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

__snake_case			      =    16
__snake_case			      =    32





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase = 16						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : Any      =AutoTokenizer.from_pretrained('''bert-base-cased'''						)
      UpperCAmelCase    : Optional[Any]      =load_dataset('''glue'''		,					'''mrpc'''						)

      def tokenize_function(__lowerCAmelCase						):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase    : int      =tokenizer(examples['''sentence1''']		,					examples['''sentence2''']		,					truncation=__lowerCAmelCase		,					max_length=__lowerCAmelCase						)
            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():
            UpperCAmelCase    : Dict      =datasets.map(
                __lowerCAmelCase		,					batched=__lowerCAmelCase		,					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
      UpperCAmelCase    : Dict      =tokenized_datasets.rename_column('''label'''		,					'''labels'''						)

      def collate_fn(__lowerCAmelCase						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase    : Optional[int]      =1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  UpperCAmelCase    : Tuple      =16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase    : Optional[int]      =8
            else:
                  UpperCAmelCase    : int      =None

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

      # Instantiate dataloaders.
      UpperCAmelCase    : Any      =DataLoader(
          tokenized_datasets['''train''']		,					shuffle=__lowerCAmelCase		,					collate_fn=__lowerCAmelCase		,					batch_size=__lowerCAmelCase						)
      UpperCAmelCase    : Dict      =DataLoader(
          tokenized_datasets['''validation''']		,					shuffle=__lowerCAmelCase		,					collate_fn=__lowerCAmelCase		,					batch_size=__lowerCAmelCase						)

      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

     __snake_case			      =    mocked_dataloaders  # noqa: F811





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  Union[str, Any]:






      '''simple docstring'''





      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,					__lowerCAmelCase						) == "1":
            UpperCAmelCase    : Union[str, Any]      =2
      # Initialize accelerator
      UpperCAmelCase    : str      =Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase    : Tuple      =config['''lr''']
      UpperCAmelCase    : Union[str, Any]      =int(config['''num_epochs''']						)
      UpperCAmelCase    : str      =int(config['''seed''']						)
      UpperCAmelCase    : List[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":
                  UpperCAmelCase    : int      =args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  UpperCAmelCase    : Optional[Any]      =int(args.checkpointing_steps						)
            else:
                  raise ValueError(
                      f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
      else:
            UpperCAmelCase    : Union[str, Any]      =None

      set_seed(__lowerCAmelCase						)

      UpperCAmelCase  ,						UpperCAmelCase    : Optional[int]      =get_dataloaders(__lowerCAmelCase		,					__lowerCAmelCase						)
      UpperCAmelCase    : Any      =evaluate.load('''glue'''		,					'''mrpc'''						)

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

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase    : Dict      =AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,					return_dict=__lowerCAmelCase						)

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

      # Instantiate optimizer
      UpperCAmelCase    : int      =AdamW(params=model.parameters()		,					lr=__lowerCAmelCase						)

      # Instantiate scheduler
      UpperCAmelCase    : List[Any]      =get_linear_schedule_with_warmup(
          optimizer=__lowerCAmelCase		,					num_warmup_steps=1_00		,					num_training_steps=(len(__lowerCAmelCase						) * 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.
      UpperCAmelCase  ,						UpperCAmelCase  ,						UpperCAmelCase  ,						UpperCAmelCase  ,						UpperCAmelCase    : int      =accelerator.prepare(
          __lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)

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

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

    # Now we train the model
      for epoch in range(__lowerCAmelCase		,					__lowerCAmelCase						):
            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
                  UpperCAmelCase    : str      =accelerator.skip_first_batches(__lowerCAmelCase		,					__lowerCAmelCase						)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  UpperCAmelCase    : List[Any]      =train_dataloader
            for step, batch in enumerate(__lowerCAmelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  UpperCAmelCase    : List[Any]      =model(**__lowerCAmelCase						)
                  UpperCAmelCase    : List[Any]      =outputs.loss
                  UpperCAmelCase    : Union[str, Any]      =loss / gradient_accumulation_steps
                  accelerator.backward(__lowerCAmelCase						)
                  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(__lowerCAmelCase		,					__lowerCAmelCase						):
                        UpperCAmelCase    : Optional[int]      =f'''step_{overall_step}'''
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    UpperCAmelCase    : Tuple      =os.path.join(args.output_dir		,					__lowerCAmelCase						)
                              accelerator.save_state(__lowerCAmelCase						)

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

            UpperCAmelCase    : str      =metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'''epoch {epoch}:'''		,					__lowerCAmelCase						)

            # 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":
                  UpperCAmelCase    : List[str]      =f'''epoch_{epoch}'''
                  if args.output_dir is not None:
                        UpperCAmelCase    : Optional[int]      =os.path.join(args.output_dir		,					__lowerCAmelCase						)
                  accelerator.save_state(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  int:






      '''simple docstring'''





      UpperCAmelCase    : Tuple      =argparse.ArgumentParser(description='''Simple example of training script.'''						)
      parser.add_argument(
          '''--mixed_precision'''		,					type=__lowerCAmelCase		,					default=__lowerCAmelCase		,					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=__lowerCAmelCase		,					default=__lowerCAmelCase		,					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=__lowerCAmelCase		,					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=__lowerCAmelCase		,					default=__lowerCAmelCase		,					help='''If the training should continue from a checkpoint folder.'''		,					)
      UpperCAmelCase    : Dict      =parser.parse_args()
      UpperCAmelCase    : Optional[int]      ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(__lowerCAmelCase		,					__lowerCAmelCase						)


if __name__ == "__main__":
     main()

from collections import defaultdict





def   lowerCAmelCase_ (				__lowerCAmelCase = 5						)->  int:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      =defaultdict(__lowerCAmelCase						)
      UpperCAmelCase    : Tuple      =0

      while True:
            UpperCAmelCase    : Any      =get_digits(__lowerCAmelCase						)
            freqs[digits].append(__lowerCAmelCase						)

            if len(freqs[digits]						) == max_base:
                  UpperCAmelCase    : Optional[int]      =freqs[digits][0] ** 3
                  return base

            num += 1





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  str:






      '''simple docstring'''





      return "".join(sorted(str(num**3						)						)						)


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