





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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class       __A					(					lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		StableDiffusionInpaintPipeline
			lowerCAmelCase_							  =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCAmelCase_							  =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCAmelCase_							  =		frozenset(
			    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCAmelCase_							  =		frozenset([]				)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__              =	UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=9 ,						out_channels=4 ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,						cross_attention_dim=3_2 ,						attention_head_dim=(2, 4) ,						use_linear_projection=_SCREAMING_SNAKE_CASE ,						)
				lowerCamelCase__              =	PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE							)
				torch.manual_seed(0							)
				lowerCamelCase__              =	AutoencoderKL(
				    block_out_channels=[3_2, 6_4] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,						latent_channels=4 ,						sample_size=1_2_8 ,						)
				torch.manual_seed(0							)
				lowerCamelCase__              =	CLIPTextConfig(
				    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_0_0_0 ,						hidden_act='''gelu''' ,						projection_dim=5_1_2 ,						)
				lowerCamelCase__              =	CLIPTextModel(_SCREAMING_SNAKE_CASE							)
				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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=0							):



				'''simple docstring'''







				lowerCamelCase__              =	floats_tensor((1, 3, 3_2, 3_2) ,						rng=random.Random(_SCREAMING_SNAKE_CASE							)							).to(_SCREAMING_SNAKE_CASE							)
				lowerCamelCase__              =	image.cpu().permute(0 ,						2 ,						3 ,						1							)[0]
				lowerCamelCase__              =	Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE							)							).convert('''RGB'''							).resize((6_4, 6_4)							)
				lowerCamelCase__              =	Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((6_4, 6_4)							)
				if str(_SCREAMING_SNAKE_CASE							).startswith('''mps'''							):
					lowerCamelCase__              =	torch.manual_seed(_SCREAMING_SNAKE_CASE							)
				else:
					lowerCamelCase__              =	torch.Generator(device=_SCREAMING_SNAKE_CASE							).manual_seed(_SCREAMING_SNAKE_CASE							)
				lowerCamelCase__              =	{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''image''': init_image,
				    '''mask_image''': mask_image,
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				lowerCamelCase__              =	self.get_dummy_components()
				lowerCamelCase__              =	StableDiffusionInpaintPipeline(**_SCREAMING_SNAKE_CASE							)
				lowerCamelCase__              =	sd_pipe.to(_SCREAMING_SNAKE_CASE							)
				sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

				lowerCamelCase__              =	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)
				lowerCamelCase__              =	sd_pipe(**_SCREAMING_SNAKE_CASE							).images
				lowerCamelCase__              =	image[0, -3:, -3:, -1]

				assert image.shape == (1, 6_4, 6_4, 3)
				lowerCamelCase__              =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				super().test_inference_batch_single_identical(expected_max_diff=3E-3							)






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

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/sd2-inpaint/init_image.png'''							)
				lowerCamelCase__              =	load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
				lowerCamelCase__              =	load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
				    '''/yellow_cat_sitting_on_a_park_bench.npy'''							)

				lowerCamelCase__              =	'''stabilityai/stable-diffusion-2-inpainting'''
				lowerCamelCase__              =	StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE ,						safety_checker=_SCREAMING_SNAKE_CASE							)
				pipe.to(_SCREAMING_SNAKE_CASE							)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
				pipe.enable_attention_slicing()

				lowerCamelCase__              =	'''Face of a yellow cat, high resolution, sitting on a park bench'''

				lowerCamelCase__              =	torch.manual_seed(0							)
				lowerCamelCase__              =	pipe(
				    prompt=_SCREAMING_SNAKE_CASE ,						image=_SCREAMING_SNAKE_CASE ,						mask_image=_SCREAMING_SNAKE_CASE ,						generator=_SCREAMING_SNAKE_CASE ,						output_type='''np''' ,						)
				lowerCamelCase__              =	output.images[0]

				assert image.shape == (5_1_2, 5_1_2, 3)
				assert np.abs(expected_image - image							).max() < 9E-3





