






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     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							@property
							def      A_  (      self				:     Dict    )	->List[str]:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)

							@property
							def      A_  (      self				:     Dict    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ort.SessionOptions()
													SCREAMING_SNAKE_CASE__     :				Any   =					False
													return options

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

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					"A red cat sitting on a park bench"

													SCREAMING_SNAKE_CASE__     :				Dict   =					np.random.RandomState(0    )
													SCREAMING_SNAKE_CASE__     :				Any   =					pipe(
													    prompt=a							,	image=a							,	mask_image=a							,	guidance_scale=7.5							,	num_inference_steps=10							,	generator=a							,	output_type="np"							,	)
													SCREAMING_SNAKE_CASE__     :				List[str]   =					output.images
													SCREAMING_SNAKE_CASE__     :				int   =					images[0, 2_55:2_58, 2_55:2_58, -1]

													assert images.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :				Any   =					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				:     List[Any]    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				str   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					LMSDiscreteScheduler.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"							,	subfolder="scheduler"							,	revision="onnx"    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"							,	revision="onnx"							,	scheduler=a							,	safety_checker=a							,	feature_extractor=a							,	provider=self.gpu_provider							,	sess_options=self.gpu_options							,	)
													pipe.set_progress_bar_config(disable=a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"A red cat sitting on a park bench"

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					np.random.RandomState(0    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe(
													    prompt=a							,	image=a							,	mask_image=a							,	guidance_scale=7.5							,	num_inference_steps=20							,	generator=a							,	output_type="np"							,	)
													SCREAMING_SNAKE_CASE__     :				List[str]   =					output.images
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images[0, 2_55:2_58, 2_55:2_58, -1]

													assert images.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE__     :				List[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






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) < k or k < 0:
												raise ValueError("Invalid Input"     )
						SCREAMING_SNAKE_CASE__     :				int   =					sum(array[:k]     )
						for i in range(len(_lowerCamelCase     ) - k     ):
												SCREAMING_SNAKE_CASE__     :				str   =					current_sum - array[i] + array[i + k]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max(_lowerCamelCase			,   _lowerCamelCase     )
						return max_sum


if __name__ == "__main__":
				from doctest import testmod
				from random import randint

				testmod()
				__lowercase       :List[str]       =  [randint(-1_000, 1_000) for i in range(100)]
				__lowercase       :Any       =  randint(0, 110)
				print(f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")