





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     IFPipeline
 lowercase_						=     TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
 lowercase_						=     TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_						=     PipelineTesterMixin.required_optional_params - {"""latents"""}

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->List[str]:



   '''simple docstring'''



   return self._get_dummy_components()

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Any=0)			->Optional[Any]:



   '''simple docstring'''



   if str(a_).startswith("mps"):
     lowerCamelCase__: Dict         =torch.manual_seed(a_)
   else:
     lowerCamelCase__: List[Any]         =torch.Generator(device=a_).manual_seed(a_)

   lowerCamelCase__: Optional[Any]         ={
       'prompt': 'A painting of a squirrel eating a burger',
       'generator': generator,
       'num_inference_steps': 2,
       'output_type': 'numpy',
   }

   return inputs

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->List[str]:



   '''simple docstring'''



   self._test_save_load_optional_components()

 @unittest.skipIf(torch_device != "cuda"			,					reason="float16 requires CUDA")
 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Union[str, Any]:



   '''simple docstring'''



   super().test_save_load_floataa(expected_max_diff=1E-1)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Dict:



   '''simple docstring'''



   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->List[Any]:



   '''simple docstring'''



   self._test_save_load_local()

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->int:



   '''simple docstring'''



   self._test_inference_batch_single_identical(
       expected_max_diff=1E-2			,					)







 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available()			,					reason="XFormers attention is only available with CUDA and `xformers` installed"			,					)
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Tuple:



   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->List[Any]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"			,					variant="fp16"			,					torch_dtype=torch.floataa)

   lowerCamelCase__: Dict         =IFSuperResolutionPipeline.from_pretrained(
       "DeepFloyd/IF-II-L-v1.0"			,					variant="fp16"			,					torch_dtype=torch.floataa			,					text_encoder=a_			,					tokenizer=a_)

   # pre compute text embeddings and remove T5 to save memory

   pipe_a.text_encoder.to("cuda")

   lowerCamelCase__: str         =pipe_a.encode_prompt("anime turtle"			,					device="cuda")

   del pipe_a.tokenizer
   del pipe_a.text_encoder
   gc.collect()

   lowerCamelCase__: int         =None
   lowerCamelCase__: Union[str, Any]         =None

   pipe_a.enable_model_cpu_offload()
   pipe_a.enable_model_cpu_offload()

   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

   self._test_if(a_			,					a_			,					a_			,					a_)

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # img2img

   lowerCamelCase__: Optional[int]         =IFImgaImgPipeline(**pipe_a.components)
   lowerCamelCase__: Union[str, Any]         =IFImgaImgSuperResolutionPipeline(**pipe_a.components)

   pipe_a.enable_model_cpu_offload()
   pipe_a.enable_model_cpu_offload()

   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

   self._test_if_imgaimg(a_			,					a_			,					a_			,					a_)

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # inpainting

   lowerCamelCase__: Optional[int]         =IFInpaintingPipeline(**pipe_a.components)
   lowerCamelCase__: Tuple         =IFInpaintingSuperResolutionPipeline(**pipe_a.components)

   pipe_a.enable_model_cpu_offload()
   pipe_a.enable_model_cpu_offload()

   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

   self._test_if_inpainting(a_			,					a_			,					a_			,					a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  int			,					UpperCAmelCase_							:  Union[str, Any])			->Union[str, Any]:



   '''simple docstring'''



   _start_torch_memory_measurement()

   lowerCamelCase__: Union[str, Any]         =torch.Generator(device="cpu").manual_seed(0)
   lowerCamelCase__: int         =pipe_a(
       prompt_embeds=a_			,					negative_prompt_embeds=a_			,					num_inference_steps=2			,					generator=a_			,					output_type="np"			,					)

   lowerCamelCase__: Optional[Any]         =output.images[0]

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

   lowerCamelCase__: Union[str, Any]         =torch.cuda.max_memory_allocated()
   assert mem_bytes < 13 * 10**9

   lowerCamelCase__: List[Any]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy")
   assert_mean_pixel_difference(a_			,					a_)

   # pipeline 2

   _start_torch_memory_measurement()

   lowerCamelCase__: Dict         =torch.Generator(device="cpu").manual_seed(0)

   lowerCamelCase__: Any         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(0)).to(a_)

   lowerCamelCase__: List[Any]         =pipe_a(
       prompt_embeds=a_			,					negative_prompt_embeds=a_			,					image=a_			,					generator=a_			,					num_inference_steps=2			,					output_type="np"			,					)

   lowerCamelCase__: int         =output.images[0]

   assert image.shape == (256, 256, 3)

   lowerCamelCase__: Dict         =torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   lowerCamelCase__: Dict         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy")
   assert_mean_pixel_difference(a_			,					a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Tuple)			->List[str]:



