


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


lowerCamelCase						 =	16
lowerCamelCase						 =	32




def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				int					):
       '''simple docstring'''





       if args.with_tracking:
              _lowerCamelCase	:		List[str]          =Accelerator(
                  cpu=args.cpu		,      mixed_precision=args.mixed_precision		,      log_with='all'		,      project_dir=args.project_dir					)
       else:
              _lowerCamelCase	:		int          =Accelerator(cpu=args.cpu		,      mixed_precision=args.mixed_precision					)

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

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

       _lowerCamelCase	:		Any          =AutoTokenizer.from_pretrained('bert-base-cased'					)
       _lowerCamelCase	:		Optional[int]          =load_dataset('glue'		,      'mrpc'					)
       _lowerCamelCase	:		Dict          =evaluate.load('glue'		,      'mrpc'					)

       def tokenize_function(SCREAMING_SNAKE_CASE__ :				List[str]					):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCamelCase	:		Dict          =tokenizer(examples['sentence1']		,      examples['sentence2']		,      truncation=SCREAMING_SNAKE_CASE__		,      max_length=SCREAMING_SNAKE_CASE__					)
              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():
              _lowerCamelCase	:		Optional[Any]          =datasets.map(
                  SCREAMING_SNAKE_CASE__		,      batched=SCREAMING_SNAKE_CASE__		,      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
       _lowerCamelCase	:		Union[str, Any]          =tokenized_datasets.rename_column('label'		,      'labels'					)

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

       def collate_fn(SCREAMING_SNAKE_CASE__ :				Any					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _lowerCamelCase	:		Dict          =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":
                     _lowerCamelCase	:		Union[str, Any]          =16
              elif accelerator.mixed_precision != "no":
                     _lowerCamelCase	:		List[str]          =8
              else:
                     _lowerCamelCase	:		List[Any]          =None

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

       # Instantiate dataloaders.
       _lowerCamelCase	:		List[str]          =DataLoader(
           tokenized_datasets['train']		,      shuffle=SCREAMING_SNAKE_CASE__		,      collate_fn=SCREAMING_SNAKE_CASE__		,      batch_size=SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Tuple          =DataLoader(
           tokenized_datasets['validation']		,      shuffle=SCREAMING_SNAKE_CASE__		,      collate_fn=SCREAMING_SNAKE_CASE__		,      batch_size=SCREAMING_SNAKE_CASE__					)

       set_seed(SCREAMING_SNAKE_CASE__					)

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

       # 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).
       _lowerCamelCase	:		Union[str, Any]          =model.to(accelerator.device					)

       # Instantiate optimizer
       _lowerCamelCase	:		Optional[Any]          =AdamW(params=model.parameters()		,      lr=SCREAMING_SNAKE_CASE__					)

       # Instantiate scheduler
       _lowerCamelCase	:		Optional[int]          =get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE__		,      num_warmup_steps=100		,      num_training_steps=(len(SCREAMING_SNAKE_CASE__					) * 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.
       _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		Dict          =accelerator.prepare(
           SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

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

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

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

                     overall_step += 1

                     if isinstance(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					):
                            _lowerCamelCase	:		Optional[int]          =F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          _lowerCamelCase	:		Union[str, Any]          =os.path.join(args.output_dir		,      SCREAMING_SNAKE_CASE__					)
                                   accelerator.save_state(SCREAMING_SNAKE_CASE__					)

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

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

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

       if args.with_tracking:
              accelerator.end_training()




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =argparse.ArgumentParser(description='Simple example of training script.'					)
       parser.add_argument(
           '--mixed_precision'		,      type=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__		,      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=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__		,      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=SCREAMING_SNAKE_CASE__		,      default=SCREAMING_SNAKE_CASE__		,      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=SCREAMING_SNAKE_CASE__		,      default='.'		,      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,      )
       parser.add_argument(
           '--project_dir'		,      type=SCREAMING_SNAKE_CASE__		,      default='logs'		,      help='Location on where to store experiment tracking logs` and relevent project information'		,      )
       _lowerCamelCase	:		Optional[int]          =parser.parse_args()
       _lowerCamelCase	:		Dict          ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)


if __name__ == "__main__":
 main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase						 =	{'configuration_visual_bert': ['VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VisualBertConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase						 =	[
     'VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'VisualBertForMultipleChoice',
     'VisualBertForPreTraining',
     'VisualBertForQuestionAnswering',
     'VisualBertForRegionToPhraseAlignment',
     'VisualBertForVisualReasoning',
     'VisualBertLayer',
     'VisualBertModel',
     'VisualBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_visual_bert import (
      VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      VisualBertForMultipleChoice,
      VisualBertForPreTraining,
      VisualBertForQuestionAnswering,
      VisualBertForRegionToPhraseAlignment,
      VisualBertForVisualReasoning,
      VisualBertLayer,
      VisualBertModel,
      VisualBertPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase						 =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


