



"""simple docstring"""







import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






	@property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   1
			_lowercase				: Any     =   3
			_lowercase				: Any     =   (32, 32)

			_lowercase				: str     =   floats_tensor((batch_size, num_channels) + sizes				,rng=random.Random(0   )   ).to(UpperCAmelCase_   )
			return image






	@property
	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: Union[str, 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				,)
			return model






	@property
	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: 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				,)
			return model






	@property
	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: List[Any]     =   CLIPTextConfig(
			    bos_token_id=0				,eos_token_id=2				,hidden_size=32				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
			return CLIPTextModel(UpperCAmelCase_   )






	@property
	def    lowerCamelCase__					(     self   ):
			def extract(*UpperCAmelCase_				,**UpperCAmelCase_   ):





					class   UpperCamelCase	:




						"""simple docstring"""






						def __init__(     self   ):
								_lowercase				: Tuple     =   torch.ones([0]   )






						def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
								self.pixel_values.to(UpperCAmelCase_   )
								return self







					return Out()

			return extract






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
			_lowercase				: Union[str, Any]     =   self.dummy_cond_unet
			_lowercase				: int     =   DDIMScheduler(
			    beta_start=0.00085				,beta_end=0.012				,beta_schedule="""scaled_linear"""				,clip_sample=UpperCAmelCase_				,set_alpha_to_one=UpperCAmelCase_				,)

			_lowercase				: Union[str, Any]     =   self.dummy_vae
			_lowercase				: Optional[int]     =   self.dummy_text_encoder
			_lowercase				: str     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

			# make sure here that pndm scheduler skips prk
			_lowercase				: Dict     =   StableDiffusionPipeline(
			    unet=UpperCAmelCase_				,scheduler=UpperCAmelCase_				,vae=UpperCAmelCase_				,text_encoder=UpperCAmelCase_				,tokenizer=UpperCAmelCase_				,safety_checker=UpperCAmelCase_				,feature_extractor=self.dummy_extractor				,)
			_lowercase				: List[Any]     =   sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: List[str]     =   """A painting of a squirrel eating a burger"""

			_lowercase				: List[str]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
			_lowercase				: Tuple     =   sd_pipe([prompt]				,generator=UpperCAmelCase_				,guidance_scale=6.0				,num_inference_steps=2				,output_type="""np"""   )
			_lowercase				: List[Any]     =   output.images

			_lowercase				: Optional[int]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
			_lowercase				: Optional[int]     =   sd_pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=6.0				,num_inference_steps=2				,output_type="""np"""				,return_dict=UpperCAmelCase_				,)[0]

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

			assert image.shape == (1, 64, 64, 3)
			_lowercase				: Optional[Any]     =   np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]   )

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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
			_lowercase				: Optional[int]     =   self.dummy_cond_unet
			_lowercase				: List[Any]     =   PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
			_lowercase				: str     =   self.dummy_vae
			_lowercase				: Any     =   self.dummy_text_encoder
			_lowercase				: Union[str, Any]     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

			# make sure here that pndm scheduler skips prk
			_lowercase				: Any     =   StableDiffusionPipeline(
			    unet=UpperCAmelCase_				,scheduler=UpperCAmelCase_				,vae=UpperCAmelCase_				,text_encoder=UpperCAmelCase_				,tokenizer=UpperCAmelCase_				,safety_checker=UpperCAmelCase_				,feature_extractor=self.dummy_extractor				,)
			_lowercase				: Union[str, Any]     =   sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: int     =   """A painting of a squirrel eating a burger"""
			_lowercase				: Optional[Any]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
			_lowercase				: List[str]     =   sd_pipe([prompt]				,generator=UpperCAmelCase_				,guidance_scale=6.0				,num_inference_steps=2				,output_type="""np"""   )

			_lowercase				: List[Any]     =   output.images

			_lowercase				: List[Any]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
			_lowercase				: int     =   sd_pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=6.0				,num_inference_steps=2				,output_type="""np"""				,return_dict=UpperCAmelCase_				,)[0]

			_lowercase				: Dict     =   image[0, -3:, -3:, -1]
			_lowercase				: List[Any]     =   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			_lowercase				: Union[str, Any]     =   np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]   )

