





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   ):
   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	:Optional[int] )->					Optional[int]:
         torch.manual_seed(0 )
         __A          =				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          =				PNDMScheduler(skip_prk_steps=__a )
         torch.manual_seed(0 )
         __A          =				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          =				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    ,   )
         __A          =				CLIPTextModel(__a )
         __A          =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

         __A          =				{
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Dict=0 )->					Tuple:
         __A          =				floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(__a ) ).to(__a )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 )[0]
         __A          =				Image.fromarray(np.uinta(__a ) ).convert('''RGB''' ).resize((64, 64) )
         __A          =				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
         if str(__a ).startswith('''mps''' ):
               __A          =				torch.manual_seed(__a )
         else:
               __A          =				torch.Generator(device=__a ).manual_seed(__a )
         __A          =				{
             "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	:str )->					Union[str, Any]:
         __A          =				"cpu"  # ensure determinism for the device-dependent torch.Generator
         __A          =				self.get_dummy_components()
         __A          =				StableDiffusionInpaintPipeline(**__a )
         __A          =				sd_pipe.to(__a )
         sd_pipe.set_progress_bar_config(disable=__a )

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

         assert image.shape == (1, 64, 64, 3)
         __A          =				np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] )

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






   def _lowerCAmelCase      (self	:Optional[int] )->					List[str]:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3 )





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




   def _lowerCAmelCase      (self	:Optional[Any] )->					int:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




   def _lowerCAmelCase      (self	:Tuple )->					Optional[int]:
         __A          =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png''' )
         __A          =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
         __A          =				load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench.npy''' )

         __A          =				"stabilityai/stable-diffusion-2-inpainting"
         __A          =				StableDiffusionInpaintPipeline.from_pretrained(__a    ,   safety_checker=__a )
         pipe.to(__a )
         pipe.set_progress_bar_config(disable=__a )
         pipe.enable_attention_slicing()

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

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

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




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A          =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png''' )
         __A          =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
         __A          =				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          =				"stabilityai/stable-diffusion-2-inpainting"
         __A          =				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          =				"Face of a yellow cat, high resolution, sitting on a park bench"

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

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






   def _lowerCAmelCase      (self	:int )->					Dict:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

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

         __A          =				"stabilityai/stable-diffusion-2-inpainting"
         __A          =				PNDMScheduler.from_pretrained(__a    ,   subfolder='''scheduler''' )
         __A          =				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          =				"Face of a yellow cat, high resolution, sitting on a park bench"

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

         __A          =				torch.cuda.max_memory_allocated()
         # make sure that less than 2.65 GB is allocated
         assert mem_bytes < 2.6_5 * 10**9










import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


_snake_case		=  {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if got_ver is None or want_ver is None:
														raise ValueError(
														    F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
														    F" reinstalling {pkg}."  )
							if not ops[op](version.parse(_lowerCamelCase  )				, version.parse(_lowerCamelCase  )  ):
														raise ImportError(
														    F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase = None  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    F"\n{hint}" if hint is not None else ""

							# non-versioned check
							if re.match(r"^[\w_\-\d]+$"				, _lowerCamelCase  ):
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    requirement, None, None
							else:
														_lowerCAmelCase     :							Optional[int]    =    re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"				, _lowerCamelCase  )
														if not match:
																					raise ValueError(
																					    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
																					    F" got {requirement}"  )
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    match[0]
														_lowerCAmelCase     :							Any    =    want_full.split(","  )  # there could be multiple requirements
														_lowerCAmelCase     :							Optional[int]    =    {}
														for w in want_range:
																					_lowerCAmelCase     :							Any    =    re.findall(r"^([\s!=<>]{1,2})(.+)"				, _lowerCamelCase  )
																					if not match:
																												raise ValueError(
																												    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
																												    F" but got {requirement}"  )
																					_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    match[0]
																					_lowerCAmelCase     :							Union[str, Any]    =    want_ver
																					if op not in ops:
																												raise ValueError(F"{requirement}: need one of {list(ops.keys()  )}, but got {op}"  )

    # special case
							if pkg == "python":
														_lowerCAmelCase     :							Tuple    =    ".".join([str(_lowerCamelCase  ) for x in sys.version_info[:3]]  )
														for op, want_ver in wanted.items():
																					_compare_versions(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )
														return

							# check if any version is installed
							try:
														_lowerCAmelCase     :							Any    =    importlib.metadata.version(_lowerCamelCase  )
							except importlib.metadata.PackageNotFoundError:
														raise importlib.metadata.PackageNotFoundError(
														    F"The '{requirement}' distribution was not found and is required by this application. {hint}"  )

							# check that the right version is installed if version number or a range was provided
							if want_ver is not None:
														for op, want_ver in wanted.items():
																					_compare_versions(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
							return require_version(_lowerCamelCase				, _lowerCamelCase  )
