





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image


_lowercase		:  Dict			       =["""text""", """image""", """audio"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Any 						=      []

		for input_type in input_types:
				if input_type == "text":
						inputs.append('Text input'						)
				elif input_type == "image":
						inputs.append(
						    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'						)						) / '000000039769.png'						).resize((5_12, 5_12)						)						)
				elif input_type == "audio":
						inputs.append(torch.ones(30_00						)						)
				elif isinstance(lowerCAmelCase__							,lowerCAmelCase__						):
						inputs.append(create_inputs(lowerCAmelCase__						)						)
				else:
						raise ValueError(F"Invalid type requested: {input_type}"						)

		return inputs


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Optional[Any] 						=      []

		for output in outputs:
				if isinstance(lowerCAmelCase__							,(str, AgentText)						):
						output_types.append('text'						)
				elif isinstance(lowerCAmelCase__							,(Image.Image, AgentImage)						):
						output_types.append('image'						)
				elif isinstance(lowerCAmelCase__							,(torch.Tensor, AgentAudio)						):
						output_types.append('audio'						)
				else:
						raise ValueError(F"Invalid output: {output}"						)

		return output_types






@is_tool_test
class 						UpperCamelCase_  :



					def   __a     (		self     :     str						):
							self.assertTrue(hasattr(self.tool       ,      'inputs'						)						)
							self.assertTrue(hasattr(self.tool       ,      'outputs'						)						)

							lowerCamelCase_	:     Dict 						=      self.tool.inputs
							for _input in inputs:
									if isinstance(_input       ,      lowerCamelCase						):
											for __input in _input:
													self.assertTrue(__input in authorized_types						)
									else:
											self.assertTrue(_input in authorized_types						)

							lowerCamelCase_	:     Optional[Any] 						=      self.tool.outputs
							for _output in outputs:
									self.assertTrue(_output in authorized_types						)



					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     List[Any] 						=      create_inputs(self.tool.inputs						)
							lowerCamelCase_	:     Optional[int] 						=      self.tool(*lowerCamelCase						)

							# There is a single output
							if len(self.tool.outputs						) == 1:
									lowerCamelCase_	:     str 						=      [outputs]

							self.assertListEqual(output_types(lowerCamelCase						)       ,      self.tool.outputs						)



					def   __a     (		self     :     List[str]						):
							self.assertTrue(hasattr(self.tool       ,      'description'						)						)
							self.assertTrue(hasattr(self.tool       ,      'default_checkpoint'						)						)
							self.assertTrue(self.tool.description.startswith('This is a tool that'						)						)



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     int 						=      create_inputs(self.tool.inputs						)
							lowerCamelCase_	:     Optional[int] 						=      self.tool(*lowerCamelCase						)

							if not isinstance(lowerCamelCase       ,      lowerCamelCase						):
									lowerCamelCase_	:     List[str] 						=      [outputs]

							self.assertEqual(len(lowerCamelCase						)       ,      len(self.tool.outputs						)						)

							for output, output_type in zip(lowerCamelCase       ,      self.tool.outputs						):
									lowerCamelCase_	:     Any 						=      AGENT_TYPE_MAPPING[output_type]
									self.assertTrue(isinstance(lowerCamelCase       ,      lowerCamelCase						)						)





					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Tuple 						=      create_inputs(self.tool.inputs						)

							lowerCamelCase_	:     str 						=      []

							for _input, input_type in zip(lowerCamelCase       ,      self.tool.inputs						):
									if isinstance(lowerCamelCase       ,      lowerCamelCase						):
											_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input						) for _input_type in input_type]						)
									else:
											_inputs.append(AGENT_TYPE_MAPPING[input_type](_input						)						)

        # Should not raise an error
							lowerCamelCase_	:     int 						=      self.tool(*lowerCamelCase						)

							if not isinstance(lowerCamelCase       ,      lowerCamelCase						):
									lowerCamelCase_	:     Tuple 						=      [outputs]

							self.assertEqual(len(lowerCamelCase						)       ,      len(self.tool.outputs						)						)







from __future__ import annotations

import math


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		if num <= 0:
				lowerCamelCase_	:     Optional[int] 						=      F"{num}: Invalid input, please enter a positive integer."
				raise ValueError(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      [True] * (num + 1)
		lowerCamelCase_	:     List[str] 						=      []
		lowerCamelCase_	:     Optional[int] 						=      2
		lowerCamelCase_	:     List[str] 						=      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_	:     Tuple 						=      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())))
