

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_     =       1_6
lowercase_     =       3_2
def 					a				(      A__     :       Optional[Any]				,       A__     :       int					)   ->				int:





      """simple docstring"""



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

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

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

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

      def tokenize_function(A__     :       Optional[int]					):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase     	=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     	=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     	=tokenized_datasets.rename_column('label'				,       'labels'					)

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

      def collate_fn(A__     :       int					):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            _lowercase     	=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     	=16
            elif accelerator.mixed_precision != "no":
                  _lowercase     	=8
            else:
                  _lowercase     	=None

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

      # Instantiate dataloaders.
      _lowercase     	=DataLoader(
          tokenized_datasets['train']				,       shuffle=__A				,       collate_fn=__A				,       batch_size=__A					)
      _lowercase     	=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)
      _lowercase     	=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     	=model.to(accelerator.device					)

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

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

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

            if "epoch" in training_difference:
                  _lowercase     	=int(training_difference.replace('epoch_'				,       ''					)					) + 1
                  _lowercase     	=None
            else:
                  _lowercase     	=int(training_difference.replace('step_'				,       ''					)					)
                  _lowercase     	=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:
                  _lowercase     	=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
                  _lowercase     	=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     	=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     	=model(**__A					)
                  _lowercase     	=outputs.loss
                  _lowercase     	=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					):
                        _lowercase     	=F'''step_{overall_step}'''
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    _lowercase     	=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():
                        _lowercase     	=model(**__A					)
                  _lowercase     	=outputs.logits.argmax(dim=-1					)
                  _lowercase	,	_lowercase     	=accelerator.gather_for_metrics((predictions, batch['labels'])					)
                  metric.add_batch(
                      predictions=__A				,       references=__A				,       )

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

      if args.with_tracking:
            accelerator.end_training()
def 					a				(      )   ->				List[str]:





      """simple docstring"""



      _lowercase     	=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'				,       )
      _lowercase     	=parser.parse_args()
      _lowercase     	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(__A				,       __A					)


if __name__ == "__main__":
  main()





import unittest

from cer import CER


lowercase_     =       CER()





class 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=['White House']
        _lowercase     	=['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.1818181818 ) < 1e-6 )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=['were wolf']
        _lowercase     	=['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.1111111 ) < 1e-6 )

        _lowercase     	=['werewolf']
        _lowercase     	=['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.25 ) < 1e-6 )

        # consecutive whitespaces case 1
        _lowercase     	=['were wolf']
        _lowercase     	=['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.0 ) < 1e-6 )

        # consecutive whitespaces case 2
        _lowercase     	=['were   wolf']
        _lowercase     	=['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.0 ) < 1e-6 )




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=['werewolf']
        _lowercase     	=['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.125 ) < 1e-6 )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=['werewolf']
        _lowercase     	=['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.125 ) < 1e-6 )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=['werewolf']
        _lowercase     	=['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.125 ) < 1e-6 )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=['werewolf']
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       0.0 )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=['werewolf', 'I am your father']
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertEqual(lowerCAmelCase							,       0.0 )

        _lowercase     	=['werewolf', 'I am your father', 'doge']
        _lowercase     	=['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.03571428 ) < 1e-6 )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=['My hovercraft', 'is full of eels']
        _lowercase     	=['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase							,       concatenate_texts=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.071428 ) < 1e-6 )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=['我能吞下玻璃而不伤身体']
        _lowercase     	=[' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.4545454545 ) < 1e-6 )

        _lowercase     	=['我能吞下玻璃', '而不伤身体']
        _lowercase     	=['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertTrue(abs(char_error_rate - 0.454545454545 ) < 1e-6 )

        _lowercase     	=['我能吞下玻璃而不伤身体']
        _lowercase     	=cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )
        self.assertFalse(lowerCAmelCase							,       0.0 )




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=['']
        _lowercase     	=['Hypothesis']
        with self.assertRaises(lowerCAmelCase ):
              cer.compute(predictions=lowerCAmelCase							,       references=lowerCAmelCase )


if __name__ == "__main__":
  unittest.main()


