



'''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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


_lowerCAmelCase      						=	16
_lowerCAmelCase      						=	32
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase = 16			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					AutoTokenizer.from_pretrained("""bert-base-cased"""			)
    lowerCAmelCase__ :  Optional[Any]							      =					load_dataset("""glue"""							,    """mrpc"""			)

    def tokenize_function(UpperCamelCase			):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__ :  Any							      =					tokenizer(examples["""sentence1"""]							,    examples["""sentence2"""]							,    truncation=UpperCamelCase							,    max_length=UpperCamelCase			)
        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__ :  Union[str, Any]							      =					datasets.map(
            UpperCamelCase							,    batched=UpperCamelCase							,    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__ :  int							      =					tokenized_datasets.rename_column("""label"""							,    """labels"""			)

    def collate_fn(UpperCamelCase			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase__ :  Optional[int]							      =					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__ :  Optional[Any]							      =					16
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__ :  Dict							      =					8
        else:
            lowerCAmelCase__ :  Optional[int]							      =					None

        return tokenizer.pad(
            UpperCamelCase							,    padding="""longest"""							,    max_length=UpperCamelCase							,    pad_to_multiple_of=UpperCamelCase							,    return_tensors="""pt"""							,    )

    # Instantiate dataloaders.
    lowerCAmelCase__ :  Optional[Any]							      =					DataLoader(
        tokenized_datasets["""train"""]							,    shuffle=UpperCamelCase							,    collate_fn=UpperCamelCase							,    batch_size=UpperCamelCase			)
    lowerCAmelCase__ :  List[Any]							      =					DataLoader(
        tokenized_datasets["""validation"""]							,    shuffle=UpperCamelCase							,    collate_fn=UpperCamelCase							,    batch_size=UpperCamelCase			)

    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

 _lowerCAmelCase      						=	mocked_dataloaders  # noqa: F811
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,    UpperCamelCase			) == "1":
        lowerCAmelCase__ :  Tuple							      =					2
    # Initialize accelerator
    lowerCAmelCase__ :  Any							      =					Accelerator(cpu=args.cpu							,    mixed_precision=args.mixed_precision			)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCAmelCase__ :  Dict							      =					config["""lr"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					int(config["""num_epochs"""]			)
    lowerCAmelCase__ :  List[Any]							      =					int(config["""seed"""]			)
    lowerCAmelCase__ :  Union[str, Any]							      =					int(config["""batch_size"""]			)

    lowerCAmelCase__ :  Optional[Any]							      =					evaluate.load("""glue"""							,    """mrpc"""			)

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=UpperCamelCase			)
    def inner_training_loop(UpperCamelCase			):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(UpperCamelCase			)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        lowerCAmelCase__ :  Union[str, Any]							      =					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,    return_dict=UpperCamelCase			)

        # 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__ :  int							      =					model.to(accelerator.device			)

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

        # Instantiate scheduler
        lowerCAmelCase__ :  Dict							      =					get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase							,    num_warmup_steps=100							,    num_training_steps=(len(UpperCamelCase			) * 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.
        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					accelerator.prepare(
            UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)

        # Now we train the model
        for epoch in range(UpperCamelCase			):
            model.train()
            for step, batch in enumerate(UpperCamelCase			):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device			)
                lowerCAmelCase__ :  Any							      =					model(**UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					outputs.loss
                accelerator.backward(UpperCamelCase			)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

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

    # 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  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					argparse.ArgumentParser(description="""Simple example of training script."""			)
    parser.add_argument(
        """--mixed_precision"""							,    type=UpperCamelCase							,    default=UpperCamelCase							,    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."""			)
    lowerCAmelCase__ :  Union[str, Any]							      =					parser.parse_args()
    lowerCAmelCase__ :  Any							      =					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(UpperCamelCase							,    UpperCamelCase			)


if __name__ == "__main__":
 main()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	UpperCAmelCase_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
