





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


lowercase_					= 16
lowercase_					= 32





def lowerCamelCase						(     __lowerCamelCase :    Accelerator					,       __lowerCamelCase :    int = 16    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""bert-base-cased"""    )
    _SCREAMING_SNAKE_CASE									=			load_dataset("""glue"""					,       """mrpc"""    )

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

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

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

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE									=			DataLoader(
        tokenized_datasets["""train"""]					,       shuffle=__lowerCamelCase					,       collate_fn=__lowerCamelCase					,       batch_size=__lowerCamelCase					,       drop_last=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			DataLoader(
        tokenized_datasets["""validation"""]					,       shuffle=__lowerCamelCase					,       collate_fn=__lowerCamelCase					,       batch_size=__lowerCamelCase					,       drop_last=(accelerator.mixed_precision == """fp8""")					,       )

    return train_dataloader, eval_dataloader





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    str    )						->str:
    # Initialize accelerator
    _SCREAMING_SNAKE_CASE									=			Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _SCREAMING_SNAKE_CASE									=			config["""lr"""]
    _SCREAMING_SNAKE_CASE									=			int(config["""num_epochs"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""seed"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""batch_size"""]    )

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

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

    set_seed(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			get_dataloaders(__lowerCamelCase					,       __lowerCamelCase    )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _SCREAMING_SNAKE_CASE									=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					,       return_dict=__lowerCamelCase    )

    # 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).
    _SCREAMING_SNAKE_CASE									=			model.to(accelerator.device    )
    # Instantiate optimizer
    _SCREAMING_SNAKE_CASE									=			AdamW(params=model.parameters()					,       lr=__lowerCamelCase    )

    # Instantiate scheduler
    _SCREAMING_SNAKE_CASE									=			get_linear_schedule_with_warmup(
        optimizer=__lowerCamelCase					,       num_warmup_steps=100					,       num_training_steps=(len(__lowerCamelCase    ) * 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.

    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.prepare(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    # Now we train the model
    for epoch in range(__lowerCamelCase    ):
        model.train()
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			outputs.loss
            _SCREAMING_SNAKE_CASE									=			loss / gradient_accumulation_steps
            accelerator.backward(__lowerCamelCase    )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

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





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


if __name__ == "__main__":
      main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
