



"""simple docstring"""

import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__    :  Optional[int]							    =	16
lowercase__    :  Optional[int]							    =	32




def __lowercase			(							_a		,			_a = 16		,			_a = "bert-base-cased"    ):
    snake_case_ :     Optional[int]				=	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
    snake_case_ :     str				=	load_dataset('''glue'''		,			'''mrpc'''    )

    def tokenize_function(_a    ):
        # max_length=None => use the model max length (it's actually the default)
        snake_case_ :     Optional[Any]				=	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
    snake_case_ :     Optional[Any]				=	datasets.map(
        _SCREAMING_SNAKE_CASE		,			batched=_SCREAMING_SNAKE_CASE		,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,			load_from_cache_file=_SCREAMING_SNAKE_CASE    )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case_ :     Optional[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.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_SCREAMING_SNAKE_CASE		,			padding='''max_length'''		,			max_length=128		,			return_tensors='''pt'''    )
        return tokenizer.pad(_SCREAMING_SNAKE_CASE		,			padding='''longest'''		,			return_tensors='''pt'''    )

    # Instantiate dataloaders.
    snake_case_ :     Any				=	DataLoader(
        tokenized_datasets['''train''']		,			shuffle=_SCREAMING_SNAKE_CASE		,			collate_fn=_SCREAMING_SNAKE_CASE		,			batch_size=_SCREAMING_SNAKE_CASE    )
    snake_case_ :     Optional[int]				=	DataLoader(
        tokenized_datasets['''validation''']		,			shuffle=_SCREAMING_SNAKE_CASE		,			collate_fn=_SCREAMING_SNAKE_CASE		,			batch_size=_SCREAMING_SNAKE_CASE    )

    return train_dataloader, eval_dataloader




def __lowercase			(							_a		,			_a    ):
    # Initialize accelerator
    snake_case_ :     List[str]				=	Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case_ :     int				=	config['lr']
    snake_case_ :     str				=	int(config['''num_epochs''']    )
    snake_case_ :     str				=	int(config['''seed''']    )
    snake_case_ :     Tuple				=	int(config['''batch_size''']    )
    snake_case_ :     List[str]				=	args.model_name_or_path

    set_seed(_SCREAMING_SNAKE_CASE    )
    snake_case_ :     List[str]				=	get_dataloaders(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_ :     str				=	AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		,			return_dict=_SCREAMING_SNAKE_CASE    )

    # Instantiate optimizer
    snake_case_ :     Optional[int]				=	(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case_ :     Optional[int]				=	optimizer_cls(params=model.parameters()		,			lr=_SCREAMING_SNAKE_CASE    )

    if accelerator.state.deepspeed_plugin is not None:
        snake_case_ :     Dict				=	accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        snake_case_ :     str				=	1
    snake_case_ :     Union[str, Any]				=	(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case_ :     Tuple				=	get_linear_schedule_with_warmup(
            optimizer=_SCREAMING_SNAKE_CASE		,			num_warmup_steps=0		,			num_training_steps=_SCREAMING_SNAKE_CASE		,			)
    else:
        snake_case_ :     Tuple				=	DummyScheduler(_SCREAMING_SNAKE_CASE		,			total_num_steps=_SCREAMING_SNAKE_CASE		,			warmup_num_steps=0    )

    # 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.
    snake_case_ :     Union[str, Any]				=	accelerator.prepare(
        _SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    )

    # We need to keep track of how many total steps we have iterated over
    snake_case_ :     List[str]				=	0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case_ :     Optional[int]				=	0

    # Now we train the model
    snake_case_ :     str				=	evaluate.load('''glue'''		,			'''mrpc'''    )
    snake_case_ :     Optional[Any]				=	0
    snake_case_ :     str				=	{}
    for epoch in range(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    ):
        model.train()
        for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
            snake_case_ :     List[Any]				=	model(**_SCREAMING_SNAKE_CASE    )
            snake_case_ :     Optional[Any]				=	outputs.loss
            snake_case_ :     str				=	loss / gradient_accumulation_steps
            accelerator.backward(_SCREAMING_SNAKE_CASE    )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        snake_case_ :     List[str]				=	0
        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():
                snake_case_ :     int				=	model(**_SCREAMING_SNAKE_CASE    )
            snake_case_ :     Optional[int]				=	outputs.logits.argmax(dim=-1    )
            # It is slightly faster to call this once, than multiple times
            snake_case_ :     List[str]				=	accelerator.gather(
                (predictions, batch['''labels'''])    )  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(_SCREAMING_SNAKE_CASE    ) - 1:
                    snake_case_ :     Any				=	predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                    snake_case_ :     Union[str, Any]				=	references[: len(eval_dataloader.dataset    ) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=_SCREAMING_SNAKE_CASE		,			references=_SCREAMING_SNAKE_CASE		,			)

        snake_case_ :     Union[str, Any]				=	metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:"		,			_SCREAMING_SNAKE_CASE    )
        snake_case_ :     Union[str, Any]				=	eval_metric['accuracy']

        if best_performance < eval_metric["accuracy"]:
            snake_case_ :     Optional[Any]				=	eval_metric['accuracy']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir		,			'''all_results.json'''    )		,			'''w'''    ) as f:
            json.dump(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    )




def __lowercase			(							):
    snake_case_ :     str				=	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''    )
    parser.add_argument(
        '''--model_name_or_path'''		,			type=_SCREAMING_SNAKE_CASE		,			default='''bert-base-cased'''		,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''		,			required=_SCREAMING_SNAKE_CASE		,			)
    parser.add_argument(
        '''--output_dir'''		,			type=_SCREAMING_SNAKE_CASE		,			default='''.'''		,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,			)
    parser.add_argument(
        '''--performance_lower_bound'''		,			type=_SCREAMING_SNAKE_CASE		,			default=_SCREAMING_SNAKE_CASE		,			help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''		,			)
    parser.add_argument(
        '''--num_epochs'''		,			type=_SCREAMING_SNAKE_CASE		,			default=3		,			help='''Number of train epochs.'''		,			)
    snake_case_ :     List[Any]				=	parser.parse_args()
    snake_case_ :     Any				=	{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
    training_function(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
     main()






"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE = 1000000   )	->int:
  a__:     int										=	limit + 1
  a__:     Optional[int]										=	[0] * limit
  for first_term in range(1		,     _SCREAMING_SNAKE_CASE   ):
    for n in range(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[Any]										=	first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:     Any										=	sum(1 for x in frequency[1:limit] if x == 10   )

  return count


if __name__ == "__main__":
 print(f"{solution() = }")


