
'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE	 =							16
_SCREAMING_SNAKE_CASE	 =							32


def 	__a(SCREAMING_SNAKE_CASE_  :					Accelerator		,       SCREAMING_SNAKE_CASE_  :					int = 16    ):



    '''simple docstring'''





    _lowerCAmelCase				=      AutoTokenizer.from_pretrained("bert-base-cased"    )
    _lowerCAmelCase				=      load_dataset("glue"		,       "mrpc"    )

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

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

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

    # Instantiate dataloaders.
    _lowerCAmelCase				=      DataLoader(
        tokenized_datasets["train"]		,       shuffle=SCREAMING_SNAKE_CASE_		,       collate_fn=SCREAMING_SNAKE_CASE_		,       batch_size=SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      DataLoader(
        tokenized_datasets["validation"]		,       shuffle=SCREAMING_SNAKE_CASE_		,       collate_fn=SCREAMING_SNAKE_CASE_		,       batch_size=SCREAMING_SNAKE_CASE_    )

    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

       _SCREAMING_SNAKE_CASE	 =							mocked_dataloaders  # noqa: F811


def 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,       SCREAMING_SNAKE_CASE_    ) == "1":
        _lowerCAmelCase				=      2
    # New Code #
    _lowerCAmelCase				=      int(args.gradient_accumulation_steps    )
    _lowerCAmelCase				=      int(args.local_sgd_steps    )
    # Initialize accelerator
    _lowerCAmelCase				=      Accelerator(
        cpu=args.cpu		,       mixed_precision=args.mixed_precision		,       gradient_accumulation_steps=SCREAMING_SNAKE_CASE_    )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowerCAmelCase				=      config["lr"]
    _lowerCAmelCase				=      int(config["num_epochs"]    )
    _lowerCAmelCase				=      int(config["seed"]    )
    _lowerCAmelCase				=      int(config["batch_size"]    )

    _lowerCAmelCase				=      evaluate.load("glue"		,       "mrpc"    )

    set_seed(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase			,			_lowerCAmelCase				=      get_dataloaders(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCAmelCase				=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,       return_dict=SCREAMING_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).
    _lowerCAmelCase				=      model.to(accelerator.device    )

    # Instantiate optimizer
    _lowerCAmelCase				=      AdamW(params=model.parameters()		,       lr=SCREAMING_SNAKE_CASE_    )

    # Instantiate scheduler
    _lowerCAmelCase				=      get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE_		,       num_warmup_steps=100		,       num_training_steps=(len(SCREAMING_SNAKE_CASE_    ) * 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				=      accelerator.prepare(
        SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )

    # Now we train the model
    for epoch in range(SCREAMING_SNAKE_CASE_    ):
        model.train()
        with LocalSGD(
            accelerator=SCREAMING_SNAKE_CASE_		,       model=SCREAMING_SNAKE_CASE_		,       local_sgd_steps=SCREAMING_SNAKE_CASE_		,       enabled=local_sgd_steps is not None    ) as local_sgd:
            for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device    )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(SCREAMING_SNAKE_CASE_    ):
                    _lowerCAmelCase				=      model(**SCREAMING_SNAKE_CASE_    )
                    _lowerCAmelCase				=      output.loss
                    accelerator.backward(SCREAMING_SNAKE_CASE_    )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

        _lowerCAmelCase				=      metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''		,       SCREAMING_SNAKE_CASE_    )


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      argparse.ArgumentParser(description="Simple example of training script."    )
    parser.add_argument(
        "--mixed_precision"		,       type=SCREAMING_SNAKE_CASE_		,       default=SCREAMING_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."		,       )
    # New Code #
    parser.add_argument(
        "--gradient_accumulation_steps"		,       type=SCREAMING_SNAKE_CASE_		,       default=1		,       help="The number of minibatches to be ran before gradients are accumulated."		,       )
    parser.add_argument(
        "--local_sgd_steps"		,       type=SCREAMING_SNAKE_CASE_		,       default=8		,       help="Number of local SGD steps or None to disable local SGD"    )
    parser.add_argument("--cpu"		,       action="store_true"		,       help="If passed, will train on the CPU."    )
    _lowerCAmelCase				=      parser.parse_args()
    _lowerCAmelCase				=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       main()









'''simple docstring'''


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)


