

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  UpperCAmelCase__	( lowerCAmelCase__		,  lowerCAmelCase__		,  lowerCAmelCase__		,  unittest.TestCase ):






  """simple docstring"""


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






  def    lowercase_	(							self		:	Optional[int]      )	->							List[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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=_SCREAMING_SNAKE_CASE			,     )
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE      )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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__					=					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__					=					CLIPTextModel(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

       SCREAMING_SNAKE_CASE__					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	str=0      )	->							str:
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_SCREAMING_SNAKE_CASE      )      ).to(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE__					=					image.cpu().permute(0			,     2			,     3			,     1      )[0]
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE      )      ).convert('''RGB'''      ).resize((64, 64)      )
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
       if str(_SCREAMING_SNAKE_CASE      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(_SCREAMING_SNAKE_CASE      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=_SCREAMING_SNAKE_CASE      ).manual_seed(_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE__					=					{
           '''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    lowercase_	(							self		:	str      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInpaintPipeline(**_SCREAMING_SNAKE_CASE      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(_SCREAMING_SNAKE_CASE      )
       sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )

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

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	int      )	->							Tuple:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






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






  """simple docstring"""


  def    lowercase_	(							self		:	Any      )	->							List[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	Union[str, Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png'''      )
       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
       SCREAMING_SNAKE_CASE__					=					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__					=					'''stabilityai/stable-diffusion-2-inpainting'''
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE			,     safety_checker=_SCREAMING_SNAKE_CASE      )
       pipe.to(_SCREAMING_SNAKE_CASE      )
       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					'''Face of a yellow cat, high resolution, sitting on a park bench'''

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

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






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

       SCREAMING_SNAKE_CASE__					=					'''Face of a yellow cat, high resolution, sitting on a park bench'''

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

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







  def    lowercase_	(							self		:	Any      )	->							Optional[int]:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-2-inpainting'''
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE			,     subfolder='''scheduler'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInpaintPipeline.from_pretrained(
           _SCREAMING_SNAKE_CASE			,     safety_checker=_SCREAMING_SNAKE_CASE			,     scheduler=_SCREAMING_SNAKE_CASE			,     torch_dtype=torch.floataa			,     )
       pipe.to(_SCREAMING_SNAKE_CASE      )
       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE      )
       pipe.enable_attention_slicing(1      )
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__					=					'''Face of a yellow cat, high resolution, sitting on a park bench'''

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

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





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