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/sd2-inpaint/init_image.png'''							)
				lowerCamelCase__              =	load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
				lowerCamelCase__              =	load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
				    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''							)

				lowerCamelCase__              =	'''stabilityai/stable-diffusion-2-inpainting'''
				lowerCamelCase__              =	StableDiffusionInpaintPipeline.from_pretrained(
				    _SCREAMING_SNAKE_CASE ,						torch_dtype=torch.floataa ,						safety_checker=_SCREAMING_SNAKE_CASE ,						)
				pipe.to(_SCREAMING_SNAKE_CASE							)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
				pipe.enable_attention_slicing()

				lowerCamelCase__              =	'''Face of a yellow cat, high resolution, sitting on a park bench'''

				lowerCamelCase__              =	torch.manual_seed(0							)
				lowerCamelCase__              =	pipe(
				    prompt=_SCREAMING_SNAKE_CASE ,						image=_SCREAMING_SNAKE_CASE ,						mask_image=_SCREAMING_SNAKE_CASE ,						generator=_SCREAMING_SNAKE_CASE ,						output_type='''np''' ,						)
				lowerCamelCase__              =	output.images[0]

				assert image.shape == (5_1_2, 5_1_2, 3)
				assert np.abs(expected_image - image							).max() < 5E-1





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/sd2-inpaint/init_image.png'''							)
				lowerCamelCase__              =	load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)

				lowerCamelCase__              =	'''stabilityai/stable-diffusion-2-inpainting'''
				lowerCamelCase__              =	PNDMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE ,						subfolder='''scheduler'''							)
				lowerCamelCase__              =	StableDiffusionInpaintPipeline.from_pretrained(
				    _SCREAMING_SNAKE_CASE ,						safety_checker=_SCREAMING_SNAKE_CASE ,						scheduler=_SCREAMING_SNAKE_CASE ,						torch_dtype=torch.floataa ,						)
				pipe.to(_SCREAMING_SNAKE_CASE							)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
				pipe.enable_attention_slicing(1							)
				pipe.enable_sequential_cpu_offload()

				lowerCamelCase__              =	'''Face of a yellow cat, high resolution, sitting on a park bench'''

				lowerCamelCase__              =	torch.manual_seed(0							)
				lowerCamelCase__              =	pipe(
				    prompt=_SCREAMING_SNAKE_CASE ,						image=_SCREAMING_SNAKE_CASE ,						mask_image=_SCREAMING_SNAKE_CASE ,						generator=_SCREAMING_SNAKE_CASE ,						num_inference_steps=2 ,						output_type='''np''' ,						)

				lowerCamelCase__              =	torch.cuda.max_memory_allocated()
				# make sure that less than 2.65 GB is allocated
				assert mem_bytes < 2.65 * 1_0**9










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	if days_between_payments <= 0:
		raise ValueError('''days_between_payments must be > 0'''							)
	if daily_interest_rate < 0:
		raise ValueError('''daily_interest_rate must be >= 0'''							)
	if principal <= 0:
		raise ValueError('''principal must be > 0'''							)
	return principal * daily_interest_rate * days_between_payments





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,)       ->   float:







	'''simple docstring'''







	if number_of_compounding_periods <= 0:
		raise ValueError('''number_of_compounding_periods must be > 0'''							)
	if nominal_annual_interest_rate_percentage < 0:
		raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0'''							)
	if principal <= 0:
		raise ValueError('''principal must be > 0'''							)

	return principal * (
	    (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
	    - 1
	)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,)       ->   float:







	'''simple docstring'''







	if number_of_years <= 0:
		raise ValueError('''number_of_years must be > 0'''							)
	if nominal_annual_percentage_rate < 0:
		raise ValueError('''nominal_annual_percentage_rate must be >= 0'''							)
	if principal <= 0:
		raise ValueError('''principal must be > 0'''							)

	return compound_interest(
	    __snake_case ,nominal_annual_percentage_rate / 365 ,number_of_years * 365							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



