





'''simple docstring'''






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   KandinskyVaaPriorPipeline
   _UpperCamelCase       		=   ["""prompt"""]
   _UpperCamelCase       		=   ["""prompt""", """negative_prompt"""]
   _UpperCamelCase       		=   [
       """num_images_per_prompt""",
       """generator""",
       """num_inference_steps""",
       """latents""",
       """negative_prompt""",
       """guidance_scale""",
       """output_type""",
       """return_dict""",
   ]
   _UpperCamelCase       		=   False


   @property
   def 					snake_case				( self					):
       return 32


   @property
   def 					snake_case				( self					):
       return 32


   @property
   def 					snake_case				( self					):
       return self.time_input_dim


   @property
   def 					snake_case				( self					):
       return self.time_input_dim * 4


   @property
   def 					snake_case				( self					):
       return 100


   @property
   def 					snake_case				( self					):
       A       :       List[Any]							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
       return tokenizer


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)
       A       :       int							=						CLIPTextConfig(
           bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=self.text_embedder_hidden_size    ,   projection_dim=self.text_embedder_hidden_size    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1_000    ,   )
       return CLIPTextModelWithProjection(_UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)

       A       :       int							=						{
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       A       :       Tuple							=						PriorTransformer(**_UpperCAmelCase					)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       A       :       Any							=						nn.Parameter(torch.ones(model.clip_std.shape					)					)
       return model


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)
       A       :       Tuple							=						CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size    ,   image_size=224    ,   projection_dim=self.text_embedder_hidden_size    ,   intermediate_size=37    ,   num_attention_heads=4    ,   num_channels=3    ,   num_hidden_layers=5    ,   patch_size=14    ,   )

       A       :       Dict							=						CLIPVisionModelWithProjection(_UpperCAmelCase					)
       return model


   @property
   def 					snake_case				( self					):
       A       :       Any							=						CLIPImageProcessor(
           crop_size=224    ,   do_center_crop=_UpperCAmelCase    ,   do_normalize=_UpperCAmelCase    ,   do_resize=_UpperCAmelCase    ,   image_mean=[0.48145466, 0.4578275, 0.40821073]    ,   image_std=[0.26862954, 0.26130258, 0.27577711]    ,   resample=3    ,   size=224    ,   )

       return image_processor


   def 					snake_case				( self					):
       A       :       Tuple							=						self.dummy_prior
       A       :       List[str]							=						self.dummy_image_encoder
       A       :       Any							=						self.dummy_text_encoder
       A       :       Optional[int]							=						self.dummy_tokenizer
       A       :       Optional[Any]							=						self.dummy_image_processor

       A       :       int							=						UnCLIPScheduler(
           variance_type='''fixed_small_log'''    ,   prediction_type='''sample'''    ,   num_train_timesteps=1_000    ,   clip_sample=_UpperCAmelCase    ,   clip_sample_range=10.0    ,   )

       A       :       List[Any]							=						{
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0					):
       if str(_UpperCAmelCase					).startswith('''mps'''					):
           A       :       Optional[Any]							=						torch.manual_seed(_UpperCAmelCase					)
       else:
           A       :       List[str]							=						torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)
       A       :       Optional[Any]							=						{
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						'''cpu'''

       A       :       Optional[Any]							=						self.get_dummy_components()

       A       :       Dict							=						self.pipeline_class(**_UpperCAmelCase					)
       A       :       Tuple							=						pipe.to(_UpperCAmelCase					)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

       A       :       Tuple							=						pipe(**self.get_dummy_inputs(_UpperCAmelCase					)					)
       A       :       Union[str, Any]							=						output.image_embeds

       A       :       List[Any]							=						pipe(
           **self.get_dummy_inputs(_UpperCAmelCase					)    ,   return_dict=_UpperCAmelCase    ,   )[0]

       A       :       int							=						image[0, -10:]
       A       :       Tuple							=						image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       A       :       Any							=						np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2


   @skip_mps
   def 					snake_case				( self					):
       A       :       Optional[Any]							=						torch_device == '''cpu'''
       A       :       Optional[int]							=						True
       A       :       int							=						False

       self._test_inference_batch_single_identical(
           test_max_difference=_UpperCAmelCase    ,   relax_max_difference=_UpperCAmelCase    ,   test_mean_pixel_difference=_UpperCAmelCase    ,   )


   @skip_mps
   def 					snake_case				( self					):
       A       :       Optional[Any]							=						torch_device == '''cpu'''
       A       :       Tuple							=						False

       self._test_attention_slicing_forward_pass(
           test_max_difference=_UpperCAmelCase    ,   test_mean_pixel_difference=_UpperCAmelCase    ,   )







'''simple docstring'''






from __future__ import annotations

import math
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[int]:

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

    A       :       int							=						[True] * (num + 1)
    A       :       Dict							=						[]
    A       :       str							=						2
    A       :       Any							=						int(math.sqrt(UpperCamelCase__		)		)

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

            # Set multiples of start be False
            for i in range(start * start    ,       num + 1    ,       UpperCamelCase__		):
                if sieve[i] is True:
                    A       :       Union[str, Any]							=						False

        start += 1

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

    return prime


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