




'''simple docstring'''







import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


A									='true'





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							Tuple=8_2					,     _a			:							List[str]=1_6  ):
    set_seed(4_2  )
    UpperCAmelCase     =	RegressionModel()
    UpperCAmelCase     =	deepcopy(_a  )
    UpperCAmelCase     =	RegressionDataset(length=_a  )
    UpperCAmelCase     =	DataLoader(_a					,     batch_size=_a  )
    model.to(accelerator.device  )
    UpperCAmelCase ,  UpperCAmelCase     =	accelerator.prepare(_a					,     _a  )
    return model, ddp_model, dataloader





def 		snake_case_							(_a			:							Accelerator					,     _a			:							Optional[Any]=False  ):
    UpperCAmelCase     =	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''  )
    UpperCAmelCase     =	load_dataset('''glue'''					,     '''mrpc'''					,     split='''validation'''  )

    def tokenize_function(_a			:							List[str]  ):
        UpperCAmelCase     =	tokenizer(examples['''sentence1''']					,     examples['''sentence2''']					,     truncation=_a					,     max_length=_a  )
        return outputs

    with accelerator.main_process_first():
        UpperCAmelCase     =	dataset.map(
            _a					,     batched=_a					,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,     )

    UpperCAmelCase     =	tokenized_datasets.rename_column('''label'''					,     '''labels'''  )

    def collate_fn(_a			:							List[Any]  ):
        if use_longest:
            return tokenizer.pad(_a					,     padding='''longest'''					,     return_tensors='''pt'''  )
        return tokenizer.pad(_a					,     padding='''max_length'''					,     max_length=1_2_8					,     return_tensors='''pt'''  )

    return DataLoader(_a					,     shuffle=_a					,     collate_fn=_a					,     batch_size=1_6  )





def 		snake_case_							(_a			:							Dict					,     _a			:							Optional[Any]  ):
    UpperCAmelCase     =	Accelerator(dispatch_batches=_a					,     split_batches=_a  )
    UpperCAmelCase     =	get_dataloader(_a					,     not dispatch_batches  )
    UpperCAmelCase     =	AutoModelForSequenceClassification.from_pretrained(
        '''hf-internal-testing/mrpc-bert-base-cased'''					,     return_dict=_a  )
    UpperCAmelCase ,  UpperCAmelCase     =	accelerator.prepare(_a					,     _a  )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							int					,     _a			:							Optional[int]  ):
    UpperCAmelCase     =	[]
    for batch in dataloader:
        UpperCAmelCase ,  UpperCAmelCase     =	batch.values()
        with torch.no_grad():
            UpperCAmelCase     =	model(_a  )
            UpperCAmelCase ,  UpperCAmelCase     =	accelerator.gather_for_metrics((logit, target)  )
            logits_and_targets.append((logit, target)  )
    UpperCAmelCase ,  UpperCAmelCase     =	[], []
    for logit, targ in logits_and_targets:
        logits.append(_a  )
        targs.append(_a  )
    UpperCAmelCase ,  UpperCAmelCase     =	torch.cat(_a  ), torch.cat(_a  )
    return logits, targs





def 		snake_case_							(_a			:							Accelerator					,     _a			:							List[str]=8_2					,     _a			:							Dict=False					,     _a			:							int=False					,     _a			:							Tuple=1_6  ):
    UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	get_basic_setup(_a					,     _a					,     _a  )
    UpperCAmelCase ,  UpperCAmelCase     =	generate_predictions(_a					,     _a					,     _a  )
    assert (
        len(_a  ) == num_samples
    ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a  )}"





def 		snake_case_							(_a			:							bool = False					,     _a			:							bool = False  ):
    UpperCAmelCase     =	evaluate.load('''glue'''					,     '''mrpc'''  )
    UpperCAmelCase ,  UpperCAmelCase     =	get_mrpc_setup(_a					,     _a  )
    # First do baseline
    UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	setup['''no''']
    model.to(_a  )
    model.eval()
    for batch in dataloader:
        batch.to(_a  )
        with torch.inference_mode():
            UpperCAmelCase     =	model(**_a  )
        UpperCAmelCase     =	outputs.logits.argmax(dim=-1  )
        metric.add_batch(predictions=_a					,     references=batch['''labels''']  )
    UpperCAmelCase     =	metric.compute()

    # Then do distributed
    UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	setup['''ddp''']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            UpperCAmelCase     =	model(**_a  )
        UpperCAmelCase     =	outputs.logits.argmax(dim=-1  )
        UpperCAmelCase     =	batch['''labels''']
        UpperCAmelCase ,  UpperCAmelCase     =	accelerator.gather_for_metrics((preds, references)  )
        metric.add_batch(predictions=_a					,     references=_a  )
    UpperCAmelCase     =	metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]					,     distributed[key]  ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"





def 		snake_case_							():
    UpperCAmelCase     =	Accelerator(split_batches=_a					,     dispatch_batches=_a  )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print('''**Testing gather_for_metrics**'''  )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"  )
                test_mrpc(_a					,     _a  )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('''**Test torch metrics**'''  )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            UpperCAmelCase     =	Accelerator(split_batches=_a					,     dispatch_batches=_a  )
            if accelerator.is_local_main_process:
                print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"  )
            test_torch_metrics(_a					,     9_9  )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('''**Test last batch is not dropped when perfectly divisible**'''  )
    UpperCAmelCase     =	Accelerator()
    test_torch_metrics(_a					,     5_1_2  )
    accelerator.state._reset_state()





def 		snake_case_							(_a			:							List[str]  ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
   main()










'''simple docstring'''







def 		snake_case_							():
    for n in range(1					,     1_0_0_0_0_0_0  ):
        yield n * (n + 1) // 2





def 		snake_case_							(_a			:							Union[str, Any]  ):
    UpperCAmelCase     =	1
    UpperCAmelCase     =	2
    while i * i <= n:
        UpperCAmelCase     =	0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count





def 		snake_case_							():
    return next(i for i in triangle_number_generator() if count_divisors(_a  ) > 5_0_0  )


if __name__ == "__main__":
   print(solution())




