




'''simple docstring'''


import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

UpperCamelCase     =				32
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase = 1_6  ) ->   Tuple:
     A:		Any        =		AutoTokenizer.from_pretrained('''bert-base-cased'''  )
     A:		List[str]        =		load_dataset('''glue'''		,							'''mrpc'''  )

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

     def collate_fn(__lowercase  ):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               A:		Optional[int]        =		1_6
          elif accelerator.mixed_precision != "no":
               A:		str        =		8
          else:
               A:		Tuple        =		None

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

     # Instantiate dataloaders.
     A:		str        =		DataLoader(
         tokenized_datasets['''train''']		,							shuffle=__lowercase		,							collate_fn=__lowercase		,							batch_size=__lowercase  )
     A:		int        =		DataLoader(
         tokenized_datasets['''validation''']		,							shuffle=__lowercase		,							collate_fn=__lowercase		,							batch_size=__lowercase  )

     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

     UpperCamelCase     =				mocked_dataloaders  # noqa: F811
def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase  ) ->   int:
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,							__lowercase  ) == "1":
          A:		Dict        =		2
     # Initialize accelerator
     A:		str        =		Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision  )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A:		Optional[int]        =		config['''lr''']
     A:		List[Any]        =		int(config['''num_epochs''']  )
     A:		Dict        =		int(config['''seed''']  )
     A:		List[str]        =		int(config['''batch_size''']  )

     A:		Any        =		evaluate.load('''glue'''		,							'''mrpc'''  )

     # New Code #
     # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
     # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
     # half each time. From this, we can calculate the number of gradient accumulation steps needed
     # and modify the Accelerator object as a result
     @find_executable_batch_size(starting_batch_size=int(__lowercase  )  )
     def inner_training_loop(__lowercase  ):
          # Since we need to modify the outside accelerator object, we need to bring it
          # to the local scope
          nonlocal accelerator

          # We can calculate the number of gradient accumulation steps based on the current
          # batch size vs the starting batch size
          A:		Union[str, Any]        =		observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          A:		int        =		num_gradient_accumulation_steps

          # Next we need to free all of the stored model references in the Accelerator each time
          accelerator.free_memory()

          # And set the seed so our results are reproducable each reset
          set_seed(__lowercase  )

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

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

          # Instantiate optimizer
          A:		Union[str, Any]        =		AdamW(params=model.parameters()		,							lr=__lowercase  )
          A    ,						A:		str        =		get_dataloaders(__lowercase		,							__lowercase  )

          # Instantiate scheduler
          A:		int        =		get_linear_schedule_with_warmup(
              optimizer=__lowercase		,							num_warmup_steps=1_0_0		,							num_training_steps=(len(__lowercase  ) * num_epochs)		,							)

          # 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    ,						A    ,						A    ,						A    ,						A:		str        =		accelerator.prepare(
              __lowercase		,							__lowercase		,							__lowercase		,							__lowercase		,							__lowercase  )

          # Now we train the model
          for epoch in range(__lowercase  ):
               model.train()
               for step, batch in enumerate(__lowercase  ):
                    # And perform gradient accumulation
                    with accelerator.accumulate(__lowercase  ):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device  )
                         A:		Tuple        =		model(**__lowercase  )
                         A:		Optional[int]        =		outputs.loss
                         accelerator.backward(__lowercase  )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()

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

               A:		Union[str, Any]        =		metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F"""epoch {epoch}:"""		,							__lowercase  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()
def 				SCREAMING_SNAKE_CASE(      ) ->   Tuple:
     A:		Any        =		argparse.ArgumentParser(description='''Simple example of training script.'''  )
     parser.add_argument(
         '''--mixed_precision'''		,							type=__lowercase		,							default=__lowercase		,							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:		int        =		parser.parse_args()
     # New Code #
     # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
     A:		List[str]        =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
     training_function(__lowercase		,							__lowercase  )


if __name__ == "__main__":
     main()












'''simple docstring'''


def 				SCREAMING_SNAKE_CASE(      __lowercase  ) ->   int:
     A:		str        =		str(__lowercase  ).strip()
     if not bin_string:
          raise ValueError('''Empty string was passed to the function'''  )
     A:		int        =		bin_string[0] == '''-'''
     if is_negative:
          A:		Optional[Any]        =		bin_string[1:]
     if not all(char in '''01''' for char in bin_string  ):
          raise ValueError('''Non-binary value was passed to the function'''  )
     A:		Any        =		0
     for char in bin_string:
          A:		Dict        =		2 * decimal_number + int(__lowercase  )
     return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
     from doctest import testmod

     testmod()






