






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_	:	Optional[int]				=				16
A_	:	Tuple				=				32

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Union[str, Any]:
  # Initialize accelerator
  if args.with_tracking:
    UpperCamelCase_:      Tuple					   =       Accelerator(
        cpu=args.cpu			,					mixed_precision=args.mixed_precision			,					log_with='all'			,					project_dir=args.project_dir						)
  else:
    UpperCamelCase_:      List[str]					   =       Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision						)

  if hasattr(args.checkpointing_steps			,					'isdigit'						):
    if args.checkpointing_steps == "epoch":
      UpperCamelCase_:      List[str]					   =       args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCamelCase_:      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:
    UpperCamelCase_:      Tuple					   =       None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCamelCase_:      Dict					   =       config['lr']
  UpperCamelCase_:      List[str]					   =       int(config['num_epochs']						)
  UpperCamelCase_:      str					   =       int(config['seed']						)
  UpperCamelCase_:      Any					   =       int(config['batch_size']						)

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

  UpperCamelCase_:      Optional[Any]					   =       AutoTokenizer.from_pretrained('bert-base-cased'						)
  UpperCamelCase_:      List[str]					   =       load_dataset('glue'			,					'mrpc'						)
  UpperCamelCase_:      Optional[Any]					   =       evaluate.load('glue'			,					'mrpc'						)

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

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

  def collate_fn(UpperCAmelCase__						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    UpperCamelCase_:      Union[str, Any]					   =       1_2_8 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_:      str					   =       1_6
    elif accelerator.mixed_precision != "no":
      UpperCamelCase_:      Optional[int]					   =       8
    else:
      UpperCamelCase_:      List[Any]					   =       None

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

  # Instantiate dataloaders.
  UpperCamelCase_:      Optional[Any]					   =       DataLoader(
      tokenized_datasets['train']			,					shuffle=UpperCAmelCase__			,					collate_fn=UpperCAmelCase__			,					batch_size=UpperCAmelCase__						)
  UpperCamelCase_:      Tuple					   =       DataLoader(
      tokenized_datasets['validation']			,					shuffle=UpperCAmelCase__			,					collate_fn=UpperCAmelCase__			,					batch_size=UpperCAmelCase__						)

  set_seed(UpperCAmelCase__						)

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

  # 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_:      Tuple					   =       model.to(accelerator.device						)

  # Instantiate optimizer
  UpperCamelCase_:      int					   =       AdamW(params=model.parameters()			,					lr=UpperCAmelCase__						)

  # Instantiate scheduler
  UpperCamelCase_:      Union[str, Any]					   =       get_linear_schedule_with_warmup(
      optimizer=UpperCAmelCase__			,					num_warmup_steps=1_0_0			,					num_training_steps=(len(UpperCAmelCase__						) * 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_:      List[Any]					   =       accelerator.prepare(
      UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

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

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

    # Now we train the model
  for epoch in range(UpperCAmelCase__			,					UpperCAmelCase__						):
    model.train()
    if args.with_tracking:
      UpperCamelCase_:      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
      UpperCamelCase_:      List[str]					   =       accelerator.skip_first_batches(UpperCAmelCase__			,					UpperCAmelCase__						)
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      UpperCamelCase_:      List[str]					   =       train_dataloader
    for step, batch in enumerate(UpperCAmelCase__						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      UpperCamelCase_:      Tuple					   =       model(**UpperCAmelCase__						)
      UpperCamelCase_:      Optional[Any]					   =       outputs.loss
      UpperCamelCase_:      List[str]					   =       loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(UpperCAmelCase__						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

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

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

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

    if checkpointing_steps == "epoch":
      UpperCamelCase_:      Any					   =       F'''epoch_{epoch}'''
      if args.output_dir is not None:
        UpperCamelCase_:      Optional[int]					   =       os.path.join(args.output_dir			,					UpperCAmelCase__						)
      accelerator.save_state(UpperCAmelCase__						)

  if args.with_tracking:
    accelerator.end_training()

def 						snake_case      () ->       List[str]:
  UpperCamelCase_:      Dict					   =       argparse.ArgumentParser(description='Simple example of training script.'						)
  parser.add_argument(
      '--mixed_precision'			,					type=UpperCAmelCase__			,					default=UpperCAmelCase__			,					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=UpperCAmelCase__			,					default=UpperCAmelCase__			,					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=UpperCAmelCase__			,					default=UpperCAmelCase__			,					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=UpperCAmelCase__			,					default='.'			,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,					)
  parser.add_argument(
      '--project_dir'			,					type=UpperCAmelCase__			,					default='logs'			,					help='Location on where to store experiment tracking logs` and relevent project information'			,					)
  UpperCamelCase_:      List[Any]					   =       parser.parse_args()
  UpperCamelCase_:      List[str]					   =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
  training_function(UpperCAmelCase__			,					UpperCAmelCase__						)


if __name__ == "__main__":
   main()







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_reformer import ReformerTokenizer
else:
   A_	:	Union[str, Any]				=				None


A_	:	List[Any]				=				logging.get_logger(__name__)


A_	:	List[str]				=				'▁'

A_	:	int				=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:	Optional[Any]				=				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None							):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'							)

         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_:      str					   =       os.path.join(
             _lowerCamelCase						,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
           copyfile(self.vocab_file						,    _lowerCamelCase							)

         return (out_vocab_file,)