



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



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Tuple:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=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       ,		)
     SCREAMING_SNAKE_CASE_: List[str]     			=PNDMScheduler(skip_prk_steps=_A		)
     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=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		)
     SCREAMING_SNAKE_CASE_: Tuple     			=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       ,		)
     SCREAMING_SNAKE_CASE_: List[str]     			=CLIPTextModel(_A		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Optional[int]=0		)     ->					Dict:


     '''simple docstring'''




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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			="""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.get_dummy_components()
     SCREAMING_SNAKE_CASE_: Dict     			=StableDiffusionInpaintPipeline(**_A		)
     SCREAMING_SNAKE_CASE_: Dict     			=sd_pipe.to(_A		)
     sd_pipe.set_progress_bar_config(disable=_A		)

     SCREAMING_SNAKE_CASE_: int     			=self.get_dummy_inputs(_A		)
     SCREAMING_SNAKE_CASE_: List[str]     			=sd_pipe(**_A		).images
     SCREAMING_SNAKE_CASE_: str     			=image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_: Tuple     			=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]		)     ->					Optional[int]:


     '''simple docstring'''




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







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



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Dict		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-inpaint/init_image.png"""		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
     SCREAMING_SNAKE_CASE_: Dict     			=load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
         """/yellow_cat_sitting_on_a_park_bench.npy"""		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""stabilityai/stable-diffusion-2-inpainting"""
     SCREAMING_SNAKE_CASE_: Tuple     			=StableDiffusionInpaintPipeline.from_pretrained(_A       ,		safety_checker=_A		)
     pipe.to(_A		)
     pipe.set_progress_bar_config(disable=_A		)
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE_: List[Any]     			="""Face of a yellow cat, high resolution, sitting on a park bench"""

     SCREAMING_SNAKE_CASE_: Dict     			=torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Tuple     			=pipe(
         prompt=_A       ,		image=_A       ,		mask_image=_A       ,		generator=_A       ,		output_type="""np"""       ,		)
     SCREAMING_SNAKE_CASE_: List[str]     			=output.images[0]

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



  def 	lowerCamelCase__       (						self :					int		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-inpaint/init_image.png"""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
         """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""stabilityai/stable-diffusion-2-inpainting"""
     SCREAMING_SNAKE_CASE_: 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()

     SCREAMING_SNAKE_CASE_: int     			="""Face of a yellow cat, high resolution, sitting on a park bench"""

     SCREAMING_SNAKE_CASE_: Dict     			=torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=pipe(
         prompt=_A       ,		image=_A       ,		mask_image=_A       ,		generator=_A       ,		output_type="""np"""       ,		)
     SCREAMING_SNAKE_CASE_: List[str]     			=output.images[0]

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





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE_: Tuple     			="""stabilityai/stable-diffusion-2-inpainting"""
     SCREAMING_SNAKE_CASE_: List[str]     			=PNDMScheduler.from_pretrained(_A       ,		subfolder="""scheduler"""		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=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()

     SCREAMING_SNAKE_CASE_: Dict     			="""Face of a yellow cat, high resolution, sitting on a park bench"""

     SCREAMING_SNAKE_CASE_: str     			=torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=pipe(
         prompt=_A       ,		image=_A       ,		mask_image=_A       ,		generator=_A       ,		num_inference_steps=2       ,		output_type="""np"""       ,		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.cuda.max_memory_allocated()
     # make sure that less than 2.65 GB is allocated
     assert mem_bytes < 2.6_5 * 10**9








"""simple docstring"""
from __future__ import annotations





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=sorted(numsa + numsa )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=divmod(len(lowercase )							,			2 )
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of first array: """).split()]
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



