
from __future__ import annotations







def    _snake_case (       __snake_case    )     ->     list[int]:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		[True] * limit
      UpperCAmelCase_  :	Any				      =		False
      UpperCAmelCase_  :	Dict				      =		False
      UpperCAmelCase_  :	Optional[int]				      =		True

      for i in range(3					,  int(limit**0.5 + 1    )					,  2    ):
            UpperCAmelCase_  :	List[Any]				      =		i * 2
            while index < limit:
                  UpperCAmelCase_  :	Dict				      =		False
                  UpperCAmelCase_  :	int				      =		index + i

      UpperCAmelCase_  :	Optional[Any]				      =		[2]

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

      return primes







def    _snake_case (       __snake_case = 1_0_0_0_0_0_0    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	str				      =		prime_sieve(__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		0
      UpperCAmelCase_  :	Optional[Any]				      =		0

      for i in range(len(__snake_case    )    ):
            for j in range(i + length					,  len(__snake_case    )    ):
                  UpperCAmelCase_  :	Tuple				      =		sum(primes[i:j]    )
                  if sol >= ceiling:
                        break

                  if sol in primes:
                        UpperCAmelCase_  :	str				      =		j - i
                        UpperCAmelCase_  :	Dict				      =		sol

      return largest


if __name__ == "__main__":
   print(F'''{solution() = }''')





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				AltDiffusionPipeline
       _lowerCamelCase			    =				TEXT_TO_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_BATCH_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS



       def  A_   (     self):

             """simple docstring"""





             torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[Any]				      =		UNetaDConditionModel(
                 block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,cross_attention_dim=32     ,)
             UpperCAmelCase_  :	Any				      =		DDIMScheduler(
                 beta_start=0.0_0085     ,beta_end=0.012     ,beta_schedule="scaled_linear"     ,clip_sample=lowercase     ,set_alpha_to_one=lowercase     ,)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		AutoencoderKL(
                 block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,latent_channels=4     ,)

             # TODO: address the non-deterministic text encoder (fails for save-load tests)
             # torch.manual_seed(0)
             # text_encoder_config = RobertaSeriesConfig(
             #     hidden_size=32,
             #     project_dim=32,
             #     intermediate_size=37,
             #     layer_norm_eps=1e-05,
             #     num_attention_heads=4,
             #     num_hidden_layers=5,
             #     vocab_size=5002,
             # )
             # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

             torch.manual_seed(0)
             UpperCAmelCase_  :	Tuple				      =		CLIPTextConfig(
                 bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=5002     ,)
             UpperCAmelCase_  :	Dict				      =		CLIPTextModel(lowercase)

             UpperCAmelCase_  :	List[Any]				      =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
             UpperCAmelCase_  :	List[str]				      =		77

             UpperCAmelCase_  :	Union[str, Any]				      =		{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
             return components



       def  A_   (     self     ,lowercase     ,lowercase=0):

             """simple docstring"""





             if str(lowercase).startswith("mps"):
                   UpperCAmelCase_  :	Any				      =		torch.manual_seed(lowercase)
             else:
                   UpperCAmelCase_  :	Dict				      =		torch.Generator(device=lowercase).manual_seed(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		{
                 "prompt": "A painting of a squirrel eating a burger",
                 "generator": generator,
                 "num_inference_steps": 2,
                 "guidance_scale": 6.0,
                 "output_type": "numpy",
             }
             return inputs



       def  A_   (     self):

             """simple docstring"""





             super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	Any				      =		self.get_dummy_components()
             torch.manual_seed(0)
             UpperCAmelCase_  :	List[Any]				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Any				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		text_encoder

             UpperCAmelCase_  :	List[Any]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Tuple				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		"A photo of an astronaut"
             UpperCAmelCase_  :	str				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	List[str]				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Optional[Any]				      =		np.array(
                 [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	int				      =		self.get_dummy_components()
             UpperCAmelCase_  :	Optional[Any]				      =		PNDMScheduler(skip_prk_steps=lowercase)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Union[str, Any]				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Dict				      =		text_encoder
             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	List[Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	int				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Tuple				      =		np.array(
                 [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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


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




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,safety_checker=lowercase)
             UpperCAmelCase_  :	Any				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Any				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Any				      =		torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[int]				      =		alt_pipe([prompt]     ,generator=lowercase     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="np")

             UpperCAmelCase_  :	Dict				      =		output.images

             UpperCAmelCase_  :	List[str]				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	Dict				      =		np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,subfolder="scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,scheduler=lowercase     ,safety_checker=lowercase)
             UpperCAmelCase_  :	List[str]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Optional[Any]				      =		torch.manual_seed(0)

             UpperCAmelCase_  :	List[str]				      =		alt_pipe([prompt]     ,generator=lowercase     ,num_inference_steps=2     ,output_type="numpy")
             UpperCAmelCase_  :	int				      =		output.images

             UpperCAmelCase_  :	str				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	List[Any]				      =		np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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