


'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCAmelCase		(							__lowerCAmelCase  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							KandinskyVaaControlnetImgaImgPipeline
  a_  					=							["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
  a_  					=							["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
  a_  					=							[
      """generator""",
      """height""",
      """width""",
      """strength""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        return self.time_input_dim






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        return self.time_input_dim * 4






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        return 100






  @property
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  Optional[Any]          =				{
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        A_						:  List[Any]          =				UNetaDConditionModel(**_UpperCamelCase )
        return model






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  List[Any]          =				VQModel(**self.dummy_movq_kwargs )
        return model






  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.dummy_unet
        A_						:  Dict          =				self.dummy_movq

        A_						:  List[str]          =				{
            """num_train_timesteps""": 1000,
            """beta_schedule""": """linear""",
            """beta_start""": 0.00085,
            """beta_end""": 0.012,
            """clip_sample""": False,
            """set_alpha_to_one""": False,
            """steps_offset""": 0,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
        }

        A_						:  Dict          =				DDIMScheduler(**_UpperCamelCase )

        A_						:  Optional[Any]          =				{
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components






  def        _a							(							self :       Tuple	,_a :       List[Any]	,_a :       Any=0 ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
        A_						:  str          =				floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1 ) ).to(
            _UpperCamelCase )
        # create init_image
        A_						:  int          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
        A_						:  Optional[Any]          =				image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  int          =				Image.fromarray(np.uinta(_UpperCamelCase ) ).convert("""RGB""" ).resize((256, 256) )
        # create hint
        A_						:  List[Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )

        if str(_UpperCamelCase ).startswith("""mps""" ):
              A_						:  Any          =				torch.manual_seed(_UpperCamelCase )
        else:
              A_						:  Any          =				torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
        A_						:  List[Any]          =				{
            """image""": init_image,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 10,
            """guidance_scale""": 7.0,
            """strength""": 0.2,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  str          =				"""cpu"""

        A_						:  Tuple          =				self.get_dummy_components()

        A_						:  Union[str, Any]          =				self.pipeline_class(**_UpperCamelCase )
        A_						:  str          =				pipe.to(_UpperCamelCase )

        pipe.set_progress_bar_config(disable=_UpperCamelCase )

        A_						:  List[str]          =				pipe(**self.get_dummy_inputs(_UpperCamelCase ) )
        A_						:  Any          =				output.images

        A_						:  Dict          =				pipe(
            **self.get_dummy_inputs(_UpperCamelCase )	,return_dict=_UpperCamelCase	,)[0]

        A_						:  Optional[int]          =				image[0, -3:, -3:, -1]
        A_						:  int          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A_						:  str          =				np.array(
            [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
        assert (
            np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
        ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       Any ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )

        A_						:  Any          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
        A_						:  Optional[int]          =				init_image.resize((512, 512) )

        A_						:  Dict          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png""" )
        A_						:  int          =				torch.from_numpy(np.array(_UpperCamelCase ) ).float() / 255.0
        A_						:  Dict          =				hint.permute(2	,0	,1 ).unsqueeze(0 )

        A_						:  Tuple          =				"""A robot, 4k photo"""

        A_						:  Optional[int]          =				KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""	,torch_dtype=torch.floataa )
        pipe_prior.to(_UpperCamelCase )

        A_						:  str          =				KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,torch_dtype=torch.floataa )
        A_						:  List[str]          =				pipeline.to(_UpperCamelCase )

        pipeline.set_progress_bar_config(disable=_UpperCamelCase )

        A_						:  str          =				torch.Generator(device="""cpu""" ).manual_seed(0 )

        A_						:  Union[str, Any]          =				pipe_prior(
            _UpperCamelCase	,image=_UpperCamelCase	,strength=0.85	,generator=_UpperCamelCase	,negative_prompt=""""""	,).to_tuple()

        A_						:  Optional[Any]          =				pipeline(
            image=_UpperCamelCase	,image_embeds=_UpperCamelCase	,negative_image_embeds=_UpperCamelCase	,hint=_UpperCamelCase	,generator=_UpperCamelCase	,num_inference_steps=100	,height=512	,width=512	,strength=0.5	,output_type="""np"""	,)

        A_						:  List[Any]          =				output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_UpperCamelCase	,_UpperCamelCase )




'''simple docstring'''


from __future__ import annotations

import math




def      lowerCamelCase					(      lowerCamelCase  :      int):

      if num <= 0:
            A_						:  List[Any]          =				F'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCamelCase)

      A_						:  str          =				[True] * (num + 1)
      A_						:  Tuple          =				[]
      A_						:  str          =				2
      A_						:  Any          =				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:
                              A_						:  Union[str, Any]          =				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())))
