





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						=		'''true'''






def 					lowerCamelCase_   (	_a     ,       _a=82     ,       _a=16	):




		"""simple docstring"""






		set_seed(42	)
		lowerCAmelCase__       :      Any   					= RegressionModel()
		lowerCAmelCase__       :      List[Any]   					= deepcopy(_a	)
		lowerCAmelCase__       :      List[Any]   					= RegressionDataset(length=_a	)
		lowerCAmelCase__       :      Optional[int]   					= DataLoader(_a     ,       batch_size=_a	)
		model.to(accelerator.device	)
		lowerCAmelCase__       :      Optional[Any]   					= accelerator.prepare(_a     ,       _a	)
		return model, ddp_model, dataloader






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      Tuple   					= AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''	)
		lowerCAmelCase__       :      Dict   					= load_dataset('''glue'''     ,       '''mrpc'''     ,       split='''validation'''	)

		def tokenize_function(_a	):
				lowerCAmelCase__       :      Dict   					= tokenizer(examples['''sentence1''']     ,       examples['''sentence2''']     ,       truncation=_a     ,       max_length=_a	)
				return outputs

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

		lowerCAmelCase__       :      List[str]   					= tokenized_datasets.rename_column('''label'''     ,       '''labels'''	)

		def collate_fn(_a	):
				if use_longest:
						return tokenizer.pad(_a     ,       padding='''longest'''     ,       return_tensors='''pt'''	)
				return tokenizer.pad(_a     ,       padding='''max_length'''     ,       max_length=128     ,       return_tensors='''pt'''	)

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






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= []
		for batch in dataloader:
				lowerCAmelCase__       :      Union[str, Any]   					= batch.values()
				with torch.no_grad():
						lowerCAmelCase__       :      Optional[int]   					= model(_a	)
						lowerCAmelCase__       :      Tuple   					= accelerator.gather_for_metrics((logit, target)	)
						logits_and_targets.append((logit, target)	)
		lowerCAmelCase__       :      List[Any]   					= [], []
		for logit, targ in logits_and_targets:
				logits.append(_a	)
				targs.append(_a	)
		lowerCAmelCase__       :      Any   					= torch.cat(_a	), torch.cat(_a	)
		return logits, targs






def 					lowerCamelCase_   (	_a     ,       _a=82     ,       _a=False     ,       _a=False     ,       _a=16	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= get_basic_setup(_a     ,       _a     ,       _a	)
		lowerCAmelCase__       :      Any   					= generate_predictions(_a     ,       _a     ,       _a	)
		assert (
		    len(_a	) == num_samples
		), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a	)}'






def 					lowerCamelCase_   (	_a = False     ,       _a = False	):




		"""simple docstring"""






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

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




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= 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]:
						lowerCAmelCase__       :      Union[str, Any]   					= 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     ,       99	)
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('''**Test last batch is not dropped when perfectly divisible**'''	)
		lowerCAmelCase__       :      Any   					= Accelerator()
		test_torch_metrics(_a     ,       512	)
		accelerator.state._reset_state()






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		main()


if __name__ == "__main__":
		main()








import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_   (	_a     ,       _a=1_000	):




		"""simple docstring"""






		if n < 2:
				return False

		if n % 2 == 0:
				return n == 2

		# this means n is odd
		lowerCAmelCase__       :      int   					= n - 1
		lowerCAmelCase__       :      Any   					= 0
		while d % 2 == 0:
				d /= 2
				exp += 1

		# n - 1=d*(2**exp)
		lowerCAmelCase__       :      Optional[Any]   					= 0
		while count < prec:
				lowerCAmelCase__       :      Optional[Any]   					= random.randint(2     ,       n - 1	)
				lowerCAmelCase__       :      List[Any]   					= bin_exp_mod(_a     ,       _a     ,       _a	)
				if b != 1:
						lowerCAmelCase__       :      Dict   					= True
						for _ in range(_a	):
								if b == n - 1:
										lowerCAmelCase__       :      Union[str, Any]   					= False
										break
								lowerCAmelCase__       :      Tuple   					= b * b
								b %= n
						if flag:
								return False
						count += 1
		return True


if __name__ == "__main__":
		lowerCamelCase						=		abs(int(input('''Enter bound : ''').strip()))
		print('''Here\'s the list of primes:''')
		print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))




