
'''simple docstring'''




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						):
     __magic_name__						=		StableDiffusionInpaintPipeline
     __magic_name__						=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __magic_name__						=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __magic_name__						=		frozenset(
         []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __magic_name__						=		frozenset([]						)





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






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   Optional[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=snake_case_						,			)
         A       :   Optional[Any]					=						PNDMScheduler(skip_prk_steps=snake_case_	)
         torch.manual_seed(0	)
         A       :   List[str]					=						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       :   str					=						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       :   Any					=						CLIPTextModel(snake_case_	)
         A       :   Union[str, Any]					=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

         A       :   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						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				Any:






         """simple docstring"""






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






         """simple docstring"""






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

         A       :   Dict					=						self.get_dummy_inputs(snake_case_	)
         A       :   Dict					=						sd_pipe(**snake_case_	).images
         A       :   List[Any]					=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A       :   Optional[Any]					=						np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]	)

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





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






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






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

         A       :   Optional[int]					=						'stabilityai/stable-diffusion-2-inpainting'
         A       :   Any					=						StableDiffusionInpaintPipeline.from_pretrained(snake_case_						,			safety_checker=snake_case_	)
         pipe.to(snake_case_	)
         pipe.set_progress_bar_config(disable=snake_case_	)
         pipe.enable_attention_slicing()

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

         A       :   str					=						torch.manual_seed(0	)
         A       :   Optional[int]					=						pipe(
             prompt=snake_case_						,			image=snake_case_						,			mask_image=snake_case_						,			generator=snake_case_						,			output_type='''np'''						,			)
         A       :   Union[str, Any]					=						output.images[0]

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





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   Any					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''	)
         A       :   Optional[int]					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''	)
         A       :   Any					=						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       :   Dict					=						'stabilityai/stable-diffusion-2-inpainting'
         A       :   List[Any]					=						StableDiffusionInpaintPipeline.from_pretrained(
             snake_case_						,			torch_dtype=torch.floataa						,			safety_checker=snake_case_						,			)
         pipe.to(snake_case_	)
         pipe.set_progress_bar_config(disable=snake_case_	)
         pipe.enable_attention_slicing()

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

         A       :   Tuple					=						torch.manual_seed(0	)
         A       :   Union[str, Any]					=						pipe(
             prompt=snake_case_						,			image=snake_case_						,			mask_image=snake_case_						,			generator=snake_case_						,			output_type='''np'''						,			)
         A       :   Optional[Any]					=						output.images[0]

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





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






         """simple docstring"""






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

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

         A       :   Union[str, Any]					=						'stabilityai/stable-diffusion-2-inpainting'
         A       :   int					=						PNDMScheduler.from_pretrained(snake_case_						,			subfolder='''scheduler'''	)
         A       :   Dict					=						StableDiffusionInpaintPipeline.from_pretrained(
             snake_case_						,			safety_checker=snake_case_						,			scheduler=snake_case_						,			torch_dtype=torch.floataa						,			)
         pipe.to(snake_case_	)
         pipe.set_progress_bar_config(disable=snake_case_	)
         pipe.enable_attention_slicing(1	)
         pipe.enable_sequential_cpu_offload()

         A       :   Dict					=						'Face of a yellow cat, high resolution, sitting on a park bench'

         A       :   Dict					=						torch.manual_seed(0	)
         A       :   int					=						pipe(
             prompt=snake_case_						,			image=snake_case_						,			mask_image=snake_case_						,			generator=snake_case_						,			num_inference_steps=2						,			output_type='''np'''						,			)

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











"""simple docstring"""




import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_     :						List[str]						=					[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Union[str, Any]										=  os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
  A_  :       Tuple										=  os.path.join(_UpperCAmelCase ,					'words.txt'					)

  A_  :       List[Any]										=  ''
  with open(_UpperCAmelCase					) as f:
    A_  :       int										=  f.readline()

  A_  :       Optional[Any]										=  [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
  A_  :       Dict										=  [
      word
      for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
      if word in TRIANGULAR_NUMBERS
  ]
  return len(_UpperCAmelCase					)


if __name__ == "__main__":
   print(solution())