






"""simple docstring"""



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 			lowerCAmelCase__			(    __magic_name__							,						unittest.TestCase   ):
   # FIXME: add fast tests
   pass




@nightly
@require_onnxruntime
@require_torch_gpu
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @property
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
   @property
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							ort.SessionOptions()
        UpperCAmelCase__						:    int							=							False
        return options
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"							)
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"							)
        UpperCAmelCase__						:    Dict							=							OnnxStableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting"							,			revision="onnx"							,			safety_checker=snake_case__							,			feature_extractor=snake_case__							,			provider=self.gpu_provider							,			sess_options=self.gpu_options							,			)
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Tuple							=							"A red cat sitting on a park bench"

        UpperCAmelCase__						:    Tuple							=							np.random.RandomState(0							)
        UpperCAmelCase__						:    Dict							=							pipe(
            prompt=snake_case__							,			image=snake_case__							,			mask_image=snake_case__							,			guidance_scale=7.5							,			num_inference_steps=1_0							,			generator=snake_case__							,			output_type="np"							,			)
        UpperCAmelCase__						:    Tuple							=							output.images
        UpperCAmelCase__						:    Any							=							images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

        assert images.shape == (1, 5_1_2, 5_1_2, 3)
        UpperCAmelCase__						:    Optional[int]							=							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       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo.png"							)
        UpperCAmelCase__						:    Optional[Any]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"							)
        UpperCAmelCase__						:    Tuple							=							LMSDiscreteScheduler.from_pretrained(
            "runwayml/stable-diffusion-inpainting"							,			subfolder="scheduler"							,			revision="onnx"							)
        UpperCAmelCase__						:    Union[str, Any]							=							OnnxStableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting"							,			revision="onnx"							,			scheduler=snake_case__							,			safety_checker=snake_case__							,			feature_extractor=snake_case__							,			provider=self.gpu_provider							,			sess_options=self.gpu_options							,			)
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Tuple							=							"A red cat sitting on a park bench"

        UpperCAmelCase__						:    Any							=							np.random.RandomState(0							)
        UpperCAmelCase__						:    int							=							pipe(
            prompt=snake_case__							,			image=snake_case__							,			mask_image=snake_case__							,			guidance_scale=7.5							,			num_inference_steps=2_0							,			generator=snake_case__							,			output_type="np"							,			)
        UpperCAmelCase__						:    int							=							output.images
        UpperCAmelCase__						:    Dict							=							images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

        assert images.shape == (1, 5_1_2, 5_1_2, 3)
        UpperCAmelCase__						:    str							=							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










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     for i in range(length - 1				):
          UpperCAmelCase__						:    Any							=							i
          for k in range(i + 1			,					snake_case				):
               if collection[k] < collection[least]:
                    UpperCAmelCase__						:    Union[str, Any]							=							k
          if least != i:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
 _lowerCAmelCase	:	List[str]				=      input("""Enter numbers separated by a comma:\n""").strip()
 _lowerCAmelCase	:	Optional[int]				=      [int(item) for item in user_input.split(""",""")]
 print(selection_sort(unsorted))


