





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	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  StableDiffusionInpaintPipeline
       SCREAMING_SNAKE_CASE_      : List[str]      =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       SCREAMING_SNAKE_CASE_      : Tuple      =  frozenset(
           []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       SCREAMING_SNAKE_CASE_      : List[str]      =  frozenset([]	)







       def    __UpperCAmelCase (    self	:	Dict  )						->					str:
         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=__A						,)
         _lowercase							= PNDMScheduler(skip_prk_steps=__A  )
         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-05						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,hidden_act='gelu'						,projection_dim=512						,)
         _lowercase							= CLIPTextModel(__A  )
         _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    __UpperCAmelCase (    self	:	str						,__A	:	Optional[Any]						,__A	:	Union[str, Any]=0  )						->					Optional[int]:
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         _lowercase							= floats_tensor((1, 3, 32, 32)						,rng=random.Random(__A  )  ).to(__A  )
         _lowercase							= image.cpu().permute(0						,2						,3						,1  )[0]
         _lowercase							= Image.fromarray(np.uinta(__A  )  ).convert('RGB'  ).resize((64, 64)  )
         _lowercase							= Image.fromarray(np.uinta(image + 4  )  ).convert('RGB'  ).resize((64, 64)  )
         if str(__A  ).startswith('mps'  ):
           _lowercase							= torch.manual_seed(__A  )
         else:
           _lowercase							= torch.Generator(device=__A  ).manual_seed(__A  )
         _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    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         _lowercase							= "cpu"  # ensure determinism for the device-dependent torch.Generator
         _lowercase							= self.get_dummy_components()
         _lowercase							= StableDiffusionInpaintPipeline(**__A  )
         _lowercase							= sd_pipe.to(__A  )
         sd_pipe.set_progress_bar_config(disable=__A  )

         _lowercase							= self.get_dummy_inputs(__A  )
         _lowercase							= sd_pipe(**__A  ).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    __UpperCAmelCase (    self	:	str  )						->					int:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





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







       """simple docstring"""





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







       def    __UpperCAmelCase (    self	:	Any  )						->					List[Any]:
         _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(__A						,safety_checker=__A  )
         pipe.to(__A  )
         pipe.set_progress_bar_config(disable=__A  )
         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=__A						,image=__A						,mask_image=__A						,generator=__A						,output_type='np'						,)
         _lowercase							= output.images[0]

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







       def    __UpperCAmelCase (    self	:	Any  )						->					Any:
         _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(
             __A						,torch_dtype=torch.floataa						,safety_checker=__A						,)
         pipe.to(__A  )
         pipe.set_progress_bar_config(disable=__A  )
         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=__A						,image=__A						,mask_image=__A						,generator=__A						,output_type='np'						,)
         _lowercase							= output.images[0]

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







       def    __UpperCAmelCase (    self	:	List[str]  )						->					List[Any]:
         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(__A						,subfolder='scheduler'  )
         _lowercase							= 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()

         _lowercase							= "Face of a yellow cat, high resolution, sitting on a park bench"

         _lowercase							= torch.manual_seed(0  )
         _lowercase							= pipe(
             prompt=__A						,image=__A						,mask_image=__A						,generator=__A						,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






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :int     )      ->						list[str]:
  return [sentence[i : i + ngram_size] for i in range(len(snake_case__     ) - ngram_size + 1     )]


if __name__ == "__main__":
      from doctest import testmod

      testmod()