"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__						:						Union[str, Any]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@nightly
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Any    ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							generator.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'cyberpunk 2077'
									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]    )

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

									UpperCAmelCase__    =							'A painting of a squirrel eating a burger '
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.text_to_image(
									    prompt=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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

									UpperCAmelCase__    =							pipe.image_variation(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]    )

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




"""simple docstring"""






from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 						a_		(      lowerCamelCase ):
				return "".join(sorted(lowerCamelCase ) )



def 						a_		(      lowerCamelCase ):
				return word_by_signature[signature(lowerCamelCase )]


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

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

if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			{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))



