



'''simple docstring'''





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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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__ :  Optional[Any]							      =					AutoTokenizer.from_pretrained("""bert-base-cased"""			)
    lowerCAmelCase__ :  Dict							      =					load_dataset("""glue"""							,    """mrpc"""			)

    def tokenize_function(UpperCamelCase			):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__ :  Union[str, 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__ :  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__ :  List[str]							      =					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__ :  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__ :  Dict							      =					16
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__ :  Optional[Any]							      =					8
        else:
            lowerCAmelCase__ :  Optional[Any]							      =					None

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

    # Instantiate dataloaders.
    lowerCAmelCase__ :  List[str]							      =					DataLoader(
        tokenized_datasets["""train"""]							,    shuffle=UpperCamelCase							,    collate_fn=UpperCamelCase							,    batch_size=UpperCamelCase			)
    lowerCAmelCase__ :  Union[str, 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__ :  Any							      =					2
    # Initialize accelerator
    lowerCAmelCase__ :  List[str]							      =					Accelerator(cpu=args.cpu							,    mixed_precision=args.mixed_precision			)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCAmelCase__ :  Optional[int]							      =					config["""lr"""]
    lowerCAmelCase__ :  Dict							      =					int(config["""num_epochs"""]			)
    lowerCAmelCase__ :  Optional[int]							      =					int(config["""seed"""]			)
    lowerCAmelCase__ :  Optional[Any]							      =					int(config["""batch_size"""]			)

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

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

    set_seed(UpperCamelCase			)
    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					get_dataloaders(UpperCamelCase							,    UpperCamelCase			)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCAmelCase__ :  Tuple							      =					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__ :  Optional[int]							      =					model.to(accelerator.device			)

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

    # Instantiate scheduler
    lowerCAmelCase__ :  Tuple							      =					get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase							,    num_warmup_steps=100							,    num_training_steps=(len(UpperCamelCase			) * 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__ :  Optional[int]							      =					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__ :  List[str]							      =					model(**UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					outputs.loss
            lowerCAmelCase__ :  Union[str, Any]							      =					loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        lowerCAmelCase__ :  str							      =					0
        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__ :  List[str]							      =					model(**UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					outputs.logits.argmax(dim=-1			)
            lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					accelerator.gather((predictions, batch["""labels"""])			)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(UpperCamelCase			) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    lowerCAmelCase__ :  int							      =					predictions[: len(eval_dataloader.dataset			) - samples_seen]
                    lowerCAmelCase__ :  int							      =					references[: len(eval_dataloader.dataset			) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # 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			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[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__ :  Optional[int]							      =					parser.parse_args()
    lowerCAmelCase__ :  Tuple							      =					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(UpperCamelCase							,    UpperCamelCase			)


if __name__ == "__main__":
 main()



'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

