



'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase__						(		UpperCamelCase__				,							UpperCamelCase__				,							unittest.TestCase			):
					a   :  Tuple   =				StableDiffusionParadigmsPipeline
					a   :  Optional[int]   =				TEXT_TO_IMAGE_PARAMS
					a   :  Dict   =				TEXT_TO_IMAGE_BATCH_PARAMS
					a   :  List[str]   =				TEXT_TO_IMAGE_IMAGE_PARAMS
					a   :  Tuple   =				TEXT_TO_IMAGE_IMAGE_PARAMS



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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      ,					attention_head_dim=(2, 4)      ,					use_linear_projection=UpperCamelCase      ,					)
						__lowerCAmelCase       				=	DDIMParallelScheduler(
						    beta_start=0.0_00_85      ,					beta_end=0.0_12      ,					beta_schedule="scaled_linear"      ,					clip_sample=UpperCamelCase      ,					set_alpha_to_one=UpperCamelCase      ,					)
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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      ,					sample_size=128      ,					)
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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=1000      ,					hidden_act="gelu"      ,					projection_dim=512      ,					)
						__lowerCAmelCase       				=	CLIPTextModel(UpperCamelCase  )
						__lowerCAmelCase       				=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

						__lowerCAmelCase       				=	{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase=0  )  ->      Union[str, Any]:
						if str(UpperCamelCase  ).startswith("mps"  ):
							__lowerCAmelCase       				=	torch.manual_seed(UpperCamelCase  )
						else:
							__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	{
						    "prompt": "a photograph of an astronaut riding a horse",
						    "generator": generator,
						    "num_inference_steps": 10,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						    "parallel": 3,
						    "debug": True,
						}
						return inputs



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						__lowerCAmelCase       				=	StableDiffusionParadigmsPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs(UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe(**UpperCamelCase  ).images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase       				=	np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]  )

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



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	DDPMParallelScheduler()
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	StableDiffusionParadigmsPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs(UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe(**UpperCamelCase  ).images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase       				=	np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]  )

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



					def 							UpperCAmelCase_			(     self  )  ->      str:
						super().test_inference_batch_consistent(batch_sizes=[1, 2]  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						super().test_inference_batch_single_identical(batch_size=2      ,					expected_max_diff=3E-3  )



					def 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.get_dummy_components()
						__lowerCAmelCase       				=	StableDiffusionParadigmsPipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs(UpperCamelCase  )
						__lowerCAmelCase       				=	"french fries"
						__lowerCAmelCase       				=	sd_pipe(**UpperCamelCase      ,					negative_prompt=UpperCamelCase  )
						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]

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

						__lowerCAmelCase       				=	np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]  )

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






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



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase=0  )  ->      Tuple:
						__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	{
						    "prompt": "a photograph of an astronaut riding a horse",
						    "generator": generator,
						    "num_inference_steps": 10,
						    "guidance_scale": 7.5,
						    "output_type": "numpy",
						    "parallel": 3,
						    "debug": True,
						}
						return inputs



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	"stabilityai/stable-diffusion-2-base"
						__lowerCAmelCase       				=	DDIMParallelScheduler.from_pretrained(UpperCamelCase      ,					subfolder="scheduler"  )
						__lowerCAmelCase       				=	StableDiffusionParadigmsPipeline.from_pretrained(UpperCamelCase      ,					scheduler=UpperCamelCase      ,					safety_checker=UpperCamelCase  )
						pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )
						pipe.enable_attention_slicing()

						__lowerCAmelCase       				=	self.get_inputs()
						__lowerCAmelCase       				=	pipe(**UpperCamelCase  ).images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)

						__lowerCAmelCase       				=	np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]  )

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




'''simple docstring'''



import math



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if not isinstance(lowerCamelCase					,		lowerCamelCase       ):
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be an integer'''
		raise TypeError(lowerCamelCase       )
	if number < 1:
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be a positive integer'''
		raise ValueError(lowerCamelCase       )
	__lowerCAmelCase       				=	[number]
	while number != 1:
		if number % 2 == 0:
			__lowerCAmelCase       				=	math.floor(math.sqrt(lowerCamelCase       )       )
		else:
			__lowerCAmelCase       				=	math.floor(
			    math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       )       )
		sequence.append(lowerCamelCase       )
	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()