

'''simple docstring'''



def 						__lowerCamelCase	(       __snake_case					:		int | float | str	)	->							tuple[int, int]:






						"""simple docstring"""





						try:
												A__					:       List[str]     =float(__snake_case	)
						except ValueError:
												raise ValueError("""Please enter a valid number"""	)
						A__					:       Tuple     =decimal - int(__snake_case	)
						if fractional_part == 0:
												return int(__snake_case	), 1
						else:
												A__					:       int     =len(str(__snake_case	).split("""."""	)[1]	)
												A__					:       Optional[int]     =int(decimal * (10**number_of_frac_digits)	)
												A__					:       str     =10**number_of_frac_digits
												A__   ,						A__					:       Dict     =denominator, numerator
												while True:
																		A__					:       Tuple     =dividend % divisor
																		if remainder == 0:
																								break
																		A__   ,						A__					:       Optional[Any]     =divisor, remainder
												A__   ,						A__					:       List[str]     =numerator / divisor, denominator / divisor
												return int(__snake_case	), int(__snake_case	)


if __name__ == "__main__":
		print(F"""{decimal_to_fraction(2) = }""")
		print(F"""{decimal_to_fraction(89.0) = }""")
		print(F"""{decimal_to_fraction('67') = }""")
		print(F"""{decimal_to_fraction('45.0') = }""")
		print(F"""{decimal_to_fraction(1.5) = }""")
		print(F"""{decimal_to_fraction('6.25') = }""")
		print(F"""{decimal_to_fraction('78td') = }""")




'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class lowerCamelCase			(  lowercase_	,						lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	StableDiffusionInstructPixaPixPipeline
			__snake_case        =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
			__snake_case        =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case        =	IMAGE_TO_IMAGE_IMAGE_PARAMS
			__snake_case        =	IMAGE_TO_IMAGE_IMAGE_PARAMS


			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       int     =UNetaDConditionModel(
									    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=8		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      )
									A__					:       str     =PNDMScheduler(skip_prk_steps=lowerCAmelCase_			)
									torch.manual_seed(0			)
									A__					:       Dict     =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		,      )
									torch.manual_seed(0			)
									A__					:       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		,      )
									A__					:       Tuple     =CLIPTextModel(lowerCAmelCase_			)
									A__					:       int     =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

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


			def lowercase__						(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						Dict=0			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =floats_tensor((1, 3, 32, 32)		,      rng=random.Random(lowerCAmelCase_			)			).to(lowerCAmelCase_			)
									A__					:       str     =image.cpu().permute(0		,      2		,      3		,      1			)[0]
									A__					:       List[str]     =Image.fromarray(np.uinta(lowerCAmelCase_			)			).convert("""RGB"""			)
									if str(lowerCAmelCase_			).startswith("""mps"""			):
															A__					:       Any     =torch.manual_seed(lowerCAmelCase_			)
									else:
															A__					:       int     =torch.Generator(device=lowerCAmelCase_			).manual_seed(lowerCAmelCase_			)
									A__					:       Optional[Any]     ={
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """image_guidance_scale""": 1,
									    """output_type""": """numpy""",
									}
									return inputs


			def lowercase__						(				self	:						Any			)      ->		str:





									'''simple docstring'''
									A__					:       Any     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       Any     =self.get_dummy_components()
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Dict     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       List[Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       List[Any]     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       Dict     =image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A__					:       Tuple     =np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3


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





									'''simple docstring'''
									A__					:       Optional[int]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       List[str]     =self.get_dummy_components()
									A__					:       Union[str, Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Optional[Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Optional[int]     ="""french fries"""
									A__					:       Tuple     =sd_pipe(**lowerCAmelCase_		,      negative_prompt=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =output.images
									A__					:       List[str]     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A__					:       Tuple     =np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       str     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       str     =self.get_dummy_components()
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Tuple     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Dict     =[inputs["""prompt"""]] * 2

									A__					:       Optional[int]     =np.array(inputs["""image"""]			).astype(np.floataa			) / 255.0
									A__					:       List[str]     =torch.from_numpy(lowerCAmelCase_			).unsqueeze(0			).to(lowerCAmelCase_			)
									A__					:       Union[str, Any]     =image / 2 + 0.5
									A__					:       Optional[int]     =image.permute(0		,      3		,      1		,      2			)
									A__					:       Dict     =image.repeat(2		,      1		,      1		,      1			)

									A__					:       int     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A__					:       List[Any]     =np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3


			def lowercase__						(				self	:						str			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[Any]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       List[str]     =self.get_dummy_components()
									A__					:       List[str]     =EulerAncestralDiscreteScheduler(
									    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""			)
									A__					:       str     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       int     =sd_pipe.to(lowerCAmelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Optional[Any]     =sd_pipe(**lowerCAmelCase_			).images
									A__					:       Tuple     =image[0, -3:, -3:, -1]

