






def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
					# formula for sum of series
					return total




def      lowercase__      (     ):


					'''simple docstring'''


					print(sum_of_series(1     ,       1     ,       10  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()










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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       int					    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=9						,				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=__a						,				)
						__a			:       str					    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a			:       Union[str, 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						,				sample_size=128						,				)
						torch.manual_seed(0							)
						__a			:       List[str]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
						__a			:       Dict					    =  CLIPTextModel(__a							)
						__a			:       Union[str, Any]					    =  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       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       List[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       Tuple					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((64, 64)							)
						__a			:       Tuple					    =  Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((64, 64)							)
						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       str					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    '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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a			:       str					    =  self.get_dummy_components()
						__a			:       Union[str, Any]					    =  StableDiffusionInpaintPipeline(**__a							)
						__a			:       List[Any]					    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  self.get_dummy_inputs(__a							)
						__a			:       Dict					    =  sd_pipe(**__a							).images
						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a			:       List[Any]					    =  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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a			:       Optional[int]					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Optional[int]					    =  StableDiffusionInpaintPipeline.from_pretrained(__a						,				safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Dict					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       int					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       Dict					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       List[str]					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				torch_dtype=torch.floataa						,				safety_checker=__a						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Union[str, Any]					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       int					    =  torch.manual_seed(0							)
						__a			:       Optional[Any]					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       int					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5E-1





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a			:       str					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

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

						__a			:       str					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       str					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				num_inference_steps=2						,				output_type='np'						,				)

						__a			:       List[str]					    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9



