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
# and perform 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
#
########################################################################


lowerCAmelCase			:							Optional[Any]     =16
lowerCAmelCase			:							Dict     =32







def 						A__  (					__A   , __A = 16	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
    _lowerCamelCase     :		Optional[int] 						=						load_dataset("""glue"""   , """mrpc"""	)

    def tokenize_function(__A	):
        # max_length=None => use the model max length (it's actually the default)
        _lowerCamelCase     :		Optional[Any] 						=						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():
        _lowerCamelCase     :		Tuple 						=						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
    _lowerCamelCase     :		Union[str, Any] 						=						tokenized_datasets.rename_column("""label"""   , """labels"""	)

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

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

    # Instantiate dataloaders.
    _lowerCamelCase     :		int 						=						DataLoader(
        tokenized_datasets["""train"""]   , shuffle=__A   , collate_fn=__A   , batch_size=__A	)
    _lowerCamelCase     :		Any 						=						DataLoader(
        tokenized_datasets["""validation"""]   , shuffle=__A   , collate_fn=__A   , batch_size=__A	)

    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			:							List[Any]     =mocked_dataloaders  # noqa: F811







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    # For testing only
    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   , __A	) == "1":
        _lowerCamelCase     :		Any 						=						2
    # New Code #
    _lowerCamelCase     :		Any 						=						int(args.gradient_accumulation_steps	)
    # Initialize accelerator
    _lowerCamelCase     :		Union[str, Any] 						=						Accelerator(
        cpu=args.cpu   , mixed_precision=args.mixed_precision   , gradient_accumulation_steps=__A	)
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowerCamelCase     :		List[str] 						=						config["""lr"""]
    _lowerCamelCase     :		Tuple 						=						int(config["""num_epochs"""]	)
    _lowerCamelCase     :		Tuple 						=						int(config["""seed"""]	)
    _lowerCamelCase     :		Union[str, Any] 						=						int(config["""batch_size"""]	)

    _lowerCamelCase     :		List[str] 						=						evaluate.load("""glue"""   , """mrpc"""	)

    set_seed(__A	)
    _lowerCamelCase						,							_lowerCamelCase     :		str 						=						get_dataloaders(__A   , __A	)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCamelCase     :		str 						=						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).
    _lowerCamelCase     :		str 						=						model.to(accelerator.device	)

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

    # Instantiate scheduler
    _lowerCamelCase     :		Any 						=						get_linear_schedule_with_warmup(
        optimizer=__A   , num_warmup_steps=100   , num_training_steps=(len(__A	) * 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     :		str 						=						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	)
            # 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(__A	):
                _lowerCamelCase     :		Dict 						=						model(**__A	)
                _lowerCamelCase     :		str 						=						output.loss
                accelerator.backward(__A	)
                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():
                _lowerCamelCase     :		Optional[Any] 						=						model(**__A	)
            _lowerCamelCase     :		Optional[int] 						=						outputs.logits.argmax(dim=-1	)
            _lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
            metric.add_batch(
                predictions=__A   , references=__A   , )

        _lowerCamelCase     :		Optional[int] 						=						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""   , __A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						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."""   , )
    # New Code #
    parser.add_argument(
        """--gradient_accumulation_steps"""   , type=__A   , default=1   , help="""The number of minibatches to be ran before gradients are accumulated."""   , )
    parser.add_argument("""--cpu"""   , action="""store_true"""   , help="""If passed, will train on the CPU."""	)
    _lowerCamelCase     :		Any 						=						parser.parse_args()
    _lowerCamelCase     :		Dict 						=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(__A   , __A	)


if __name__ == "__main__":
    main()





lowerCAmelCase			:							Tuple     =0  # The first color of the flag.
lowerCAmelCase			:							Union[str, Any]     =1  # The second color of the flag.
lowerCAmelCase			:							Any     =2  # The third color of the flag.
lowerCAmelCase			:							List[str]     =(red, white, blue)







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase			:							List[str]     =input("Enter numbers separated by commas:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item.strip()) for item in user_input.split(",")]
    print(F"""{dutch_national_flag_sort(unsorted)}""")




