




'''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
#
########################################################################

A					       =32





def 		snake_case_				(_a      :				Accelerator       ,  _a      :				int = 1_6	):
    UpperCAmelCase     			=						AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    UpperCAmelCase     			=						load_dataset('''glue'''       ,  '''mrpc'''	)

    def tokenize_function(_a      :				Union[str, Any]	):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase     			=						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():
        UpperCAmelCase     			=						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
    UpperCAmelCase     			=						tokenized_datasets.rename_column('''label'''       ,  '''labels'''	)

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

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

    # Instantiate dataloaders.
    UpperCAmelCase     			=						DataLoader(
        tokenized_datasets['''train''']       ,  shuffle=_a       ,  collate_fn=_a       ,  batch_size=_a	)
    UpperCAmelCase     			=						DataLoader(
        tokenized_datasets['''validation''']       ,  shuffle=_a       ,  collate_fn=_a       ,  batch_size=_a	)

    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

   A					       =mocked_dataloaders  # noqa: F811





def 		snake_case_				(_a      :				Union[str, Any]       ,  _a      :				Optional[int]	):
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,  _a	) == "1":
        UpperCAmelCase     			=						2
    # Initialize accelerator
    UpperCAmelCase     			=						Accelerator(cpu=args.cpu       ,  mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCAmelCase     			=						config['''lr''']
    UpperCAmelCase     			=						int(config['''num_epochs''']	)
    UpperCAmelCase     			=						int(config['''seed''']	)
    UpperCAmelCase     			=						int(config['''batch_size''']	)

    UpperCAmelCase     			=						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(_a	)	)
    def inner_training_loop(_a      :				str	):
        # 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
        UpperCAmelCase     			=						observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCAmelCase     			=						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(_a	)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        UpperCAmelCase     			=						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).
        UpperCAmelCase     			=						model.to(accelerator.device	)

        # Instantiate optimizer
        UpperCAmelCase     			=						AdamW(params=model.parameters()       ,  lr=_a	)
        UpperCAmelCase			,	UpperCAmelCase     			=						get_dataloaders(_a       ,  _a	)

        # Instantiate scheduler
        UpperCAmelCase     			=						get_linear_schedule_with_warmup(
            optimizer=_a       ,  num_warmup_steps=1_0_0       ,  num_training_steps=(len(_a	) * 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.
        UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						accelerator.prepare(
            _a       ,  _a       ,  _a       ,  _a       ,  _a	)

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

            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():
                    UpperCAmelCase     			=						model(**_a	)
                UpperCAmelCase     			=						outputs.logits.argmax(dim=-1	)
                UpperCAmelCase			,	UpperCAmelCase     			=						accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                metric.add_batch(
                    predictions=_a       ,  references=_a       ,  )

            UpperCAmelCase     			=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"epoch {epoch}:"       ,  _a	)

    # 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 		snake_case_				():
    UpperCAmelCase     			=						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.'''	)
    UpperCAmelCase     			=						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
    UpperCAmelCase     			=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
    training_function(_a       ,  _a	)


if __name__ == "__main__":
   main()










'''simple docstring'''







from ..utils import DummyObject, requires_backends


class      _a    (      metaclass=__a					):
       __a							:	str  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Any  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Optional[int]  ,							*lowercase		:      List[str]  ,							**lowercase		:      str							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      List[Any]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)






class      _a    (      metaclass=__a					):
       __a							:	List[Any]  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Tuple  ,							**lowercase		:      List[str]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Any  ,							*lowercase		:      Any  ,							**lowercase		:      Tuple							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)




