store experiment tracking logs` and relevent project information'''  ,     )
     _a     =  parser.parse_args()
     _a     =  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__A  ,     __A)


if __name__ == "__main__":
  main()








'''simple docstring'''





from math import gcd




def lowerCAmelCase  (__A = 12_000):



     """simple docstring"""



     _a     =  0
     for d in range(max_d + 1):
          for n in range(d // 3 + 1  ,     (d + 1) // 2):
               if gcd(__A  ,     __A) == 1:
                    fractions_number += 1
     return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")