   '''simple docstring'''



   _start_torch_memory_measurement()

   lowerCamelCase__: Union[str, Any]         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(0)).to(a_)

   lowerCamelCase__: Any         =torch.Generator(device="cpu").manual_seed(0)

   lowerCamelCase__: List[str]         =pipe_a(
       prompt_embeds=a_			,					negative_prompt_embeds=a_			,					image=a_			,					num_inference_steps=2			,					generator=a_			,					output_type="np"			,					)

   lowerCamelCase__: Optional[int]         =output.images[0]

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

   lowerCamelCase__: Union[str, Any]         =torch.cuda.max_memory_allocated()
   assert mem_bytes < 10 * 10**9

   lowerCamelCase__: Union[str, Any]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy")
   assert_mean_pixel_difference(a_			,					a_)

   # pipeline 2

   _start_torch_memory_measurement()

   lowerCamelCase__: List[str]         =torch.Generator(device="cpu").manual_seed(0)

   lowerCamelCase__: str         =floats_tensor((1, 3, 256, 256)			,					rng=random.Random(0)).to(a_)
   lowerCamelCase__: Optional[Any]         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(0)).to(a_)

   lowerCamelCase__: Union[str, Any]         =pipe_a(
       prompt_embeds=a_			,					negative_prompt_embeds=a_			,					image=a_			,					original_image=a_			,					generator=a_			,					num_inference_steps=2			,					output_type="np"			,					)

   lowerCamelCase__: Optional[int]         =output.images[0]

   assert image.shape == (256, 256, 3)

   lowerCamelCase__: Any         =torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   lowerCamelCase__: Optional[int]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy")
   assert_mean_pixel_difference(a_			,					a_)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  str)			->Dict:



   '''simple docstring'''



   _start_torch_memory_measurement()

   lowerCamelCase__: Dict         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(0)).to(a_)
   lowerCamelCase__: Dict         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(1)).to(a_)

   lowerCamelCase__: List[str]         =torch.Generator(device="cpu").manual_seed(0)
   lowerCamelCase__: Any         =pipe_a(
       prompt_embeds=a_			,					negative_prompt_embeds=a_			,					image=a_			,					mask_image=a_			,					num_inference_steps=2			,					generator=a_			,					output_type="np"			,					)

   lowerCamelCase__: Any         =output.images[0]

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

   lowerCamelCase__: Optional[int]         =torch.cuda.max_memory_allocated()
   assert mem_bytes < 10 * 10**9

   lowerCamelCase__: Union[str, Any]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy")
   assert_mean_pixel_difference(a_			,					a_)

   # pipeline 2

   _start_torch_memory_measurement()

   lowerCamelCase__: int         =torch.Generator(device="cpu").manual_seed(0)

   lowerCamelCase__: Any         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(0)).to(a_)
   lowerCamelCase__: int         =floats_tensor((1, 3, 256, 256)			,					rng=random.Random(0)).to(a_)
   lowerCamelCase__: Optional[int]         =floats_tensor((1, 3, 256, 256)			,					rng=random.Random(1)).to(a_)

   lowerCamelCase__: Optional[int]         =pipe_a(
       prompt_embeds=a_			,					negative_prompt_embeds=a_			,					image=a_			,					mask_image=a_			,					original_image=a_			,					generator=a_			,					num_inference_steps=2			,					output_type="np"			,					)

   lowerCamelCase__: List[str]         =output.images[0]

   assert image.shape == (256, 256, 3)

   lowerCamelCase__: Optional[Any]         =torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   lowerCamelCase__: List[str]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy")
   assert_mean_pixel_difference(a_			,					a_)





def 	lowerCAmelCase_					(			)	->						int:







  """simple docstring"""





  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()






'''simple docstring'''

from __future__ import annotations







def a_     (    __UpperCAmelCase					)				->					list[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=[True] * limit
			snake_case:    Optional[int]      							=False
			snake_case:    Union[str, Any]      							=False
			snake_case:    List[Any]      							=True

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

			snake_case:    Union[str, Any]      							=[2]

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

			return primes







def a_     (    __UpperCAmelCase = 1_00_00_00					)				->					int:





			"""simple docstring"""







			snake_case:    str      							=prime_sieve(__UpperCAmelCase					)
			snake_case:    str      							=0
			snake_case:    str      							=0

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

									if sol in primes:
												snake_case:    List[str]      							=j - i
												snake_case:    Optional[int]      							=sol

			return largest


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




