





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    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  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Any						   =       KandinskyVaaInpaintPipeline
      _UpperCAmelCase     :int						   =       ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _UpperCAmelCase     :int						   =       [
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _UpperCAmelCase     :int						   =       [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase     :List[Any]						   =       False





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 32





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 32





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 100





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       torch.manual_seed(0       )

       UpperCamelCase     : int	     =       {
           "in_channels": 9,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image",
           "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,
       }

       UpperCamelCase     : Tuple	     =       UNetaDConditionModel(**A_       )
       return model





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["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",
           ],
           "vq_embed_dim": 4,
       }





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       torch.manual_seed(0       )
       UpperCamelCase     : Dict	     =       VQModel(**self.dummy_movq_kwargs       )
       return model





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.dummy_unet
       UpperCamelCase     : Optional[Any]	     =       self.dummy_movq

       UpperCamelCase     : List[Any]	     =       DDIMScheduler(
           num_train_timesteps=1000	,							beta_schedule="linear"	,							beta_start=0.0_00_85	,							beta_end=0.0_12	,							clip_sample=A_	,							set_alpha_to_one=A_	,							steps_offset=1	,							prediction_type="epsilon"	,							thresholding=A_	,							)

       UpperCamelCase     : List[str]	     =       {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





      def 	__UpperCamelCase(			self	,							A_	,							A_=0       ):
       '''simple docstring'''




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

       if str(A_       ).startswith("mps"       ):
        UpperCamelCase     : int	     =       torch.manual_seed(A_       )
       else:
        UpperCamelCase     : List[str]	     =       torch.Generator(device=A_       ).manual_seed(A_       )
       UpperCamelCase     : Dict	     =       {
           "image": init_image,
           "mask_image": mask,
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "num_inference_steps": 2,
           "guidance_scale": 4.0,
           "output_type": "np",
       }
       return inputs





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       "cpu"

       UpperCamelCase     : Tuple	     =       self.get_dummy_components()

       UpperCamelCase     : int	     =       self.pipeline_class(**A_       )
       UpperCamelCase     : str	     =       pipe.to(A_       )

       pipe.set_progress_bar_config(disable=A_       )

       UpperCamelCase     : Dict	     =       pipe(**self.get_dummy_inputs(A_       )       )
       UpperCamelCase     : Optional[Any]	     =       output.images

       UpperCamelCase     : List[str]	     =       pipe(
           **self.get_dummy_inputs(A_       )	,							return_dict=A_	,							)[0]

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

       print(F"""image.shape {image.shape}"""       )

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

       UpperCamelCase     : Optional[Any]	     =       np.array(
           [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]       )

       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()}"""





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )



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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"       )

       UpperCamelCase     : Union[str, Any]	     =       load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
       UpperCamelCase     : Any	     =       np.ones((768, 768)	,							dtype=np.floataa       )
       UpperCamelCase     : str	     =       0

       UpperCamelCase     : int	     =       "a hat"

       UpperCamelCase     : Any	     =       KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"	,							torch_dtype=torch.floataa       )
       pipe_prior.to(A_       )

       UpperCamelCase     : Tuple	     =       KandinskyVaaInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder-inpaint"	,							torch_dtype=torch.floataa       )
       UpperCamelCase     : Tuple	     =       pipeline.to(A_       )
       pipeline.set_progress_bar_config(disable=A_       )

       UpperCamelCase     : Tuple	     =       torch.Generator(device="cpu"       ).manual_seed(0       )
       UpperCamelCase     ,   UpperCamelCase     : Tuple	     =       pipe_prior(
           A_	,							generator=A_	,							num_inference_steps=5	,							negative_prompt=""	,							).to_tuple()

       UpperCamelCase     : Tuple	     =       pipeline(
           image=A_	,							mask_image=A_	,							image_embeds=A_	,							negative_image_embeds=A_	,							generator=A_	,							num_inference_steps=100	,							height=768	,							width=768	,							output_type="np"	,							)

       UpperCamelCase     : Union[str, Any]	     =       output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(A_	,							A_       )







import math



def 		A_    (					_lowerCAmelCase					)						->			list:
 UpperCamelCase     : str	     =       [True] * n
 UpperCamelCase     : Optional[int]	     =       False
 UpperCamelCase     : str	     =       False
 UpperCamelCase     : List[Any]	     =       True

 for i in range(3							,       int(n**0.5 + 1					)							,       2					):
  UpperCamelCase     : int	     =       i * 2
  while index < n:
   UpperCamelCase     : Optional[Any]	     =       False
   UpperCamelCase     : Optional[Any]	     =       index + i

 UpperCamelCase     : Optional[Any]	     =       [2]

 for i in range(3							,       _lowerCAmelCase							,       2					):
  if is_prime[i]:
   primes.append(_lowerCAmelCase					)

 return primes



def 		A_    (					_lowerCAmelCase = 9999_6666_3333					)						->			int:
 UpperCamelCase     : Tuple	     =       math.floor(math.sqrt(_lowerCAmelCase					)					) + 100
 UpperCamelCase     : Dict	     =       prime_sieve(_lowerCAmelCase					)

 UpperCamelCase     : Optional[Any]	     =       0
 UpperCamelCase     : Tuple	     =       0
 UpperCamelCase     : List[str]	     =       primes[prime_index]

 while (last_prime**2) <= limit:
  UpperCamelCase     : Dict	     =       primes[prime_index + 1]

  UpperCamelCase     : Any	     =       last_prime**2
  UpperCamelCase     : Union[str, Any]	     =       next_prime**2

  # Get numbers divisible by lps(current)
  UpperCamelCase     : Dict	     =       lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  UpperCamelCase     : List[str]	     =       upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  UpperCamelCase     : Dict	     =       0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  UpperCamelCase     : str	     =       next_prime
  prime_index += 1

 return matches_sum


if __name__ == "__main__":
    print(solution())
