"""simple docstring"""


# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__UpperCamelCase									=			Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__UpperCamelCase									=			{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

__UpperCamelCase									=			'''zero2'''
__UpperCamelCase									=			'''zero3'''
__UpperCamelCase									=			[ZEROa, ZEROa]







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      Optional[Any]:
     # customize the test name generator function as we want both params to appear in the sub-test
     # name, as by default it shows only the first param
     snake_case_					       =					parameterized.to_safe_name('_'.join(str(UpperCAmelCase					) for x in param.args					)					)
     return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
__UpperCamelCase									=			list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class  UpperCamelCase					(				lowerCAmelCase__   ):







       @parameterized.expand(lowerCAmelCase__,							name_func=lowerCAmelCase__)
       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      int:
            self.run_and_check(
                stage=lowerCAmelCase__,							model=lowerCAmelCase__,							distributed=lowerCAmelCase__,							fpaa=lowerCAmelCase__,							)







       @require_torch_multi_gpu
       @parameterized.expand(lowerCAmelCase__,							name_func=lowerCAmelCase__)
       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[str]:
            self.run_and_check(
                stage=lowerCAmelCase__,							model=lowerCAmelCase__,							distributed=lowerCAmelCase__,							fpaa=lowerCAmelCase__,							)







       @parameterized.expand(lowerCAmelCase__,							name_func=lowerCAmelCase__)
       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Any:
            self.run_and_check(
                stage=lowerCAmelCase__,							model=lowerCAmelCase__,							distributed=lowerCAmelCase__,							fpaa=lowerCAmelCase__,							)







       @require_torch_multi_gpu
       @parameterized.expand(lowerCAmelCase__,							name_func=lowerCAmelCase__)
       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Optional[int]:
            self.run_and_check(
                stage=lowerCAmelCase__,							model=lowerCAmelCase__,							distributed=lowerCAmelCase__,							fpaa=lowerCAmelCase__,							)







       def 		a_						( self,							lowerCAmelCase__)      ->      List[Any]:
            # XXX: run_asr is premature and doesn't save any results
            # so all we check for now is that the process didn't fail
            pass







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = 10,							lowerCAmelCase__ = True,							lowerCAmelCase__ = True,							lowerCAmelCase__ = True,							)      ->      Optional[Any]:
            snake_case_					       =					models[model]

            snake_case_					       =					self.run_trainer(
                stage=lowerCAmelCase__,							model_name=lowerCAmelCase__,							eval_steps=lowerCAmelCase__,							num_train_epochs=1,							distributed=lowerCAmelCase__,							fpaa=lowerCAmelCase__,							)

            self.do_checks(lowerCAmelCase__)

            return output_dir







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = 10,							lowerCAmelCase__ = 1,							lowerCAmelCase__ = True,							lowerCAmelCase__ = True,							)      ->      int:
            snake_case_					       =					self.get_auto_remove_tmp_dir('./xxx',							after=lowerCAmelCase__)
            snake_case_					       =					f'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowerCAmelCase__)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

            if fpaa:
                 args.extend(['--fp16'])

            # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
            # hence the separate config files
            snake_case_					       =					f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
            snake_case_					       =					[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
            snake_case_					       =					self.get_launcher(lowerCAmelCase__)

            snake_case_					       =					launcher + script + args + ds_args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(lowerCAmelCase__,							env=self.get_env())

            return output_dir







       def 		a_						( self,							lowerCAmelCase__=False)      ->      Union[str, Any]:
            # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
            # - it won't be able to handle that
            # 2. for now testing with just 2 gpus max (since some quality tests may give different
            # results with mode gpus because we use very little data)
            snake_case_					       =					min(2,							get_gpu_count()) if distributed else 1
            return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()








'''simple docstring'''

from __future__ import annotations

import math





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[int]:

    if num <= 0:
        _SCREAMING_SNAKE_CASE									=			F'{num}: Invalid input, please enter a positive integer.'
        raise ValueError(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			[True] * (num + 1)
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			2
    _SCREAMING_SNAKE_CASE									=			int(math.sqrt(__lowerCamelCase    )    )

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(__lowerCamelCase    )

            # Set multiples of start be False
            for i in range(start * start					,       num + 1					,       __lowerCamelCase    ):
                if sieve[i] is True:
                    _SCREAMING_SNAKE_CASE									=			False

        start += 1

    for j in range(end + 1					,       num + 1    ):
        if sieve[j] is True:
            prime.append(__lowerCamelCase    )

    return prime


if __name__ == "__main__":
      print(prime_sieve(int(input("""Enter a positive integer: """).strip())))



