






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			):






 '''simple docstring'''




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


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				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							,  )
     _UpperCAmelCase				:      Dict			  =				PNDMScheduler(skip_prk_steps=_A)
     torch.manual_seed(0)
     _UpperCAmelCase				:      List[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)
     _UpperCAmelCase				:      Dict			  =				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							,  )
     _UpperCAmelCase				:      Tuple			  =				CLIPTextModel(_A)
     _UpperCAmelCase				:      int			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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


 def  snake_case__				(	self							,  _A							,  _A=0) ->		Dict:

     """simple docstring"""



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

     """simple docstring"""



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

     _UpperCAmelCase				:      Union[str, Any]			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      List[str]			  =				sd_pipe(**_A).images
     _UpperCAmelCase				:      Optional[Any]			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _UpperCAmelCase				:      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  snake_case__				(	self) ->		str:

     """simple docstring"""



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



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






 '''simple docstring'''




 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



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


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

     """simple docstring"""



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

     _UpperCAmelCase				:      Tuple			  =				'''stabilityai/stable-diffusion-2-inpainting'''
     _UpperCAmelCase				:      Any			  =				StableDiffusionInpaintPipeline.from_pretrained(_A							,  safety_checker=_A)
     pipe.to(_A)
     pipe.set_progress_bar_config(disable=_A)
     pipe.enable_attention_slicing()

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

     _UpperCAmelCase				:      Any			  =				torch.manual_seed(0)
     _UpperCAmelCase				:      List[Any]			  =				pipe(
         prompt=_A							,  image=_A							,  mask_image=_A							,  generator=_A							,  output_type='''np'''							,  )
     _UpperCAmelCase				:      Tuple			  =				output.images[0]

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


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

     """simple docstring"""



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

     _UpperCAmelCase				:      Tuple			  =				'''stabilityai/stable-diffusion-2-inpainting'''
     _UpperCAmelCase				:      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()

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

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

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


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



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

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

     _UpperCAmelCase				:      List[Any]			  =				'''stabilityai/stable-diffusion-2-inpainting'''
     _UpperCAmelCase				:      List[str]			  =				PNDMScheduler.from_pretrained(_A							,  subfolder='''scheduler''')
     _UpperCAmelCase				:      int			  =				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()

     _UpperCAmelCase				:      int			  =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

     _UpperCAmelCase				:      Optional[Any]			  =				torch.manual_seed(0)
     _UpperCAmelCase				:      Optional[int]			  =				pipe(
         prompt=_A							,  image=_A							,  mask_image=_A							,  generator=_A							,  num_inference_steps=2							,  output_type='''np'''							,  )

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












import math

def      _lowerCamelCase	(						__A    :							int )      ->					int:

    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      List[Any]			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )

    if number < 1:
        _UpperCAmelCase				:      Optional[Any]			  =				f'''Input value of [number={number}] must be > 0'''
        raise ValueError(__A )
    elif number == 1:
        return 3
    elif number == 2:
        return 5
    else:
        _UpperCAmelCase				:      Any			  =				int(math.log(number // 3		,		2 ) ) + 2

        _UpperCAmelCase				:      Dict			  =				[3, 5]
        _UpperCAmelCase				:      Tuple			  =				2
        _UpperCAmelCase				:      Dict			  =				3
        for block in range(1		,		__A ):
            for _ in range(__A ):
                proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
                proth_index += 1
            increment *= 2

    return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    SCREAMING_SNAKE_CASE 							=						0
    try:
      SCREAMING_SNAKE_CASE 							=						proth(number)
    except ValueError:
      print(F'ValueError: there is no {number}th Proth number')
      continue

    print(F'The {number}th Proth number: {value}')




