




'''simple docstring'''







import argparse

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
#
# 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
#
########################################################################


A     =16
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	:							Tuple     ):
        # 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	:							Optional[int]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        UpperCAmelCase			       =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # 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'''    ,   max_length=_a    ,   pad_to_multiple_of=_a    ,   return_tensors='''pt'''    ,   )

    # Instantiate dataloaders.
    UpperCAmelCase			       =		DataLoader(
        tokenized_datasets['''train''']    ,   shuffle=_a    ,   collate_fn=_a    ,   batch_size=_a    ,   drop_last=_a     )
    UpperCAmelCase			       =		DataLoader(
        tokenized_datasets['''validation''']    ,   shuffle=_a    ,   collate_fn=_a    ,   batch_size=_a    ,   drop_last=(accelerator.mixed_precision == '''fp8''')    ,   )

    return train_dataloader, eval_dataloader





def 						snake_case_							(_a	:							Optional[Any]    ,   _a	:							Union[str, Any]     ):
    # 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'''     )

    # If the batch size is too big we use gradient accumulation
    UpperCAmelCase			       =		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        UpperCAmelCase			       =		batch_size // MAX_GPU_BATCH_SIZE
        UpperCAmelCase			       =		MAX_GPU_BATCH_SIZE

    set_seed(_a     )
    UpperCAmelCase       ,     UpperCAmelCase			       =		get_dataloaders(_a    ,   _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     )

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

    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     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            UpperCAmelCase			       =		model(**_a     )
            UpperCAmelCase			       =		outputs.loss
            UpperCAmelCase			       =		loss / gradient_accumulation_steps
            accelerator.backward(_a     )
            if step % gradient_accumulation_steps == 0:
                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     )





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()
    UpperCAmelCase			       =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
    training_function(_a    ,   _a     )


if __name__ == "__main__":
   main()










from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






