


"""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
#
########################################################################


a_       							=    16
a_       							=    32






def      a__ (				__lowercase     ,			__lowercase = 16   )	->	Tuple:
    _A					  =			AutoTokenizer.from_pretrained("bert-base-cased"   )
    _A					  =			load_dataset("glue"     ,			"mrpc"   )

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

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

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

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

    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

  a_       							=    mocked_dataloaders  # noqa: F811






def      a__ (				__lowercase     ,			__lowercase   )	->	str:
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,			__lowercase   ) == "1":
        _A					  =			2
    # Initialize accelerator
    _A					  =			Accelerator(cpu=args.cpu     ,			mixed_precision=args.mixed_precision   )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _A					  =			config["lr"]
    _A					  =			int(config["num_epochs"]   )
    _A					  =			int(config["seed"]   )
    _A					  =			int(config["batch_size"]   )

    _A					  =			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=__lowercase   )
    def inner_training_loop(__lowercase   ):
        # 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(__lowercase   )

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

        # 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).
        _A					  =			model.to(accelerator.device   )

        # Instantiate optimizer
        _A					  =			AdamW(params=model.parameters()     ,			lr=__lowercase   )
        _A  ,		_A					  =			get_dataloaders(__lowercase     ,			__lowercase   )

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

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

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

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

    # 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      a__ (				)	->	int:
    _A					  =			argparse.ArgumentParser(description="Simple example of training script."   )
    parser.add_argument(
        "--mixed_precision"     ,			type=__lowercase     ,			default=__lowercase     ,			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."   )
    _A					  =			parser.parse_args()
    _A					  =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(__lowercase     ,			__lowercase   )


if __name__ == "__main__":
  main()



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)