




"""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


lowerCAmelCase							:			List[Any]     							=							"""true"""



def     a__ (				snake_case__       , snake_case__=82       , snake_case__=16				)						->					Optional[Any]:
			set_seed(42				)
			lowerCamelCase			      =					RegressionModel()
			lowerCamelCase			      =					deepcopy(SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					RegressionDataset(length=SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					DataLoader(SCREAMING_SNAKE_CASE__       , batch_size=SCREAMING_SNAKE_CASE__				)
			model.to(accelerator.device				)
			lowerCamelCase      ,   lowerCamelCase			      =					accelerator.prepare(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__				)
			return model, ddp_model, dataloader



def     a__ (				snake_case__       , snake_case__=False				)						->					int:
			lowerCamelCase			      =					AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""				)
			lowerCamelCase			      =					load_dataset("""glue"""       , """mrpc"""       , split="""validation"""				)

			def tokenize_function(snake_case__				):
						lowerCamelCase			      =					tokenizer(examples["""sentence1"""]       , examples["""sentence2"""]       , truncation=SCREAMING_SNAKE_CASE__       , max_length=SCREAMING_SNAKE_CASE__				)
						return outputs

			with accelerator.main_process_first():
						lowerCamelCase			      =					dataset.map(
						    SCREAMING_SNAKE_CASE__       , batched=SCREAMING_SNAKE_CASE__       , remove_columns=["""idx""", """sentence1""", """sentence2"""]       , )

			lowerCamelCase			      =					tokenized_datasets.rename_column("""label"""       , """labels"""				)

			def collate_fn(snake_case__				):
						if use_longest:
									return tokenizer.pad(SCREAMING_SNAKE_CASE__       , padding="""longest"""       , return_tensors="""pt"""				)
						return tokenizer.pad(SCREAMING_SNAKE_CASE__       , padding="""max_length"""       , max_length=1_28       , return_tensors="""pt"""				)

			return DataLoader(SCREAMING_SNAKE_CASE__       , shuffle=SCREAMING_SNAKE_CASE__       , collate_fn=SCREAMING_SNAKE_CASE__       , batch_size=16				)



def     a__ (				snake_case__       , snake_case__				)						->					str:
			lowerCamelCase			      =					Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__       , split_batches=SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					get_dataloader(SCREAMING_SNAKE_CASE__       , not dispatch_batches				)
			lowerCamelCase			      =					AutoModelForSequenceClassification.from_pretrained(
			    """hf-internal-testing/mrpc-bert-base-cased"""       , return_dict=SCREAMING_SNAKE_CASE__				)
			lowerCamelCase      ,   lowerCamelCase			      =					accelerator.prepare(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__				)
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					List[str]:
			lowerCamelCase			      =					[]
			for batch in dataloader:
						lowerCamelCase      ,   lowerCamelCase			      =					batch.values()
						with torch.no_grad():
									lowerCamelCase			      =					model(SCREAMING_SNAKE_CASE__				)
									lowerCamelCase      ,   lowerCamelCase			      =					accelerator.gather_for_metrics((logit, target)				)
									logits_and_targets.append((logit, target)				)
			lowerCamelCase      ,   lowerCamelCase			      =					[], []
			for logit, targ in logits_and_targets:
						logits.append(SCREAMING_SNAKE_CASE__				)
						targs.append(SCREAMING_SNAKE_CASE__				)
			lowerCamelCase      ,   lowerCamelCase			      =					torch.cat(SCREAMING_SNAKE_CASE__				), torch.cat(SCREAMING_SNAKE_CASE__				)
			return logits, targs



def     a__ (				snake_case__       , snake_case__=82       , snake_case__=False       , snake_case__=False       , snake_case__=16				)						->					List[Any]:
			lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					get_basic_setup(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__				)
			lowerCamelCase      ,   lowerCamelCase			      =					generate_predictions(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__				)
			assert (
			    len(SCREAMING_SNAKE_CASE__				) == num_samples
			), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__				)}'



def     a__ (				snake_case__ = False       , snake_case__ = False				)						->					str:
			lowerCamelCase			      =					evaluate.load("""glue"""       , """mrpc"""				)
			lowerCamelCase      ,   lowerCamelCase			      =					get_mrpc_setup(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__				)
			# First do baseline
			lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					setup["""no"""]
			model.to(SCREAMING_SNAKE_CASE__				)
			model.eval()
			for batch in dataloader:
						batch.to(SCREAMING_SNAKE_CASE__				)
						with torch.inference_mode():
									lowerCamelCase			      =					model(**SCREAMING_SNAKE_CASE__				)
						lowerCamelCase			      =					outputs.logits.argmax(dim=-1				)
						metric.add_batch(predictions=SCREAMING_SNAKE_CASE__       , references=batch["""labels"""]				)
			lowerCamelCase			      =					metric.compute()

			# Then do distributed
			lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					setup["""ddp"""]
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									lowerCamelCase			      =					model(**SCREAMING_SNAKE_CASE__				)
						lowerCamelCase			      =					outputs.logits.argmax(dim=-1				)
						lowerCamelCase			      =					batch["""labels"""]
						lowerCamelCase      ,   lowerCamelCase			      =					accelerator.gather_for_metrics((preds, references)				)
						metric.add_batch(predictions=SCREAMING_SNAKE_CASE__       , references=SCREAMING_SNAKE_CASE__				)
			lowerCamelCase			      =					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     a__ (				)						->					Optional[Any]:
			lowerCamelCase			      =					Accelerator(split_batches=SCREAMING_SNAKE_CASE__       , dispatch_batches=SCREAMING_SNAKE_CASE__				)
			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(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__				)
												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]:
									lowerCamelCase			      =					Accelerator(split_batches=SCREAMING_SNAKE_CASE__       , dispatch_batches=SCREAMING_SNAKE_CASE__				)
									if accelerator.is_local_main_process:
												print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'				)
									test_torch_metrics(SCREAMING_SNAKE_CASE__       , 99				)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print("""**Test last batch is not dropped when perfectly divisible**"""				)
			lowerCamelCase			      =					Accelerator()
			test_torch_metrics(SCREAMING_SNAKE_CASE__       , 5_12				)
			accelerator.state._reset_state()



def     a__ (				snake_case__				)						->					Union[str, Any]:
			main()


if __name__ == "__main__":
					main()






from timeit import timeit
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        number &= number - 1
        result += 1
    return result
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int      )							->      int:





    '''simple docstring'''






    if number < 0:
        raise ValueError('the value of input must not be negative'      )
    A__          =   0
    while number:
        if number % 2 == 1:
            result += 1
        number >>= 1
    return result
def 						_snake_case(    )							->      None:





    '''simple docstring'''






    def do_benchmark(SCREAMING_SNAKE_CASE__			:	int      ) -> None:
        A__          =   'import __main__ as z'
        print(f'Benchmark when {number = }:'      )
        print(f'{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,			setup=SCREAMING_SNAKE_CASE__      )
        print(f'timeit() runs in {timing} seconds'      )
        print(f'{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE__      ) = }'      )
        A__          =   timeit(
            'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,			setup=SCREAMING_SNAKE_CASE__    ,			)
        print(f'timeit() runs in {timing} seconds'      )

    for number in (25, 37, 58, 0):
        do_benchmark(SCREAMING_SNAKE_CASE__      )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

