
from __future__ import annotations

import math






def    lowerCamelCase__				(				__lowerCAmelCase  :  int				):




			"""simple docstring"""


			if num <= 0:
						lowerCAmelCase_						=	F"""{num}: Invalid input, please enter a positive integer."""
						raise ValueError(__lowerCAmelCase				)

			lowerCAmelCase_						=	[True] * (num + 1)
			lowerCAmelCase_						=	[]
			lowerCAmelCase_						=	2
			lowerCAmelCase_						=	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:
															lowerCAmelCase_						=	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())))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_A   =     {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextTokenizer"]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextFeatureExtractor"]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFSpeech2TextForConditionalGeneration",
			    "TFSpeech2TextModel",
			    "TFSpeech2TextPreTrainedModel",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Speech2TextForConditionalGeneration",
			    "Speech2TextModel",
			    "Speech2TextPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



