





import argparse

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)
#
# 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__:      Dict									=		16
a__:      Optional[Any]									=		32


def 	UpperCamelCase__(       UpperCamelCase__			:       Accelerator ,     UpperCamelCase__			:       int = 16  )->List[Any]:
       A__											=						AutoTokenizer.from_pretrained('''bert-base-cased'''  )
       A__											=						load_dataset('''glue''' ,     '''mrpc'''  )

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

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

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

       # Instantiate dataloaders.
       A__											=						DataLoader(
           tokenized_datasets['''train'''] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case ,     drop_last=__snake_case  )
       A__											=						DataLoader(
           tokenized_datasets['''validation'''] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case ,     drop_last=(accelerator.mixed_precision == '''fp8''') ,     )

       return train_dataloader, eval_dataloader


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Dict  )->List[Any]:
       # Initialize accelerator
       A__											=						Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision  )
       # 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''']  )

       A__											=						evaluate.load('''glue''' ,     '''mrpc'''  )

       # 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

       set_seed(__snake_case  )
       A__											=						get_dataloaders(__snake_case ,     __snake_case  )
       # 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=__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).
       A__											=						model.to(accelerator.device  )
       # Instantiate optimizer
       A__											=						AdamW(params=model.parameters() ,     lr=__snake_case  )

       # Instantiate scheduler
       A__											=						get_linear_schedule_with_warmup(
           optimizer=__snake_case ,     num_warmup_steps=1_00 ,     num_training_steps=(len(__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.

       A__											=						accelerator.prepare(
           __snake_case ,     __snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )

       # Now we train the model
       for epoch in range(__snake_case  ):
              model.train()
              for step, batch in enumerate(__snake_case  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     A__											=						model(**__snake_case  )
                     A__											=						outputs.loss
                     A__											=						loss / gradient_accumulation_steps
                     accelerator.backward(__snake_case  )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              A__											=						metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:" ,     __snake_case  )


def 	UpperCamelCase__(       )->int:
       A__											=						argparse.ArgumentParser(description='''Simple example of training script.'''  )
       parser.add_argument(
           '''--mixed_precision''' ,     type=__snake_case ,     default=__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.'''  )
       A__											=						parser.parse_args()
       A__											=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(__snake_case ,     __snake_case  )


if __name__ == "__main__":
       main()












'''simple docstring'''




lowercase__    :      Dict						=    {
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}