





"""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


a_					=     16
a_					=     32







def 						__lowercase	(  snake_case_    :    Accelerator							,snake_case_    :    int = 16							,snake_case_    :    str = "bert-base-cased"		)  ->Tuple:



    '''simple docstring'''







    __A       :		Optional[int]							=	AutoTokenizer.from_pretrained(snake_case_		)
    __A       :		Union[str, Any]							=	load_dataset('''glue'''							,'''mrpc'''		)

    def tokenize_function(snake_case_    :    Union[str, Any]		):
        # max_length=None => use the model max length (it's actually the default)
        __A       :		Optional[Any]							=	tokenizer(examples['''sentence1''']							,examples['''sentence2''']							,truncation=snake_case_							,max_length=snake_case_		)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    __A       :		Optional[Any]							=	datasets.map(
        snake_case_							,batched=snake_case_							,remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,load_from_cache_file=snake_case_		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __A       :		List[str]							=	tokenized_datasets.rename_column('''label'''							,'''labels'''		)

    def collate_fn(snake_case_    :    Tuple		):
        # 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(snake_case_							,padding='''max_length'''							,max_length=128							,return_tensors='''pt'''		)
        return tokenizer.pad(snake_case_							,padding='''longest'''							,return_tensors='''pt'''		)

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

    return train_dataloader, eval_dataloader







def 						__lowercase	(  snake_case_    :    Optional[Any]							,snake_case_    :    Union[str, Any]		)  ->List[str]:



    '''simple docstring'''







    __A       :		Tuple							=	Accelerator()

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

    set_seed(snake_case_		)
    __A					,			__A       :		Union[str, Any]							=	get_dataloaders(snake_case_							,snake_case_							,snake_case_		)

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

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

    if accelerator.state.deepspeed_plugin is not None:
        __A       :		Any							=	accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __A       :		int							=	1
    __A       :		Union[str, Any]							=	(len(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
    ):
        __A       :		Optional[Any]							=	get_linear_schedule_with_warmup(
            optimizer=snake_case_							,num_warmup_steps=0							,num_training_steps=snake_case_							,)
    else:
        __A       :		int							=	DummyScheduler(snake_case_							,total_num_steps=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.
    __A					,			__A					,			__A					,			__A					,			__A       :		Dict							=	accelerator.prepare(
        snake_case_							,snake_case_							,snake_case_							,snake_case_							,snake_case_		)

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

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

            overall_step += 1

        model.eval()
        __A       :		Optional[Any]							=	0
        for step, batch in enumerate(snake_case_		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            with torch.no_grad():
                __A       :		Tuple							=	model(**snake_case_		)
            __A       :		Optional[Any]							=	outputs.logits.argmax(dim=-1		)
            # It is slightly faster to call this once, than multiple times
            __A					,			__A       :		Optional[int]							=	accelerator.gather(
                (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(snake_case_		) - 1:
                    __A       :		int							=	predictions[: len(eval_dataloader.dataset		) - samples_seen]
                    __A       :		List[Any]							=	references[: len(eval_dataloader.dataset		) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=snake_case_							,references=snake_case_							,)

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

        if best_performance < eval_metric["accuracy"]:
            __A       :		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(snake_case_							,snake_case_		)







def 						__lowercase	(  )  ->Union[str, Any]:



    '''simple docstring'''







    __A       :		Optional[int]							=	argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
    parser.add_argument(
        '''--model_name_or_path'''							,type=snake_case_							,default='''bert-base-cased'''							,help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,required=snake_case_							,)
    parser.add_argument(
        '''--output_dir'''							,type=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=snake_case_							,default=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=snake_case_							,default=3							,help='''Number of train epochs.'''							,)
    __A       :		Dict							=	parser.parse_args()
    __A       :		List[str]							=	{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case_							,snake_case_		)


if __name__ == "__main__":
       main()











"""simple docstring"""



from collections import defaultdict
from math import gcd







def 						__lowercase	(  snake_case_    :    int = 1500000		)  ->int:



    '''simple docstring'''







    __A       :		defaultdict							=	defaultdict(snake_case_		)
    __A       :		Any							=	2
    while 2 * euclid_m * (euclid_m + 1) <= limit:
        for euclid_n in range((euclid_m % 2) + 1							,snake_case_							,2		):
            if gcd(snake_case_							,snake_case_		) > 1:
                continue
            __A       :		List[Any]							=	2 * euclid_m * (euclid_m + euclid_n)
            for perimeter in range(snake_case_							,limit + 1							,snake_case_		):
                frequencies[perimeter] += 1
        euclid_m += 1
    return sum(1 for frequency in frequencies.values() if frequency == 1		)


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




