

'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					int   )      -> float:
							return base * power(lowerCAmelCase__		,(exponent - 1)   ) if exponent else 1


if __name__ == "__main__":
							print("Raise base to the power of exponent using recursion...")
							SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			int(input("Enter the base: ").strip())
							SCREAMING_SNAKE_CASE							:				List[str]     	=			int(input("Enter the exponent: ").strip())
							SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			power(base, abs(exponent))
							if exponent < 0:  # power() does not properly deal w/ negative exponents
														SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			1 / result
							print(f"{base} to the power of {exponent} is {result}")








'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



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



				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				3
											SCREAMING_SNAKE_CASE_				=				(32, 32)

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



				@property
				def   a__					(    self       )     ->							Any:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							Tuple:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				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   a__					(    self       )     ->							Tuple:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				RobertaSeriesConfig(
											    hidden_size=32,     project_dim=32,     intermediate_size=37,     layer_norm_eps=1E-05,     num_attention_heads=4,     num_hidden_layers=5,     pad_token_id=1,     vocab_size=5006,     )
											return RobertaSeriesModelWithTransformation(_lowercase       )



				@property
				def   a__					(    self       )     ->							List[str]:
											def extract(*_lowercase,     **_lowercase       ):






																		class    snake_case     :






																						"""simple docstring"""



																						def __init__(    self       )     ->							Dict:
																													SCREAMING_SNAKE_CASE_				=				torch.ones([0]       )





																						def   a__					(    self,     _lowercase       )     ->							List[str]:
																													self.pixel_values.to(_lowercase       )
																													return self


																		return Out()

											return extract



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				'cpu'  # ensure determinism for the device-dependent torch.Generator
											SCREAMING_SNAKE_CASE_				=				self.dummy_cond_unet
											SCREAMING_SNAKE_CASE_				=				PNDMScheduler(skip_prk_steps=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.dummy_vae
											SCREAMING_SNAKE_CASE_				=				self.dummy_text_encoder
											SCREAMING_SNAKE_CASE_				=				XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
											SCREAMING_SNAKE_CASE_				=				77

											SCREAMING_SNAKE_CASE_				=				self.dummy_image.to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				init_image / 2 + 0.5

											# make sure here that pndm scheduler skips prk
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline(
											    unet=_lowercase,     scheduler=_lowercase,     vae=_lowercase,     text_encoder=_lowercase,     tokenizer=_lowercase,     safety_checker=_lowercase,     feature_extractor=self.dummy_extractor,     )
											SCREAMING_SNAKE_CASE_				=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,     do_normalize=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe.to(_lowercase       )
											alt_pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'A painting of a squirrel eating a burger'
											SCREAMING_SNAKE_CASE_				=				torch.Generator(device=_lowercase       ).manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe(
											    [prompt],     generator=_lowercase,     guidance_scale=6.0,     num_inference_steps=2,     output_type='np',     image=_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				output.images

											SCREAMING_SNAKE_CASE_				=				torch.Generator(device=_lowercase       ).manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe(
											    [prompt],     generator=_lowercase,     guidance_scale=6.0,     num_inference_steps=2,     output_type='np',     image=_lowercase,     return_dict=_lowercase,     )[0]

											SCREAMING_SNAKE_CASE_				=				image[0, -3:, -3:, -1]
											SCREAMING_SNAKE_CASE_				=				image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											SCREAMING_SNAKE_CASE_				=				np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]       )

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



				@unittest.skipIf(torch_device != 'cuda',     'This test requires a GPU'       )
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				self.dummy_cond_unet
											SCREAMING_SNAKE_CASE_				=				PNDMScheduler(skip_prk_steps=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.dummy_vae
											SCREAMING_SNAKE_CASE_				=				self.dummy_text_encoder
											SCREAMING_SNAKE_CASE_				=				XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
											SCREAMING_SNAKE_CASE_				=				77

											SCREAMING_SNAKE_CASE_				=				self.dummy_image.to(_lowercase       )

											# put models in fp16
											SCREAMING_SNAKE_CASE_				=				unet.half()
											SCREAMING_SNAKE_CASE_				=				vae.half()
											SCREAMING_SNAKE_CASE_				=				bert.half()

											# make sure here that pndm scheduler skips prk
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline(
											    unet=_lowercase,     scheduler=_lowercase,     vae=_lowercase,     text_encoder=_lowercase,     tokenizer=_lowercase,     safety_checker=_lowercase,     feature_extractor=self.dummy_extractor,     )
											SCREAMING_SNAKE_CASE_				=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,     do_normalize=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe.to(_lowercase       )
											alt_pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'A painting of a squirrel eating a burger'
											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe(
											    [prompt],     generator=_lowercase,     num_inference_steps=2,     output_type='np',     image=_lowercase,     ).images

											assert image.shape == (1, 32, 32, 3)





				@unittest.skipIf(torch_device != 'cuda',     'This test requires a GPU'       )
				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/img2img/sketch-mountains-input.jpg'       )
											# resize to resolution that is divisible by 8 but not 16 or 32
											SCREAMING_SNAKE_CASE_				=				init_image.resize((760, 504)       )

											SCREAMING_SNAKE_CASE_				=				'BAAI/AltDiffusion'
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline.from_pretrained(
											    _lowercase,     safety_checker=_lowercase,     )
											pipe.to(_lowercase       )
											pipe.set_progress_bar_config(disable=_lowercase       )
											pipe.enable_attention_slicing()

											SCREAMING_SNAKE_CASE_				=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				pipe(
											    prompt=_lowercase,     image=_lowercase,     strength=0.75,     guidance_scale=7.5,     generator=_lowercase,     output_type='np',     )
											SCREAMING_SNAKE_CASE_				=				output.images[0]

											SCREAMING_SNAKE_CASE_				=				image[255:258, 383:386, -1]

											assert image.shape == (504, 760, 3)
											SCREAMING_SNAKE_CASE_				=				np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]       )

											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       )     ->							List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/img2img/sketch-mountains-input.jpg'       )
											SCREAMING_SNAKE_CASE_				=				init_image.resize((768, 512)       )
											SCREAMING_SNAKE_CASE_				=				load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'       )

											SCREAMING_SNAKE_CASE_				=				'BAAI/AltDiffusion'
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline.from_pretrained(
											    _lowercase,     safety_checker=_lowercase,     )
											pipe.to(_lowercase       )
											pipe.set_progress_bar_config(disable=_lowercase       )
											pipe.enable_attention_slicing()

											SCREAMING_SNAKE_CASE_				=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				pipe(
											    prompt=_lowercase,     image=_lowercase,     strength=0.75,     guidance_scale=7.5,     generator=_lowercase,     output_type='np',     )
											SCREAMING_SNAKE_CASE_				=				output.images[0]

											assert image.shape == (512, 768, 3)
											# img2img is flaky across GPUs even in fp32, so using MAE here
											assert np.abs(expected_image - image       ).max() < 1E-2