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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   StableDiffusionPipeline.from_pretrained(
			    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,safety_checker=UpperCAmelCase_   )
			assert isinstance(UpperCAmelCase_				,UpperCAmelCase_   )
			assert isinstance(pipe.scheduler				,UpperCAmelCase_   )
			assert pipe.safety_checker is None

			_lowercase				: Union[str, Any]     =   pipe("""example prompt"""				,num_inference_steps=2   ).images[0]
			assert image is not None

			# check that there's no error when saving a pipeline with one of the models being None
			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(UpperCAmelCase_   )
					_lowercase				: Dict     =   StableDiffusionPipeline.from_pretrained(UpperCAmelCase_   )

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			_lowercase				: str     =   pipe("""example prompt"""				,num_inference_steps=2   ).images[0]
			assert image is not None






	@unittest.skipIf(torch_device != """cuda"""				,"""This test requires a GPU"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   self.dummy_cond_unet
			_lowercase				: List[Any]     =   PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
			_lowercase				: List[Any]     =   self.dummy_vae
			_lowercase				: Any     =   self.dummy_text_encoder
			_lowercase				: List[str]     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

			# put models in fp16
			_lowercase				: Optional[Any]     =   unet.half()
			_lowercase				: Dict     =   vae.half()
			_lowercase				: Tuple     =   bert.half()

			# make sure here that pndm scheduler skips prk
			_lowercase				: List[str]     =   StableDiffusionPipeline(
			    unet=UpperCAmelCase_				,scheduler=UpperCAmelCase_				,vae=UpperCAmelCase_				,text_encoder=UpperCAmelCase_				,tokenizer=UpperCAmelCase_				,safety_checker=UpperCAmelCase_				,feature_extractor=self.dummy_extractor				,)
			_lowercase				: Dict     =   sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: str     =   """A painting of a squirrel eating a burger"""
			_lowercase				: Dict     =   sd_pipe([prompt]				,num_inference_steps=2				,output_type="""np"""   ).images

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







@nightly
@require_torch_gpu
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   """cuda"""
			_lowercase				: Optional[int]     =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
			_lowercase				: Union[str, Any]     =   pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   """a photo of a cat"""
			_lowercase				: Dict     =   {
			    """editing_prompt""": ["""sunglasses"""],
			    """reverse_editing_direction""": [False],
			    """edit_warmup_steps""": 10,
			    """edit_guidance_scale""": 6,
			    """edit_threshold""": 0.95,
			    """edit_momentum_scale""": 0.5,
			    """edit_mom_beta""": 0.6,
			}

			_lowercase				: List[str]     =   3
			_lowercase				: Optional[int]     =   7

			# no sega enabled
			_lowercase				: Dict     =   torch.Generator(UpperCAmelCase_   )
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,)

			_lowercase				: int     =   output.images
			_lowercase				: str     =   image[0, -3:, -3:, -1]
			_lowercase				: Dict     =   [
			    0.34673113,
			    0.38492733,
			    0.37597352,
			    0.34086335,
			    0.35650748,
			    0.35579205,
			    0.3384763,
			    0.34340236,
			    0.3573271,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: Optional[int]     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,**UpperCAmelCase_				,)

			_lowercase				: Union[str, Any]     =   output.images
			_lowercase				: int     =   image[0, -3:, -3:, -1]
			_lowercase				: Optional[int]     =   [
			    0.41887826,
			    0.37728766,
			    0.30138272,
			    0.41416335,
			    0.41664985,
			    0.36283392,
			    0.36191246,
			    0.43364465,
			    0.43001732,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   """cuda"""
			_lowercase				: Optional[int]     =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
			_lowercase				: Tuple     =   pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   """an image of a crowded boulevard, realistic, 4k"""
			_lowercase				: List[str]     =   {
			    """editing_prompt""": """crowd, crowded, people""",
			    """reverse_editing_direction""": True,
			    """edit_warmup_steps""": 10,
			    """edit_guidance_scale""": 8.3,
			    """edit_threshold""": 0.9,
			    """edit_momentum_scale""": 0.5,
			    """edit_mom_beta""": 0.6,
			}

			_lowercase				: List[str]     =   9
			_lowercase				: Optional[int]     =   7

			# no sega enabled
			_lowercase				: Tuple     =   torch.Generator(UpperCAmelCase_   )
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: List[str]     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,)