									A__					:       List[str]     =[round(lowerCAmelCase_		,      4			) for x in image_slice.flatten().tolist()]
									print(""",""".join([str(lowerCAmelCase_			) for x in slice]			)			)

									assert image.shape == (1, 32, 32, 3)
									A__					:       Any     =np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3


			def lowercase__						(				self	:						List[Any]			)      ->		int:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



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





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.get_dummy_components()
									A__					:       Optional[Any]     =StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_			)
									A__					:       Any     =VaeImageProcessor(do_resize=lowerCAmelCase_		,      do_normalize=lowerCAmelCase_			)
									A__					:       Dict     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       str     =pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_		,      input_image_type="""pt"""			)			)[0]

									A__					:       List[Any]     =components["""vae"""]
									A__					:       Dict     =self.get_dummy_inputs_by_type(lowerCAmelCase_		,      input_image_type="""pt"""			)

									for image_param in self.image_latents_params:
															if image_param in inputs.keys():
																					A__					:       List[Any]     =vae.encode(inputs[image_param]			).latent_dist.mode()

									A__					:       Optional[Any]     =pipe(**lowerCAmelCase_			)[0]

									A__					:       Dict     =np.abs(out - out_latents_inputs			).max()
									self.assertLess(lowerCAmelCase_		,      1e-4		,      """passing latents as image input generate different result from passing image"""			)



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



			'''simple docstring'''






			def lowercase__						(				self	:						List[str]			)      ->		Union[str, Any]:





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int=0			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[Any]     =torch.manual_seed(lowerCAmelCase_			)
									A__					:       Optional[Any]     =load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""			)
									A__					:       List[Any]     ={
									    """prompt""": """turn him into a cyborg""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """image_guidance_scale""": 1.0,
									    """output_type""": """numpy""",
									}
									return inputs


			def lowercase__						(				self	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									A__					:       Optional[Any]     =pipe(**lowerCAmelCase_			).images
									A__					:       Tuple     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Dict     =np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]			)

									assert np.abs(expected_slice - image_slice			).max() < 1e-3


			def lowercase__						(				self	:						str			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       str     =LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Union[str, Any]     =self.get_inputs()
									A__					:       Tuple     =pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       List[Any]     =np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]			)

									assert np.abs(expected_slice - image_slice			).max() < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       List[str]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       str     =DDIMScheduler.from_config(pipe.scheduler.config			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									A__					:       List[str]     =pipe(**lowerCAmelCase_			).images
									A__					:       List[Any]     =image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Any     =np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]			)

									assert np.abs(expected_slice - image_slice			).max() < 1e-3


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





									'''simple docstring'''
									A__					:       int     =0

									def callback_fn(lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						torch.FloatTensor			) -> None:
															A__					:       Any     =True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					A__					:       List[str]     =latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					A__					:       Optional[Any]     =latents[0, -3:, -3:, -1]
																					A__					:       Tuple     =np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]			)

																					assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
															elif step == 2:
																					A__					:       List[Any]     =latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					A__					:       Dict     =latents[0, -3:, -3:, -1]
																					A__					:       List[Any]     =np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]			)

																					assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2

									A__					:       List[str]     =False

									A__					:       Optional[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_		,      torch_dtype=torch.floataa			)
									A__					:       int     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Optional[Any]     =self.get_inputs()
									pipe(**lowerCAmelCase_		,      callback=lowerCAmelCase_		,      callback_steps=1			)
									assert callback_fn.has_been_called
									assert number_of_steps == 3


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





									'''simple docstring'''
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A__					:       Dict     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    """timbrooks/instruct-pix2pix"""		,      safety_checker=lowerCAmelCase_		,      torch_dtype=torch.floataa			)
									A__					:       Union[str, Any]     =pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing(1			)
									pipe.enable_sequential_cpu_offload()

									A__					:       List[str]     =self.get_inputs()
									A__					:       Dict     =pipe(**lowerCAmelCase_			)

									A__					:       List[str]     =torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9



			def lowercase__						(				self	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Tuple     =self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A__					:       int     =inputs["""image"""].resize((5_04, 5_04)			)

									A__					:       Optional[int]     ="""timbrooks/instruct-pix2pix"""
									A__					:       List[Any]     =StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    lowerCAmelCase_		,      safety_checker=lowerCAmelCase_		,      )
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)
									pipe.enable_attention_slicing()

									A__					:       Dict     =pipe(**lowerCAmelCase_			)
									A__					:       Dict     =output.images[0]

									A__					:       int     =image[2_55:2_58, 3_83:3_86, -1]

									assert image.shape == (5_04, 5_04, 3)
									A__					:       Dict     =np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 5e-3

