

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort

class     a__  (    snake_case__							,		unittest.TestCase	):
			"""simple docstring"""







			pass

@nightly
@require_onnxruntime
@require_torch_gpu
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


			@property
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	ort.SessionOptions()
							A__						=	False
							return options


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/in_paint/overture-creations-5sI6fQgYIuo.png"				)
							A__						=	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"				)
							A__						=	OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    "runwayml/stable-diffusion-inpainting"					,					revision="onnx"					,					safety_checker=lowercase					,					feature_extractor=lowercase					,					provider=self.gpu_provider					,					sess_options=self.gpu_options					,					)
							pipe.set_progress_bar_config(disable=lowercase				)

							A__						=	"A red cat sitting on a park bench"

							A__						=	np.random.RandomState(0				)
							A__						=	pipe(
							    prompt=lowercase					,					image=lowercase					,					mask_image=lowercase					,					guidance_scale=7.5					,					num_inference_steps=10					,					generator=lowercase					,					output_type="np"					,					)
							A__						=	output.images
							A__						=	images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							A__						=	np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/in_paint/overture-creations-5sI6fQgYIuo.png"				)
							A__						=	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"				)
							A__						=	LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-inpainting"					,					subfolder="scheduler"					,					revision="onnx"				)
							A__						=	OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    "runwayml/stable-diffusion-inpainting"					,					revision="onnx"					,					scheduler=lowercase					,					safety_checker=lowercase					,					feature_extractor=lowercase					,					provider=self.gpu_provider					,					sess_options=self.gpu_options					,					)
							pipe.set_progress_bar_config(disable=lowercase				)

							A__						=	"A red cat sitting on a park bench"

							A__						=	np.random.RandomState(0				)
							A__						=	pipe(
							    prompt=lowercase					,					image=lowercase					,					mask_image=lowercase					,					guidance_scale=7.5					,					num_inference_steps=20					,					generator=lowercase					,					output_type="np"					,					)
							A__						=	output.images
							A__						=	images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							A__						=	np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3









from __future__ import annotations

import collections
import pprint
from pathlib import Path


def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						return "".join(sorted(__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase	)->  list[str]:





						"""simple docstring"""



						return word_by_signature[signature(__lowerCAmelCase	)]


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

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

if __name__ == "__main__":
		_a      	=  {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))