			_lowercase				: int     =   output.images
			_lowercase				: Dict     =   image[0, -3:, -3:, -1]
			_lowercase				: str     =   [
			    0.43497998,
			    0.91814065,
			    0.7540739,
			    0.55580205,
			    0.8467265,
			    0.5389691,
			    0.62574506,
			    0.58897763,
			    0.50926757,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: Tuple     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,**UpperCAmelCase_				,)

			_lowercase				: Union[str, Any]     =   output.images
			_lowercase				: int     =   image[0, -3:, -3:, -1]
			_lowercase				: Union[str, Any]     =   [
			    0.3089719,
			    0.30500144,
			    0.29016042,
			    0.30630964,
			    0.325687,
			    0.29419225,
			    0.2908091,
			    0.28723598,
			    0.27696294,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   """cuda"""
			_lowercase				: List[str]     =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
			_lowercase				: Union[str, Any]     =   pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   """a castle next to a river"""
			_lowercase				: Any     =   {
			    """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
			    """reverse_editing_direction""": False,
			    """edit_warmup_steps""": [15, 18],
			    """edit_guidance_scale""": 6,
			    """edit_threshold""": [0.9, 0.8],
			    """edit_momentum_scale""": 0.5,
			    """edit_mom_beta""": 0.6,
			}

			_lowercase				: int     =   48
			_lowercase				: List[str]     =   7

			# no sega enabled
			_lowercase				: Union[str, Any]     =   torch.Generator(UpperCAmelCase_   )
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,)

			_lowercase				: Optional[int]     =   output.images
			_lowercase				: Any     =   image[0, -3:, -3:, -1]
			_lowercase				: Optional[Any]     =   [
			    0.75163555,
			    0.76037145,
			    0.61785,
			    0.9189673,
			    0.8627701,
			    0.85189694,
			    0.8512813,
			    0.87012076,
			    0.8312857,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: Dict     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,**UpperCAmelCase_				,)

			_lowercase				: Any     =   output.images
			_lowercase				: Any     =   image[0, -3:, -3:, -1]
			_lowercase				: Union[str, Any]     =   [
			    0.73553365,
			    0.7537271,
			    0.74341905,
			    0.66480356,
			    0.6472925,
			    0.63039416,
			    0.64812905,
			    0.6749717,
			    0.6517102,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   """cuda"""
			_lowercase				: List[str]     =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,torch_dtype=torch.floataa   )
			_lowercase				: Optional[Any]     =   pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: List[Any]     =   """a photo of a cat"""
			_lowercase				: Optional[int]     =   {
			    """editing_prompt""": ["""sunglasses"""],
			    """reverse_editing_direction""": [False],
			    """edit_warmup_steps""": 10,
			    """edit_guidance_scale""": 6,
			    """edit_threshold""": 0.95,
			    """edit_momentum_scale""": 0.5,
			    """edit_mom_beta""": 0.6,
			}

			_lowercase				: List[Any]     =   3
			_lowercase				: Optional[int]     =   7

			# no sega enabled
			_lowercase				: List[str]     =   torch.Generator(UpperCAmelCase_   )
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: List[str]     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,)

			_lowercase				: str     =   output.images
			_lowercase				: Union[str, Any]     =   image[0, -3:, -3:, -1]
			_lowercase				: Dict     =   [
			    0.34887695,
			    0.3876953,
			    0.375,
			    0.34423828,
			    0.3581543,
			    0.35717773,
			    0.3383789,
			    0.34570312,
			    0.359375,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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

			# with sega enabled
			# generator = torch.manual_seed(seed)
			generator.manual_seed(UpperCAmelCase_   )
			_lowercase				: List[str]     =   pipe(
			    [prompt]				,generator=UpperCAmelCase_				,guidance_scale=UpperCAmelCase_				,num_inference_steps=50				,output_type="""np"""				,width=5_12				,height=5_12				,**UpperCAmelCase_				,)

			_lowercase				: Dict     =   output.images
			_lowercase				: int     =   image[0, -3:, -3:, -1]
			_lowercase				: Any     =   [
			    0.42285156,
			    0.36914062,
			    0.29077148,
			    0.42041016,
			    0.41918945,
			    0.35498047,
			    0.3618164,
			    0.4423828,
			    0.43115234,
			]

			assert image.shape == (1, 5_12, 5_12, 3)

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





from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()




