


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				(     UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__	,      unittest.TestCase     ):
							_lowercase      :     Optional[Any]     =					StableDiffusionInpaintPipeline
							_lowercase      :     int     =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							_lowercase      :     List[str]     =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_lowercase      :     Dict     =					frozenset(
							    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							_lowercase      :     List[Any]     =					frozenset([]     )

							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									torch.manual_seed(0				)
									lowercase__				= 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=UpperCamelCase_       ,		)
									lowercase__				= PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
									torch.manual_seed(0				)
									lowercase__				= 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				)
									lowercase__				= 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=1_000       ,		hidden_act='''gelu'''       ,		projection_dim=512       ,		)
									lowercase__				= CLIPTextModel(UpperCamelCase_				)
									lowercase__				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

									lowercase__				= {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components

							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      Union[str, Any]       ,		UpperCamelCase_:      str=0				)     ->  int:





									"""simple docstring"""







									lowercase__				= floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
									lowercase__				= image.cpu().permute(0       ,		2       ,		3       ,		1				)[0]
									lowercase__				= Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				).resize((64, 64)				)
									lowercase__				= Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((64, 64)				)
									if str(UpperCamelCase_				).startswith('''mps'''				):
											lowercase__				= torch.manual_seed(UpperCamelCase_				)
									else:
											lowercase__				= torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
									lowercase__				= {
									    '''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:      Optional[int]				)     ->  Optional[int]:





									"""simple docstring"""







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

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

									assert image.shape == (1, 64, 64, 3)
									lowercase__				= 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:      Any				)     ->  Dict:





									"""simple docstring"""







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


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

							def  lowerCamelCase_					(				self:      int				)     ->  int:





									"""simple docstring"""







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

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





									"""simple docstring"""







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

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

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

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

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

							def  lowerCamelCase_					(				self:      Tuple				)     ->  Tuple:





									"""simple docstring"""







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

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

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

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

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


							def  lowerCamelCase_					(				self:      Tuple				)     ->  Dict:





									"""simple docstring"""







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

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

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

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

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

									lowercase__				= torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9





import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

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

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

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

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


